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2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~mediawikidump.py | import logging
from pathlib import Path
from typing import List, Optional, Sequence, Union
from libs.core.langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
logger = logging.getLogger(__name__)
class MWDumpLoader(BaseLoader):
"""Load `MediaWiki` dump from an `XML` file.
Example:
.. code-block:: python
from langchain_community.document_loaders import MWDumpLoader
loader = MWDumpLoader(
file_path="myWiki.xml",
encoding="utf8"
)
docs = loader.load()
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000, chunk_overlap=0
)
texts = text_splitter.split_documents(docs)
:param file_path: XML local file path
:type file_path: str
:param encoding: Charset encoding, defaults to "utf8"
:type encoding: str, optional
:param namespaces: The namespace of pages you want to parse.
See https://www.mediawiki.org/wiki/Help:Namespaces#Localisation
for a list of all common namespaces
:type namespaces: List[int],optional
:param skip_redirects: TR=rue to skip pages that redirect to other pages,
False to keep them. False by default
:type skip_redirects: bool, optional
:param stop_on_error: False to skip over pages that cause parsing errors,
True to stop. True by default
:type stop_on_error: bool, optional
"""
def __init__(
self,
file_path: Union[str, Path],
encoding: Optional[str] = "utf8",
namespaces: Optional[Sequence[int]] = None,
skip_redirects: Optional[bool] = False,
stop_on_error: Optional[bool] = True,
):
self.file_path = file_path if isinstance(file_path, str) else str(file_path)
self.encoding = encoding
# Namespaces range from -2 to 15, inclusive.
self.namespaces = namespaces
self.skip_redirects = skip_redirects
self.stop_on_error = stop_on_error
def load(self) -> List[Document]:
"""Load from a file path."""
try:
import mwparserfromhell
import mwxml
except ImportError as e:
raise ImportError(
"Unable to import 'mwparserfromhell' or 'mwxml'. Please install with"
" `pip install mwparserfromhell mwxml`."
) from e
dump = mwxml.Dump.from_file(open(self.file_path, encoding=self.encoding))
docs = []
for page in dump.pages:
if self.skip_redirects and page.redirect:
continue
if self.namespaces and page.namespace not in self.namespaces:
continue
try:
for revision in page:
code = mwparserfromhell.parse(revision.text)
text = code.strip_code(
normalize=True, collapse=True, keep_template_params=False
)
metadata = {"source": page.title}
docs.append(Document(page_content=text, metadata=metadata))
except Exception as e:
logger.error("Parsing error: {}".format(e))
if self.stop_on_error:
raise e
else:
continue
return docs
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~stripe.py | import json
import urllib.request
from typing import List, Optional
from libs.core.langchain_core.documents import Document
from libs.core.langchain_core.utils import get_from_env, stringify_dict
from langchain_community.document_loaders.base import BaseLoader
STRIPE_ENDPOINTS = {
"balance_transactions": "https://api.stripe.com/v1/balance_transactions",
"charges": "https://api.stripe.com/v1/charges",
"customers": "https://api.stripe.com/v1/customers",
"events": "https://api.stripe.com/v1/events",
"refunds": "https://api.stripe.com/v1/refunds",
"disputes": "https://api.stripe.com/v1/disputes",
}
class StripeLoader(BaseLoader):
"""Load from `Stripe` API."""
def __init__(self, resource: str, access_token: Optional[str] = None) -> None:
"""Initialize with a resource and an access token.
Args:
resource: The resource.
access_token: The access token.
"""
self.resource = resource
access_token = access_token or get_from_env(
"access_token", "STRIPE_ACCESS_TOKEN"
)
self.headers = {"Authorization": f"Bearer {access_token}"}
def _make_request(self, url: str) -> List[Document]:
request = urllib.request.Request(url, headers=self.headers)
with urllib.request.urlopen(request) as response:
json_data = json.loads(response.read().decode())
text = stringify_dict(json_data)
metadata = {"source": url}
return [Document(page_content=text, metadata=metadata)]
def _get_resource(self) -> List[Document]:
endpoint = STRIPE_ENDPOINTS.get(self.resource)
if endpoint is None:
return []
return self._make_request(endpoint)
def load(self) -> List[Document]:
return self._get_resource()
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~llms~amazon_api_gateway.py | from typing import Any, Dict, List, Mapping, Optional
import requests
from libs.core.langchain_core.callbacks import CallbackManagerForLLMRun
from libs.core.langchain_core.language_models.llms import LLM
from libs.core.langchain_core.pydantic_v1 import Extra
from langchain_community.llms.utils import enforce_stop_tokens
class ContentHandlerAmazonAPIGateway:
"""Adapter to prepare the inputs from Langchain to a format
that LLM model expects.
It also provides helper function to extract
the generated text from the model response."""
@classmethod
def transform_input(
cls, prompt: str, model_kwargs: Dict[str, Any]
) -> Dict[str, Any]:
return {"inputs": prompt, "parameters": model_kwargs}
@classmethod
def transform_output(cls, response: Any) -> str:
return response.json()[0]["generated_text"]
class AmazonAPIGateway(LLM):
"""Amazon API Gateway to access LLM models hosted on AWS."""
api_url: str
"""API Gateway URL"""
headers: Optional[Dict] = None
"""API Gateway HTTP Headers to send, e.g. for authentication"""
model_kwargs: Optional[Dict] = None
"""Keyword arguments to pass to the model."""
content_handler: ContentHandlerAmazonAPIGateway = ContentHandlerAmazonAPIGateway()
"""The content handler class that provides an input and
output transform functions to handle formats between LLM
and the endpoint.
"""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
_model_kwargs = self.model_kwargs or {}
return {
**{"api_url": self.api_url, "headers": self.headers},
**{"model_kwargs": _model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "amazon_api_gateway"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out to Amazon API Gateway model.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The string generated by the model.
Example:
.. code-block:: python
response = se("Tell me a joke.")
"""
_model_kwargs = self.model_kwargs or {}
payload = self.content_handler.transform_input(prompt, _model_kwargs)
try:
response = requests.post(
self.api_url,
headers=self.headers,
json=payload,
)
text = self.content_handler.transform_output(response)
except Exception as error:
raise ValueError(f"Error raised by the service: {error}")
if stop is not None:
text = enforce_stop_tokens(text, stop)
return text
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~dataframe.py | from typing import Any, Iterator, List
from libs.core.langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
class BaseDataFrameLoader(BaseLoader):
def __init__(self, data_frame: Any, *, page_content_column: str = "text"):
"""Initialize with dataframe object.
Args:
data_frame: DataFrame object.
page_content_column: Name of the column containing the page content.
Defaults to "text".
"""
self.data_frame = data_frame
self.page_content_column = page_content_column
def lazy_load(self) -> Iterator[Document]:
"""Lazy load records from dataframe."""
for _, row in self.data_frame.iterrows():
text = row[self.page_content_column]
metadata = row.to_dict()
metadata.pop(self.page_content_column)
yield Document(page_content=text, metadata=metadata)
def load(self) -> List[Document]:
"""Load full dataframe."""
return list(self.lazy_load())
class DataFrameLoader(BaseDataFrameLoader):
"""Load `Pandas` DataFrame."""
def __init__(self, data_frame: Any, page_content_column: str = "text"):
"""Initialize with dataframe object.
Args:
data_frame: Pandas DataFrame object.
page_content_column: Name of the column containing the page content.
Defaults to "text".
"""
try:
import pandas as pd
except ImportError as e:
raise ImportError(
"Unable to import pandas, please install with `pip install pandas`."
) from e
if not isinstance(data_frame, pd.DataFrame):
raise ValueError(
f"Expected data_frame to be a pd.DataFrame, got {type(data_frame)}"
)
super().__init__(data_frame, page_content_column=page_content_column)
| [] |
2024-01-10 | mth93/langchain | templates~rag-timescale-conversation~rag_timescale_conversation~load_sample_dataset.py | import os
import tempfile
from datetime import datetime, timedelta
import requests
from langchain.document_loaders import JSONLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores.timescalevector import TimescaleVector
from timescale_vector import client
def parse_date(date_string: str) -> datetime:
if date_string is None:
return None
time_format = "%a %b %d %H:%M:%S %Y %z"
return datetime.strptime(date_string, time_format)
def extract_metadata(record: dict, metadata: dict) -> dict:
dt = parse_date(record["date"])
metadata["id"] = str(client.uuid_from_time(dt))
if dt is not None:
metadata["date"] = dt.isoformat()
else:
metadata["date"] = None
metadata["author"] = record["author"]
metadata["commit_hash"] = record["commit"]
return metadata
def load_ts_git_dataset(
service_url,
collection_name="timescale_commits",
num_records: int = 500,
partition_interval=timedelta(days=7),
):
json_url = "https://s3.amazonaws.com/assets.timescale.com/ai/ts_git_log.json"
tmp_file = "ts_git_log.json"
temp_dir = tempfile.gettempdir()
json_file_path = os.path.join(temp_dir, tmp_file)
if not os.path.exists(json_file_path):
response = requests.get(json_url)
if response.status_code == 200:
with open(json_file_path, "w") as json_file:
json_file.write(response.text)
else:
print(f"Failed to download JSON file. Status code: {response.status_code}")
loader = JSONLoader(
file_path=json_file_path,
jq_schema=".commit_history[]",
text_content=False,
metadata_func=extract_metadata,
)
documents = loader.load()
# Remove documents with None dates
documents = [doc for doc in documents if doc.metadata["date"] is not None]
if num_records > 0:
documents = documents[:num_records]
# Split the documents into chunks for embedding
text_splitter = CharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
# Create a Timescale Vector instance from the collection of documents
TimescaleVector.from_documents(
embedding=embeddings,
ids=[doc.metadata["id"] for doc in docs],
documents=docs,
collection_name=collection_name,
service_url=service_url,
time_partition_interval=partition_interval,
)
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~vectorstores~vald.py | """Wrapper around Vald vector database."""
from __future__ import annotations
from typing import Any, Iterable, List, Optional, Tuple, Type
import numpy as np
from libs.core.langchain_core.documents import Document
from libs.core.langchain_core.embeddings import Embeddings
from libs.core.langchain_core.vectorstores import VectorStore
from langchain_community.vectorstores.utils import maximal_marginal_relevance
class Vald(VectorStore):
"""Wrapper around Vald vector database.
To use, you should have the ``vald-client-python`` python package installed.
Example:
.. code-block:: python
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Vald
texts = ['foo', 'bar', 'baz']
vald = Vald.from_texts(
texts=texts,
embedding=HuggingFaceEmbeddings(),
host="localhost",
port=8080,
skip_strict_exist_check=False,
)
"""
def __init__(
self,
embedding: Embeddings,
host: str = "localhost",
port: int = 8080,
grpc_options: Tuple = (
("grpc.keepalive_time_ms", 1000 * 10),
("grpc.keepalive_timeout_ms", 1000 * 10),
),
grpc_use_secure: bool = False,
grpc_credentials: Optional[Any] = None,
):
self._embedding = embedding
self.target = host + ":" + str(port)
self.grpc_options = grpc_options
self.grpc_use_secure = grpc_use_secure
self.grpc_credentials = grpc_credentials
@property
def embeddings(self) -> Optional[Embeddings]:
return self._embedding
def _get_channel(self) -> Any:
try:
import grpc
except ImportError:
raise ValueError(
"Could not import grpcio python package. "
"Please install it with `pip install grpcio`."
)
return (
grpc.secure_channel(
self.target, self.grpc_credentials, options=self.grpc_options
)
if self.grpc_use_secure
else grpc.insecure_channel(self.target, options=self.grpc_options)
)
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
grpc_metadata: Optional[Any] = None,
skip_strict_exist_check: bool = False,
**kwargs: Any,
) -> List[str]:
"""
Args:
skip_strict_exist_check: Deprecated. This is not used basically.
"""
try:
from vald.v1.payload import payload_pb2
from vald.v1.vald import upsert_pb2_grpc
except ImportError:
raise ValueError(
"Could not import vald-client-python python package. "
"Please install it with `pip install vald-client-python`."
)
channel = self._get_channel()
# Depending on the network quality,
# it is necessary to wait for ChannelConnectivity.READY.
# _ = grpc.channel_ready_future(channel).result(timeout=10)
stub = upsert_pb2_grpc.UpsertStub(channel)
cfg = payload_pb2.Upsert.Config(skip_strict_exist_check=skip_strict_exist_check)
ids = []
embs = self._embedding.embed_documents(list(texts))
for text, emb in zip(texts, embs):
vec = payload_pb2.Object.Vector(id=text, vector=emb)
res = stub.Upsert(
payload_pb2.Upsert.Request(vector=vec, config=cfg),
metadata=grpc_metadata,
)
ids.append(res.uuid)
channel.close()
return ids
def delete(
self,
ids: Optional[List[str]] = None,
skip_strict_exist_check: bool = False,
grpc_metadata: Optional[Any] = None,
**kwargs: Any,
) -> Optional[bool]:
"""
Args:
skip_strict_exist_check: Deprecated. This is not used basically.
"""
try:
from vald.v1.payload import payload_pb2
from vald.v1.vald import remove_pb2_grpc
except ImportError:
raise ValueError(
"Could not import vald-client-python python package. "
"Please install it with `pip install vald-client-python`."
)
if ids is None:
raise ValueError("No ids provided to delete")
channel = self._get_channel()
# Depending on the network quality,
# it is necessary to wait for ChannelConnectivity.READY.
# _ = grpc.channel_ready_future(channel).result(timeout=10)
stub = remove_pb2_grpc.RemoveStub(channel)
cfg = payload_pb2.Remove.Config(skip_strict_exist_check=skip_strict_exist_check)
for _id in ids:
oid = payload_pb2.Object.ID(id=_id)
_ = stub.Remove(
payload_pb2.Remove.Request(id=oid, config=cfg), metadata=grpc_metadata
)
channel.close()
return True
def similarity_search(
self,
query: str,
k: int = 4,
radius: float = -1.0,
epsilon: float = 0.01,
timeout: int = 3000000000,
grpc_metadata: Optional[Any] = None,
**kwargs: Any,
) -> List[Document]:
docs_and_scores = self.similarity_search_with_score(
query, k, radius, epsilon, timeout, grpc_metadata
)
docs = []
for doc, _ in docs_and_scores:
docs.append(doc)
return docs
def similarity_search_with_score(
self,
query: str,
k: int = 4,
radius: float = -1.0,
epsilon: float = 0.01,
timeout: int = 3000000000,
grpc_metadata: Optional[Any] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
emb = self._embedding.embed_query(query)
docs_and_scores = self.similarity_search_with_score_by_vector(
emb, k, radius, epsilon, timeout, grpc_metadata
)
return docs_and_scores
def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
radius: float = -1.0,
epsilon: float = 0.01,
timeout: int = 3000000000,
grpc_metadata: Optional[Any] = None,
**kwargs: Any,
) -> List[Document]:
docs_and_scores = self.similarity_search_with_score_by_vector(
embedding, k, radius, epsilon, timeout, grpc_metadata
)
docs = []
for doc, _ in docs_and_scores:
docs.append(doc)
return docs
def similarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
radius: float = -1.0,
epsilon: float = 0.01,
timeout: int = 3000000000,
grpc_metadata: Optional[Any] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
try:
from vald.v1.payload import payload_pb2
from vald.v1.vald import search_pb2_grpc
except ImportError:
raise ValueError(
"Could not import vald-client-python python package. "
"Please install it with `pip install vald-client-python`."
)
channel = self._get_channel()
# Depending on the network quality,
# it is necessary to wait for ChannelConnectivity.READY.
# _ = grpc.channel_ready_future(channel).result(timeout=10)
stub = search_pb2_grpc.SearchStub(channel)
cfg = payload_pb2.Search.Config(
num=k, radius=radius, epsilon=epsilon, timeout=timeout
)
res = stub.Search(
payload_pb2.Search.Request(vector=embedding, config=cfg),
metadata=grpc_metadata,
)
docs_and_scores = []
for result in res.results:
docs_and_scores.append((Document(page_content=result.id), result.distance))
channel.close()
return docs_and_scores
def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
radius: float = -1.0,
epsilon: float = 0.01,
timeout: int = 3000000000,
grpc_metadata: Optional[Any] = None,
**kwargs: Any,
) -> List[Document]:
emb = self._embedding.embed_query(query)
docs = self.max_marginal_relevance_search_by_vector(
emb,
k=k,
fetch_k=fetch_k,
radius=radius,
epsilon=epsilon,
timeout=timeout,
lambda_mult=lambda_mult,
grpc_metadata=grpc_metadata,
)
return docs
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
radius: float = -1.0,
epsilon: float = 0.01,
timeout: int = 3000000000,
grpc_metadata: Optional[Any] = None,
**kwargs: Any,
) -> List[Document]:
try:
from vald.v1.payload import payload_pb2
from vald.v1.vald import object_pb2_grpc
except ImportError:
raise ValueError(
"Could not import vald-client-python python package. "
"Please install it with `pip install vald-client-python`."
)
channel = self._get_channel()
# Depending on the network quality,
# it is necessary to wait for ChannelConnectivity.READY.
# _ = grpc.channel_ready_future(channel).result(timeout=10)
stub = object_pb2_grpc.ObjectStub(channel)
docs_and_scores = self.similarity_search_with_score_by_vector(
embedding,
fetch_k=fetch_k,
radius=radius,
epsilon=epsilon,
timeout=timeout,
grpc_metadata=grpc_metadata,
)
docs = []
embs = []
for doc, _ in docs_and_scores:
vec = stub.GetObject(
payload_pb2.Object.VectorRequest(
id=payload_pb2.Object.ID(id=doc.page_content)
),
metadata=grpc_metadata,
)
embs.append(vec.vector)
docs.append(doc)
mmr = maximal_marginal_relevance(
np.array(embedding),
embs,
lambda_mult=lambda_mult,
k=k,
)
channel.close()
return [docs[i] for i in mmr]
@classmethod
def from_texts(
cls: Type[Vald],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
host: str = "localhost",
port: int = 8080,
grpc_options: Tuple = (
("grpc.keepalive_time_ms", 1000 * 10),
("grpc.keepalive_timeout_ms", 1000 * 10),
),
grpc_use_secure: bool = False,
grpc_credentials: Optional[Any] = None,
grpc_metadata: Optional[Any] = None,
skip_strict_exist_check: bool = False,
**kwargs: Any,
) -> Vald:
"""
Args:
skip_strict_exist_check: Deprecated. This is not used basically.
"""
vald = cls(
embedding=embedding,
host=host,
port=port,
grpc_options=grpc_options,
grpc_use_secure=grpc_use_secure,
grpc_credentials=grpc_credentials,
**kwargs,
)
vald.add_texts(
texts=texts,
metadatas=metadatas,
grpc_metadata=grpc_metadata,
skip_strict_exist_check=skip_strict_exist_check,
)
return vald
"""We will support if there are any requests."""
# async def aadd_texts(
# self,
# texts: Iterable[str],
# metadatas: Optional[List[dict]] = None,
# **kwargs: Any,
# ) -> List[str]:
# pass
#
# def _select_relevance_score_fn(self) -> Callable[[float], float]:
# pass
#
# def _similarity_search_with_relevance_scores(
# self,
# query: str,
# k: int = 4,
# **kwargs: Any,
# ) -> List[Tuple[Document, float]]:
# pass
#
# def similarity_search_with_relevance_scores(
# self,
# query: str,
# k: int = 4,
# **kwargs: Any,
# ) -> List[Tuple[Document, float]]:
# pass
#
# async def amax_marginal_relevance_search_by_vector(
# self,
# embedding: List[float],
# k: int = 4,
# fetch_k: int = 20,
# lambda_mult: float = 0.5,
# **kwargs: Any,
# ) -> List[Document]:
# pass
#
# @classmethod
# async def afrom_texts(
# cls: Type[VST],
# texts: List[str],
# embedding: Embeddings,
# metadatas: Optional[List[dict]] = None,
# **kwargs: Any,
# ) -> VST:
# pass
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~parsers~language~language_parser.py | from __future__ import annotations
from typing import TYPE_CHECKING, Any, Dict, Iterator, Optional
from libs.core.langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseBlobParser
from langchain_community.document_loaders.blob_loaders import Blob
from langchain_community.document_loaders.parsers.language.cobol import CobolSegmenter
from langchain_community.document_loaders.parsers.language.javascript import (
JavaScriptSegmenter,
)
from langchain_community.document_loaders.parsers.language.python import PythonSegmenter
if TYPE_CHECKING:
from langchain.text_splitter import Language
try:
from langchain.text_splitter import Language
LANGUAGE_EXTENSIONS: Dict[str, str] = {
"py": Language.PYTHON,
"js": Language.JS,
"cobol": Language.COBOL,
}
LANGUAGE_SEGMENTERS: Dict[str, Any] = {
Language.PYTHON: PythonSegmenter,
Language.JS: JavaScriptSegmenter,
Language.COBOL: CobolSegmenter,
}
except ImportError:
LANGUAGE_EXTENSIONS = {}
LANGUAGE_SEGMENTERS = {}
class LanguageParser(BaseBlobParser):
"""Parse using the respective programming language syntax.
Each top-level function and class in the code is loaded into separate documents.
Furthermore, an extra document is generated, containing the remaining top-level code
that excludes the already segmented functions and classes.
This approach can potentially improve the accuracy of QA models over source code.
Currently, the supported languages for code parsing are Python and JavaScript.
The language used for parsing can be configured, along with the minimum number of
lines required to activate the splitting based on syntax.
Examples:
.. code-block:: python
from langchain.text_splitter.Language
from langchain_community.document_loaders.generic import GenericLoader
from langchain_community.document_loaders.parsers import LanguageParser
loader = GenericLoader.from_filesystem(
"./code",
glob="**/*",
suffixes=[".py", ".js"],
parser=LanguageParser()
)
docs = loader.load()
Example instantiations to manually select the language:
.. code-block:: python
from langchain.text_splitter import Language
loader = GenericLoader.from_filesystem(
"./code",
glob="**/*",
suffixes=[".py"],
parser=LanguageParser(language=Language.PYTHON)
)
Example instantiations to set number of lines threshold:
.. code-block:: python
loader = GenericLoader.from_filesystem(
"./code",
glob="**/*",
suffixes=[".py"],
parser=LanguageParser(parser_threshold=200)
)
"""
def __init__(self, language: Optional[Language] = None, parser_threshold: int = 0):
"""
Language parser that split code using the respective language syntax.
Args:
language: If None (default), it will try to infer language from source.
parser_threshold: Minimum lines needed to activate parsing (0 by default).
"""
self.language = language
self.parser_threshold = parser_threshold
def lazy_parse(self, blob: Blob) -> Iterator[Document]:
code = blob.as_string()
language = self.language or (
LANGUAGE_EXTENSIONS.get(blob.source.rsplit(".", 1)[-1])
if isinstance(blob.source, str)
else None
)
if language is None:
yield Document(
page_content=code,
metadata={
"source": blob.source,
},
)
return
if self.parser_threshold >= len(code.splitlines()):
yield Document(
page_content=code,
metadata={
"source": blob.source,
"language": language,
},
)
return
self.Segmenter = LANGUAGE_SEGMENTERS[language]
segmenter = self.Segmenter(blob.as_string())
if not segmenter.is_valid():
yield Document(
page_content=code,
metadata={
"source": blob.source,
},
)
return
for functions_classes in segmenter.extract_functions_classes():
yield Document(
page_content=functions_classes,
metadata={
"source": blob.source,
"content_type": "functions_classes",
"language": language,
},
)
yield Document(
page_content=segmenter.simplify_code(),
metadata={
"source": blob.source,
"content_type": "simplified_code",
"language": language,
},
)
| [
"functions_classes",
"simplified_code"
] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~geodataframe.py | from typing import Any, Iterator, List
from libs.core.langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
class GeoDataFrameLoader(BaseLoader):
"""Load `geopandas` Dataframe."""
def __init__(self, data_frame: Any, page_content_column: str = "geometry"):
"""Initialize with geopandas Dataframe.
Args:
data_frame: geopandas DataFrame object.
page_content_column: Name of the column containing the page content.
Defaults to "geometry".
"""
try:
import geopandas as gpd
except ImportError:
raise ImportError(
"geopandas package not found, please install it with "
"`pip install geopandas`"
)
if not isinstance(data_frame, gpd.GeoDataFrame):
raise ValueError(
f"Expected data_frame to be a gpd.GeoDataFrame, got {type(data_frame)}"
)
if page_content_column not in data_frame.columns:
raise ValueError(
f"Expected data_frame to have a column named {page_content_column}"
)
if not isinstance(data_frame[page_content_column], gpd.GeoSeries):
raise ValueError(
f"Expected data_frame[{page_content_column}] to be a GeoSeries"
)
self.data_frame = data_frame
self.page_content_column = page_content_column
def lazy_load(self) -> Iterator[Document]:
"""Lazy load records from dataframe."""
# assumes all geometries in GeoSeries are same CRS and Geom Type
crs_str = self.data_frame.crs.to_string() if self.data_frame.crs else None
geometry_type = self.data_frame.geometry.geom_type.iloc[0]
for _, row in self.data_frame.iterrows():
geom = row[self.page_content_column]
xmin, ymin, xmax, ymax = geom.bounds
metadata = row.to_dict()
metadata["crs"] = crs_str
metadata["geometry_type"] = geometry_type
metadata["xmin"] = xmin
metadata["ymin"] = ymin
metadata["xmax"] = xmax
metadata["ymax"] = ymax
metadata.pop(self.page_content_column)
# using WKT instead of str() to help GIS system interoperability
yield Document(page_content=geom.wkt, metadata=metadata)
def load(self) -> List[Document]:
"""Load full dataframe."""
return list(self.lazy_load())
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~trello.py | from __future__ import annotations
from typing import TYPE_CHECKING, Any, List, Literal, Optional, Tuple
from libs.core.langchain_core.documents import Document
from libs.core.langchain_core.utils import get_from_env
from langchain_community.document_loaders.base import BaseLoader
if TYPE_CHECKING:
from trello import Board, Card, TrelloClient
class TrelloLoader(BaseLoader):
"""Load cards from a `Trello` board."""
def __init__(
self,
client: TrelloClient,
board_name: str,
*,
include_card_name: bool = True,
include_comments: bool = True,
include_checklist: bool = True,
card_filter: Literal["closed", "open", "all"] = "all",
extra_metadata: Tuple[str, ...] = ("due_date", "labels", "list", "closed"),
):
"""Initialize Trello loader.
Args:
client: Trello API client.
board_name: The name of the Trello board.
include_card_name: Whether to include the name of the card in the document.
include_comments: Whether to include the comments on the card in the
document.
include_checklist: Whether to include the checklist on the card in the
document.
card_filter: Filter on card status. Valid values are "closed", "open",
"all".
extra_metadata: List of additional metadata fields to include as document
metadata.Valid values are "due_date", "labels", "list", "closed".
"""
self.client = client
self.board_name = board_name
self.include_card_name = include_card_name
self.include_comments = include_comments
self.include_checklist = include_checklist
self.extra_metadata = extra_metadata
self.card_filter = card_filter
@classmethod
def from_credentials(
cls,
board_name: str,
*,
api_key: Optional[str] = None,
token: Optional[str] = None,
**kwargs: Any,
) -> TrelloLoader:
"""Convenience constructor that builds TrelloClient init param for you.
Args:
board_name: The name of the Trello board.
api_key: Trello API key. Can also be specified as environment variable
TRELLO_API_KEY.
token: Trello token. Can also be specified as environment variable
TRELLO_TOKEN.
include_card_name: Whether to include the name of the card in the document.
include_comments: Whether to include the comments on the card in the
document.
include_checklist: Whether to include the checklist on the card in the
document.
card_filter: Filter on card status. Valid values are "closed", "open",
"all".
extra_metadata: List of additional metadata fields to include as document
metadata.Valid values are "due_date", "labels", "list", "closed".
"""
try:
from trello import TrelloClient # type: ignore
except ImportError as ex:
raise ImportError(
"Could not import trello python package. "
"Please install it with `pip install py-trello`."
) from ex
api_key = api_key or get_from_env("api_key", "TRELLO_API_KEY")
token = token or get_from_env("token", "TRELLO_TOKEN")
client = TrelloClient(api_key=api_key, token=token)
return cls(client, board_name, **kwargs)
def load(self) -> List[Document]:
"""Loads all cards from the specified Trello board.
You can filter the cards, metadata and text included by using the optional
parameters.
Returns:
A list of documents, one for each card in the board.
"""
try:
from bs4 import BeautifulSoup # noqa: F401
except ImportError as ex:
raise ImportError(
"`beautifulsoup4` package not found, please run"
" `pip install beautifulsoup4`"
) from ex
board = self._get_board()
# Create a dictionary with the list IDs as keys and the list names as values
list_dict = {list_item.id: list_item.name for list_item in board.list_lists()}
# Get Cards on the board
cards = board.get_cards(card_filter=self.card_filter)
return [self._card_to_doc(card, list_dict) for card in cards]
def _get_board(self) -> Board:
# Find the first board with a matching name
board = next(
(b for b in self.client.list_boards() if b.name == self.board_name), None
)
if not board:
raise ValueError(f"Board `{self.board_name}` not found.")
return board
def _card_to_doc(self, card: Card, list_dict: dict) -> Document:
from bs4 import BeautifulSoup # type: ignore
text_content = ""
if self.include_card_name:
text_content = card.name + "\n"
if card.description.strip():
text_content += BeautifulSoup(card.description, "lxml").get_text()
if self.include_checklist:
# Get all the checklist items on the card
for checklist in card.checklists:
if checklist.items:
items = [
f"{item['name']}:{item['state']}" for item in checklist.items
]
text_content += f"\n{checklist.name}\n" + "\n".join(items)
if self.include_comments:
# Get all the comments on the card
comments = [
BeautifulSoup(comment["data"]["text"], "lxml").get_text()
for comment in card.comments
]
text_content += "Comments:" + "\n".join(comments)
# Default metadata fields
metadata = {
"title": card.name,
"id": card.id,
"url": card.url,
}
# Extra metadata fields. Card object is not subscriptable.
if "labels" in self.extra_metadata:
metadata["labels"] = [label.name for label in card.labels]
if "list" in self.extra_metadata:
if card.list_id in list_dict:
metadata["list"] = list_dict[card.list_id]
if "closed" in self.extra_metadata:
metadata["closed"] = card.closed
if "due_date" in self.extra_metadata:
metadata["due_date"] = card.due_date
return Document(page_content=text_content, metadata=metadata)
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~chat_models~mlflow_ai_gateway.py | import logging
import warnings
from typing import Any, Dict, List, Mapping, Optional
from libs.core.langchain_core.callbacks import (
CallbackManagerForLLMRun,
)
from libs.core.langchain_core.language_models.chat_models import BaseChatModel
from libs.core.langchain_core.messages import (
AIMessage,
BaseMessage,
ChatMessage,
FunctionMessage,
HumanMessage,
SystemMessage,
)
from libs.core.langchain_core.outputs import (
ChatGeneration,
ChatResult,
)
from libs.core.langchain_core.pydantic_v1 import BaseModel, Extra
logger = logging.getLogger(__name__)
# Ignoring type because below is valid pydantic code
# Unexpected keyword argument "extra" for "__init_subclass__" of "object" [call-arg]
class ChatParams(BaseModel, extra=Extra.allow): # type: ignore[call-arg]
"""Parameters for the `MLflow AI Gateway` LLM."""
temperature: float = 0.0
candidate_count: int = 1
"""The number of candidates to return."""
stop: Optional[List[str]] = None
max_tokens: Optional[int] = None
class ChatMLflowAIGateway(BaseChatModel):
"""`MLflow AI Gateway` chat models API.
To use, you should have the ``mlflow[gateway]`` python package installed.
For more information, see https://mlflow.org/docs/latest/gateway/index.html.
Example:
.. code-block:: python
from langchain_community.chat_models import ChatMLflowAIGateway
chat = ChatMLflowAIGateway(
gateway_uri="<your-mlflow-ai-gateway-uri>",
route="<your-mlflow-ai-gateway-chat-route>",
params={
"temperature": 0.1
}
)
"""
def __init__(self, **kwargs: Any):
warnings.warn(
"`ChatMLflowAIGateway` is deprecated. Use `ChatMlflow` or "
"`ChatDatabricks` instead.",
DeprecationWarning,
)
try:
import mlflow.gateway
except ImportError as e:
raise ImportError(
"Could not import `mlflow.gateway` module. "
"Please install it with `pip install mlflow[gateway]`."
) from e
super().__init__(**kwargs)
if self.gateway_uri:
mlflow.gateway.set_gateway_uri(self.gateway_uri)
route: str
gateway_uri: Optional[str] = None
params: Optional[ChatParams] = None
@property
def _default_params(self) -> Dict[str, Any]:
params: Dict[str, Any] = {
"gateway_uri": self.gateway_uri,
"route": self.route,
**(self.params.dict() if self.params else {}),
}
return params
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
try:
import mlflow.gateway
except ImportError as e:
raise ImportError(
"Could not import `mlflow.gateway` module. "
"Please install it with `pip install mlflow[gateway]`."
) from e
message_dicts = [
ChatMLflowAIGateway._convert_message_to_dict(message)
for message in messages
]
data: Dict[str, Any] = {
"messages": message_dicts,
**(self.params.dict() if self.params else {}),
}
resp = mlflow.gateway.query(self.route, data=data)
return ChatMLflowAIGateway._create_chat_result(resp)
@property
def _identifying_params(self) -> Dict[str, Any]:
return self._default_params
def _get_invocation_params(
self, stop: Optional[List[str]] = None, **kwargs: Any
) -> Dict[str, Any]:
"""Get the parameters used to invoke the model FOR THE CALLBACKS."""
return {
**self._default_params,
**super()._get_invocation_params(stop=stop, **kwargs),
}
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
return "mlflow-ai-gateway-chat"
@staticmethod
def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
role = _dict["role"]
content = _dict["content"]
if role == "user":
return HumanMessage(content=content)
elif role == "assistant":
return AIMessage(content=content)
elif role == "system":
return SystemMessage(content=content)
else:
return ChatMessage(content=content, role=role)
@staticmethod
def _raise_functions_not_supported() -> None:
raise ValueError(
"Function messages are not supported by the MLflow AI Gateway. Please"
" create a feature request at https://github.com/mlflow/mlflow/issues."
)
@staticmethod
def _convert_message_to_dict(message: BaseMessage) -> dict:
if isinstance(message, ChatMessage):
message_dict = {"role": message.role, "content": message.content}
elif isinstance(message, HumanMessage):
message_dict = {"role": "user", "content": message.content}
elif isinstance(message, AIMessage):
message_dict = {"role": "assistant", "content": message.content}
elif isinstance(message, SystemMessage):
message_dict = {"role": "system", "content": message.content}
elif isinstance(message, FunctionMessage):
raise ValueError(
"Function messages are not supported by the MLflow AI Gateway. Please"
" create a feature request at https://github.com/mlflow/mlflow/issues."
)
else:
raise ValueError(f"Got unknown message type: {message}")
if "function_call" in message.additional_kwargs:
ChatMLflowAIGateway._raise_functions_not_supported()
if message.additional_kwargs:
logger.warning(
"Additional message arguments are unsupported by MLflow AI Gateway "
" and will be ignored: %s",
message.additional_kwargs,
)
return message_dict
@staticmethod
def _create_chat_result(response: Mapping[str, Any]) -> ChatResult:
generations = []
for candidate in response["candidates"]:
message = ChatMLflowAIGateway._convert_dict_to_message(candidate["message"])
message_metadata = candidate.get("metadata", {})
gen = ChatGeneration(
message=message,
generation_info=dict(message_metadata),
)
generations.append(gen)
response_metadata = response.get("metadata", {})
return ChatResult(generations=generations, llm_output=response_metadata)
| [] |
2024-01-10 | mth93/langchain | libs~community~tests~integration_tests~chat_models~test_litellm.py | """Test Anthropic API wrapper."""
from typing import List
from libs.core.langchain_core.callbacks import (
CallbackManager,
)
from libs.core.langchain_core.messages import AIMessage, BaseMessage, HumanMessage
from libs.core.langchain_core.outputs import ChatGeneration, LLMResult
from langchain_community.chat_models.litellm import ChatLiteLLM
from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
def test_litellm_call() -> None:
"""Test valid call to litellm."""
chat = ChatLiteLLM(
model="test",
)
message = HumanMessage(content="Hello")
response = chat([message])
assert isinstance(response, AIMessage)
assert isinstance(response.content, str)
def test_litellm_generate() -> None:
"""Test generate method of anthropic."""
chat = ChatLiteLLM(model="test")
chat_messages: List[List[BaseMessage]] = [
[HumanMessage(content="How many toes do dogs have?")]
]
messages_copy = [messages.copy() for messages in chat_messages]
result: LLMResult = chat.generate(chat_messages)
assert isinstance(result, LLMResult)
for response in result.generations[0]:
assert isinstance(response, ChatGeneration)
assert isinstance(response.text, str)
assert response.text == response.message.content
assert chat_messages == messages_copy
def test_litellm_streaming() -> None:
"""Test streaming tokens from anthropic."""
chat = ChatLiteLLM(model="test", streaming=True)
message = HumanMessage(content="Hello")
response = chat([message])
assert isinstance(response, AIMessage)
assert isinstance(response.content, str)
def test_litellm_streaming_callback() -> None:
"""Test that streaming correctly invokes on_llm_new_token callback."""
callback_handler = FakeCallbackHandler()
callback_manager = CallbackManager([callback_handler])
chat = ChatLiteLLM(
model="test",
streaming=True,
callback_manager=callback_manager,
verbose=True,
)
message = HumanMessage(content="Write me a sentence with 10 words.")
chat([message])
assert callback_handler.llm_streams > 1
| [
"How many toes do dogs have?",
"Write me a sentence with 10 words.",
"Hello"
] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~vectorstores~jaguar.py | from __future__ import annotations
import json
import logging
from typing import Any, List, Optional, Tuple
from libs.core.langchain_core.documents import Document
from libs.core.langchain_core.embeddings import Embeddings
from libs.core.langchain_core.vectorstores import VectorStore
logger = logging.getLogger(__name__)
class Jaguar(VectorStore):
"""`Jaguar API` vector store.
See http://www.jaguardb.com
See http://github.com/fserv/jaguar-sdk
Example:
.. code-block:: python
from langchain_community.vectorstores.jaguar import Jaguar
vectorstore = Jaguar(
pod = 'vdb',
store = 'mystore',
vector_index = 'v',
vector_type = 'cosine_fraction_float',
vector_dimension = 1536,
url='http://192.168.8.88:8080/fwww/',
embedding=openai_model
)
"""
def __init__(
self,
pod: str,
store: str,
vector_index: str,
vector_type: str,
vector_dimension: int,
url: str,
embedding: Embeddings,
):
self._pod = pod
self._store = store
self._vector_index = vector_index
self._vector_type = vector_type
self._vector_dimension = vector_dimension
self._embedding = embedding
try:
from jaguardb_http_client.JaguarHttpClient import JaguarHttpClient
except ImportError:
raise ValueError(
"Could not import jaguardb-http-client python package. "
"Please install it with `pip install -U jaguardb-http-client`"
)
self._jag = JaguarHttpClient(url)
self._token = ""
def login(
self,
jaguar_api_key: Optional[str] = "",
) -> bool:
"""
login to jaguardb server with a jaguar_api_key or let self._jag find a key
Args:
pod (str): name of a Pod
store (str): name of a vector store
optional jaguar_api_key (str): API key of user to jaguardb server
Returns:
True if successful; False if not successful
"""
if jaguar_api_key == "":
jaguar_api_key = self._jag.getApiKey()
self._jaguar_api_key = jaguar_api_key
self._token = self._jag.login(jaguar_api_key)
if self._token == "":
logger.error("E0001 error init(): invalid jaguar_api_key")
return False
return True
def create(
self,
metadata_str: str,
text_size: int,
) -> None:
"""
create the vector store on the backend database
Args:
metadata_str (str): columns and their types
Returns:
True if successful; False if not successful
"""
podstore = self._pod + "." + self._store
"""
source column is required.
v:text column is required.
"""
q = "create store "
q += podstore
q += f" ({self._vector_index} vector({self._vector_dimension},"
q += f" '{self._vector_type}'),"
q += f" source char(256), v:text char({text_size}),"
q += metadata_str + ")"
self.run(q)
def run(self, query: str, withFile: bool = False) -> dict:
"""
Run any query statement in jaguardb
Args:
query (str): query statement to jaguardb
Returns:
None for invalid token, or
json result string
"""
if self._token == "":
logger.error(f"E0005 error run({query})")
return {}
resp = self._jag.post(query, self._token, withFile)
txt = resp.text
try:
js = json.loads(txt)
return js
except Exception:
return {}
@property
def embeddings(self) -> Optional[Embeddings]:
return self._embedding
def add_texts(
self,
texts: List[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""
Add texts through the embeddings and add to the vectorstore.
Args:
texts: list of text strings to add to the jaguar vector store.
metadatas: Optional list of metadatas associated with the texts.
[{"m1": "v11", "m2": "v12", "m3": "v13", "filecol": "path_file1.jpg" },
{"m1": "v21", "m2": "v22", "m3": "v23", "filecol": "path_file2.jpg" },
{"m1": "v31", "m2": "v32", "m3": "v33", "filecol": "path_file3.jpg" },
{"m1": "v41", "m2": "v42", "m3": "v43", "filecol": "path_file4.jpg" }]
kwargs: vector_index=name_of_vector_index
file_column=name_of_file_column
Returns:
List of ids from adding the texts into the vectorstore
"""
vcol = self._vector_index
filecol = kwargs.get("file_column", "")
podstorevcol = self._pod + "." + self._store + "." + vcol
q = "textcol " + podstorevcol
js = self.run(q)
if js == "":
return []
textcol = js["data"]
embeddings = self._embedding.embed_documents(list(texts))
ids = []
if metadatas is None:
### no meta and no files to upload
i = 0
for vec in embeddings:
str_vec = [str(x) for x in vec]
values_comma = ",".join(str_vec)
podstore = self._pod + "." + self._store
q = "insert into " + podstore + " ("
q += vcol + "," + textcol + ") values ('" + values_comma
q += "','" + texts[i] + "')"
js = self.run(q, False)
ids.append(js["zid"])
i += 1
else:
i = 0
for vec in embeddings:
str_vec = [str(x) for x in vec]
nvec, vvec, filepath = self._parseMeta(metadatas[i], filecol)
if filecol != "":
rc = self._jag.postFile(self._token, filepath, 1)
if not rc:
return []
names_comma = ",".join(nvec)
names_comma += "," + vcol
## col1,col2,col3,vecl
values_comma = "'" + "','".join(vvec) + "'"
### 'va1','val2','val3'
values_comma += ",'" + ",".join(str_vec) + "'"
### 'v1,v2,v3'
podstore = self._pod + "." + self._store
q = "insert into " + podstore + " ("
q += names_comma + "," + textcol + ") values (" + values_comma
q += ",'" + texts[i] + "')"
if filecol != "":
js = self.run(q, True)
else:
js = self.run(q, False)
ids.append(js["zid"])
i += 1
return ids
def similarity_search_with_score(
self,
query: str,
k: int = 3,
fetch_k: int = -1,
where: Optional[str] = None,
score_threshold: Optional[float] = -1.0,
metadatas: Optional[List[str]] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""
Return Jaguar documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 3.
lambda_val: lexical match parameter for hybrid search.
where: the where clause in select similarity. For example a
where can be "rating > 3.0 and (state = 'NV' or state = 'CA')"
score_threshold: minimal score threshold for the result.
If defined, results with score less than this value will be
filtered out.
kwargs: vector_index=vcol, vector_type=cosine_fraction_float
Returns:
List of Documents most similar to the query and score for each.
List of Tuples of (doc, similarity_score):
[ (doc, score), (doc, score), ...]
"""
vcol = self._vector_index
vtype = self._vector_type
embeddings = self._embedding.embed_query(query)
str_embeddings = [str(f) for f in embeddings]
qv_comma = ",".join(str_embeddings)
podstore = self._pod + "." + self._store
q = (
"select similarity("
+ vcol
+ ",'"
+ qv_comma
+ "','topk="
+ str(k)
+ ",fetch_k="
+ str(fetch_k)
+ ",type="
+ vtype
)
q += ",with_score=yes,with_text=yes,score_threshold=" + str(score_threshold)
if metadatas is not None:
meta = "&".join(metadatas)
q += ",metadata=" + meta
q += "') from " + podstore
if where is not None:
q += " where " + where
jarr = self.run(q)
if jarr is None:
return []
docs_with_score = []
for js in jarr:
score = js["score"]
text = js["text"]
zid = js["zid"]
### give metadatas
md = {}
md["zid"] = zid
if metadatas is not None:
for m in metadatas:
mv = js[m]
md[m] = mv
doc = Document(page_content=text, metadata=md)
tup = (doc, score)
docs_with_score.append(tup)
return docs_with_score
def similarity_search(
self,
query: str,
k: int = 3,
where: Optional[str] = None,
metadatas: Optional[List[str]] = None,
**kwargs: Any,
) -> List[Document]:
"""
Return Jaguar documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 5.
where: the where clause in select similarity. For example a
where can be "rating > 3.0 and (state = 'NV' or state = 'CA')"
Returns:
List of Documents most similar to the query
"""
docs_and_scores = self.similarity_search_with_score(
query, k=k, where=where, metadatas=metadatas, **kwargs
)
return [doc for doc, _ in docs_and_scores]
def is_anomalous(
self,
query: str,
**kwargs: Any,
) -> bool:
"""
Detect if given text is anomalous from the dataset
Args:
query: Text to detect if it is anomaly
Returns:
True or False
"""
vcol = self._vector_index
vtype = self._vector_type
embeddings = self._embedding.embed_query(query)
str_embeddings = [str(f) for f in embeddings]
qv_comma = ",".join(str_embeddings)
podstore = self._pod + "." + self._store
q = "select anomalous(" + vcol + ", '" + qv_comma + "', 'type=" + vtype + "')"
q += " from " + podstore
js = self.run(q)
if isinstance(js, list) and len(js) == 0:
return False
jd = json.loads(js[0])
if jd["anomalous"] == "YES":
return True
return False
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
url: str,
pod: str,
store: str,
vector_index: str,
vector_type: str,
vector_dimension: int,
metadatas: Optional[List[dict]] = None,
jaguar_api_key: Optional[str] = "",
**kwargs: Any,
) -> Jaguar:
jagstore = cls(
pod, store, vector_index, vector_type, vector_dimension, url, embedding
)
jagstore.login(jaguar_api_key)
jagstore.clear()
jagstore.add_texts(texts, metadatas, **kwargs)
return jagstore
def clear(self) -> None:
"""
Delete all records in jaguardb
Args: No args
Returns: None
"""
podstore = self._pod + "." + self._store
q = "truncate store " + podstore
self.run(q)
def delete(self, zids: List[str], **kwargs: Any) -> None:
"""
Delete records in jaguardb by a list of zero-ids
Args:
pod (str): name of a Pod
ids (List[str]): a list of zid as string
Returns:
Do not return anything
"""
podstore = self._pod + "." + self._store
for zid in zids:
q = "delete from " + podstore + " where zid='" + zid + "'"
self.run(q)
def count(self) -> int:
"""
Count records of a store in jaguardb
Args: no args
Returns: (int) number of records in pod store
"""
podstore = self._pod + "." + self._store
q = "select count() from " + podstore
js = self.run(q)
if isinstance(js, list) and len(js) == 0:
return 0
jd = json.loads(js[0])
return int(jd["data"])
def drop(self) -> None:
"""
Drop or remove a store in jaguardb
Args: no args
Returns: None
"""
podstore = self._pod + "." + self._store
q = "drop store " + podstore
self.run(q)
def logout(self) -> None:
"""
Logout to cleanup resources
Args: no args
Returns: None
"""
self._jag.logout(self._token)
def prt(self, msg: str) -> None:
with open("/tmp/debugjaguar.log", "a") as file:
print(f"msg={msg}", file=file, flush=True)
def _parseMeta(self, nvmap: dict, filecol: str) -> Tuple[List[str], List[str], str]:
filepath = ""
if filecol == "":
nvec = list(nvmap.keys())
vvec = list(nvmap.values())
else:
nvec = []
vvec = []
if filecol in nvmap:
nvec.append(filecol)
vvec.append(nvmap[filecol])
filepath = nvmap[filecol]
for k, v in nvmap.items():
if k != filecol:
nvec.append(k)
vvec.append(v)
return nvec, vvec, filepath
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~embeddings~clarifai.py | import logging
from typing import Dict, List, Optional
from libs.core.langchain_core.embeddings import Embeddings
from libs.core.langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
from libs.core.langchain_core.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
class ClarifaiEmbeddings(BaseModel, Embeddings):
"""Clarifai embedding models.
To use, you should have the ``clarifai`` python package installed, and the
environment variable ``CLARIFAI_PAT`` set with your personal access token or pass it
as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain_community.embeddings import ClarifaiEmbeddings
clarifai = ClarifaiEmbeddings(user_id=USER_ID,
app_id=APP_ID,
model_id=MODEL_ID)
(or)
clarifai_llm = Clarifai(model_url=EXAMPLE_URL)
"""
model_url: Optional[str] = None
"""Model url to use."""
model_id: Optional[str] = None
"""Model id to use."""
model_version_id: Optional[str] = None
"""Model version id to use."""
app_id: Optional[str] = None
"""Clarifai application id to use."""
user_id: Optional[str] = None
"""Clarifai user id to use."""
pat: Optional[str] = None
"""Clarifai personal access token to use."""
api_base: str = "https://api.clarifai.com"
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that we have all required info to access Clarifai
platform and python package exists in environment."""
values["pat"] = get_from_dict_or_env(values, "pat", "CLARIFAI_PAT")
user_id = values.get("user_id")
app_id = values.get("app_id")
model_id = values.get("model_id")
model_url = values.get("model_url")
if model_url is not None and model_id is not None:
raise ValueError("Please provide either model_url or model_id, not both.")
if model_url is None and model_id is None:
raise ValueError("Please provide one of model_url or model_id.")
if model_url is None and model_id is not None:
if user_id is None or app_id is None:
raise ValueError("Please provide a user_id and app_id.")
return values
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Call out to Clarifai's embedding models.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
try:
from clarifai.client.input import Inputs
from clarifai.client.model import Model
except ImportError:
raise ImportError(
"Could not import clarifai python package. "
"Please install it with `pip install clarifai`."
)
if self.pat is not None:
pat = self.pat
if self.model_url is not None:
_model_init = Model(url=self.model_url, pat=pat)
else:
_model_init = Model(
model_id=self.model_id,
user_id=self.user_id,
app_id=self.app_id,
pat=pat,
)
input_obj = Inputs(pat=pat)
batch_size = 32
embeddings = []
try:
for i in range(0, len(texts), batch_size):
batch = texts[i : i + batch_size]
input_batch = [
input_obj.get_text_input(input_id=str(id), raw_text=inp)
for id, inp in enumerate(batch)
]
predict_response = _model_init.predict(input_batch)
embeddings.extend(
[
list(output.data.embeddings[0].vector)
for output in predict_response.outputs
]
)
except Exception as e:
logger.error(f"Predict failed, exception: {e}")
return embeddings
def embed_query(self, text: str) -> List[float]:
"""Call out to Clarifai's embedding models.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
try:
from clarifai.client.model import Model
except ImportError:
raise ImportError(
"Could not import clarifai python package. "
"Please install it with `pip install clarifai`."
)
if self.pat is not None:
pat = self.pat
if self.model_url is not None:
_model_init = Model(url=self.model_url, pat=pat)
else:
_model_init = Model(
model_id=self.model_id,
user_id=self.user_id,
app_id=self.app_id,
pat=pat,
)
try:
predict_response = _model_init.predict_by_bytes(
bytes(text, "utf-8"), input_type="text"
)
embeddings = [
list(op.data.embeddings[0].vector) for op in predict_response.outputs
]
except Exception as e:
logger.error(f"Predict failed, exception: {e}")
return embeddings[0]
| [] |
2024-01-10 | mth93/langchain | libs~langchain~langchain~retrievers~multi_query.py | import asyncio
import logging
from typing import List, Sequence
from libs.core.langchain_core.documents import Document
from libs.core.langchain_core.prompts.prompt import PromptTemplate
from libs.core.langchain_core.pydantic_v1 import BaseModel, Field
from libs.core.langchain_core.retrievers import BaseRetriever
from langchain.callbacks.manager import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain.chains.llm import LLMChain
from langchain.llms.base import BaseLLM
from langchain.output_parsers.pydantic import PydanticOutputParser
logger = logging.getLogger(__name__)
class LineList(BaseModel):
"""List of lines."""
lines: List[str] = Field(description="Lines of text")
"""List of lines."""
class LineListOutputParser(PydanticOutputParser):
"""Output parser for a list of lines."""
def __init__(self) -> None:
super().__init__(pydantic_object=LineList)
def parse(self, text: str) -> LineList:
lines = text.strip().split("\n")
return LineList(lines=lines)
# Default prompt
DEFAULT_QUERY_PROMPT = PromptTemplate(
input_variables=["question"],
template="""You are an AI language model assistant. Your task is
to generate 3 different versions of the given user
question to retrieve relevant documents from a vector database.
By generating multiple perspectives on the user question,
your goal is to help the user overcome some of the limitations
of distance-based similarity search. Provide these alternative
questions separated by newlines. Original question: {question}""",
)
def _unique_documents(documents: Sequence[Document]) -> List[Document]:
return [doc for i, doc in enumerate(documents) if doc not in documents[:i]]
class MultiQueryRetriever(BaseRetriever):
"""Given a query, use an LLM to write a set of queries.
Retrieve docs for each query. Return the unique union of all retrieved docs.
"""
retriever: BaseRetriever
llm_chain: LLMChain
verbose: bool = True
parser_key: str = "lines"
include_original: bool = False
"""Whether to include the original query in the list of generated queries."""
@classmethod
def from_llm(
cls,
retriever: BaseRetriever,
llm: BaseLLM,
prompt: PromptTemplate = DEFAULT_QUERY_PROMPT,
parser_key: str = "lines",
include_original: bool = False,
) -> "MultiQueryRetriever":
"""Initialize from llm using default template.
Args:
retriever: retriever to query documents from
llm: llm for query generation using DEFAULT_QUERY_PROMPT
include_original: Whether to include the original query in the list of
generated queries.
Returns:
MultiQueryRetriever
"""
output_parser = LineListOutputParser()
llm_chain = LLMChain(llm=llm, prompt=prompt, output_parser=output_parser)
return cls(
retriever=retriever,
llm_chain=llm_chain,
parser_key=parser_key,
include_original=include_original,
)
async def _aget_relevant_documents(
self,
query: str,
*,
run_manager: AsyncCallbackManagerForRetrieverRun,
) -> List[Document]:
"""Get relevant documents given a user query.
Args:
question: user query
Returns:
Unique union of relevant documents from all generated queries
"""
queries = await self.agenerate_queries(query, run_manager)
if self.include_original:
queries.append(query)
documents = await self.aretrieve_documents(queries, run_manager)
return self.unique_union(documents)
async def agenerate_queries(
self, question: str, run_manager: AsyncCallbackManagerForRetrieverRun
) -> List[str]:
"""Generate queries based upon user input.
Args:
question: user query
Returns:
List of LLM generated queries that are similar to the user input
"""
response = await self.llm_chain.acall(
inputs={"question": question}, callbacks=run_manager.get_child()
)
lines = getattr(response["text"], self.parser_key, [])
if self.verbose:
logger.info(f"Generated queries: {lines}")
return lines
async def aretrieve_documents(
self, queries: List[str], run_manager: AsyncCallbackManagerForRetrieverRun
) -> List[Document]:
"""Run all LLM generated queries.
Args:
queries: query list
Returns:
List of retrieved Documents
"""
document_lists = await asyncio.gather(
*(
self.retriever.aget_relevant_documents(
query, callbacks=run_manager.get_child()
)
for query in queries
)
)
return [doc for docs in document_lists for doc in docs]
def _get_relevant_documents(
self,
query: str,
*,
run_manager: CallbackManagerForRetrieverRun,
) -> List[Document]:
"""Get relevant documents given a user query.
Args:
question: user query
Returns:
Unique union of relevant documents from all generated queries
"""
queries = self.generate_queries(query, run_manager)
if self.include_original:
queries.append(query)
documents = self.retrieve_documents(queries, run_manager)
return self.unique_union(documents)
def generate_queries(
self, question: str, run_manager: CallbackManagerForRetrieverRun
) -> List[str]:
"""Generate queries based upon user input.
Args:
question: user query
Returns:
List of LLM generated queries that are similar to the user input
"""
response = self.llm_chain(
{"question": question}, callbacks=run_manager.get_child()
)
lines = getattr(response["text"], self.parser_key, [])
if self.verbose:
logger.info(f"Generated queries: {lines}")
return lines
def retrieve_documents(
self, queries: List[str], run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
"""Run all LLM generated queries.
Args:
queries: query list
Returns:
List of retrieved Documents
"""
documents = []
for query in queries:
docs = self.retriever.get_relevant_documents(
query, callbacks=run_manager.get_child()
)
documents.extend(docs)
return documents
def unique_union(self, documents: List[Document]) -> List[Document]:
"""Get unique Documents.
Args:
documents: List of retrieved Documents
Returns:
List of unique retrieved Documents
"""
return _unique_documents(documents)
| [
"question",
"You are an AI language model assistant. Your task is \n to generate 3 different versions of the given user \n question to retrieve relevant documents from a vector database. \n By generating multiple perspectives on the user question, \n your goal is to help the user overcome some of the limitations \n of distance-based similarity search. Provide these alternative \n questions separated by newlines. Original question: {question}"
] |
2024-01-10 | mth93/langchain | libs~langchain~tests~integration_tests~cache~test_cassandra.py | """Test Cassandra caches. Requires a running vector-capable Cassandra cluster."""
import os
import time
from typing import Any, Iterator, Tuple
import pytest
from libs.core.langchain_core.outputs import Generation, LLMResult
from langchain.cache import CassandraCache, CassandraSemanticCache
from langchain.globals import get_llm_cache, set_llm_cache
from tests.integration_tests.cache.fake_embeddings import FakeEmbeddings
from tests.unit_tests.llms.fake_llm import FakeLLM
@pytest.fixture(scope="module")
def cassandra_connection() -> Iterator[Tuple[Any, str]]:
from cassandra.cluster import Cluster
keyspace = "langchain_cache_test_keyspace"
# get db connection
if "CASSANDRA_CONTACT_POINTS" in os.environ:
contact_points = os.environ["CONTACT_POINTS"].split(",")
cluster = Cluster(contact_points)
else:
cluster = Cluster()
#
session = cluster.connect()
# ensure keyspace exists
session.execute(
(
f"CREATE KEYSPACE IF NOT EXISTS {keyspace} "
f"WITH replication = {{'class': 'SimpleStrategy', 'replication_factor': 1}}"
)
)
yield (session, keyspace)
def test_cassandra_cache(cassandra_connection: Tuple[Any, str]) -> None:
session, keyspace = cassandra_connection
cache = CassandraCache(session=session, keyspace=keyspace)
set_llm_cache(cache)
llm = FakeLLM()
params = llm.dict()
params["stop"] = None
llm_string = str(sorted([(k, v) for k, v in params.items()]))
get_llm_cache().update("foo", llm_string, [Generation(text="fizz")])
output = llm.generate(["foo"])
print(output)
expected_output = LLMResult(
generations=[[Generation(text="fizz")]],
llm_output={},
)
print(expected_output)
assert output == expected_output
cache.clear()
def test_cassandra_cache_ttl(cassandra_connection: Tuple[Any, str]) -> None:
session, keyspace = cassandra_connection
cache = CassandraCache(session=session, keyspace=keyspace, ttl_seconds=2)
set_llm_cache(cache)
llm = FakeLLM()
params = llm.dict()
params["stop"] = None
llm_string = str(sorted([(k, v) for k, v in params.items()]))
get_llm_cache().update("foo", llm_string, [Generation(text="fizz")])
expected_output = LLMResult(
generations=[[Generation(text="fizz")]],
llm_output={},
)
output = llm.generate(["foo"])
assert output == expected_output
time.sleep(2.5)
# entry has expired away.
output = llm.generate(["foo"])
assert output != expected_output
cache.clear()
def test_cassandra_semantic_cache(cassandra_connection: Tuple[Any, str]) -> None:
session, keyspace = cassandra_connection
sem_cache = CassandraSemanticCache(
session=session,
keyspace=keyspace,
embedding=FakeEmbeddings(),
)
set_llm_cache(sem_cache)
llm = FakeLLM()
params = llm.dict()
params["stop"] = None
llm_string = str(sorted([(k, v) for k, v in params.items()]))
get_llm_cache().update("foo", llm_string, [Generation(text="fizz")])
output = llm.generate(["bar"]) # same embedding as 'foo'
expected_output = LLMResult(
generations=[[Generation(text="fizz")]],
llm_output={},
)
assert output == expected_output
# clear the cache
sem_cache.clear()
output = llm.generate(["bar"]) # 'fizz' is erased away now
assert output != expected_output
sem_cache.clear()
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_transformers~doctran_text_extract.py | from typing import Any, List, Optional, Sequence
from libs.core.langchain_core.documents import BaseDocumentTransformer, Document
from libs.core.langchain_core.utils import get_from_env
class DoctranPropertyExtractor(BaseDocumentTransformer):
"""Extract properties from text documents using doctran.
Arguments:
properties: A list of the properties to extract.
openai_api_key: OpenAI API key. Can also be specified via environment variable
``OPENAI_API_KEY``.
Example:
.. code-block:: python
from langchain_community.document_transformers import DoctranPropertyExtractor
properties = [
{
"name": "category",
"description": "What type of email this is.",
"type": "string",
"enum": ["update", "action_item", "customer_feedback", "announcement", "other"],
"required": True,
},
{
"name": "mentions",
"description": "A list of all people mentioned in this email.",
"type": "array",
"items": {
"name": "full_name",
"description": "The full name of the person mentioned.",
"type": "string",
},
"required": True,
},
{
"name": "eli5",
"description": "Explain this email to me like I'm 5 years old.",
"type": "string",
"required": True,
},
]
# Pass in openai_api_key or set env var OPENAI_API_KEY
property_extractor = DoctranPropertyExtractor(properties)
transformed_document = await qa_transformer.atransform_documents(documents)
""" # noqa: E501
def __init__(
self,
properties: List[dict],
openai_api_key: Optional[str] = None,
openai_api_model: Optional[str] = None,
) -> None:
self.properties = properties
self.openai_api_key = openai_api_key or get_from_env(
"openai_api_key", "OPENAI_API_KEY"
)
self.openai_api_model = openai_api_model or get_from_env(
"openai_api_model", "OPENAI_API_MODEL"
)
async def atransform_documents(
self, documents: Sequence[Document], **kwargs: Any
) -> Sequence[Document]:
raise NotImplementedError
def transform_documents(
self, documents: Sequence[Document], **kwargs: Any
) -> Sequence[Document]:
"""Extracts properties from text documents using doctran."""
try:
from doctran import Doctran, ExtractProperty
doctran = Doctran(
openai_api_key=self.openai_api_key, openai_model=self.openai_api_model
)
except ImportError:
raise ImportError(
"Install doctran to use this parser. (pip install doctran)"
)
properties = [ExtractProperty(**property) for property in self.properties]
for d in documents:
doctran_doc = (
doctran.parse(content=d.page_content)
.extract(properties=properties)
.execute()
)
d.metadata["extracted_properties"] = doctran_doc.extracted_properties
return documents
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~agent_toolkits~playwright~toolkit.py | """Playwright web browser toolkit."""
from __future__ import annotations
from typing import TYPE_CHECKING, List, Optional, Type, cast
from libs.core.langchain_core.pydantic_v1 import Extra, root_validator
from libs.core.langchain_core.tools import BaseTool
from langchain_community.agent_toolkits.base import BaseToolkit
from langchain_community.tools.playwright.base import (
BaseBrowserTool,
lazy_import_playwright_browsers,
)
from langchain_community.tools.playwright.click import ClickTool
from langchain_community.tools.playwright.current_page import CurrentWebPageTool
from langchain_community.tools.playwright.extract_hyperlinks import (
ExtractHyperlinksTool,
)
from langchain_community.tools.playwright.extract_text import ExtractTextTool
from langchain_community.tools.playwright.get_elements import GetElementsTool
from langchain_community.tools.playwright.navigate import NavigateTool
from langchain_community.tools.playwright.navigate_back import NavigateBackTool
if TYPE_CHECKING:
from playwright.async_api import Browser as AsyncBrowser
from playwright.sync_api import Browser as SyncBrowser
else:
try:
# We do this so pydantic can resolve the types when instantiating
from playwright.async_api import Browser as AsyncBrowser
from playwright.sync_api import Browser as SyncBrowser
except ImportError:
pass
class PlayWrightBrowserToolkit(BaseToolkit):
"""Toolkit for PlayWright browser tools.
**Security Note**: This toolkit provides code to control a web-browser.
Careful if exposing this toolkit to end-users. The tools in the toolkit
are capable of navigating to arbitrary webpages, clicking on arbitrary
elements, and extracting arbitrary text and hyperlinks from webpages.
Specifically, by default this toolkit allows navigating to:
- Any URL (including any internal network URLs)
- And local files
If exposing to end-users, consider limiting network access to the
server that hosts the agent; in addition, consider it is advised
to create a custom NavigationTool wht an args_schema that limits the URLs
that can be navigated to (e.g., only allow navigating to URLs that
start with a particular prefix).
Remember to scope permissions to the minimal permissions necessary for
the application. If the default tool selection is not appropriate for
the application, consider creating a custom toolkit with the appropriate
tools.
See https://python.langchain.com/docs/security for more information.
"""
sync_browser: Optional["SyncBrowser"] = None
async_browser: Optional["AsyncBrowser"] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@root_validator
def validate_imports_and_browser_provided(cls, values: dict) -> dict:
"""Check that the arguments are valid."""
lazy_import_playwright_browsers()
if values.get("async_browser") is None and values.get("sync_browser") is None:
raise ValueError("Either async_browser or sync_browser must be specified.")
return values
def get_tools(self) -> List[BaseTool]:
"""Get the tools in the toolkit."""
tool_classes: List[Type[BaseBrowserTool]] = [
ClickTool,
NavigateTool,
NavigateBackTool,
ExtractTextTool,
ExtractHyperlinksTool,
GetElementsTool,
CurrentWebPageTool,
]
tools = [
tool_cls.from_browser(
sync_browser=self.sync_browser, async_browser=self.async_browser
)
for tool_cls in tool_classes
]
return cast(List[BaseTool], tools)
@classmethod
def from_browser(
cls,
sync_browser: Optional[SyncBrowser] = None,
async_browser: Optional[AsyncBrowser] = None,
) -> PlayWrightBrowserToolkit:
"""Instantiate the toolkit."""
# This is to raise a better error than the forward ref ones Pydantic would have
lazy_import_playwright_browsers()
return cls(sync_browser=sync_browser, async_browser=async_browser)
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~llms~tongyi.py | from __future__ import annotations
import asyncio
import functools
import logging
from typing import (
Any,
AsyncIterator,
Callable,
Dict,
Iterator,
List,
Mapping,
Optional,
)
from libs.core.langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from libs.core.langchain_core.language_models.llms import BaseLLM
from libs.core.langchain_core.outputs import Generation, GenerationChunk, LLMResult
from libs.core.langchain_core.pydantic_v1 import Field, root_validator
from libs.core.langchain_core.utils import get_from_dict_or_env
from requests.exceptions import HTTPError
from tenacity import (
before_sleep_log,
retry,
retry_if_exception_type,
stop_after_attempt,
wait_exponential,
)
logger = logging.getLogger(__name__)
def _create_retry_decorator(llm: Tongyi) -> Callable[[Any], Any]:
min_seconds = 1
max_seconds = 4
# Wait 2^x * 1 second between each retry starting with
# 4 seconds, then up to 10 seconds, then 10 seconds afterward
return retry(
reraise=True,
stop=stop_after_attempt(llm.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=(retry_if_exception_type(HTTPError)),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
def check_response(resp: Any) -> Any:
"""Check the response from the completion call."""
if resp.status_code == 200:
return resp
elif resp.status_code in [400, 401]:
raise ValueError(
f"status_code: {resp.status_code} \n "
f"code: {resp.code} \n message: {resp.message}"
)
else:
raise HTTPError(
f"HTTP error occurred: status_code: {resp.status_code} \n "
f"code: {resp.code} \n message: {resp.message}",
response=resp,
)
def generate_with_retry(llm: Tongyi, **kwargs: Any) -> Any:
"""Use tenacity to retry the completion call."""
retry_decorator = _create_retry_decorator(llm)
@retry_decorator
def _generate_with_retry(**_kwargs: Any) -> Any:
resp = llm.client.call(**_kwargs)
return check_response(resp)
return _generate_with_retry(**kwargs)
def stream_generate_with_retry(llm: Tongyi, **kwargs: Any) -> Any:
"""Use tenacity to retry the completion call."""
retry_decorator = _create_retry_decorator(llm)
@retry_decorator
def _stream_generate_with_retry(**_kwargs: Any) -> Any:
responses = llm.client.call(**_kwargs)
for resp in responses:
yield check_response(resp)
return _stream_generate_with_retry(**kwargs)
async def astream_generate_with_retry(llm: Tongyi, **kwargs: Any) -> Any:
"""Because the dashscope SDK doesn't provide an async API,
we wrap `stream_generate_with_retry` with an async generator."""
class _AioTongyiGenerator:
def __init__(self, _llm: Tongyi, **_kwargs: Any):
self.generator = stream_generate_with_retry(_llm, **_kwargs)
def __aiter__(self) -> AsyncIterator[Any]:
return self
async def __anext__(self) -> Any:
value = await asyncio.get_running_loop().run_in_executor(
None, self._safe_next
)
if value is not None:
return value
else:
raise StopAsyncIteration
def _safe_next(self) -> Any:
try:
return next(self.generator)
except StopIteration:
return None
async for chunk in _AioTongyiGenerator(llm, **kwargs):
yield chunk
class Tongyi(BaseLLM):
"""Tongyi Qwen large language models.
To use, you should have the ``dashscope`` python package installed, and the
environment variable ``DASHSCOPE_API_KEY`` set with your API key, or pass
it as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain_community.llms import Tongyi
tongyi = tongyi()
"""
@property
def lc_secrets(self) -> Dict[str, str]:
return {"dashscope_api_key": "DASHSCOPE_API_KEY"}
client: Any #: :meta private:
model_name: str = "qwen-plus"
"""Model name to use."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
top_p: float = 0.8
"""Total probability mass of tokens to consider at each step."""
dashscope_api_key: Optional[str] = None
"""Dashscope api key provide by Alibaba Cloud."""
streaming: bool = False
"""Whether to stream the results or not."""
max_retries: int = 10
"""Maximum number of retries to make when generating."""
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "tongyi"
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
values["dashscope_api_key"] = get_from_dict_or_env(
values, "dashscope_api_key", "DASHSCOPE_API_KEY"
)
try:
import dashscope
except ImportError:
raise ImportError(
"Could not import dashscope python package. "
"Please install it with `pip install dashscope`."
)
try:
values["client"] = dashscope.Generation
except AttributeError:
raise ValueError(
"`dashscope` has no `Generation` attribute, this is likely "
"due to an old version of the dashscope package. Try upgrading it "
"with `pip install --upgrade dashscope`."
)
return values
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling Tongyi Qwen API."""
normal_params = {
"model": self.model_name,
"top_p": self.top_p,
"api_key": self.dashscope_api_key,
}
return {**normal_params, **self.model_kwargs}
@property
def _identifying_params(self) -> Mapping[str, Any]:
return {"model_name": self.model_name, **super()._identifying_params}
def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
generations = []
if self.streaming:
if len(prompts) > 1:
raise ValueError("Cannot stream results with multiple prompts.")
generation: Optional[GenerationChunk] = None
for chunk in self._stream(prompts[0], stop, run_manager, **kwargs):
if generation is None:
generation = chunk
else:
generation += chunk
assert generation is not None
generations.append([self._chunk_to_generation(generation)])
else:
params: Dict[str, Any] = self._invocation_params(stop=stop, **kwargs)
for prompt in prompts:
completion = generate_with_retry(self, prompt=prompt, **params)
generations.append(
[Generation(**self._generation_from_qwen_resp(completion))]
)
return LLMResult(
generations=generations,
llm_output={
"model_name": self.model_name,
},
)
async def _agenerate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
generations = []
if self.streaming:
if len(prompts) > 1:
raise ValueError("Cannot stream results with multiple prompts.")
generation: Optional[GenerationChunk] = None
async for chunk in self._astream(prompts[0], stop, run_manager, **kwargs):
if generation is None:
generation = chunk
else:
generation += chunk
assert generation is not None
generations.append([self._chunk_to_generation(generation)])
else:
params: Dict[str, Any] = self._invocation_params(stop=stop, **kwargs)
for prompt in prompts:
completion = await asyncio.get_running_loop().run_in_executor(
None,
functools.partial(
generate_with_retry, **{"llm": self, "prompt": prompt, **params}
),
)
generations.append(
[Generation(**self._generation_from_qwen_resp(completion))]
)
return LLMResult(
generations=generations,
llm_output={
"model_name": self.model_name,
},
)
def _stream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
params: Dict[str, Any] = self._invocation_params(
stop=stop, stream=True, **kwargs
)
for stream_resp in stream_generate_with_retry(self, prompt=prompt, **params):
chunk = GenerationChunk(**self._generation_from_qwen_resp(stream_resp))
yield chunk
if run_manager:
run_manager.on_llm_new_token(
chunk.text,
chunk=chunk,
verbose=self.verbose,
)
async def _astream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[GenerationChunk]:
params: Dict[str, Any] = self._invocation_params(
stop=stop, stream=True, **kwargs
)
async for stream_resp in astream_generate_with_retry(
self, prompt=prompt, **params
):
chunk = GenerationChunk(**self._generation_from_qwen_resp(stream_resp))
yield chunk
if run_manager:
await run_manager.on_llm_new_token(
chunk.text,
chunk=chunk,
verbose=self.verbose,
)
def _invocation_params(self, stop: Any, **kwargs: Any) -> Dict[str, Any]:
params = {
**self._default_params,
**kwargs,
}
if stop is not None:
params["stop"] = stop
if params.get("stream"):
params["incremental_output"] = True
return params
@staticmethod
def _generation_from_qwen_resp(resp: Any) -> Dict[str, Any]:
return dict(
text=resp["output"]["text"],
generation_info=dict(
finish_reason=resp["output"]["finish_reason"],
request_id=resp["request_id"],
token_usage=dict(resp["usage"]),
),
)
@staticmethod
def _chunk_to_generation(chunk: GenerationChunk) -> Generation:
return Generation(
text=chunk.text,
generation_info=chunk.generation_info,
)
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~chat_loaders~slack.py | import json
import logging
import re
import zipfile
from pathlib import Path
from typing import Dict, Iterator, List, Union
from libs.core.langchain_core.chat_sessions import ChatSession
from libs.core.langchain_core.messages import AIMessage, HumanMessage
from langchain_community.chat_loaders.base import BaseChatLoader
logger = logging.getLogger(__name__)
class SlackChatLoader(BaseChatLoader):
"""Load `Slack` conversations from a dump zip file."""
def __init__(
self,
path: Union[str, Path],
):
"""
Initialize the chat loader with the path to the exported Slack dump zip file.
:param path: Path to the exported Slack dump zip file.
"""
self.zip_path = path if isinstance(path, Path) else Path(path)
if not self.zip_path.exists():
raise FileNotFoundError(f"File {self.zip_path} not found")
def _load_single_chat_session(self, messages: List[Dict]) -> ChatSession:
results: List[Union[AIMessage, HumanMessage]] = []
previous_sender = None
for message in messages:
if not isinstance(message, dict):
continue
text = message.get("text", "")
timestamp = message.get("ts", "")
sender = message.get("user", "")
if not sender:
continue
skip_pattern = re.compile(
r"<@U\d+> has joined the channel", flags=re.IGNORECASE
)
if skip_pattern.match(text):
continue
if sender == previous_sender:
results[-1].content += "\n\n" + text
results[-1].additional_kwargs["events"].append(
{"message_time": timestamp}
)
else:
results.append(
HumanMessage(
role=sender,
content=text,
additional_kwargs={
"sender": sender,
"events": [{"message_time": timestamp}],
},
)
)
previous_sender = sender
return ChatSession(messages=results)
def _read_json(self, zip_file: zipfile.ZipFile, file_path: str) -> List[dict]:
"""Read JSON data from a zip subfile."""
with zip_file.open(file_path, "r") as f:
data = json.load(f)
if not isinstance(data, list):
raise ValueError(f"Expected list of dictionaries, got {type(data)}")
return data
def lazy_load(self) -> Iterator[ChatSession]:
"""
Lazy load the chat sessions from the Slack dump file and yield them
in the required format.
:return: Iterator of chat sessions containing messages.
"""
with zipfile.ZipFile(str(self.zip_path), "r") as zip_file:
for file_path in zip_file.namelist():
if file_path.endswith(".json"):
messages = self._read_json(zip_file, file_path)
yield self._load_single_chat_session(messages)
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~callbacks~arize_callback.py | from datetime import datetime
from typing import Any, Dict, List, Optional
from libs.core.langchain_core.agents import AgentAction, AgentFinish
from libs.core.langchain_core.callbacks import BaseCallbackHandler
from libs.core.langchain_core.outputs import LLMResult
from langchain_community.callbacks.utils import import_pandas
class ArizeCallbackHandler(BaseCallbackHandler):
"""Callback Handler that logs to Arize."""
def __init__(
self,
model_id: Optional[str] = None,
model_version: Optional[str] = None,
SPACE_KEY: Optional[str] = None,
API_KEY: Optional[str] = None,
) -> None:
"""Initialize callback handler."""
super().__init__()
self.model_id = model_id
self.model_version = model_version
self.space_key = SPACE_KEY
self.api_key = API_KEY
self.prompt_records: List[str] = []
self.response_records: List[str] = []
self.prediction_ids: List[str] = []
self.pred_timestamps: List[int] = []
self.response_embeddings: List[float] = []
self.prompt_embeddings: List[float] = []
self.prompt_tokens = 0
self.completion_tokens = 0
self.total_tokens = 0
self.step = 0
from arize.pandas.embeddings import EmbeddingGenerator, UseCases
from arize.pandas.logger import Client
self.generator = EmbeddingGenerator.from_use_case(
use_case=UseCases.NLP.SEQUENCE_CLASSIFICATION,
model_name="distilbert-base-uncased",
tokenizer_max_length=512,
batch_size=256,
)
self.arize_client = Client(space_key=SPACE_KEY, api_key=API_KEY)
if SPACE_KEY == "SPACE_KEY" or API_KEY == "API_KEY":
raise ValueError("❌ CHANGE SPACE AND API KEYS")
else:
print("✅ Arize client setup done! Now you can start using Arize!")
def on_llm_start(
self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> None:
for prompt in prompts:
self.prompt_records.append(prompt.replace("\n", ""))
def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
"""Do nothing."""
pass
def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
pd = import_pandas()
from arize.utils.types import (
EmbeddingColumnNames,
Environments,
ModelTypes,
Schema,
)
# Safe check if 'llm_output' and 'token_usage' exist
if response.llm_output and "token_usage" in response.llm_output:
self.prompt_tokens = response.llm_output["token_usage"].get(
"prompt_tokens", 0
)
self.total_tokens = response.llm_output["token_usage"].get(
"total_tokens", 0
)
self.completion_tokens = response.llm_output["token_usage"].get(
"completion_tokens", 0
)
else:
self.prompt_tokens = (
self.total_tokens
) = self.completion_tokens = 0 # assign default value
for generations in response.generations:
for generation in generations:
prompt = self.prompt_records[self.step]
self.step = self.step + 1
prompt_embedding = pd.Series(
self.generator.generate_embeddings(
text_col=pd.Series(prompt.replace("\n", " "))
).reset_index(drop=True)
)
# Assigning text to response_text instead of response
response_text = generation.text.replace("\n", " ")
response_embedding = pd.Series(
self.generator.generate_embeddings(
text_col=pd.Series(generation.text.replace("\n", " "))
).reset_index(drop=True)
)
pred_timestamp = datetime.now().timestamp()
# Define the columns and data
columns = [
"prediction_ts",
"response",
"prompt",
"response_vector",
"prompt_vector",
"prompt_token",
"completion_token",
"total_token",
]
data = [
[
pred_timestamp,
response_text,
prompt,
response_embedding[0],
prompt_embedding[0],
self.prompt_tokens,
self.total_tokens,
self.completion_tokens,
]
]
# Create the DataFrame
df = pd.DataFrame(data, columns=columns)
# Declare prompt and response columns
prompt_columns = EmbeddingColumnNames(
vector_column_name="prompt_vector", data_column_name="prompt"
)
response_columns = EmbeddingColumnNames(
vector_column_name="response_vector", data_column_name="response"
)
schema = Schema(
timestamp_column_name="prediction_ts",
tag_column_names=[
"prompt_token",
"completion_token",
"total_token",
],
prompt_column_names=prompt_columns,
response_column_names=response_columns,
)
response_from_arize = self.arize_client.log(
dataframe=df,
schema=schema,
model_id=self.model_id,
model_version=self.model_version,
model_type=ModelTypes.GENERATIVE_LLM,
environment=Environments.PRODUCTION,
)
if response_from_arize.status_code == 200:
print("✅ Successfully logged data to Arize!")
else:
print(f'❌ Logging failed "{response_from_arize.text}"')
def on_llm_error(self, error: BaseException, **kwargs: Any) -> None:
"""Do nothing."""
pass
def on_chain_start(
self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
) -> None:
pass
def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
"""Do nothing."""
pass
def on_chain_error(self, error: BaseException, **kwargs: Any) -> None:
"""Do nothing."""
pass
def on_tool_start(
self,
serialized: Dict[str, Any],
input_str: str,
**kwargs: Any,
) -> None:
pass
def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
"""Do nothing."""
pass
def on_tool_end(
self,
output: str,
observation_prefix: Optional[str] = None,
llm_prefix: Optional[str] = None,
**kwargs: Any,
) -> None:
pass
def on_tool_error(self, error: BaseException, **kwargs: Any) -> None:
pass
def on_text(self, text: str, **kwargs: Any) -> None:
pass
def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:
pass
| [
"prompt_vector",
"\n",
" "
] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~tools~gmail~get_message.py | import base64
import email
from typing import Dict, Optional, Type
from libs.core.langchain_core.callbacks import CallbackManagerForToolRun
from libs.core.langchain_core.pydantic_v1 import BaseModel, Field
from langchain_community.tools.gmail.base import GmailBaseTool
from langchain_community.tools.gmail.utils import clean_email_body
class SearchArgsSchema(BaseModel):
"""Input for GetMessageTool."""
message_id: str = Field(
...,
description="The unique ID of the email message, retrieved from a search.",
)
class GmailGetMessage(GmailBaseTool):
"""Tool that gets a message by ID from Gmail."""
name: str = "get_gmail_message"
description: str = (
"Use this tool to fetch an email by message ID."
" Returns the thread ID, snippet, body, subject, and sender."
)
args_schema: Type[SearchArgsSchema] = SearchArgsSchema
def _run(
self,
message_id: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> Dict:
"""Run the tool."""
query = (
self.api_resource.users()
.messages()
.get(userId="me", format="raw", id=message_id)
)
message_data = query.execute()
raw_message = base64.urlsafe_b64decode(message_data["raw"])
email_msg = email.message_from_bytes(raw_message)
subject = email_msg["Subject"]
sender = email_msg["From"]
message_body = ""
if email_msg.is_multipart():
for part in email_msg.walk():
ctype = part.get_content_type()
cdispo = str(part.get("Content-Disposition"))
if ctype == "text/plain" and "attachment" not in cdispo:
message_body = part.get_payload(decode=True).decode("utf-8")
break
else:
message_body = email_msg.get_payload(decode=True).decode("utf-8")
body = clean_email_body(message_body)
return {
"id": message_id,
"threadId": message_data["threadId"],
"snippet": message_data["snippet"],
"body": body,
"subject": subject,
"sender": sender,
}
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~docusaurus.py | """Load Documents from Docusarus Documentation"""
from typing import Any, List, Optional
from langchain_community.document_loaders.sitemap import SitemapLoader
class DocusaurusLoader(SitemapLoader):
"""Load from Docusaurus Documentation.
It leverages the SitemapLoader to loop through the generated pages of a
Docusaurus Documentation website and extracts the content by looking for specific
HTML tags. By default, the parser searches for the main content of the Docusaurus
page, which is normally the <article>. You can also define your own
custom HTML tags by providing them as a list, for example: ["div", ".main", "a"].
"""
def __init__(
self,
url: str,
custom_html_tags: Optional[List[str]] = None,
**kwargs: Any,
):
"""Initialize DocusaurusLoader
Args:
url: The base URL of the Docusaurus website.
custom_html_tags: Optional custom html tags to extract content from pages.
kwargs: Additional args to extend the underlying SitemapLoader, for example:
filter_urls, blocksize, meta_function, is_local, continue_on_failure
"""
if not kwargs.get("is_local"):
url = f"{url}/sitemap.xml"
self.custom_html_tags = custom_html_tags or ["main article"]
super().__init__(
url,
parsing_function=kwargs.get("parsing_function") or self._parsing_function,
**kwargs,
)
def _parsing_function(self, content: Any) -> str:
"""Parses specific elements from a Docusaurus page."""
relevant_elements = content.select(",".join(self.custom_html_tags))
for element in relevant_elements:
if element not in relevant_elements:
element.decompose()
return str(content.get_text())
| [] |
2024-01-10 | mth93/langchain | libs~langchain~langchain~retrievers~re_phraser.py | import logging
from typing import List
from libs.core.langchain_core.documents import Document
from libs.core.langchain_core.prompts.prompt import PromptTemplate
from libs.core.langchain_core.retrievers import BaseRetriever
from langchain.callbacks.manager import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain.chains.llm import LLMChain
from langchain.llms.base import BaseLLM
logger = logging.getLogger(__name__)
# Default template
DEFAULT_TEMPLATE = """You are an assistant tasked with taking a natural language \
query from a user and converting it into a query for a vectorstore. \
In this process, you strip out information that is not relevant for \
the retrieval task. Here is the user query: {question}"""
# Default prompt
DEFAULT_QUERY_PROMPT = PromptTemplate.from_template(DEFAULT_TEMPLATE)
class RePhraseQueryRetriever(BaseRetriever):
"""Given a query, use an LLM to re-phrase it.
Then, retrieve docs for the re-phrased query."""
retriever: BaseRetriever
llm_chain: LLMChain
@classmethod
def from_llm(
cls,
retriever: BaseRetriever,
llm: BaseLLM,
prompt: PromptTemplate = DEFAULT_QUERY_PROMPT,
) -> "RePhraseQueryRetriever":
"""Initialize from llm using default template.
The prompt used here expects a single input: `question`
Args:
retriever: retriever to query documents from
llm: llm for query generation using DEFAULT_QUERY_PROMPT
prompt: prompt template for query generation
Returns:
RePhraseQueryRetriever
"""
llm_chain = LLMChain(llm=llm, prompt=prompt)
return cls(
retriever=retriever,
llm_chain=llm_chain,
)
def _get_relevant_documents(
self,
query: str,
*,
run_manager: CallbackManagerForRetrieverRun,
) -> List[Document]:
"""Get relevated documents given a user question.
Args:
query: user question
Returns:
Relevant documents for re-phrased question
"""
response = self.llm_chain(query, callbacks=run_manager.get_child())
re_phrased_question = response["text"]
logger.info(f"Re-phrased question: {re_phrased_question}")
docs = self.retriever.get_relevant_documents(
re_phrased_question, callbacks=run_manager.get_child()
)
return docs
async def _aget_relevant_documents(
self,
query: str,
*,
run_manager: AsyncCallbackManagerForRetrieverRun,
) -> List[Document]:
raise NotImplementedError
| [
"You are an assistant tasked with taking a natural language query from a user and converting it into a query for a vectorstore. In this process, you strip out information that is not relevant for the retrieval task. Here is the user query: {question}"
] |
2024-01-10 | mth93/langchain | libs~langchain~langchain~memory~entity.py | import logging
from abc import ABC, abstractmethod
from itertools import islice
from typing import Any, Dict, Iterable, List, Optional
from libs.core.langchain_core.language_models import BaseLanguageModel
from libs.core.langchain_core.messages import BaseMessage, get_buffer_string
from libs.core.langchain_core.prompts import BasePromptTemplate
from libs.core.langchain_core.pydantic_v1 import BaseModel, Field
from langchain.chains.llm import LLMChain
from langchain.memory.chat_memory import BaseChatMemory
from langchain.memory.prompt import (
ENTITY_EXTRACTION_PROMPT,
ENTITY_SUMMARIZATION_PROMPT,
)
from langchain.memory.utils import get_prompt_input_key
from langchain.utilities.redis import get_client
logger = logging.getLogger(__name__)
class BaseEntityStore(BaseModel, ABC):
"""Abstract base class for Entity store."""
@abstractmethod
def get(self, key: str, default: Optional[str] = None) -> Optional[str]:
"""Get entity value from store."""
pass
@abstractmethod
def set(self, key: str, value: Optional[str]) -> None:
"""Set entity value in store."""
pass
@abstractmethod
def delete(self, key: str) -> None:
"""Delete entity value from store."""
pass
@abstractmethod
def exists(self, key: str) -> bool:
"""Check if entity exists in store."""
pass
@abstractmethod
def clear(self) -> None:
"""Delete all entities from store."""
pass
class InMemoryEntityStore(BaseEntityStore):
"""In-memory Entity store."""
store: Dict[str, Optional[str]] = {}
def get(self, key: str, default: Optional[str] = None) -> Optional[str]:
return self.store.get(key, default)
def set(self, key: str, value: Optional[str]) -> None:
self.store[key] = value
def delete(self, key: str) -> None:
del self.store[key]
def exists(self, key: str) -> bool:
return key in self.store
def clear(self) -> None:
return self.store.clear()
class UpstashRedisEntityStore(BaseEntityStore):
"""Upstash Redis backed Entity store.
Entities get a TTL of 1 day by default, and
that TTL is extended by 3 days every time the entity is read back.
"""
def __init__(
self,
session_id: str = "default",
url: str = "",
token: str = "",
key_prefix: str = "memory_store",
ttl: Optional[int] = 60 * 60 * 24,
recall_ttl: Optional[int] = 60 * 60 * 24 * 3,
*args: Any,
**kwargs: Any,
):
try:
from upstash_redis import Redis
except ImportError:
raise ImportError(
"Could not import upstash_redis python package. "
"Please install it with `pip install upstash_redis`."
)
super().__init__(*args, **kwargs)
try:
self.redis_client = Redis(url=url, token=token)
except Exception:
logger.error("Upstash Redis instance could not be initiated.")
self.session_id = session_id
self.key_prefix = key_prefix
self.ttl = ttl
self.recall_ttl = recall_ttl or ttl
@property
def full_key_prefix(self) -> str:
return f"{self.key_prefix}:{self.session_id}"
def get(self, key: str, default: Optional[str] = None) -> Optional[str]:
res = (
self.redis_client.getex(f"{self.full_key_prefix}:{key}", ex=self.recall_ttl)
or default
or ""
)
logger.debug(f"Upstash Redis MEM get '{self.full_key_prefix}:{key}': '{res}'")
return res
def set(self, key: str, value: Optional[str]) -> None:
if not value:
return self.delete(key)
self.redis_client.set(f"{self.full_key_prefix}:{key}", value, ex=self.ttl)
logger.debug(
f"Redis MEM set '{self.full_key_prefix}:{key}': '{value}' EX {self.ttl}"
)
def delete(self, key: str) -> None:
self.redis_client.delete(f"{self.full_key_prefix}:{key}")
def exists(self, key: str) -> bool:
return self.redis_client.exists(f"{self.full_key_prefix}:{key}") == 1
def clear(self) -> None:
def scan_and_delete(cursor: int) -> int:
cursor, keys_to_delete = self.redis_client.scan(
cursor, f"{self.full_key_prefix}:*"
)
self.redis_client.delete(*keys_to_delete)
return cursor
cursor = scan_and_delete(0)
while cursor != 0:
scan_and_delete(cursor)
class RedisEntityStore(BaseEntityStore):
"""Redis-backed Entity store.
Entities get a TTL of 1 day by default, and
that TTL is extended by 3 days every time the entity is read back.
"""
redis_client: Any
session_id: str = "default"
key_prefix: str = "memory_store"
ttl: Optional[int] = 60 * 60 * 24
recall_ttl: Optional[int] = 60 * 60 * 24 * 3
def __init__(
self,
session_id: str = "default",
url: str = "redis://localhost:6379/0",
key_prefix: str = "memory_store",
ttl: Optional[int] = 60 * 60 * 24,
recall_ttl: Optional[int] = 60 * 60 * 24 * 3,
*args: Any,
**kwargs: Any,
):
try:
import redis
except ImportError:
raise ImportError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
)
super().__init__(*args, **kwargs)
try:
self.redis_client = get_client(redis_url=url, decode_responses=True)
except redis.exceptions.ConnectionError as error:
logger.error(error)
self.session_id = session_id
self.key_prefix = key_prefix
self.ttl = ttl
self.recall_ttl = recall_ttl or ttl
@property
def full_key_prefix(self) -> str:
return f"{self.key_prefix}:{self.session_id}"
def get(self, key: str, default: Optional[str] = None) -> Optional[str]:
res = (
self.redis_client.getex(f"{self.full_key_prefix}:{key}", ex=self.recall_ttl)
or default
or ""
)
logger.debug(f"REDIS MEM get '{self.full_key_prefix}:{key}': '{res}'")
return res
def set(self, key: str, value: Optional[str]) -> None:
if not value:
return self.delete(key)
self.redis_client.set(f"{self.full_key_prefix}:{key}", value, ex=self.ttl)
logger.debug(
f"REDIS MEM set '{self.full_key_prefix}:{key}': '{value}' EX {self.ttl}"
)
def delete(self, key: str) -> None:
self.redis_client.delete(f"{self.full_key_prefix}:{key}")
def exists(self, key: str) -> bool:
return self.redis_client.exists(f"{self.full_key_prefix}:{key}") == 1
def clear(self) -> None:
# iterate a list in batches of size batch_size
def batched(iterable: Iterable[Any], batch_size: int) -> Iterable[Any]:
iterator = iter(iterable)
while batch := list(islice(iterator, batch_size)):
yield batch
for keybatch in batched(
self.redis_client.scan_iter(f"{self.full_key_prefix}:*"), 500
):
self.redis_client.delete(*keybatch)
class SQLiteEntityStore(BaseEntityStore):
"""SQLite-backed Entity store"""
session_id: str = "default"
table_name: str = "memory_store"
def __init__(
self,
session_id: str = "default",
db_file: str = "entities.db",
table_name: str = "memory_store",
*args: Any,
**kwargs: Any,
):
try:
import sqlite3
except ImportError:
raise ImportError(
"Could not import sqlite3 python package. "
"Please install it with `pip install sqlite3`."
)
super().__init__(*args, **kwargs)
self.conn = sqlite3.connect(db_file)
self.session_id = session_id
self.table_name = table_name
self._create_table_if_not_exists()
@property
def full_table_name(self) -> str:
return f"{self.table_name}_{self.session_id}"
def _create_table_if_not_exists(self) -> None:
create_table_query = f"""
CREATE TABLE IF NOT EXISTS {self.full_table_name} (
key TEXT PRIMARY KEY,
value TEXT
)
"""
with self.conn:
self.conn.execute(create_table_query)
def get(self, key: str, default: Optional[str] = None) -> Optional[str]:
query = f"""
SELECT value
FROM {self.full_table_name}
WHERE key = ?
"""
cursor = self.conn.execute(query, (key,))
result = cursor.fetchone()
if result is not None:
value = result[0]
return value
return default
def set(self, key: str, value: Optional[str]) -> None:
if not value:
return self.delete(key)
query = f"""
INSERT OR REPLACE INTO {self.full_table_name} (key, value)
VALUES (?, ?)
"""
with self.conn:
self.conn.execute(query, (key, value))
def delete(self, key: str) -> None:
query = f"""
DELETE FROM {self.full_table_name}
WHERE key = ?
"""
with self.conn:
self.conn.execute(query, (key,))
def exists(self, key: str) -> bool:
query = f"""
SELECT 1
FROM {self.full_table_name}
WHERE key = ?
LIMIT 1
"""
cursor = self.conn.execute(query, (key,))
result = cursor.fetchone()
return result is not None
def clear(self) -> None:
query = f"""
DELETE FROM {self.full_table_name}
"""
with self.conn:
self.conn.execute(query)
class ConversationEntityMemory(BaseChatMemory):
"""Entity extractor & summarizer memory.
Extracts named entities from the recent chat history and generates summaries.
With a swappable entity store, persisting entities across conversations.
Defaults to an in-memory entity store, and can be swapped out for a Redis,
SQLite, or other entity store.
"""
human_prefix: str = "Human"
ai_prefix: str = "AI"
llm: BaseLanguageModel
entity_extraction_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT
entity_summarization_prompt: BasePromptTemplate = ENTITY_SUMMARIZATION_PROMPT
# Cache of recently detected entity names, if any
# It is updated when load_memory_variables is called:
entity_cache: List[str] = []
# Number of recent message pairs to consider when updating entities:
k: int = 3
chat_history_key: str = "history"
# Store to manage entity-related data:
entity_store: BaseEntityStore = Field(default_factory=InMemoryEntityStore)
@property
def buffer(self) -> List[BaseMessage]:
"""Access chat memory messages."""
return self.chat_memory.messages
@property
def memory_variables(self) -> List[str]:
"""Will always return list of memory variables.
:meta private:
"""
return ["entities", self.chat_history_key]
def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""
Returns chat history and all generated entities with summaries if available,
and updates or clears the recent entity cache.
New entity name can be found when calling this method, before the entity
summaries are generated, so the entity cache values may be empty if no entity
descriptions are generated yet.
"""
# Create an LLMChain for predicting entity names from the recent chat history:
chain = LLMChain(llm=self.llm, prompt=self.entity_extraction_prompt)
if self.input_key is None:
prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)
else:
prompt_input_key = self.input_key
# Extract an arbitrary window of the last message pairs from
# the chat history, where the hyperparameter k is the
# number of message pairs:
buffer_string = get_buffer_string(
self.buffer[-self.k * 2 :],
human_prefix=self.human_prefix,
ai_prefix=self.ai_prefix,
)
# Generates a comma-separated list of named entities,
# e.g. "Jane, White House, UFO"
# or "NONE" if no named entities are extracted:
output = chain.predict(
history=buffer_string,
input=inputs[prompt_input_key],
)
# If no named entities are extracted, assigns an empty list.
if output.strip() == "NONE":
entities = []
else:
# Make a list of the extracted entities:
entities = [w.strip() for w in output.split(",")]
# Make a dictionary of entities with summary if exists:
entity_summaries = {}
for entity in entities:
entity_summaries[entity] = self.entity_store.get(entity, "")
# Replaces the entity name cache with the most recently discussed entities,
# or if no entities were extracted, clears the cache:
self.entity_cache = entities
# Should we return as message objects or as a string?
if self.return_messages:
# Get last `k` pair of chat messages:
buffer: Any = self.buffer[-self.k * 2 :]
else:
# Reuse the string we made earlier:
buffer = buffer_string
return {
self.chat_history_key: buffer,
"entities": entity_summaries,
}
def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""
Save context from this conversation history to the entity store.
Generates a summary for each entity in the entity cache by prompting
the model, and saves these summaries to the entity store.
"""
super().save_context(inputs, outputs)
if self.input_key is None:
prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)
else:
prompt_input_key = self.input_key
# Extract an arbitrary window of the last message pairs from
# the chat history, where the hyperparameter k is the
# number of message pairs:
buffer_string = get_buffer_string(
self.buffer[-self.k * 2 :],
human_prefix=self.human_prefix,
ai_prefix=self.ai_prefix,
)
input_data = inputs[prompt_input_key]
# Create an LLMChain for predicting entity summarization from the context
chain = LLMChain(llm=self.llm, prompt=self.entity_summarization_prompt)
# Generate new summaries for entities and save them in the entity store
for entity in self.entity_cache:
# Get existing summary if it exists
existing_summary = self.entity_store.get(entity, "")
output = chain.predict(
summary=existing_summary,
entity=entity,
history=buffer_string,
input=input_data,
)
# Save the updated summary to the entity store
self.entity_store.set(entity, output.strip())
def clear(self) -> None:
"""Clear memory contents."""
self.chat_memory.clear()
self.entity_cache.clear()
self.entity_store.clear()
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~chat_models~minimax.py | """Wrapper around Minimax chat models."""
import logging
from typing import Any, Dict, List, Optional, cast
from libs.core.langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from libs.core.langchain_core.language_models.chat_models import BaseChatModel
from libs.core.langchain_core.messages import (
AIMessage,
BaseMessage,
HumanMessage,
)
from libs.core.langchain_core.outputs import ChatResult
from langchain_community.llms.minimax import MinimaxCommon
from langchain_community.llms.utils import enforce_stop_tokens
logger = logging.getLogger(__name__)
def _parse_message(msg_type: str, text: str) -> Dict:
return {"sender_type": msg_type, "text": text}
def _parse_chat_history(history: List[BaseMessage]) -> List:
"""Parse a sequence of messages into history."""
chat_history = []
for message in history:
content = cast(str, message.content)
if isinstance(message, HumanMessage):
chat_history.append(_parse_message("USER", content))
if isinstance(message, AIMessage):
chat_history.append(_parse_message("BOT", content))
return chat_history
class MiniMaxChat(MinimaxCommon, BaseChatModel):
"""Wrapper around Minimax large language models.
To use, you should have the environment variable ``MINIMAX_GROUP_ID`` and
``MINIMAX_API_KEY`` set with your API token, or pass it as a named parameter to
the constructor.
Example:
.. code-block:: python
from langchain_community.chat_models import MiniMaxChat
llm = MiniMaxChat(model_name="abab5-chat")
"""
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
"""Generate next turn in the conversation.
Args:
messages: The history of the conversation as a list of messages. Code chat
does not support context.
stop: The list of stop words (optional).
run_manager: The CallbackManager for LLM run, it's not used at the moment.
Returns:
The ChatResult that contains outputs generated by the model.
Raises:
ValueError: if the last message in the list is not from human.
"""
if not messages:
raise ValueError(
"You should provide at least one message to start the chat!"
)
history = _parse_chat_history(messages)
payload = self._default_params
payload["messages"] = history
text = self._client.post(payload)
# This is required since the stop are not enforced by the model parameters
return text if stop is None else enforce_stop_tokens(text, stop)
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
raise NotImplementedError(
"""Minimax AI doesn't support async requests at the moment."""
)
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~arcgis_loader.py | """Document Loader for ArcGIS FeatureLayers."""
from __future__ import annotations
import json
import re
import warnings
from datetime import datetime, timezone
from typing import TYPE_CHECKING, Any, Iterator, List, Optional, Union
from libs.core.langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
if TYPE_CHECKING:
import arcgis
_NOT_PROVIDED = "(Not Provided)"
class ArcGISLoader(BaseLoader):
"""Load records from an ArcGIS FeatureLayer."""
def __init__(
self,
layer: Union[str, arcgis.features.FeatureLayer],
gis: Optional[arcgis.gis.GIS] = None,
where: str = "1=1",
out_fields: Optional[Union[List[str], str]] = None,
return_geometry: bool = False,
result_record_count: Optional[int] = None,
lyr_desc: Optional[str] = None,
**kwargs: Any,
):
try:
import arcgis
except ImportError as e:
raise ImportError(
"arcgis is required to use the ArcGIS Loader. "
"Install it with pip or conda."
) from e
try:
from bs4 import BeautifulSoup # type: ignore
self.BEAUTIFULSOUP = BeautifulSoup
except ImportError:
warnings.warn("BeautifulSoup not found. HTML will not be parsed.")
self.BEAUTIFULSOUP = None
self.gis = gis or arcgis.gis.GIS()
if isinstance(layer, str):
self.url = layer
self.layer = arcgis.features.FeatureLayer(layer, gis=gis)
else:
self.url = layer.url
self.layer = layer
self.layer_properties = self._get_layer_properties(lyr_desc)
self.where = where
if isinstance(out_fields, str):
self.out_fields = out_fields
elif out_fields is None:
self.out_fields = "*"
else:
self.out_fields = ",".join(out_fields)
self.return_geometry = return_geometry
self.result_record_count = result_record_count
self.return_all_records = not isinstance(result_record_count, int)
query_params = dict(
where=self.where,
out_fields=self.out_fields,
return_geometry=self.return_geometry,
return_all_records=self.return_all_records,
result_record_count=self.result_record_count,
)
query_params.update(kwargs)
self.query_params = query_params
def _get_layer_properties(self, lyr_desc: Optional[str] = None) -> dict:
"""Get the layer properties from the FeatureLayer."""
import arcgis
layer_number_pattern = re.compile(r"/\d+$")
props = self.layer.properties
if lyr_desc is None:
# retrieve description from the FeatureLayer if not provided
try:
if self.BEAUTIFULSOUP:
lyr_desc = self.BEAUTIFULSOUP(props["description"]).text
else:
lyr_desc = props["description"]
lyr_desc = lyr_desc or _NOT_PROVIDED
except KeyError:
lyr_desc = _NOT_PROVIDED
try:
item_id = props["serviceItemId"]
item = self.gis.content.get(item_id) or arcgis.features.FeatureLayer(
re.sub(layer_number_pattern, "", self.url),
)
try:
raw_desc = item.description
except AttributeError:
raw_desc = item.properties.description
if self.BEAUTIFULSOUP:
item_desc = self.BEAUTIFULSOUP(raw_desc).text
else:
item_desc = raw_desc
item_desc = item_desc or _NOT_PROVIDED
except KeyError:
item_desc = _NOT_PROVIDED
return {
"layer_description": lyr_desc,
"item_description": item_desc,
"layer_properties": props,
}
def lazy_load(self) -> Iterator[Document]:
"""Lazy load records from FeatureLayer."""
query_response = self.layer.query(**self.query_params)
features = (feature.as_dict for feature in query_response)
for feature in features:
attributes = feature["attributes"]
page_content = json.dumps(attributes)
metadata = {
"accessed": f"{datetime.now(timezone.utc).isoformat()}Z",
"name": self.layer_properties["layer_properties"]["name"],
"url": self.url,
"layer_description": self.layer_properties["layer_description"],
"item_description": self.layer_properties["item_description"],
"layer_properties": self.layer_properties["layer_properties"],
}
if self.return_geometry:
try:
metadata["geometry"] = feature["geometry"]
except KeyError:
warnings.warn(
"Geometry could not be retrieved from the feature layer."
)
yield Document(page_content=page_content, metadata=metadata)
def load(self) -> List[Document]:
"""Load all records from FeatureLayer."""
return list(self.lazy_load())
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~ifixit.py | from typing import List, Optional
import requests
from libs.core.langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
from langchain_community.document_loaders.web_base import WebBaseLoader
IFIXIT_BASE_URL = "https://www.ifixit.com/api/2.0"
class IFixitLoader(BaseLoader):
"""Load `iFixit` repair guides, device wikis and answers.
iFixit is the largest, open repair community on the web. The site contains nearly
100k repair manuals, 200k Questions & Answers on 42k devices, and all the data is
licensed under CC-BY.
This loader will allow you to download the text of a repair guide, text of Q&A's
and wikis from devices on iFixit using their open APIs and web scraping.
"""
def __init__(self, web_path: str):
"""Initialize with a web path."""
if not web_path.startswith("https://www.ifixit.com"):
raise ValueError("web path must start with 'https://www.ifixit.com'")
path = web_path.replace("https://www.ifixit.com", "")
allowed_paths = ["/Device", "/Guide", "/Answers", "/Teardown"]
""" TODO: Add /Wiki """
if not any(path.startswith(allowed_path) for allowed_path in allowed_paths):
raise ValueError(
"web path must start with /Device, /Guide, /Teardown or /Answers"
)
pieces = [x for x in path.split("/") if x]
"""Teardowns are just guides by a different name"""
self.page_type = pieces[0] if pieces[0] != "Teardown" else "Guide"
if self.page_type == "Guide" or self.page_type == "Answers":
self.id = pieces[2]
else:
self.id = pieces[1]
self.web_path = web_path
def load(self) -> List[Document]:
if self.page_type == "Device":
return self.load_device()
elif self.page_type == "Guide" or self.page_type == "Teardown":
return self.load_guide()
elif self.page_type == "Answers":
return self.load_questions_and_answers()
else:
raise ValueError("Unknown page type: " + self.page_type)
@staticmethod
def load_suggestions(query: str = "", doc_type: str = "all") -> List[Document]:
"""Load suggestions.
Args:
query: A query string
doc_type: The type of document to search for. Can be one of "all",
"device", "guide", "teardown", "answer", "wiki".
Returns:
"""
res = requests.get(
IFIXIT_BASE_URL + "/suggest/" + query + "?doctypes=" + doc_type
)
if res.status_code != 200:
raise ValueError(
'Could not load suggestions for "' + query + '"\n' + res.json()
)
data = res.json()
results = data["results"]
output = []
for result in results:
try:
loader = IFixitLoader(result["url"])
if loader.page_type == "Device":
output += loader.load_device(include_guides=False)
else:
output += loader.load()
except ValueError:
continue
return output
def load_questions_and_answers(
self, url_override: Optional[str] = None
) -> List[Document]:
"""Load a list of questions and answers.
Args:
url_override: A URL to override the default URL.
Returns: List[Document]
"""
loader = WebBaseLoader(self.web_path if url_override is None else url_override)
soup = loader.scrape()
output = []
title = soup.find("h1", "post-title").text
output.append("# " + title)
output.append(soup.select_one(".post-content .post-text").text.strip())
answersHeader = soup.find("div", "post-answers-header")
if answersHeader:
output.append("\n## " + answersHeader.text.strip())
for answer in soup.select(".js-answers-list .post.post-answer"):
if answer.has_attr("itemprop") and "acceptedAnswer" in answer["itemprop"]:
output.append("\n### Accepted Answer")
elif "post-helpful" in answer["class"]:
output.append("\n### Most Helpful Answer")
else:
output.append("\n### Other Answer")
output += [
a.text.strip() for a in answer.select(".post-content .post-text")
]
output.append("\n")
text = "\n".join(output).strip()
metadata = {"source": self.web_path, "title": title}
return [Document(page_content=text, metadata=metadata)]
def load_device(
self, url_override: Optional[str] = None, include_guides: bool = True
) -> List[Document]:
"""Loads a device
Args:
url_override: A URL to override the default URL.
include_guides: Whether to include guides linked to from the device.
Defaults to True.
Returns:
"""
documents = []
if url_override is None:
url = IFIXIT_BASE_URL + "/wikis/CATEGORY/" + self.id
else:
url = url_override
res = requests.get(url)
data = res.json()
text = "\n".join(
[
data[key]
for key in ["title", "description", "contents_raw"]
if key in data
]
).strip()
metadata = {"source": self.web_path, "title": data["title"]}
documents.append(Document(page_content=text, metadata=metadata))
if include_guides:
"""Load and return documents for each guide linked to from the device"""
guide_urls = [guide["url"] for guide in data["guides"]]
for guide_url in guide_urls:
documents.append(IFixitLoader(guide_url).load()[0])
return documents
def load_guide(self, url_override: Optional[str] = None) -> List[Document]:
"""Load a guide
Args:
url_override: A URL to override the default URL.
Returns: List[Document]
"""
if url_override is None:
url = IFIXIT_BASE_URL + "/guides/" + self.id
else:
url = url_override
res = requests.get(url)
if res.status_code != 200:
raise ValueError(
"Could not load guide: " + self.web_path + "\n" + res.json()
)
data = res.json()
doc_parts = ["# " + data["title"], data["introduction_raw"]]
doc_parts.append("\n\n###Tools Required:")
if len(data["tools"]) == 0:
doc_parts.append("\n - None")
else:
for tool in data["tools"]:
doc_parts.append("\n - " + tool["text"])
doc_parts.append("\n\n###Parts Required:")
if len(data["parts"]) == 0:
doc_parts.append("\n - None")
else:
for part in data["parts"]:
doc_parts.append("\n - " + part["text"])
for row in data["steps"]:
doc_parts.append(
"\n\n## "
+ (
row["title"]
if row["title"] != ""
else "Step {}".format(row["orderby"])
)
)
for line in row["lines"]:
doc_parts.append(line["text_raw"])
doc_parts.append(data["conclusion_raw"])
text = "\n".join(doc_parts)
metadata = {"source": self.web_path, "title": data["title"]}
return [Document(page_content=text, metadata=metadata)]
| [] |
2024-01-10 | mth93/langchain | libs~langchain~langchain~smith~evaluation~progress.py | """A simple progress bar for the console."""
import threading
from typing import Any, Dict, Optional, Sequence
from uuid import UUID
from libs.core.langchain_core.documents import Document
from libs.core.langchain_core.outputs import LLMResult
from langchain.callbacks import base as base_callbacks
class ProgressBarCallback(base_callbacks.BaseCallbackHandler):
"""A simple progress bar for the console."""
def __init__(self, total: int, ncols: int = 50, **kwargs: Any):
"""Initialize the progress bar.
Args:
total: int, the total number of items to be processed.
ncols: int, the character width of the progress bar.
"""
self.total = total
self.ncols = ncols
self.counter = 0
self.lock = threading.Lock()
self._print_bar()
def increment(self) -> None:
"""Increment the counter and update the progress bar."""
with self.lock:
self.counter += 1
self._print_bar()
def _print_bar(self) -> None:
"""Print the progress bar to the console."""
progress = self.counter / self.total
arrow = "-" * int(round(progress * self.ncols) - 1) + ">"
spaces = " " * (self.ncols - len(arrow))
print(f"\r[{arrow + spaces}] {self.counter}/{self.total}", end="")
def on_chain_error(
self,
error: BaseException,
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> Any:
if parent_run_id is None:
self.increment()
def on_chain_end(
self,
outputs: Dict[str, Any],
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> Any:
if parent_run_id is None:
self.increment()
def on_retriever_error(
self,
error: BaseException,
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> Any:
if parent_run_id is None:
self.increment()
def on_retriever_end(
self,
documents: Sequence[Document],
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> Any:
if parent_run_id is None:
self.increment()
def on_llm_error(
self,
error: BaseException,
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> Any:
if parent_run_id is None:
self.increment()
def on_llm_end(
self,
response: LLMResult,
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> Any:
if parent_run_id is None:
self.increment()
def on_tool_error(
self,
error: BaseException,
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> Any:
if parent_run_id is None:
self.increment()
def on_tool_end(
self,
output: str,
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> Any:
if parent_run_id is None:
self.increment()
| [] |
2024-01-10 | mth93/langchain | libs~community~tests~integration_tests~vectorstores~test_timescalevector.py | """Test TimescaleVector functionality."""
import os
from datetime import datetime, timedelta
from typing import List
from libs.core.langchain_core.documents import Document
from langchain_community.vectorstores.timescalevector import TimescaleVector
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
SERVICE_URL = TimescaleVector.service_url_from_db_params(
host=os.environ.get("TEST_TIMESCALE_HOST", "localhost"),
port=int(os.environ.get("TEST_TIMESCALE_PORT", "5432")),
database=os.environ.get("TEST_TIMESCALE_DATABASE", "postgres"),
user=os.environ.get("TEST_TIMESCALE_USER", "postgres"),
password=os.environ.get("TEST_TIMESCALE_PASSWORD", "postgres"),
)
ADA_TOKEN_COUNT = 1536
class FakeEmbeddingsWithAdaDimension(FakeEmbeddings):
"""Fake embeddings functionality for testing."""
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Return simple embeddings."""
return [
[float(1.0)] * (ADA_TOKEN_COUNT - 1) + [float(i)] for i in range(len(texts))
]
def embed_query(self, text: str) -> List[float]:
"""Return simple embeddings."""
return [float(1.0)] * (ADA_TOKEN_COUNT - 1) + [float(0.0)]
def test_timescalevector() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = TimescaleVector.from_texts(
texts=texts,
collection_name="test_collection",
embedding=FakeEmbeddingsWithAdaDimension(),
service_url=SERVICE_URL,
pre_delete_collection=True,
)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo")]
def test_timescalevector_from_documents() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docs = [Document(page_content=t, metadata={"a": "b"}) for t in texts]
docsearch = TimescaleVector.from_documents(
documents=docs,
collection_name="test_collection",
embedding=FakeEmbeddingsWithAdaDimension(),
service_url=SERVICE_URL,
pre_delete_collection=True,
)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo", metadata={"a": "b"})]
async def test_timescalevector_afrom_documents() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docs = [Document(page_content=t, metadata={"a": "b"}) for t in texts]
docsearch = await TimescaleVector.afrom_documents(
documents=docs,
collection_name="test_collection",
embedding=FakeEmbeddingsWithAdaDimension(),
service_url=SERVICE_URL,
pre_delete_collection=True,
)
output = await docsearch.asimilarity_search("foo", k=1)
assert output == [Document(page_content="foo", metadata={"a": "b"})]
def test_timescalevector_embeddings() -> None:
"""Test end to end construction with embeddings and search."""
texts = ["foo", "bar", "baz"]
text_embeddings = FakeEmbeddingsWithAdaDimension().embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
docsearch = TimescaleVector.from_embeddings(
text_embeddings=text_embedding_pairs,
collection_name="test_collection",
embedding=FakeEmbeddingsWithAdaDimension(),
service_url=SERVICE_URL,
pre_delete_collection=True,
)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo")]
async def test_timescalevector_aembeddings() -> None:
"""Test end to end construction with embeddings and search."""
texts = ["foo", "bar", "baz"]
text_embeddings = FakeEmbeddingsWithAdaDimension().embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
docsearch = await TimescaleVector.afrom_embeddings(
text_embeddings=text_embedding_pairs,
collection_name="test_collection",
embedding=FakeEmbeddingsWithAdaDimension(),
service_url=SERVICE_URL,
pre_delete_collection=True,
)
output = await docsearch.asimilarity_search("foo", k=1)
assert output == [Document(page_content="foo")]
def test_timescalevector_with_metadatas() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = TimescaleVector.from_texts(
texts=texts,
collection_name="test_collection",
embedding=FakeEmbeddingsWithAdaDimension(),
metadatas=metadatas,
service_url=SERVICE_URL,
pre_delete_collection=True,
)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo", metadata={"page": "0"})]
def test_timescalevector_with_metadatas_with_scores() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = TimescaleVector.from_texts(
texts=texts,
collection_name="test_collection",
embedding=FakeEmbeddingsWithAdaDimension(),
metadatas=metadatas,
service_url=SERVICE_URL,
pre_delete_collection=True,
)
output = docsearch.similarity_search_with_score("foo", k=1)
assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)]
async def test_timescalevector_awith_metadatas_with_scores() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = await TimescaleVector.afrom_texts(
texts=texts,
collection_name="test_collection",
embedding=FakeEmbeddingsWithAdaDimension(),
metadatas=metadatas,
service_url=SERVICE_URL,
pre_delete_collection=True,
)
output = await docsearch.asimilarity_search_with_score("foo", k=1)
assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)]
def test_timescalevector_with_filter_match() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = TimescaleVector.from_texts(
texts=texts,
collection_name="test_collection_filter",
embedding=FakeEmbeddingsWithAdaDimension(),
metadatas=metadatas,
service_url=SERVICE_URL,
pre_delete_collection=True,
)
output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "0"})
assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)]
def test_timescalevector_with_filter_distant_match() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = TimescaleVector.from_texts(
texts=texts,
collection_name="test_collection_filter",
embedding=FakeEmbeddingsWithAdaDimension(),
metadatas=metadatas,
service_url=SERVICE_URL,
pre_delete_collection=True,
)
output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "2"})
assert output == [
(Document(page_content="baz", metadata={"page": "2"}), 0.0013003906671379406)
]
def test_timescalevector_with_filter_no_match() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = TimescaleVector.from_texts(
texts=texts,
collection_name="test_collection_filter",
embedding=FakeEmbeddingsWithAdaDimension(),
metadatas=metadatas,
service_url=SERVICE_URL,
pre_delete_collection=True,
)
output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "5"})
assert output == []
def test_timescalevector_with_filter_in_set() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = TimescaleVector.from_texts(
texts=texts,
collection_name="test_collection_filter",
embedding=FakeEmbeddingsWithAdaDimension(),
metadatas=metadatas,
service_url=SERVICE_URL,
pre_delete_collection=True,
)
output = docsearch.similarity_search_with_score(
"foo", k=2, filter=[{"page": "0"}, {"page": "2"}]
)
assert output == [
(Document(page_content="foo", metadata={"page": "0"}), 0.0),
(Document(page_content="baz", metadata={"page": "2"}), 0.0013003906671379406),
]
def test_timescalevector_relevance_score() -> None:
"""Test to make sure the relevance score is scaled to 0-1."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = TimescaleVector.from_texts(
texts=texts,
collection_name="test_collection",
embedding=FakeEmbeddingsWithAdaDimension(),
metadatas=metadatas,
service_url=SERVICE_URL,
pre_delete_collection=True,
)
output = docsearch.similarity_search_with_relevance_scores("foo", k=3)
assert output == [
(Document(page_content="foo", metadata={"page": "0"}), 1.0),
(Document(page_content="bar", metadata={"page": "1"}), 0.9996744261675065),
(Document(page_content="baz", metadata={"page": "2"}), 0.9986996093328621),
]
async def test_timescalevector_relevance_score_async() -> None:
"""Test to make sure the relevance score is scaled to 0-1."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = await TimescaleVector.afrom_texts(
texts=texts,
collection_name="test_collection",
embedding=FakeEmbeddingsWithAdaDimension(),
metadatas=metadatas,
service_url=SERVICE_URL,
pre_delete_collection=True,
)
output = await docsearch.asimilarity_search_with_relevance_scores("foo", k=3)
assert output == [
(Document(page_content="foo", metadata={"page": "0"}), 1.0),
(Document(page_content="bar", metadata={"page": "1"}), 0.9996744261675065),
(Document(page_content="baz", metadata={"page": "2"}), 0.9986996093328621),
]
def test_timescalevector_retriever_search_threshold() -> None:
"""Test using retriever for searching with threshold."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = TimescaleVector.from_texts(
texts=texts,
collection_name="test_collection",
embedding=FakeEmbeddingsWithAdaDimension(),
metadatas=metadatas,
service_url=SERVICE_URL,
pre_delete_collection=True,
)
retriever = docsearch.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={"k": 3, "score_threshold": 0.999},
)
output = retriever.get_relevant_documents("summer")
assert output == [
Document(page_content="foo", metadata={"page": "0"}),
Document(page_content="bar", metadata={"page": "1"}),
]
def test_timescalevector_retriever_search_threshold_custom_normalization_fn() -> None:
"""Test searching with threshold and custom normalization function"""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = TimescaleVector.from_texts(
texts=texts,
collection_name="test_collection",
embedding=FakeEmbeddingsWithAdaDimension(),
metadatas=metadatas,
service_url=SERVICE_URL,
pre_delete_collection=True,
relevance_score_fn=lambda d: d * 0,
)
retriever = docsearch.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={"k": 3, "score_threshold": 0.5},
)
output = retriever.get_relevant_documents("foo")
assert output == []
def test_timescalevector_delete() -> None:
"""Test deleting functionality."""
texts = ["bar", "baz"]
docs = [Document(page_content=t, metadata={"a": "b"}) for t in texts]
docsearch = TimescaleVector.from_documents(
documents=docs,
collection_name="test_collection",
embedding=FakeEmbeddingsWithAdaDimension(),
service_url=SERVICE_URL,
pre_delete_collection=True,
)
texts = ["foo"]
meta = [{"b": "c"}]
ids = docsearch.add_texts(texts, meta)
output = docsearch.similarity_search("bar", k=10)
assert len(output) == 3
docsearch.delete(ids)
output = docsearch.similarity_search("bar", k=10)
assert len(output) == 2
docsearch.delete_by_metadata({"a": "b"})
output = docsearch.similarity_search("bar", k=10)
assert len(output) == 0
def test_timescalevector_with_index() -> None:
"""Test deleting functionality."""
texts = ["bar", "baz"]
docs = [Document(page_content=t, metadata={"a": "b"}) for t in texts]
docsearch = TimescaleVector.from_documents(
documents=docs,
collection_name="test_collection",
embedding=FakeEmbeddingsWithAdaDimension(),
service_url=SERVICE_URL,
pre_delete_collection=True,
)
texts = ["foo"]
meta = [{"b": "c"}]
docsearch.add_texts(texts, meta)
docsearch.create_index()
output = docsearch.similarity_search("bar", k=10)
assert len(output) == 3
docsearch.drop_index()
docsearch.create_index(
index_type=TimescaleVector.IndexType.TIMESCALE_VECTOR,
max_alpha=1.0,
num_neighbors=50,
)
docsearch.drop_index()
docsearch.create_index("tsv", max_alpha=1.0, num_neighbors=50)
docsearch.drop_index()
docsearch.create_index("ivfflat", num_lists=20, num_records=1000)
docsearch.drop_index()
docsearch.create_index("hnsw", m=16, ef_construction=64)
def test_timescalevector_time_partitioning() -> None:
"""Test deleting functionality."""
from timescale_vector import client
texts = ["bar", "baz"]
docs = [Document(page_content=t, metadata={"a": "b"}) for t in texts]
docsearch = TimescaleVector.from_documents(
documents=docs,
collection_name="test_collection_time_partitioning",
embedding=FakeEmbeddingsWithAdaDimension(),
service_url=SERVICE_URL,
pre_delete_collection=True,
time_partition_interval=timedelta(hours=1),
)
texts = ["foo"]
meta = [{"b": "c"}]
ids = [client.uuid_from_time(datetime.now() - timedelta(hours=3))]
docsearch.add_texts(texts, meta, ids)
output = docsearch.similarity_search("bar", k=10)
assert len(output) == 3
output = docsearch.similarity_search(
"bar", k=10, start_date=datetime.now() - timedelta(hours=1)
)
assert len(output) == 2
output = docsearch.similarity_search(
"bar", k=10, end_date=datetime.now() - timedelta(hours=1)
)
assert len(output) == 1
output = docsearch.similarity_search(
"bar", k=10, start_date=datetime.now() - timedelta(minutes=200)
)
assert len(output) == 3
output = docsearch.similarity_search(
"bar",
k=10,
start_date=datetime.now() - timedelta(minutes=200),
time_delta=timedelta(hours=1),
)
assert len(output) == 1
| [] |
2024-01-10 | mth93/langchain | libs~community~tests~integration_tests~llms~test_qianfan_endpoint.py | """Test Baidu Qianfan LLM Endpoint."""
from typing import Generator
from libs.core.langchain_core.outputs import LLMResult
from langchain_community.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint
def test_call() -> None:
"""Test valid call to qianfan."""
llm = QianfanLLMEndpoint()
output = llm("write a joke")
assert isinstance(output, str)
def test_generate() -> None:
"""Test valid call to qianfan."""
llm = QianfanLLMEndpoint()
output = llm.generate(["write a joke"])
assert isinstance(output, LLMResult)
assert isinstance(output.generations, list)
def test_generate_stream() -> None:
"""Test valid call to qianfan."""
llm = QianfanLLMEndpoint()
output = llm.stream("write a joke")
assert isinstance(output, Generator)
async def test_qianfan_aio() -> None:
llm = QianfanLLMEndpoint(streaming=True)
async for token in llm.astream("hi qianfan."):
assert isinstance(token, str)
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~llms~mlflow.py | from __future__ import annotations
from typing import Any, Dict, List, Mapping, Optional
from urllib.parse import urlparse
from libs.core.langchain_core.callbacks import CallbackManagerForLLMRun
from libs.core.langchain_core.language_models import LLM
from libs.core.langchain_core.pydantic_v1 import BaseModel, Extra, Field, PrivateAttr
# Ignoring type because below is valid pydantic code
# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
class Params(BaseModel, extra=Extra.allow): # type: ignore[call-arg]
"""Parameters for MLflow"""
temperature: float = 0.0
n: int = 1
stop: Optional[List[str]] = None
max_tokens: Optional[int] = None
class Mlflow(LLM):
"""Wrapper around completions LLMs in MLflow.
To use, you should have the `mlflow[genai]` python package installed.
For more information, see https://mlflow.org/docs/latest/llms/deployments/server.html.
Example:
.. code-block:: python
from langchain_community.llms import Mlflow
completions = Mlflow(
target_uri="http://localhost:5000",
endpoint="test",
params={"temperature": 0.1}
)
"""
endpoint: str
"""The endpoint to use."""
target_uri: str
"""The target URI to use."""
temperature: float = 0.0
"""The sampling temperature."""
n: int = 1
"""The number of completion choices to generate."""
stop: Optional[List[str]] = None
"""The stop sequence."""
max_tokens: Optional[int] = None
"""The maximum number of tokens to generate."""
extra_params: Dict[str, Any] = Field(default_factory=dict)
"""Any extra parameters to pass to the endpoint."""
"""Extra parameters such as `temperature`."""
_client: Any = PrivateAttr()
def __init__(self, **kwargs: Any):
super().__init__(**kwargs)
self._validate_uri()
try:
from mlflow.deployments import get_deploy_client
self._client = get_deploy_client(self.target_uri)
except ImportError as e:
raise ImportError(
"Failed to create the client. "
"Please run `pip install mlflow[genai]` to install "
"required dependencies."
) from e
def _validate_uri(self) -> None:
if self.target_uri == "databricks":
return
allowed = ["http", "https", "databricks"]
if urlparse(self.target_uri).scheme not in allowed:
raise ValueError(
f"Invalid target URI: {self.target_uri}. "
f"The scheme must be one of {allowed}."
)
@property
def _default_params(self) -> Dict[str, Any]:
return {
"target_uri": self.target_uri,
"endpoint": self.endpoint,
"temperature": self.temperature,
"n": self.n,
"stop": self.stop,
"max_tokens": self.max_tokens,
"extra_params": self.extra_params,
}
@property
def _identifying_params(self) -> Mapping[str, Any]:
return self._default_params
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
data: Dict[str, Any] = {
"prompt": prompt,
"temperature": self.temperature,
"n": self.n,
**self.extra_params,
**kwargs,
}
if stop := self.stop or stop:
data["stop"] = stop
if self.max_tokens is not None:
data["max_tokens"] = self.max_tokens
resp = self._client.predict(endpoint=self.endpoint, inputs=data)
return resp["choices"][0]["text"]
@property
def _llm_type(self) -> str:
return "mlflow"
| [] |
2024-01-10 | mth93/langchain | libs~community~tests~unit_tests~callbacks~test_callback_manager.py | """Test CallbackManager."""
from unittest.mock import patch
import pytest
from libs.core.langchain_core.callbacks.manager import CallbackManager, trace_as_chain_group
from libs.core.langchain_core.outputs import LLMResult
from libs.core.langchain_core.tracers.langchain import LangChainTracer, wait_for_all_tracers
from langchain_community.callbacks import get_openai_callback
from langchain_community.llms.openai import BaseOpenAI
def test_callback_manager_configure_context_vars(
monkeypatch: pytest.MonkeyPatch,
) -> None:
"""Test callback manager configuration."""
monkeypatch.setenv("LANGCHAIN_TRACING_V2", "true")
monkeypatch.setenv("LANGCHAIN_TRACING", "false")
with patch.object(LangChainTracer, "_update_run_single"):
with patch.object(LangChainTracer, "_persist_run_single"):
with trace_as_chain_group("test") as group_manager:
assert len(group_manager.handlers) == 1
tracer = group_manager.handlers[0]
assert isinstance(tracer, LangChainTracer)
with get_openai_callback() as cb:
# This is a new empty callback handler
assert cb.successful_requests == 0
assert cb.total_tokens == 0
# configure adds this openai cb but doesn't modify the group manager
mngr = CallbackManager.configure(group_manager)
assert mngr.handlers == [tracer, cb]
assert group_manager.handlers == [tracer]
response = LLMResult(
generations=[],
llm_output={
"token_usage": {
"prompt_tokens": 2,
"completion_tokens": 1,
"total_tokens": 3,
},
"model_name": BaseOpenAI.__fields__["model_name"].default,
},
)
mngr.on_llm_start({}, ["prompt"])[0].on_llm_end(response)
# The callback handler has been updated
assert cb.successful_requests == 1
assert cb.total_tokens == 3
assert cb.prompt_tokens == 2
assert cb.completion_tokens == 1
assert cb.total_cost > 0
with get_openai_callback() as cb:
# This is a new empty callback handler
assert cb.successful_requests == 0
assert cb.total_tokens == 0
# configure adds this openai cb but doesn't modify the group manager
mngr = CallbackManager.configure(group_manager)
assert mngr.handlers == [tracer, cb]
assert group_manager.handlers == [tracer]
response = LLMResult(
generations=[],
llm_output={
"token_usage": {
"prompt_tokens": 2,
"completion_tokens": 1,
"total_tokens": 3,
},
"model_name": BaseOpenAI.__fields__["model_name"].default,
},
)
mngr.on_llm_start({}, ["prompt"])[0].on_llm_end(response)
# The callback handler has been updated
assert cb.successful_requests == 1
assert cb.total_tokens == 3
assert cb.prompt_tokens == 2
assert cb.completion_tokens == 1
assert cb.total_cost > 0
wait_for_all_tracers()
assert LangChainTracer._persist_run_single.call_count == 1 # type: ignore
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~llms~volcengine_maas.py | from __future__ import annotations
from typing import Any, Dict, Iterator, List, Optional
from libs.core.langchain_core.callbacks import CallbackManagerForLLMRun
from libs.core.langchain_core.language_models.llms import LLM
from libs.core.langchain_core.outputs import GenerationChunk
from libs.core.langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
from libs.core.langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
class VolcEngineMaasBase(BaseModel):
"""Base class for VolcEngineMaas models."""
client: Any
volc_engine_maas_ak: Optional[SecretStr] = None
"""access key for volc engine"""
volc_engine_maas_sk: Optional[SecretStr] = None
"""secret key for volc engine"""
endpoint: Optional[str] = "maas-api.ml-platform-cn-beijing.volces.com"
"""Endpoint of the VolcEngineMaas LLM."""
region: Optional[str] = "Region"
"""Region of the VolcEngineMaas LLM."""
model: str = "skylark-lite-public"
"""Model name. you could check this model details here
https://www.volcengine.com/docs/82379/1133187
and you could choose other models by change this field"""
model_version: Optional[str] = None
"""Model version. Only used in moonshot large language model.
you could check details here https://www.volcengine.com/docs/82379/1158281"""
top_p: Optional[float] = 0.8
"""Total probability mass of tokens to consider at each step."""
temperature: Optional[float] = 0.95
"""A non-negative float that tunes the degree of randomness in generation."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""model special arguments, you could check detail on model page"""
streaming: bool = False
"""Whether to stream the results."""
connect_timeout: Optional[int] = 60
"""Timeout for connect to volc engine maas endpoint. Default is 60 seconds."""
read_timeout: Optional[int] = 60
"""Timeout for read response from volc engine maas endpoint.
Default is 60 seconds."""
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
volc_engine_maas_ak = convert_to_secret_str(
get_from_dict_or_env(values, "volc_engine_maas_ak", "VOLC_ACCESSKEY")
)
volc_engine_maas_sk = convert_to_secret_str(
get_from_dict_or_env(values, "volc_engine_maas_sk", "VOLC_SECRETKEY")
)
endpoint = values["endpoint"]
if values["endpoint"] is not None and values["endpoint"] != "":
endpoint = values["endpoint"]
try:
from volcengine.maas import MaasService
maas = MaasService(
endpoint,
values["region"],
connection_timeout=values["connect_timeout"],
socket_timeout=values["read_timeout"],
)
maas.set_ak(volc_engine_maas_ak.get_secret_value())
maas.set_sk(volc_engine_maas_sk.get_secret_value())
values["volc_engine_maas_ak"] = volc_engine_maas_ak
values["volc_engine_maas_sk"] = volc_engine_maas_sk
values["client"] = maas
except ImportError:
raise ImportError(
"volcengine package not found, please install it with "
"`pip install volcengine`"
)
return values
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling VolcEngineMaas API."""
normal_params = {
"top_p": self.top_p,
"temperature": self.temperature,
}
return {**normal_params, **self.model_kwargs}
class VolcEngineMaasLLM(LLM, VolcEngineMaasBase):
"""volc engine maas hosts a plethora of models.
You can utilize these models through this class.
To use, you should have the ``volcengine`` python package installed.
and set access key and secret key by environment variable or direct pass those to
this class.
access key, secret key are required parameters which you could get help
https://www.volcengine.com/docs/6291/65568
In order to use them, it is necessary to install the 'volcengine' Python package.
The access key and secret key must be set either via environment variables or
passed directly to this class.
access key and secret key are mandatory parameters for which assistance can be
sought at https://www.volcengine.com/docs/6291/65568.
Example:
.. code-block:: python
from langchain_community.llms import VolcEngineMaasLLM
model = VolcEngineMaasLLM(model="skylark-lite-public",
volc_engine_maas_ak="your_ak",
volc_engine_maas_sk="your_sk")
"""
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "volc-engine-maas-llm"
def _convert_prompt_msg_params(
self,
prompt: str,
**kwargs: Any,
) -> dict:
model_req = {
"model": {
"name": self.model,
}
}
if self.model_version is not None:
model_req["model"]["version"] = self.model_version
return {
**model_req,
"messages": [{"role": "user", "content": prompt}],
"parameters": {**self._default_params, **kwargs},
}
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
if self.streaming:
completion = ""
for chunk in self._stream(prompt, stop, run_manager, **kwargs):
completion += chunk.text
return completion
params = self._convert_prompt_msg_params(prompt, **kwargs)
response = self.client.chat(params)
return response.get("choice", {}).get("message", {}).get("content", "")
def _stream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
params = self._convert_prompt_msg_params(prompt, **kwargs)
for res in self.client.stream_chat(params):
if res:
chunk = GenerationChunk(
text=res.get("choice", {}).get("message", {}).get("content", "")
)
yield chunk
if run_manager:
run_manager.on_llm_new_token(chunk.text, chunk=chunk)
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~sitemap.py | import itertools
import re
from typing import Any, Callable, Generator, Iterable, List, Optional, Tuple
from urllib.parse import urlparse
from libs.core.langchain_core.documents import Document
from langchain_community.document_loaders.web_base import WebBaseLoader
def _default_parsing_function(content: Any) -> str:
return str(content.get_text())
def _default_meta_function(meta: dict, _content: Any) -> dict:
return {"source": meta["loc"], **meta}
def _batch_block(iterable: Iterable, size: int) -> Generator[List[dict], None, None]:
it = iter(iterable)
while item := list(itertools.islice(it, size)):
yield item
def _extract_scheme_and_domain(url: str) -> Tuple[str, str]:
"""Extract the scheme + domain from a given URL.
Args:
url (str): The input URL.
Returns:
return a 2-tuple of scheme and domain
"""
parsed_uri = urlparse(url)
return parsed_uri.scheme, parsed_uri.netloc
class SitemapLoader(WebBaseLoader):
"""Load a sitemap and its URLs.
**Security Note**: This loader can be used to load all URLs specified in a sitemap.
If a malicious actor gets access to the sitemap, they could force
the server to load URLs from other domains by modifying the sitemap.
This could lead to server-side request forgery (SSRF) attacks; e.g.,
with the attacker forcing the server to load URLs from internal
service endpoints that are not publicly accessible. While the attacker
may not immediately gain access to this data, this data could leak
into downstream systems (e.g., data loader is used to load data for indexing).
This loader is a crawler and web crawlers should generally NOT be deployed
with network access to any internal servers.
Control access to who can submit crawling requests and what network access
the crawler has.
By default, the loader will only load URLs from the same domain as the sitemap
if the site map is not a local file. This can be disabled by setting
restrict_to_same_domain to False (not recommended).
If the site map is a local file, no such risk mitigation is applied by default.
Use the filter URLs argument to limit which URLs can be loaded.
See https://python.langchain.com/docs/security
"""
def __init__(
self,
web_path: str,
filter_urls: Optional[List[str]] = None,
parsing_function: Optional[Callable] = None,
blocksize: Optional[int] = None,
blocknum: int = 0,
meta_function: Optional[Callable] = None,
is_local: bool = False,
continue_on_failure: bool = False,
restrict_to_same_domain: bool = True,
**kwargs: Any,
):
"""Initialize with webpage path and optional filter URLs.
Args:
web_path: url of the sitemap. can also be a local path
filter_urls: a list of regexes. If specified, only
URLS that match one of the filter URLs will be loaded.
*WARNING* The filter URLs are interpreted as regular expressions.
Remember to escape special characters if you do not want them to be
interpreted as regular expression syntax. For example, `.` appears
frequently in URLs and should be escaped if you want to match a literal
`.` rather than any character.
restrict_to_same_domain takes precedence over filter_urls when
restrict_to_same_domain is True and the sitemap is not a local file.
parsing_function: Function to parse bs4.Soup output
blocksize: number of sitemap locations per block
blocknum: the number of the block that should be loaded - zero indexed.
Default: 0
meta_function: Function to parse bs4.Soup output for metadata
remember when setting this method to also copy metadata["loc"]
to metadata["source"] if you are using this field
is_local: whether the sitemap is a local file. Default: False
continue_on_failure: whether to continue loading the sitemap if an error
occurs loading a url, emitting a warning instead of raising an
exception. Setting this to True makes the loader more robust, but also
may result in missing data. Default: False
restrict_to_same_domain: whether to restrict loading to URLs to the same
domain as the sitemap. Attention: This is only applied if the sitemap
is not a local file!
"""
if blocksize is not None and blocksize < 1:
raise ValueError("Sitemap blocksize should be at least 1")
if blocknum < 0:
raise ValueError("Sitemap blocknum can not be lower then 0")
try:
import lxml # noqa:F401
except ImportError:
raise ImportError(
"lxml package not found, please install it with `pip install lxml`"
)
super().__init__(web_paths=[web_path], **kwargs)
# Define a list of URL patterns (interpreted as regular expressions) that
# will be allowed to be loaded.
# restrict_to_same_domain takes precedence over filter_urls when
# restrict_to_same_domain is True and the sitemap is not a local file.
self.allow_url_patterns = filter_urls
self.restrict_to_same_domain = restrict_to_same_domain
self.parsing_function = parsing_function or _default_parsing_function
self.meta_function = meta_function or _default_meta_function
self.blocksize = blocksize
self.blocknum = blocknum
self.is_local = is_local
self.continue_on_failure = continue_on_failure
def parse_sitemap(self, soup: Any) -> List[dict]:
"""Parse sitemap xml and load into a list of dicts.
Args:
soup: BeautifulSoup object.
Returns:
List of dicts.
"""
els = []
for url in soup.find_all("url"):
loc = url.find("loc")
if not loc:
continue
# Strip leading and trailing whitespace and newlines
loc_text = loc.text.strip()
if self.restrict_to_same_domain and not self.is_local:
if _extract_scheme_and_domain(loc_text) != _extract_scheme_and_domain(
self.web_path
):
continue
if self.allow_url_patterns and not any(
re.match(regexp_pattern, loc_text)
for regexp_pattern in self.allow_url_patterns
):
continue
els.append(
{
tag: prop.text
for tag in ["loc", "lastmod", "changefreq", "priority"]
if (prop := url.find(tag))
}
)
for sitemap in soup.find_all("sitemap"):
loc = sitemap.find("loc")
if not loc:
continue
soup_child = self.scrape_all([loc.text], "xml")[0]
els.extend(self.parse_sitemap(soup_child))
return els
def load(self) -> List[Document]:
"""Load sitemap."""
if self.is_local:
try:
import bs4
except ImportError:
raise ImportError(
"beautifulsoup4 package not found, please install it"
" with `pip install beautifulsoup4`"
)
fp = open(self.web_path)
soup = bs4.BeautifulSoup(fp, "xml")
else:
soup = self._scrape(self.web_path, parser="xml")
els = self.parse_sitemap(soup)
if self.blocksize is not None:
elblocks = list(_batch_block(els, self.blocksize))
blockcount = len(elblocks)
if blockcount - 1 < self.blocknum:
raise ValueError(
"Selected sitemap does not contain enough blocks for given blocknum"
)
else:
els = elblocks[self.blocknum]
results = self.scrape_all([el["loc"].strip() for el in els if "loc" in el])
return [
Document(
page_content=self.parsing_function(results[i]),
metadata=self.meta_function(els[i], results[i]),
)
for i in range(len(results))
]
| [] |
2024-01-10 | mth93/langchain | libs~langchain~langchain~evaluation~scoring~eval_chain.py | """Base classes for scoring the output of a model on a scale of 1-10."""
from __future__ import annotations
import logging
import re
from typing import Any, Dict, List, Optional, Union
from libs.core.langchain_core.language_models import BaseLanguageModel
from libs.core.langchain_core.output_parsers import BaseOutputParser
from libs.core.langchain_core.prompts.prompt import PromptTemplate
from libs.core.langchain_core.pydantic_v1 import Extra, Field
from langchain.callbacks.manager import Callbacks
from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple
from langchain.chains.llm import LLMChain
from langchain.chat_models.azure_openai import AzureChatOpenAI
from langchain.chat_models.openai import ChatOpenAI
from langchain.evaluation.criteria.eval_chain import (
CRITERIA_TYPE,
Criteria,
)
from langchain.evaluation.schema import LLMEvalChain, StringEvaluator
from langchain.evaluation.scoring.prompt import (
CRITERIA_INSTRUCTIONS,
DEFAULT_CRITERIA,
SCORING_TEMPLATE,
SCORING_TEMPLATE_WITH_REFERENCE,
)
from langchain.schema import RUN_KEY
logger = logging.getLogger(__name__)
_FIND_DOUBLE_BRACKETS = re.compile(r"\[\[(.*?)\]\]")
_SUPPORTED_CRITERIA = {
Criteria.CONCISENESS: "Is the submission concise and to the point?",
Criteria.RELEVANCE: "Is the submission referring to a real quote from the text?",
Criteria.CORRECTNESS: "Is the submission correct, accurate, and factual?",
Criteria.COHERENCE: "Is the submission coherent, well-structured, and organized?",
Criteria.HARMFULNESS: "Is the submission harmful, offensive, or inappropriate?",
Criteria.MALICIOUSNESS: "Is the submission malicious in any way?",
Criteria.HELPFULNESS: "Is the submission helpful, insightful, and appropriate?",
Criteria.CONTROVERSIALITY: "Is the submission controversial or debatable?",
Criteria.MISOGYNY: "Is the submission misogynistic or sexist?",
Criteria.CRIMINALITY: "Is the submission criminal in any way?",
Criteria.INSENSITIVITY: "Is the submission insensitive to any group of people?",
Criteria.DEPTH: "Does the submission demonstrate depth of thought?",
Criteria.CREATIVITY: "Does the submission demonstrate novelty or unique ideas?",
Criteria.DETAIL: "Does the submission demonstrate attention to detail?",
}
def resolve_criteria(
criteria: Optional[Union[CRITERIA_TYPE, str, List[CRITERIA_TYPE]]],
) -> dict:
"""Resolve the criteria for the pairwise evaluator.
Args:
criteria (Union[CRITERIA_TYPE, str], optional): The criteria to use.
Returns:
dict: The resolved criteria.
"""
if criteria is None:
_default_criteria = [
Criteria.HELPFULNESS,
Criteria.RELEVANCE,
Criteria.CORRECTNESS,
Criteria.DEPTH,
]
return {k.value: _SUPPORTED_CRITERIA[k] for k in _default_criteria}
elif isinstance(criteria, Criteria):
criteria_ = {criteria.value: _SUPPORTED_CRITERIA[criteria]}
elif isinstance(criteria, str):
if criteria in _SUPPORTED_CRITERIA:
criteria_ = {criteria: _SUPPORTED_CRITERIA[Criteria(criteria)]}
else:
criteria_ = {criteria: ""}
elif isinstance(criteria, ConstitutionalPrinciple):
criteria_ = {criteria.name: criteria.critique_request}
elif isinstance(criteria, (list, tuple)):
criteria_ = {
k: v
for criterion in criteria
for k, v in resolve_criteria(criterion).items()
}
else:
if not criteria:
raise ValueError(
"Criteria cannot be empty. "
"Please provide a criterion name or a mapping of the criterion name"
" to its description."
)
criteria_ = dict(criteria)
return criteria_
class ScoreStringResultOutputParser(BaseOutputParser[dict]):
"""A parser for the output of the ScoreStringEvalChain.
Attributes:
_type (str): The type of the output parser.
"""
@property
def _type(self) -> str:
"""Return the type of the output parser.
Returns:
str: The type of the output parser.
"""
return "pairwise_string_result"
def parse(self, text: str) -> Dict[str, Any]:
"""Parse the output text.
Args:
text (str): The output text to parse.
Returns:
Dict: The parsed output.
Raises:
ValueError: If the verdict is invalid.
"""
match = _FIND_DOUBLE_BRACKETS.search(text)
if match:
verdict = match.group(1)
if not match or verdict not in list("123456789") + ["10"]:
raise ValueError(
f"Invalid output: {text}. "
"Output must contain a double bracketed string\
with the verdict between 1 and 10."
)
return {
"reasoning": text,
"score": int(verdict),
}
class ScoreStringEvalChain(StringEvaluator, LLMEvalChain, LLMChain):
"""A chain for scoring on a scale of 1-10 the output of a model.
Attributes:
output_parser (BaseOutputParser): The output parser for the chain.
Example:
>>> from langchain.chat_models import ChatOpenAI
>>> from langchain.evaluation.scoring import ScoreStringEvalChain
>>> llm = ChatOpenAI(temperature=0, model_name="gpt-4")
>>> chain = ScoreStringEvalChain.from_llm(llm=llm)
>>> result = chain.evaluate_strings(
... input = "What is the chemical formula for water?",
... prediction = "H2O",
... reference = "The chemical formula for water is H2O.",
... )
>>> print(result)
# {
# "score": 8,
# "comment": "The response accurately states "
# "that the chemical formula for water is H2O."
# "However, it does not provide an explanation of what the formula means."
# }
"""
output_key: str = "results" #: :meta private:
output_parser: BaseOutputParser = Field(
default_factory=ScoreStringResultOutputParser
)
normalize_by: Optional[float] = None
"""The value to normalize the score by, if specified."""
criterion_name: str
"""The name of the criterion being evaluated."""
class Config:
"""Configuration for the ScoreStringEvalChain."""
extra = Extra.ignore
@classmethod
def is_lc_serializable(cls) -> bool:
return False
@property
def requires_reference(self) -> bool:
"""Return whether the chain requires a reference.
Returns:
bool: True if the chain requires a reference, False otherwise.
"""
return False
@property
def requires_input(self) -> bool:
"""Return whether the chain requires an input.
Returns:
bool: True if the chain requires an input, False otherwise.
"""
return True
@property
def evaluation_name(self) -> str:
"""Get the name of the evaluation.
Returns
-------
str
The name of the evaluation.
"""
return f"score_string:{self.criterion_name}"
@property
def _skip_reference_warning(self) -> str:
"""Return the warning to show when reference is ignored.
Returns:
str: The warning to show when reference is ignored.
"""
return (
f"Ignoring reference in {self.__class__.__name__}, as it is not expected."
"\nTo use a reference, use the LabeledScoreStringEvalChain instead."
" (EvaluatorType.LABELED_SCORE_STRING) instead."
)
@classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
*,
prompt: Optional[PromptTemplate] = None,
criteria: Optional[Union[CRITERIA_TYPE, str]] = None,
normalize_by: Optional[float] = None,
**kwargs: Any,
) -> ScoreStringEvalChain:
"""Initialize the ScoreStringEvalChain from an LLM.
Args:
llm (BaseChatModel): The LLM to use (GPT-4 recommended).
prompt (PromptTemplate, optional): The prompt to use.
**kwargs (Any): Additional keyword arguments.
Returns:
ScoreStringEvalChain: The initialized ScoreStringEvalChain.
Raises:
ValueError: If the input variables are not as expected.
"""
if not (
isinstance(llm, (ChatOpenAI, AzureChatOpenAI))
and llm.model_name.startswith("gpt-4")
):
logger.warning(
"This chain was only tested with GPT-4. \
Performance may be significantly worse with other models."
)
expected_input_vars = {"prediction", "input", "criteria"}
prompt_ = prompt or SCORING_TEMPLATE.partial(reference="")
if expected_input_vars != set(prompt_.input_variables):
raise ValueError(
f"Input variables should be {expected_input_vars}, "
f"but got {prompt_.input_variables}"
)
criteria_ = resolve_criteria(criteria)
criteria_str = "\n".join(
f"{k}: {v}" if v else k for k, v in criteria_.items()
).strip()
criteria_str = (
CRITERIA_INSTRUCTIONS + f"{criteria_str}\n"
if criteria_str
else DEFAULT_CRITERIA
)
return cls(
llm=llm,
prompt=prompt_.partial(criteria=criteria_str),
normalize_by=normalize_by,
criterion_name="-".join(criteria_),
**kwargs,
)
def _prepare_input(
self,
prediction: str,
input: Optional[str],
reference: Optional[str],
) -> dict:
"""Prepare the input for the chain.
Args:
prediction (str): The output string from the first model.
prediction_b (str): The output string from the second model.
input (str, optional): The input or task string.
reference (str, optional): The reference string, if any.
Returns:
dict: The prepared input for the chain.
"""
input_ = {
"prediction": prediction,
"input": input,
}
if self.requires_reference:
input_["reference"] = reference
return input_
def _prepare_output(self, result: dict) -> dict:
"""Prepare the output."""
parsed = result[self.output_key]
if RUN_KEY in result:
parsed[RUN_KEY] = result[RUN_KEY]
if "score" in parsed and self.normalize_by is not None:
parsed["score"] = parsed["score"] / self.normalize_by
return parsed
def _evaluate_strings(
self,
*,
prediction: str,
input: Optional[str] = None,
reference: Optional[str] = None,
callbacks: Callbacks = None,
tags: Optional[List[str]] = None,
metadata: Optional[Dict[str, Any]] = None,
include_run_info: bool = False,
**kwargs: Any,
) -> dict:
"""Score the output string.
Args:
prediction (str): The output string from the first model.
input (str, optional): The input or task string.
callbacks (Callbacks, optional): The callbacks to use.
reference (str, optional): The reference string, if any.
**kwargs (Any): Additional keyword arguments.
Returns:
dict: A dictionary containing:
- reasoning: The reasoning for the preference.
- score: A score between 1 and 10.
"""
input_ = self._prepare_input(prediction, input, reference)
result = self(
inputs=input_,
callbacks=callbacks,
tags=tags,
metadata=metadata,
include_run_info=include_run_info,
)
return self._prepare_output(result)
async def _aevaluate_string_pairs(
self,
*,
prediction: str,
reference: Optional[str] = None,
input: Optional[str] = None,
callbacks: Callbacks = None,
tags: Optional[List[str]] = None,
metadata: Optional[Dict[str, Any]] = None,
include_run_info: bool = False,
**kwargs: Any,
) -> dict:
"""Asynchronously score the output string.
Args:
prediction (str): The output string from the first model.
input (str, optional): The input or task string.
callbacks (Callbacks, optional): The callbacks to use.
reference (str, optional): The reference string, if any.
**kwargs (Any): Additional keyword arguments.
Returns:
dict: A dictionary containing:
- reasoning: The reasoning for the preference.
- score: A score between 1 and 10.
"""
input_ = self._prepare_input(prediction, input, reference)
result = await self.acall(
inputs=input_,
callbacks=callbacks,
tags=tags,
metadata=metadata,
include_run_info=include_run_info,
)
return self._prepare_output(result)
class LabeledScoreStringEvalChain(ScoreStringEvalChain):
"""A chain for scoring the output of a model on a scale of 1-10.
Attributes:
output_parser (BaseOutputParser): The output parser for the chain.
"""
@property
def requires_reference(self) -> bool:
"""Return whether the chain requires a reference.
Returns:
bool: True if the chain requires a reference, False otherwise.
"""
return True
@classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
*,
prompt: Optional[PromptTemplate] = None,
criteria: Optional[Union[CRITERIA_TYPE, str]] = None,
normalize_by: Optional[float] = None,
**kwargs: Any,
) -> LabeledScoreStringEvalChain:
"""Initialize the LabeledScoreStringEvalChain from an LLM.
Args:
llm (BaseLanguageModel): The LLM to use.
prompt (PromptTemplate, optional): The prompt to use.
criteria (Union[CRITERIA_TYPE, str], optional): The criteria to use.
normalize_by (float, optional): The value to normalize the score by.
**kwargs (Any): Additional keyword arguments.
Returns:
LabeledScoreStringEvalChain: The initialized LabeledScoreStringEvalChain.
Raises:
ValueError: If the input variables are not as expected.
""" # noqa: E501
expected_input_vars = {
"prediction",
"input",
"reference",
"criteria",
}
prompt_ = prompt or SCORING_TEMPLATE_WITH_REFERENCE
if expected_input_vars != set(prompt_.input_variables):
raise ValueError(
f"Input variables should be {expected_input_vars}, "
f"but got {prompt_.input_variables}"
)
criteria_ = resolve_criteria(criteria)
criteria_str = "\n".join(f"{k}: {v}" for k, v in criteria_.items()).strip()
criteria_str = (
CRITERIA_INSTRUCTIONS + f"{criteria_str}\n"
if criteria_str
else DEFAULT_CRITERIA
)
return cls(
llm=llm,
prompt=prompt_.partial(criteria=criteria_str),
normalize_by=normalize_by,
criterion_name="-".join(criteria_),
**kwargs,
)
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~generic.py | from __future__ import annotations
from pathlib import Path
from typing import (
TYPE_CHECKING,
Any,
Iterator,
List,
Literal,
Optional,
Sequence,
Union,
)
from libs.core.langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseBlobParser, BaseLoader
from langchain_community.document_loaders.blob_loaders import (
BlobLoader,
FileSystemBlobLoader,
)
from langchain_community.document_loaders.parsers.registry import get_parser
if TYPE_CHECKING:
from langchain.text_splitter import TextSplitter
_PathLike = Union[str, Path]
DEFAULT = Literal["default"]
class GenericLoader(BaseLoader):
"""Generic Document Loader.
A generic document loader that allows combining an arbitrary blob loader with
a blob parser.
Examples:
Parse a specific PDF file:
.. code-block:: python
from langchain_community.document_loaders import GenericLoader
from langchain_community.document_loaders.parsers.pdf import PyPDFParser
# Recursively load all text files in a directory.
loader = GenericLoader.from_filesystem(
"my_lovely_pdf.pdf",
parser=PyPDFParser()
)
.. code-block:: python
from langchain_community.document_loaders import GenericLoader
from langchain_community.document_loaders.blob_loaders import FileSystemBlobLoader
loader = GenericLoader.from_filesystem(
path="path/to/directory",
glob="**/[!.]*",
suffixes=[".pdf"],
show_progress=True,
)
docs = loader.lazy_load()
next(docs)
Example instantiations to change which files are loaded:
.. code-block:: python
# Recursively load all text files in a directory.
loader = GenericLoader.from_filesystem("/path/to/dir", glob="**/*.txt")
# Recursively load all non-hidden files in a directory.
loader = GenericLoader.from_filesystem("/path/to/dir", glob="**/[!.]*")
# Load all files in a directory without recursion.
loader = GenericLoader.from_filesystem("/path/to/dir", glob="*")
Example instantiations to change which parser is used:
.. code-block:: python
from langchain_community.document_loaders.parsers.pdf import PyPDFParser
# Recursively load all text files in a directory.
loader = GenericLoader.from_filesystem(
"/path/to/dir",
glob="**/*.pdf",
parser=PyPDFParser()
)
""" # noqa: E501
def __init__(
self,
blob_loader: BlobLoader,
blob_parser: BaseBlobParser,
) -> None:
"""A generic document loader.
Args:
blob_loader: A blob loader which knows how to yield blobs
blob_parser: A blob parser which knows how to parse blobs into documents
"""
self.blob_loader = blob_loader
self.blob_parser = blob_parser
def lazy_load(
self,
) -> Iterator[Document]:
"""Load documents lazily. Use this when working at a large scale."""
for blob in self.blob_loader.yield_blobs():
yield from self.blob_parser.lazy_parse(blob)
def load(self) -> List[Document]:
"""Load all documents."""
return list(self.lazy_load())
def load_and_split(
self, text_splitter: Optional[TextSplitter] = None
) -> List[Document]:
"""Load all documents and split them into sentences."""
raise NotImplementedError(
"Loading and splitting is not yet implemented for generic loaders. "
"When they will be implemented they will be added via the initializer. "
"This method should not be used going forward."
)
@classmethod
def from_filesystem(
cls,
path: _PathLike,
*,
glob: str = "**/[!.]*",
exclude: Sequence[str] = (),
suffixes: Optional[Sequence[str]] = None,
show_progress: bool = False,
parser: Union[DEFAULT, BaseBlobParser] = "default",
parser_kwargs: Optional[dict] = None,
) -> GenericLoader:
"""Create a generic document loader using a filesystem blob loader.
Args:
path: The path to the directory to load documents from OR the path to a
single file to load. If this is a file, glob, exclude, suffixes
will be ignored.
glob: The glob pattern to use to find documents.
suffixes: The suffixes to use to filter documents. If None, all files
matching the glob will be loaded.
exclude: A list of patterns to exclude from the loader.
show_progress: Whether to show a progress bar or not (requires tqdm).
Proxies to the file system loader.
parser: A blob parser which knows how to parse blobs into documents,
will instantiate a default parser if not provided.
The default can be overridden by either passing a parser or
setting the class attribute `blob_parser` (the latter
should be used with inheritance).
parser_kwargs: Keyword arguments to pass to the parser.
Returns:
A generic document loader.
"""
blob_loader = FileSystemBlobLoader(
path,
glob=glob,
exclude=exclude,
suffixes=suffixes,
show_progress=show_progress,
)
if isinstance(parser, str):
if parser == "default":
try:
# If there is an implementation of get_parser on the class, use it.
blob_parser = cls.get_parser(**(parser_kwargs or {}))
except NotImplementedError:
# if not then use the global registry.
blob_parser = get_parser(parser)
else:
blob_parser = get_parser(parser)
else:
blob_parser = parser
return cls(blob_loader, blob_parser)
@staticmethod
def get_parser(**kwargs: Any) -> BaseBlobParser:
"""Override this method to associate a default parser with the class."""
raise NotImplementedError()
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_transformers~beautiful_soup_transformer.py | from typing import Any, Iterator, List, Sequence, cast
from libs.core.langchain_core.documents import BaseDocumentTransformer, Document
class BeautifulSoupTransformer(BaseDocumentTransformer):
"""Transform HTML content by extracting specific tags and removing unwanted ones.
Example:
.. code-block:: python
from langchain_community.document_transformers import BeautifulSoupTransformer
bs4_transformer = BeautifulSoupTransformer()
docs_transformed = bs4_transformer.transform_documents(docs)
""" # noqa: E501
def __init__(self) -> None:
"""
Initialize the transformer.
This checks if the BeautifulSoup4 package is installed.
If not, it raises an ImportError.
"""
try:
import bs4 # noqa:F401
except ImportError:
raise ImportError(
"BeautifulSoup4 is required for BeautifulSoupTransformer. "
"Please install it with `pip install beautifulsoup4`."
)
def transform_documents(
self,
documents: Sequence[Document],
unwanted_tags: List[str] = ["script", "style"],
tags_to_extract: List[str] = ["p", "li", "div", "a"],
remove_lines: bool = True,
**kwargs: Any,
) -> Sequence[Document]:
"""
Transform a list of Document objects by cleaning their HTML content.
Args:
documents: A sequence of Document objects containing HTML content.
unwanted_tags: A list of tags to be removed from the HTML.
tags_to_extract: A list of tags whose content will be extracted.
remove_lines: If set to True, unnecessary lines will be
removed from the HTML content.
Returns:
A sequence of Document objects with transformed content.
"""
for doc in documents:
cleaned_content = doc.page_content
cleaned_content = self.remove_unwanted_tags(cleaned_content, unwanted_tags)
cleaned_content = self.extract_tags(cleaned_content, tags_to_extract)
if remove_lines:
cleaned_content = self.remove_unnecessary_lines(cleaned_content)
doc.page_content = cleaned_content
return documents
@staticmethod
def remove_unwanted_tags(html_content: str, unwanted_tags: List[str]) -> str:
"""
Remove unwanted tags from a given HTML content.
Args:
html_content: The original HTML content string.
unwanted_tags: A list of tags to be removed from the HTML.
Returns:
A cleaned HTML string with unwanted tags removed.
"""
from bs4 import BeautifulSoup
soup = BeautifulSoup(html_content, "html.parser")
for tag in unwanted_tags:
for element in soup.find_all(tag):
element.decompose()
return str(soup)
@staticmethod
def extract_tags(html_content: str, tags: List[str]) -> str:
"""
Extract specific tags from a given HTML content.
Args:
html_content: The original HTML content string.
tags: A list of tags to be extracted from the HTML.
Returns:
A string combining the content of the extracted tags.
"""
from bs4 import BeautifulSoup
soup = BeautifulSoup(html_content, "html.parser")
text_parts: List[str] = []
for element in soup.find_all():
if element.name in tags:
# Extract all navigable strings recursively from this element.
text_parts += get_navigable_strings(element)
# To avoid duplicate text, remove all descendants from the soup.
element.decompose()
return " ".join(text_parts)
@staticmethod
def remove_unnecessary_lines(content: str) -> str:
"""
Clean up the content by removing unnecessary lines.
Args:
content: A string, which may contain unnecessary lines or spaces.
Returns:
A cleaned string with unnecessary lines removed.
"""
lines = content.split("\n")
stripped_lines = [line.strip() for line in lines]
non_empty_lines = [line for line in stripped_lines if line]
cleaned_content = " ".join(non_empty_lines)
return cleaned_content
async def atransform_documents(
self,
documents: Sequence[Document],
**kwargs: Any,
) -> Sequence[Document]:
raise NotImplementedError
def get_navigable_strings(element: Any) -> Iterator[str]:
"""Get all navigable strings from a BeautifulSoup element.
Args:
element: A BeautifulSoup element.
Returns:
A generator of strings.
"""
from bs4 import NavigableString, Tag
for child in cast(Tag, element).children:
if isinstance(child, Tag):
yield from get_navigable_strings(child)
elif isinstance(child, NavigableString):
if (element.name == "a") and (href := element.get("href")):
yield f"{child.strip()} ({href})"
else:
yield child.strip()
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~discord.py | from __future__ import annotations
from typing import TYPE_CHECKING, List
from libs.core.langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
if TYPE_CHECKING:
import pandas as pd
class DiscordChatLoader(BaseLoader):
"""Load `Discord` chat logs."""
def __init__(self, chat_log: pd.DataFrame, user_id_col: str = "ID"):
"""Initialize with a Pandas DataFrame containing chat logs.
Args:
chat_log: Pandas DataFrame containing chat logs.
user_id_col: Name of the column containing the user ID. Defaults to "ID".
"""
if not isinstance(chat_log, pd.DataFrame):
raise ValueError(
f"Expected chat_log to be a pd.DataFrame, got {type(chat_log)}"
)
self.chat_log = chat_log
self.user_id_col = user_id_col
def load(self) -> List[Document]:
"""Load all chat messages."""
result = []
for _, row in self.chat_log.iterrows():
user_id = row[self.user_id_col]
metadata = row.to_dict()
metadata.pop(self.user_id_col)
result.append(Document(page_content=user_id, metadata=metadata))
return result
| [] |
2024-01-10 | mth93/langchain | libs~community~tests~integration_tests~vectorstores~test_zep.py | # mypy: disable-error-code=attr-defined
import copy
from random import random
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
from uuid import uuid4
import pytest
from libs.core.langchain_core.documents import Document
from pytest_mock import MockerFixture
from langchain_community.vectorstores import ZepVectorStore
from langchain_community.vectorstores.zep import CollectionConfig
if TYPE_CHECKING:
from zep_python.document import Document as ZepDocument
from zep_python.document import DocumentCollection
VECTOR_DIMS = 5
def gen_vector() -> List[float]:
return [random() for _ in range(VECTOR_DIMS)]
def gen_mock_zep_document(
collection_name: str,
embedding_dimensions: Optional[int] = None,
) -> "ZepDocument":
from zep_python.document import Document as ZepDocument
embedding = (
[random() for _ in range(embedding_dimensions)]
if embedding_dimensions
else None
)
return ZepDocument(
uuid=str(uuid4()),
collection_name=collection_name,
content="Test Document",
embedding=embedding,
metadata={"key": "value"},
)
@pytest.fixture
def texts_metadatas() -> Dict[str, Any]:
return {
"texts": ["Test Document" for _ in range(2)],
"metadatas": [{"key": "value"} for _ in range(2)],
}
@pytest.fixture
def mock_documents() -> List[Document]:
return [
Document(
page_content="Test Document",
metadata={"key": "value"},
)
for _ in range(2)
]
@pytest.fixture
def texts_metadatas_as_zep_documents() -> List["ZepDocument"]:
from zep_python.document import Document as ZepDocument
return [
ZepDocument(
content="Test Document",
metadata={"key": "value"},
)
for _ in range(2)
]
@pytest.fixture
def search_results() -> List["ZepDocument"]:
return [
gen_mock_zep_document(
collection_name="test_collection", embedding_dimensions=VECTOR_DIMS
)
for _ in range(2)
]
@pytest.fixture
def search_results_with_query_embedding() -> Tuple[List["ZepDocument"], List[float]]:
return_count = 2
return [
gen_mock_zep_document(
collection_name="test_collection", embedding_dimensions=VECTOR_DIMS
)
for _ in range(return_count)
], gen_vector()
@pytest.fixture
def mock_collection_config() -> CollectionConfig:
return CollectionConfig(
name="test_collection",
description="Test Collection",
metadata={"key": "value"},
embedding_dimensions=VECTOR_DIMS,
is_auto_embedded=True,
)
@pytest.fixture
@pytest.mark.requires("zep_python")
def mock_collection(
mocker: MockerFixture,
mock_collection_config: CollectionConfig,
search_results: List[Document],
search_results_with_query_embedding: Tuple[List[Document], List[float]],
) -> "DocumentCollection":
from zep_python.document import DocumentCollection
mock_collection: DocumentCollection = mocker.patch(
"zep_python.document.collections.DocumentCollection", autospec=True
)
mock_collection.search.return_value = copy.deepcopy(search_results)
mock_collection.asearch.return_value = copy.deepcopy(search_results)
temp_value = copy.deepcopy(search_results_with_query_embedding)
mock_collection.search_return_query_vector.return_value = copy.deepcopy(temp_value)
mock_collection.asearch_return_query_vector.return_value = copy.deepcopy(temp_value)
mock_collection.name = mock_collection_config.name
mock_collection.is_auto_embedded = mock_collection_config.is_auto_embedded
mock_collection.embedding_dimensions = mock_collection_config.embedding_dimensions
return mock_collection
@pytest.fixture
@pytest.mark.requires("zep_python")
def zep_vectorstore(
mocker: MockerFixture,
mock_collection: "DocumentCollection",
mock_collection_config: CollectionConfig,
) -> ZepVectorStore:
mock_document_client = mocker.patch(
"zep_python.document.client.DocumentClient", autospec=True
)
mock_document_client.get_collection.return_value = mock_collection
mock_client = mocker.patch("zep_python.ZepClient", autospec=True)
mock_client.return_value.document = mock_document_client
vs = ZepVectorStore(
mock_collection_config.name,
"http://localhost:8080",
api_key="test",
config=mock_collection_config,
)
return vs
@pytest.mark.requires("zep_python")
def test_from_texts(
zep_vectorstore: ZepVectorStore,
mock_collection_config: CollectionConfig,
mock_collection: "DocumentCollection",
texts_metadatas: Dict[str, Any],
texts_metadatas_as_zep_documents: List["ZepDocument"],
) -> None:
vs = zep_vectorstore.from_texts(
**texts_metadatas,
collection_name=mock_collection_config.name,
api_url="http://localhost:8000",
)
vs._collection.add_documents.assert_called_once_with( # type: ignore
texts_metadatas_as_zep_documents
)
@pytest.mark.requires("zep_python")
def test_add_documents(
zep_vectorstore: ZepVectorStore,
mock_collection: "DocumentCollection",
mock_documents: List[Document],
texts_metadatas_as_zep_documents: List["ZepDocument"],
) -> None:
zep_vectorstore.add_documents(mock_documents)
mock_collection.add_documents.assert_called_once_with( # type: ignore
texts_metadatas_as_zep_documents
)
@pytest.mark.requires("zep_python")
async def test_asearch_similarity(
zep_vectorstore: ZepVectorStore,
) -> None:
r = await zep_vectorstore.asearch(
query="Test Document", search_type="similarity", k=2
)
assert len(r) == 2
assert r[0].page_content == "Test Document"
assert r[0].metadata == {"key": "value"}
@pytest.mark.requires("zep_python")
async def test_asearch_mmr(
zep_vectorstore: ZepVectorStore,
) -> None:
r = await zep_vectorstore.asearch(query="Test Document", search_type="mmr", k=1)
assert len(r) == 1
assert r[0].page_content == "Test Document"
assert r[0].metadata == {"key": "value"}
@pytest.mark.requires("zep_python")
def test_search_similarity(
zep_vectorstore: ZepVectorStore,
) -> None:
r = zep_vectorstore.search(query="Test Document", search_type="similarity", k=2)
assert len(r) == 2
assert r[0].page_content == "Test Document"
assert r[0].metadata == {"key": "value"}
@pytest.mark.requires("zep_python")
def test_search_mmr(
zep_vectorstore: ZepVectorStore,
) -> None:
r = zep_vectorstore.search(query="Test Document", search_type="mmr", k=1)
assert len(r) == 1
assert r[0].page_content == "Test Document"
assert r[0].metadata == {"key": "value"}
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~brave_search.py | from typing import Iterator, List, Optional
from libs.core.langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
from langchain_community.utilities.brave_search import BraveSearchWrapper
class BraveSearchLoader(BaseLoader):
"""Load with `Brave Search` engine."""
def __init__(self, query: str, api_key: str, search_kwargs: Optional[dict] = None):
"""Initializes the BraveLoader.
Args:
query: The query to search for.
api_key: The API key to use.
search_kwargs: The search kwargs to use.
"""
self.query = query
self.api_key = api_key
self.search_kwargs = search_kwargs or {}
def load(self) -> List[Document]:
brave_client = BraveSearchWrapper(
api_key=self.api_key,
search_kwargs=self.search_kwargs,
)
return brave_client.download_documents(self.query)
def lazy_load(self) -> Iterator[Document]:
for doc in self.load():
yield doc
| [] |
2024-01-10 | mth93/langchain | libs~langchain~tests~integration_tests~smith~evaluation~test_runner_utils.py | from typing import Iterator, List
from uuid import uuid4
import pytest
from libs.core.langchain_core.messages import BaseMessage, HumanMessage
from libs.core.langchain_core.prompts.chat import ChatPromptTemplate
from langsmith import Client as Client
from langsmith.schemas import DataType
from langchain.chains.llm import LLMChain
from langchain.chat_models import ChatOpenAI
from langchain.evaluation import EvaluatorType
from langchain.llms.openai import OpenAI
from langchain.smith import RunEvalConfig, run_on_dataset
from langchain.smith.evaluation import InputFormatError
from langchain.smith.evaluation.runner_utils import arun_on_dataset
def _check_all_feedback_passed(_project_name: str, client: Client) -> None:
# Assert that all runs completed, all feedback completed, and that the
# chain or llm passes for the feedback provided.
runs = list(client.list_runs(project_name=_project_name, execution_order=1))
if not runs:
# Queue delays. We are mainly just smoke checking rn.
return
feedback = list(client.list_feedback(run_ids=[run.id for run in runs]))
if not feedback:
return
assert all([f.score == 1 for f in feedback])
@pytest.fixture
def eval_project_name() -> str:
return f"lcp integration tests - {str(uuid4())[-8:]}"
@pytest.fixture(scope="module")
def client() -> Client:
return Client()
@pytest.fixture(
scope="module",
)
def kv_dataset_name() -> Iterator[str]:
import pandas as pd
client = Client()
df = pd.DataFrame(
{
"some_input": [
"What's the capital of California?",
"What's the capital of Nevada?",
"What's the capital of Oregon?",
"What's the capital of Washington?",
],
"other_input": [
"a",
"b",
"c",
"d",
],
"some_output": ["Sacramento", "Carson City", "Salem", "Olympia"],
"other_output": ["e", "f", "g", "h"],
}
)
uid = str(uuid4())[-8:]
_dataset_name = f"lcp kv dataset integration tests - {uid}"
client.upload_dataframe(
df,
name=_dataset_name,
input_keys=["some_input", "other_input"],
output_keys=["some_output", "other_output"],
description="Integration test dataset",
)
yield _dataset_name
def test_chat_model(
kv_dataset_name: str, eval_project_name: str, client: Client
) -> None:
llm = ChatOpenAI(temperature=0)
eval_config = RunEvalConfig(evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA])
with pytest.raises(ValueError, match="Must specify reference_key"):
run_on_dataset(
dataset_name=kv_dataset_name,
llm_or_chain_factory=llm,
evaluation=eval_config,
client=client,
)
eval_config = RunEvalConfig(
evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA],
reference_key="some_output",
)
with pytest.raises(
InputFormatError, match="Example inputs do not match language model"
):
run_on_dataset(
dataset_name=kv_dataset_name,
llm_or_chain_factory=llm,
evaluation=eval_config,
client=client,
)
def input_mapper(d: dict) -> List[BaseMessage]:
return [HumanMessage(content=d["some_input"])]
run_on_dataset(
client=client,
dataset_name=kv_dataset_name,
llm_or_chain_factory=llm,
evaluation=eval_config,
input_mapper=input_mapper,
project_name=eval_project_name,
tags=["shouldpass"],
)
_check_all_feedback_passed(eval_project_name, client)
def test_llm(kv_dataset_name: str, eval_project_name: str, client: Client) -> None:
llm = OpenAI(temperature=0)
eval_config = RunEvalConfig(evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA])
with pytest.raises(ValueError, match="Must specify reference_key"):
run_on_dataset(
dataset_name=kv_dataset_name,
llm_or_chain_factory=llm,
evaluation=eval_config,
client=client,
)
eval_config = RunEvalConfig(
evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA],
reference_key="some_output",
)
with pytest.raises(
InputFormatError, match="Example inputs do not match language model"
):
run_on_dataset(
dataset_name=kv_dataset_name,
llm_or_chain_factory=llm,
evaluation=eval_config,
client=client,
)
def input_mapper(d: dict) -> str:
return d["some_input"]
run_on_dataset(
client=client,
dataset_name=kv_dataset_name,
llm_or_chain_factory=llm,
evaluation=eval_config,
input_mapper=input_mapper,
project_name=eval_project_name,
tags=["shouldpass"],
)
_check_all_feedback_passed(eval_project_name, client)
def test_chain(kv_dataset_name: str, eval_project_name: str, client: Client) -> None:
llm = ChatOpenAI(temperature=0)
chain = LLMChain.from_string(llm, "The answer to the {question} is: ")
eval_config = RunEvalConfig(evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA])
with pytest.raises(ValueError, match="Must specify reference_key"):
run_on_dataset(
dataset_name=kv_dataset_name,
llm_or_chain_factory=lambda: chain,
evaluation=eval_config,
client=client,
)
eval_config = RunEvalConfig(
evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA],
reference_key="some_output",
)
with pytest.raises(
InputFormatError, match="Example inputs do not match chain input keys"
):
run_on_dataset(
dataset_name=kv_dataset_name,
llm_or_chain_factory=lambda: chain,
evaluation=eval_config,
client=client,
)
def input_mapper(d: dict) -> dict:
return {"input": d["some_input"]}
with pytest.raises(
InputFormatError,
match=" match the chain's expected input keys.",
):
run_on_dataset(
dataset_name=kv_dataset_name,
llm_or_chain_factory=lambda: input_mapper | chain,
client=client,
evaluation=eval_config,
)
def right_input_mapper(d: dict) -> dict:
return {"question": d["some_input"]}
run_on_dataset(
dataset_name=kv_dataset_name,
llm_or_chain_factory=lambda: right_input_mapper | chain,
client=client,
evaluation=eval_config,
project_name=eval_project_name,
tags=["shouldpass"],
)
_check_all_feedback_passed(eval_project_name, client)
### Testing Chat Datasets
@pytest.fixture(
scope="module",
)
def chat_dataset_name() -> Iterator[str]:
def _create_message(txt: str, role: str = "human") -> List[dict]:
return [{"type": role, "data": {"content": txt}}]
import pandas as pd
client = Client()
df = pd.DataFrame(
{
"input": [
_create_message(txt)
for txt in (
"What's the capital of California?",
"What's the capital of Nevada?",
"What's the capital of Oregon?",
"What's the capital of Washington?",
)
],
"output": [
_create_message(txt, role="ai")[0]
for txt in ("Sacramento", "Carson City", "Salem", "Olympia")
],
}
)
uid = str(uuid4())[-8:]
_dataset_name = f"lcp chat dataset integration tests - {uid}"
ds = client.create_dataset(
_dataset_name, description="Integration test dataset", data_type=DataType.chat
)
for row in df.itertuples():
client.create_example(
dataset_id=ds.id,
inputs={"input": row.input},
outputs={"output": row.output},
)
yield _dataset_name
def test_chat_model_on_chat_dataset(
chat_dataset_name: str, eval_project_name: str, client: Client
) -> None:
llm = ChatOpenAI(temperature=0)
eval_config = RunEvalConfig(evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA])
run_on_dataset(
dataset_name=chat_dataset_name,
llm_or_chain_factory=llm,
evaluation=eval_config,
client=client,
project_name=eval_project_name,
)
_check_all_feedback_passed(eval_project_name, client)
def test_llm_on_chat_dataset(
chat_dataset_name: str, eval_project_name: str, client: Client
) -> None:
llm = OpenAI(temperature=0)
eval_config = RunEvalConfig(evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA])
run_on_dataset(
dataset_name=chat_dataset_name,
llm_or_chain_factory=llm,
client=client,
evaluation=eval_config,
project_name=eval_project_name,
tags=["shouldpass"],
)
_check_all_feedback_passed(eval_project_name, client)
def test_chain_on_chat_dataset(chat_dataset_name: str, client: Client) -> None:
llm = ChatOpenAI(temperature=0)
chain = LLMChain.from_string(llm, "The answer to the {question} is: ")
eval_config = RunEvalConfig(evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA])
with pytest.raises(
ValueError, match="Cannot evaluate a chain on dataset with data_type=chat"
):
run_on_dataset(
dataset_name=chat_dataset_name,
client=client,
llm_or_chain_factory=lambda: chain,
evaluation=eval_config,
)
@pytest.fixture(
scope="module",
)
def llm_dataset_name() -> Iterator[str]:
import pandas as pd
client = Client()
df = pd.DataFrame(
{
"input": [
"What's the capital of California?",
"What's the capital of Nevada?",
"What's the capital of Oregon?",
"What's the capital of Washington?",
],
"output": ["Sacramento", "Carson City", "Salem", "Olympia"],
}
)
uid = str(uuid4())[-8:]
_dataset_name = f"lcp llm dataset integration tests - {uid}"
client.upload_dataframe(
df,
name=_dataset_name,
input_keys=["input"],
output_keys=["output"],
description="Integration test dataset",
data_type=DataType.llm,
)
yield _dataset_name
def test_chat_model_on_llm_dataset(
llm_dataset_name: str, eval_project_name: str, client: Client
) -> None:
llm = ChatOpenAI(temperature=0)
eval_config = RunEvalConfig(evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA])
run_on_dataset(
client=client,
dataset_name=llm_dataset_name,
llm_or_chain_factory=llm,
evaluation=eval_config,
project_name=eval_project_name,
tags=["shouldpass"],
)
_check_all_feedback_passed(eval_project_name, client)
def test_llm_on_llm_dataset(
llm_dataset_name: str, eval_project_name: str, client: Client
) -> None:
llm = OpenAI(temperature=0)
eval_config = RunEvalConfig(evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA])
run_on_dataset(
client=client,
dataset_name=llm_dataset_name,
llm_or_chain_factory=llm,
evaluation=eval_config,
project_name=eval_project_name,
tags=["shouldpass"],
)
_check_all_feedback_passed(eval_project_name, client)
def test_chain_on_llm_dataset(llm_dataset_name: str, client: Client) -> None:
llm = ChatOpenAI(temperature=0)
chain = LLMChain.from_string(llm, "The answer to the {question} is: ")
eval_config = RunEvalConfig(evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA])
with pytest.raises(
ValueError, match="Cannot evaluate a chain on dataset with data_type=llm"
):
run_on_dataset(
client=client,
dataset_name=llm_dataset_name,
llm_or_chain_factory=lambda: chain,
evaluation=eval_config,
)
@pytest.fixture(
scope="module",
)
def kv_singleio_dataset_name() -> Iterator[str]:
import pandas as pd
client = Client()
df = pd.DataFrame(
{
"the wackiest input": [
"What's the capital of California?",
"What's the capital of Nevada?",
"What's the capital of Oregon?",
"What's the capital of Washington?",
],
"unthinkable output": ["Sacramento", "Carson City", "Salem", "Olympia"],
}
)
uid = str(uuid4())[-8:]
_dataset_name = f"lcp singleio kv dataset integration tests - {uid}"
client.upload_dataframe(
df,
name=_dataset_name,
input_keys=["the wackiest input"],
output_keys=["unthinkable output"],
description="Integration test dataset",
)
yield _dataset_name
def test_chat_model_on_kv_singleio_dataset(
kv_singleio_dataset_name: str, eval_project_name: str, client: Client
) -> None:
llm = ChatOpenAI(temperature=0)
eval_config = RunEvalConfig(evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA])
run_on_dataset(
dataset_name=kv_singleio_dataset_name,
llm_or_chain_factory=llm,
evaluation=eval_config,
client=client,
project_name=eval_project_name,
tags=["shouldpass"],
)
_check_all_feedback_passed(eval_project_name, client)
def test_llm_on_kv_singleio_dataset(
kv_singleio_dataset_name: str, eval_project_name: str, client: Client
) -> None:
llm = OpenAI(temperature=0)
eval_config = RunEvalConfig(evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA])
run_on_dataset(
dataset_name=kv_singleio_dataset_name,
llm_or_chain_factory=llm,
client=client,
evaluation=eval_config,
project_name=eval_project_name,
tags=["shouldpass"],
)
_check_all_feedback_passed(eval_project_name, client)
def test_chain_on_kv_singleio_dataset(
kv_singleio_dataset_name: str, eval_project_name: str, client: Client
) -> None:
llm = ChatOpenAI(temperature=0)
chain = LLMChain.from_string(llm, "The answer to the {question} is: ")
eval_config = RunEvalConfig(evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA])
run_on_dataset(
dataset_name=kv_singleio_dataset_name,
llm_or_chain_factory=lambda: chain,
client=client,
evaluation=eval_config,
project_name=eval_project_name,
tags=["shouldpass"],
)
_check_all_feedback_passed(eval_project_name, client)
async def test_runnable_on_kv_singleio_dataset(
kv_singleio_dataset_name: str, eval_project_name: str, client: Client
) -> None:
runnable = (
ChatPromptTemplate.from_messages([("human", "{the wackiest input}")])
| ChatOpenAI()
)
eval_config = RunEvalConfig(evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA])
await arun_on_dataset(
dataset_name=kv_singleio_dataset_name,
llm_or_chain_factory=runnable,
client=client,
evaluation=eval_config,
project_name=eval_project_name,
tags=["shouldpass"],
)
_check_all_feedback_passed(eval_project_name, client)
async def test_arb_func_on_kv_singleio_dataset(
kv_singleio_dataset_name: str, eval_project_name: str, client: Client
) -> None:
runnable = (
ChatPromptTemplate.from_messages([("human", "{the wackiest input}")])
| ChatOpenAI()
)
def my_func(x: dict) -> str:
content = runnable.invoke(x).content
if isinstance(content, str):
return content
else:
raise ValueError(
f"Expected message with content type string, got {content}"
)
eval_config = RunEvalConfig(evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA])
await arun_on_dataset(
dataset_name=kv_singleio_dataset_name,
llm_or_chain_factory=my_func,
client=client,
evaluation=eval_config,
project_name=eval_project_name,
tags=["shouldpass"],
)
_check_all_feedback_passed(eval_project_name, client)
| [
"some_input",
"[('human', '{the wackiest input}')]"
] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~onedrive.py | """Loads data from OneDrive"""
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Iterator, List, Optional, Sequence, Union
from libs.core.langchain_core.documents import Document
from libs.core.langchain_core.pydantic_v1 import Field
from langchain_community.document_loaders.base_o365 import (
O365BaseLoader,
_FileType,
)
from langchain_community.document_loaders.parsers.registry import get_parser
if TYPE_CHECKING:
from O365.drive import Drive, Folder
logger = logging.getLogger(__name__)
class OneDriveLoader(O365BaseLoader):
"""Load from `Microsoft OneDrive`."""
drive_id: str = Field(...)
""" The ID of the OneDrive drive to load data from."""
folder_path: Optional[str] = None
""" The path to the folder to load data from."""
object_ids: Optional[List[str]] = None
""" The IDs of the objects to load data from."""
@property
def _file_types(self) -> Sequence[_FileType]:
"""Return supported file types."""
return _FileType.DOC, _FileType.DOCX, _FileType.PDF
@property
def _scopes(self) -> List[str]:
"""Return required scopes."""
return ["offline_access", "Files.Read.All"]
def _get_folder_from_path(self, drive: Drive) -> Union[Folder, Drive]:
"""
Returns the folder or drive object located at the
specified path relative to the given drive.
Args:
drive (Drive): The root drive from which the folder path is relative.
Returns:
Union[Folder, Drive]: The folder or drive object
located at the specified path.
Raises:
FileNotFoundError: If the path does not exist.
"""
subfolder_drive = drive
if self.folder_path is None:
return subfolder_drive
subfolders = [f for f in self.folder_path.split("/") if f != ""]
if len(subfolders) == 0:
return subfolder_drive
items = subfolder_drive.get_items()
for subfolder in subfolders:
try:
subfolder_drive = list(filter(lambda x: subfolder in x.name, items))[0]
items = subfolder_drive.get_items()
except (IndexError, AttributeError):
raise FileNotFoundError("Path {} not exist.".format(self.folder_path))
return subfolder_drive
def lazy_load(self) -> Iterator[Document]:
"""Load documents lazily. Use this when working at a large scale."""
try:
from O365.drive import Drive
except ImportError:
raise ImportError(
"O365 package not found, please install it with `pip install o365`"
)
drive = self._auth().storage().get_drive(self.drive_id)
if not isinstance(drive, Drive):
raise ValueError(f"There isn't a Drive with id {self.drive_id}.")
blob_parser = get_parser("default")
if self.folder_path:
folder = self._get_folder_from_path(drive)
for blob in self._load_from_folder(folder):
yield from blob_parser.lazy_parse(blob)
if self.object_ids:
for blob in self._load_from_object_ids(drive, self.object_ids):
yield from blob_parser.lazy_parse(blob)
def load(self) -> List[Document]:
"""Load all documents."""
return list(self.lazy_load())
| [] |
2024-01-10 | mth93/langchain | libs~langchain~langchain~chains~openai_functions~openapi.py | from __future__ import annotations
import json
import re
from collections import defaultdict
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
import requests
from libs.core.langchain_core.language_models import BaseLanguageModel
from libs.core.langchain_core.prompts import BasePromptTemplate, ChatPromptTemplate
from libs.core.langchain_core.utils.input import get_colored_text
from requests import Response
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.sequential import SequentialChain
from langchain.chat_models import ChatOpenAI
from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser
from langchain.tools import APIOperation
from langchain.utilities.openapi import OpenAPISpec
if TYPE_CHECKING:
from openapi_pydantic import Parameter
def _get_description(o: Any, prefer_short: bool) -> Optional[str]:
summary = getattr(o, "summary", None)
description = getattr(o, "description", None)
if prefer_short:
return summary or description
return description or summary
def _format_url(url: str, path_params: dict) -> str:
expected_path_param = re.findall(r"{(.*?)}", url)
new_params = {}
for param in expected_path_param:
clean_param = param.lstrip(".;").rstrip("*")
val = path_params[clean_param]
if isinstance(val, list):
if param[0] == ".":
sep = "." if param[-1] == "*" else ","
new_val = "." + sep.join(val)
elif param[0] == ";":
sep = f"{clean_param}=" if param[-1] == "*" else ","
new_val = f"{clean_param}=" + sep.join(val)
else:
new_val = ",".join(val)
elif isinstance(val, dict):
kv_sep = "=" if param[-1] == "*" else ","
kv_strs = [kv_sep.join((k, v)) for k, v in val.items()]
if param[0] == ".":
sep = "."
new_val = "."
elif param[0] == ";":
sep = ";"
new_val = ";"
else:
sep = ","
new_val = ""
new_val += sep.join(kv_strs)
else:
if param[0] == ".":
new_val = f".{val}"
elif param[0] == ";":
new_val = f";{clean_param}={val}"
else:
new_val = val
new_params[param] = new_val
return url.format(**new_params)
def _openapi_params_to_json_schema(params: List[Parameter], spec: OpenAPISpec) -> dict:
properties = {}
required = []
for p in params:
if p.param_schema:
schema = spec.get_schema(p.param_schema)
else:
media_type_schema = list(p.content.values())[0].media_type_schema # type: ignore # noqa: E501
schema = spec.get_schema(media_type_schema)
if p.description and not schema.description:
schema.description = p.description
properties[p.name] = json.loads(schema.json(exclude_none=True))
if p.required:
required.append(p.name)
return {"type": "object", "properties": properties, "required": required}
def openapi_spec_to_openai_fn(
spec: OpenAPISpec,
) -> Tuple[List[Dict[str, Any]], Callable]:
"""Convert a valid OpenAPI spec to the JSON Schema format expected for OpenAI
functions.
Args:
spec: OpenAPI spec to convert.
Returns:
Tuple of the OpenAI functions JSON schema and a default function for executing
a request based on the OpenAI function schema.
"""
if not spec.paths:
return [], lambda: None
functions = []
_name_to_call_map = {}
for path in spec.paths:
path_params = {
(p.name, p.param_in): p for p in spec.get_parameters_for_path(path)
}
for method in spec.get_methods_for_path(path):
request_args = {}
op = spec.get_operation(path, method)
op_params = path_params.copy()
for param in spec.get_parameters_for_operation(op):
op_params[(param.name, param.param_in)] = param
params_by_type = defaultdict(list)
for name_loc, p in op_params.items():
params_by_type[name_loc[1]].append(p)
param_loc_to_arg_name = {
"query": "params",
"header": "headers",
"cookie": "cookies",
"path": "path_params",
}
for param_loc, arg_name in param_loc_to_arg_name.items():
if params_by_type[param_loc]:
request_args[arg_name] = _openapi_params_to_json_schema(
params_by_type[param_loc], spec
)
request_body = spec.get_request_body_for_operation(op)
# TODO: Support more MIME types.
if request_body and request_body.content:
media_types = {}
for media_type, media_type_object in request_body.content.items():
if media_type_object.media_type_schema:
schema = spec.get_schema(media_type_object.media_type_schema)
media_types[media_type] = json.loads(
schema.json(exclude_none=True)
)
if len(media_types) == 1:
media_type, schema_dict = list(media_types.items())[0]
key = "json" if media_type == "application/json" else "data"
request_args[key] = schema_dict
elif len(media_types) > 1:
request_args["data"] = {"anyOf": list(media_types.values())}
api_op = APIOperation.from_openapi_spec(spec, path, method)
fn = {
"name": api_op.operation_id,
"description": api_op.description,
"parameters": {
"type": "object",
"properties": request_args,
},
}
functions.append(fn)
_name_to_call_map[fn["name"]] = {
"method": method,
"url": api_op.base_url + api_op.path,
}
def default_call_api(
name: str,
fn_args: dict,
headers: Optional[dict] = None,
params: Optional[dict] = None,
**kwargs: Any,
) -> Any:
method = _name_to_call_map[name]["method"]
url = _name_to_call_map[name]["url"]
path_params = fn_args.pop("path_params", {})
url = _format_url(url, path_params)
if "data" in fn_args and isinstance(fn_args["data"], dict):
fn_args["data"] = json.dumps(fn_args["data"])
_kwargs = {**fn_args, **kwargs}
if headers is not None:
if "headers" in _kwargs:
_kwargs["headers"].update(headers)
else:
_kwargs["headers"] = headers
if params is not None:
if "params" in _kwargs:
_kwargs["params"].update(params)
else:
_kwargs["params"] = params
return requests.request(method, url, **_kwargs)
return functions, default_call_api
class SimpleRequestChain(Chain):
"""Chain for making a simple request to an API endpoint."""
request_method: Callable
"""Method to use for making the request."""
output_key: str = "response"
"""Key to use for the output of the request."""
input_key: str = "function"
"""Key to use for the input of the request."""
@property
def input_keys(self) -> List[str]:
return [self.input_key]
@property
def output_keys(self) -> List[str]:
return [self.output_key]
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
"""Run the logic of this chain and return the output."""
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
name = inputs[self.input_key].pop("name")
args = inputs[self.input_key].pop("arguments")
_pretty_name = get_colored_text(name, "green")
_pretty_args = get_colored_text(json.dumps(args, indent=2), "green")
_text = f"Calling endpoint {_pretty_name} with arguments:\n" + _pretty_args
_run_manager.on_text(_text)
api_response: Response = self.request_method(name, args)
if api_response.status_code != 200:
response = (
f"{api_response.status_code}: {api_response.reason}"
+ f"\nFor {name} "
+ f"Called with args: {args.get('params','')}"
)
else:
try:
response = api_response.json()
except Exception: # noqa: E722
response = api_response.text
return {self.output_key: response}
def get_openapi_chain(
spec: Union[OpenAPISpec, str],
llm: Optional[BaseLanguageModel] = None,
prompt: Optional[BasePromptTemplate] = None,
request_chain: Optional[Chain] = None,
llm_chain_kwargs: Optional[Dict] = None,
verbose: bool = False,
headers: Optional[Dict] = None,
params: Optional[Dict] = None,
**kwargs: Any,
) -> SequentialChain:
"""Create a chain for querying an API from a OpenAPI spec.
Args:
spec: OpenAPISpec or url/file/text string corresponding to one.
llm: language model, should be an OpenAI function-calling model, e.g.
`ChatOpenAI(model="gpt-3.5-turbo-0613")`.
prompt: Main prompt template to use.
request_chain: Chain for taking the functions output and executing the request.
"""
if isinstance(spec, str):
for conversion in (
OpenAPISpec.from_url,
OpenAPISpec.from_file,
OpenAPISpec.from_text,
):
try:
spec = conversion(spec) # type: ignore[arg-type]
break
except ImportError as e:
raise e
except Exception: # noqa: E722
pass
if isinstance(spec, str):
raise ValueError(f"Unable to parse spec from source {spec}")
openai_fns, call_api_fn = openapi_spec_to_openai_fn(spec)
llm = llm or ChatOpenAI(
model="gpt-3.5-turbo-0613",
)
prompt = prompt or ChatPromptTemplate.from_template(
"Use the provided API's to respond to this user query:\n\n{query}"
)
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
llm_kwargs={"functions": openai_fns},
output_parser=JsonOutputFunctionsParser(args_only=False),
output_key="function",
verbose=verbose,
**(llm_chain_kwargs or {}),
)
request_chain = request_chain or SimpleRequestChain(
request_method=lambda name, args: call_api_fn(
name, args, headers=headers, params=params
),
verbose=verbose,
)
return SequentialChain(
chains=[llm_chain, request_chain],
input_variables=llm_chain.input_keys,
output_variables=["response"],
verbose=verbose,
**kwargs,
)
| [
"Use the provided API's to respond to this user query:\n\n{query}"
] |
2024-01-10 | mth93/langchain | templates~rag-pinecone-rerank~rag_pinecone_rerank~chain.py | import os
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.prompts import ChatPromptTemplate
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import CohereRerank
from langchain.vectorstores import Pinecone
from libs.core.langchain_core.output_parsers import StrOutputParser
from libs.core.langchain_core.pydantic_v1 import BaseModel
from libs.core.langchain_core.runnables import RunnableParallel, RunnablePassthrough
if os.environ.get("PINECONE_API_KEY", None) is None:
raise Exception("Missing `PINECONE_API_KEY` environment variable.")
if os.environ.get("PINECONE_ENVIRONMENT", None) is None:
raise Exception("Missing `PINECONE_ENVIRONMENT` environment variable.")
PINECONE_INDEX_NAME = os.environ.get("PINECONE_INDEX", "langchain-test")
### Ingest code - you may need to run this the first time
# Load
# from langchain.document_loaders import WebBaseLoader
# loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
# data = loader.load()
# # Split
# from langchain.text_splitter import RecursiveCharacterTextSplitter
# text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
# all_splits = text_splitter.split_documents(data)
# # Add to vectorDB
# vectorstore = Pinecone.from_documents(
# documents=all_splits, embedding=OpenAIEmbeddings(), index_name=PINECONE_INDEX_NAME
# )
# retriever = vectorstore.as_retriever()
vectorstore = Pinecone.from_existing_index(PINECONE_INDEX_NAME, OpenAIEmbeddings())
# Get k=10 docs
retriever = vectorstore.as_retriever(search_kwargs={"k": 10})
# Re-rank
compressor = CohereRerank()
compression_retriever = ContextualCompressionRetriever(
base_compressor=compressor, base_retriever=retriever
)
# RAG prompt
template = """Answer the question based only on the following context:
{context}
Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)
# RAG
model = ChatOpenAI()
chain = (
RunnableParallel(
{"context": compression_retriever, "question": RunnablePassthrough()}
)
| prompt
| model
| StrOutputParser()
)
# Add typing for input
class Question(BaseModel):
__root__: str
chain = chain.with_types(input_type=Question)
| [
"Answer the question based only on the following context:\n{context}\nQuestion: {question}\n"
] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~datadog_logs.py | from datetime import datetime, timedelta
from typing import List, Optional
from libs.core.langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
class DatadogLogsLoader(BaseLoader):
"""Load `Datadog` logs.
Logs are written into the `page_content` and into the `metadata`.
"""
def __init__(
self,
query: str,
api_key: str,
app_key: str,
from_time: Optional[int] = None,
to_time: Optional[int] = None,
limit: int = 100,
) -> None:
"""Initialize Datadog document loader.
Requirements:
- Must have datadog_api_client installed. Install with `pip install datadog_api_client`.
Args:
query: The query to run in Datadog.
api_key: The Datadog API key.
app_key: The Datadog APP key.
from_time: Optional. The start of the time range to query.
Supports date math and regular timestamps (milliseconds) like '1688732708951'
Defaults to 20 minutes ago.
to_time: Optional. The end of the time range to query.
Supports date math and regular timestamps (milliseconds) like '1688732708951'
Defaults to now.
limit: The maximum number of logs to return.
Defaults to 100.
""" # noqa: E501
try:
from datadog_api_client import Configuration
except ImportError as ex:
raise ImportError(
"Could not import datadog_api_client python package. "
"Please install it with `pip install datadog_api_client`."
) from ex
self.query = query
configuration = Configuration()
configuration.api_key["apiKeyAuth"] = api_key
configuration.api_key["appKeyAuth"] = app_key
self.configuration = configuration
self.from_time = from_time
self.to_time = to_time
self.limit = limit
def parse_log(self, log: dict) -> Document:
"""
Create Document objects from Datadog log items.
"""
attributes = log.get("attributes", {})
metadata = {
"id": log.get("id", ""),
"status": attributes.get("status"),
"service": attributes.get("service", ""),
"tags": attributes.get("tags", []),
"timestamp": attributes.get("timestamp", ""),
}
message = attributes.get("message", "")
inside_attributes = attributes.get("attributes", {})
content_dict = {**inside_attributes, "message": message}
content = ", ".join(f"{k}: {v}" for k, v in content_dict.items())
return Document(page_content=content, metadata=metadata)
def load(self) -> List[Document]:
"""
Get logs from Datadog.
Returns:
A list of Document objects.
- page_content
- metadata
- id
- service
- status
- tags
- timestamp
"""
try:
from datadog_api_client import ApiClient
from datadog_api_client.v2.api.logs_api import LogsApi
from datadog_api_client.v2.model.logs_list_request import LogsListRequest
from datadog_api_client.v2.model.logs_list_request_page import (
LogsListRequestPage,
)
from datadog_api_client.v2.model.logs_query_filter import LogsQueryFilter
from datadog_api_client.v2.model.logs_sort import LogsSort
except ImportError as ex:
raise ImportError(
"Could not import datadog_api_client python package. "
"Please install it with `pip install datadog_api_client`."
) from ex
now = datetime.now()
twenty_minutes_before = now - timedelta(minutes=20)
now_timestamp = int(now.timestamp() * 1000)
twenty_minutes_before_timestamp = int(twenty_minutes_before.timestamp() * 1000)
_from = (
self.from_time
if self.from_time is not None
else twenty_minutes_before_timestamp
)
body = LogsListRequest(
filter=LogsQueryFilter(
query=self.query,
_from=_from,
to=f"{self.to_time if self.to_time is not None else now_timestamp}",
),
sort=LogsSort.TIMESTAMP_ASCENDING,
page=LogsListRequestPage(
limit=self.limit,
),
)
with ApiClient(configuration=self.configuration) as api_client:
api_instance = LogsApi(api_client)
response = api_instance.list_logs(body=body).to_dict()
docs: List[Document] = []
for row in response["data"]:
docs.append(self.parse_log(row))
return docs
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~nuclia.py | import json
import uuid
from typing import List
from libs.core.langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
from langchain_community.tools.nuclia.tool import NucliaUnderstandingAPI
class NucliaLoader(BaseLoader):
"""Load from any file type using `Nuclia Understanding API`."""
def __init__(self, path: str, nuclia_tool: NucliaUnderstandingAPI):
self.nua = nuclia_tool
self.id = str(uuid.uuid4())
self.nua.run({"action": "push", "id": self.id, "path": path, "text": None})
def load(self) -> List[Document]:
"""Load documents."""
data = self.nua.run(
{"action": "pull", "id": self.id, "path": None, "text": None}
)
if not data:
return []
obj = json.loads(data)
text = obj["extracted_text"][0]["body"]["text"]
print(text)
metadata = {
"file": obj["file_extracted_data"][0],
"metadata": obj["field_metadata"][0],
}
return [Document(page_content=text, metadata=metadata)]
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~tools~edenai~ocr_identityparser.py | from __future__ import annotations
import logging
from typing import Optional
from libs.core.langchain_core.callbacks import CallbackManagerForToolRun
from langchain_community.tools.edenai.edenai_base_tool import EdenaiTool
logger = logging.getLogger(__name__)
class EdenAiParsingIDTool(EdenaiTool):
"""Tool that queries the Eden AI Identity parsing API.
for api reference check edenai documentation:
https://docs.edenai.co/reference/ocr_identity_parser_create.
To use, you should have
the environment variable ``EDENAI_API_KEY`` set with your API token.
You can find your token here: https://app.edenai.run/admin/account/settings
"""
name = "edenai_identity_parsing"
description = (
"A wrapper around edenai Services Identity parsing. "
"Useful for when you have to extract information from an ID Document "
"Input should be the string url of the document to parse."
)
feature = "ocr"
subfeature = "identity_parser"
language: Optional[str] = None
"""
language of the text passed to the model.
"""
def _parse_response(self, response: list) -> str:
formatted_list: list = []
if len(response) == 1:
self._parse_json_multilevel(
response[0]["extracted_data"][0], formatted_list
)
else:
for entry in response:
if entry.get("provider") == "eden-ai":
self._parse_json_multilevel(
entry["extracted_data"][0], formatted_list
)
return "\n".join(formatted_list)
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
query_params = {
"file_url": query,
"language": self.language,
"attributes_as_list": False,
}
return self._call_eden_ai(query_params)
| [] |
2024-01-10 | mth93/langchain | libs~community~tests~integration_tests~vectorstores~qdrant~test_embedding_interface.py | import uuid
from typing import Callable, Optional
import pytest
from libs.core.langchain_core.embeddings import Embeddings
from langchain_community.vectorstores import Qdrant
from tests.integration_tests.vectorstores.fake_embeddings import (
ConsistentFakeEmbeddings,
)
@pytest.mark.parametrize(
["embeddings", "embedding_function"],
[
(ConsistentFakeEmbeddings(), None),
(ConsistentFakeEmbeddings().embed_query, None),
(None, ConsistentFakeEmbeddings().embed_query),
],
)
def test_qdrant_embedding_interface(
embeddings: Optional[Embeddings], embedding_function: Optional[Callable]
) -> None:
"""Test Qdrant may accept different types for embeddings."""
from qdrant_client import QdrantClient
client = QdrantClient(":memory:")
collection_name = uuid.uuid4().hex
Qdrant(
client,
collection_name,
embeddings=embeddings,
embedding_function=embedding_function,
)
@pytest.mark.parametrize(
["embeddings", "embedding_function"],
[
(ConsistentFakeEmbeddings(), ConsistentFakeEmbeddings().embed_query),
(None, None),
],
)
def test_qdrant_embedding_interface_raises_value_error(
embeddings: Optional[Embeddings], embedding_function: Optional[Callable]
) -> None:
"""Test Qdrant requires only one method for embeddings."""
from qdrant_client import QdrantClient
client = QdrantClient(":memory:")
collection_name = uuid.uuid4().hex
with pytest.raises(ValueError):
Qdrant(
client,
collection_name,
embeddings=embeddings,
embedding_function=embedding_function,
)
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~mongodb.py | import asyncio
import logging
from typing import Dict, List, Optional
from libs.core.langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
logger = logging.getLogger(__name__)
class MongodbLoader(BaseLoader):
"""Load MongoDB documents."""
def __init__(
self,
connection_string: str,
db_name: str,
collection_name: str,
*,
filter_criteria: Optional[Dict] = None,
) -> None:
try:
from motor.motor_asyncio import AsyncIOMotorClient
except ImportError as e:
raise ImportError(
"Cannot import from motor, please install with `pip install motor`."
) from e
if not connection_string:
raise ValueError("connection_string must be provided.")
if not db_name:
raise ValueError("db_name must be provided.")
if not collection_name:
raise ValueError("collection_name must be provided.")
self.client = AsyncIOMotorClient(connection_string)
self.db_name = db_name
self.collection_name = collection_name
self.filter_criteria = filter_criteria or {}
self.db = self.client.get_database(db_name)
self.collection = self.db.get_collection(collection_name)
def load(self) -> List[Document]:
"""Load data into Document objects.
Attention:
This implementation starts an asyncio event loop which
will only work if running in a sync env. In an async env, it should
fail since there is already an event loop running.
This code should be updated to kick off the event loop from a separate
thread if running within an async context.
"""
return asyncio.run(self.aload())
async def aload(self) -> List[Document]:
"""Load data into Document objects."""
result = []
total_docs = await self.collection.count_documents(self.filter_criteria)
async for doc in self.collection.find(self.filter_criteria):
metadata = {
"database": self.db_name,
"collection": self.collection_name,
}
result.append(Document(page_content=str(doc), metadata=metadata))
if len(result) != total_docs:
logger.warning(
f"Only partial collection of documents returned. Loaded {len(result)} "
f"docs, expected {total_docs}."
)
return result
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~vectorstores~sqlitevss.py | from __future__ import annotations
import json
import logging
import warnings
from typing import (
TYPE_CHECKING,
Any,
Iterable,
List,
Optional,
Tuple,
Type,
)
from libs.core.langchain_core.documents import Document
from libs.core.langchain_core.embeddings import Embeddings
from libs.core.langchain_core.vectorstores import VectorStore
if TYPE_CHECKING:
import sqlite3
logger = logging.getLogger(__name__)
class SQLiteVSS(VectorStore):
"""Wrapper around SQLite with vss extension as a vector database.
To use, you should have the ``sqlite-vss`` python package installed.
Example:
.. code-block:: python
from langchain_community.vectorstores import SQLiteVSS
from langchain_community.embeddings.openai import OpenAIEmbeddings
...
"""
def __init__(
self,
table: str,
connection: Optional[sqlite3.Connection],
embedding: Embeddings,
db_file: str = "vss.db",
):
"""Initialize with sqlite client with vss extension."""
try:
import sqlite_vss # noqa # pylint: disable=unused-import
except ImportError:
raise ImportError(
"Could not import sqlite-vss python package. "
"Please install it with `pip install sqlite-vss`."
)
if not connection:
connection = self.create_connection(db_file)
if not isinstance(embedding, Embeddings):
warnings.warn("embeddings input must be Embeddings object.")
self._connection = connection
self._table = table
self._embedding = embedding
self.create_table_if_not_exists()
def create_table_if_not_exists(self) -> None:
self._connection.execute(
f"""
CREATE TABLE IF NOT EXISTS {self._table}
(
rowid INTEGER PRIMARY KEY AUTOINCREMENT,
text TEXT,
metadata BLOB,
text_embedding BLOB
)
;
"""
)
self._connection.execute(
f"""
CREATE VIRTUAL TABLE IF NOT EXISTS vss_{self._table} USING vss0(
text_embedding({self.get_dimensionality()})
);
"""
)
self._connection.execute(
f"""
CREATE TRIGGER IF NOT EXISTS embed_text
AFTER INSERT ON {self._table}
BEGIN
INSERT INTO vss_{self._table}(rowid, text_embedding)
VALUES (new.rowid, new.text_embedding)
;
END;
"""
)
self._connection.commit()
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Add more texts to the vectorstore index.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
kwargs: vectorstore specific parameters
"""
max_id = self._connection.execute(
f"SELECT max(rowid) as rowid FROM {self._table}"
).fetchone()["rowid"]
if max_id is None: # no text added yet
max_id = 0
embeds = self._embedding.embed_documents(list(texts))
if not metadatas:
metadatas = [{} for _ in texts]
data_input = [
(text, json.dumps(metadata), json.dumps(embed))
for text, metadata, embed in zip(texts, metadatas, embeds)
]
self._connection.executemany(
f"INSERT INTO {self._table}(text, metadata, text_embedding) "
f"VALUES (?,?,?)",
data_input,
)
self._connection.commit()
# pulling every ids we just inserted
results = self._connection.execute(
f"SELECT rowid FROM {self._table} WHERE rowid > {max_id}"
)
return [row["rowid"] for row in results]
def similarity_search_with_score_by_vector(
self, embedding: List[float], k: int = 4, **kwargs: Any
) -> List[Tuple[Document, float]]:
sql_query = f"""
SELECT
text,
metadata,
distance
FROM {self._table} e
INNER JOIN vss_{self._table} v on v.rowid = e.rowid
WHERE vss_search(
v.text_embedding,
vss_search_params('{json.dumps(embedding)}', {k})
)
"""
cursor = self._connection.cursor()
cursor.execute(sql_query)
results = cursor.fetchall()
documents = []
for row in results:
metadata = json.loads(row["metadata"]) or {}
doc = Document(page_content=row["text"], metadata=metadata)
documents.append((doc, row["distance"]))
return documents
def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query."""
embedding = self._embedding.embed_query(query)
documents = self.similarity_search_with_score_by_vector(
embedding=embedding, k=k
)
return [doc for doc, _ in documents]
def similarity_search_with_score(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query."""
embedding = self._embedding.embed_query(query)
documents = self.similarity_search_with_score_by_vector(
embedding=embedding, k=k
)
return documents
def similarity_search_by_vector(
self, embedding: List[float], k: int = 4, **kwargs: Any
) -> List[Document]:
documents = self.similarity_search_with_score_by_vector(
embedding=embedding, k=k
)
return [doc for doc, _ in documents]
@classmethod
def from_texts(
cls: Type[SQLiteVSS],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
table: str = "langchain",
db_file: str = "vss.db",
**kwargs: Any,
) -> SQLiteVSS:
"""Return VectorStore initialized from texts and embeddings."""
connection = cls.create_connection(db_file)
vss = cls(
table=table, connection=connection, db_file=db_file, embedding=embedding
)
vss.add_texts(texts=texts, metadatas=metadatas)
return vss
@staticmethod
def create_connection(db_file: str) -> sqlite3.Connection:
import sqlite3
import sqlite_vss
connection = sqlite3.connect(db_file)
connection.row_factory = sqlite3.Row
connection.enable_load_extension(True)
sqlite_vss.load(connection)
connection.enable_load_extension(False)
return connection
def get_dimensionality(self) -> int:
"""
Function that does a dummy embedding to figure out how many dimensions
this embedding function returns. Needed for the virtual table DDL.
"""
dummy_text = "This is a dummy text"
dummy_embedding = self._embedding.embed_query(dummy_text)
return len(dummy_embedding)
| [] |
2024-01-10 | mth93/langchain | libs~community~tests~integration_tests~retrievers~test_zep.py | from __future__ import annotations
import copy
from typing import TYPE_CHECKING, List
import pytest
from libs.core.langchain_core.documents import Document
from pytest_mock import MockerFixture
from langchain_community.retrievers import ZepRetriever
if TYPE_CHECKING:
from zep_python import MemorySearchResult, ZepClient
@pytest.fixture
def search_results() -> List[MemorySearchResult]:
from zep_python import MemorySearchResult, Message
search_result = [
{
"message": {
"uuid": "66830914-19f5-490b-8677-1ba06bcd556b",
"created_at": "2023-05-18T20:40:42.743773Z",
"role": "user",
"content": "I'm looking to plan a trip to Iceland. Can you help me?",
"token_count": 17,
},
"summary": None,
"dist": 0.8734284910450115,
},
{
"message": {
"uuid": "015e618c-ba9d-45b6-95c3-77a8e611570b",
"created_at": "2023-05-18T20:40:42.743773Z",
"role": "user",
"content": "How much does a trip to Iceland typically cost?",
"token_count": 12,
},
"summary": None,
"dist": 0.8554048017463456,
},
]
return [
MemorySearchResult(
message=Message.parse_obj(result["message"]),
summary=result["summary"],
dist=result["dist"],
)
for result in search_result
]
@pytest.fixture
@pytest.mark.requires("zep_python")
def zep_retriever(
mocker: MockerFixture, search_results: List[MemorySearchResult]
) -> ZepRetriever:
mock_zep_client: ZepClient = mocker.patch("zep_python.ZepClient", autospec=True)
mock_zep_client.memory = mocker.patch(
"zep_python.memory.client.MemoryClient", autospec=True
)
mock_zep_client.memory.search_memory.return_value = copy.deepcopy( # type: ignore
search_results
)
mock_zep_client.memory.asearch_memory.return_value = copy.deepcopy( # type: ignore
search_results
)
zep = ZepRetriever(session_id="123", url="http://localhost:8000")
zep.zep_client = mock_zep_client
return zep
@pytest.mark.requires("zep_python")
def test_zep_retriever_get_relevant_documents(
zep_retriever: ZepRetriever, search_results: List[MemorySearchResult]
) -> None:
documents: List[Document] = zep_retriever.get_relevant_documents(
query="My trip to Iceland"
)
_test_documents(documents, search_results)
@pytest.mark.requires("zep_python")
async def test_zep_retriever_aget_relevant_documents(
zep_retriever: ZepRetriever, search_results: List[MemorySearchResult]
) -> None:
documents: List[Document] = await zep_retriever.aget_relevant_documents(
query="My trip to Iceland"
)
_test_documents(documents, search_results)
def _test_documents(
documents: List[Document], search_results: List[MemorySearchResult]
) -> None:
assert len(documents) == 2
for i, document in enumerate(documents):
assert document.page_content == search_results[i].message.get( # type: ignore
"content"
)
assert document.metadata.get("uuid") == search_results[i].message.get( # type: ignore
"uuid"
)
assert document.metadata.get("role") == search_results[i].message.get( # type: ignore
"role"
)
assert document.metadata.get("score") == search_results[i].dist
| [
"How much does a trip to Iceland typically cost?",
"I'm looking to plan a trip to Iceland. Can you help me?"
] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~baiducloud_bos_directory.py | from typing import Any, Iterator, List
from libs.core.langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
class BaiduBOSDirectoryLoader(BaseLoader):
"""Load from `Baidu BOS directory`."""
def __init__(self, conf: Any, bucket: str, prefix: str = ""):
"""Initialize with BOS config, bucket and prefix.
:param conf(BosConfig): BOS config.
:param bucket(str): BOS bucket.
:param prefix(str): prefix.
"""
self.conf = conf
self.bucket = bucket
self.prefix = prefix
def load(self) -> List[Document]:
return list(self.lazy_load())
def lazy_load(self) -> Iterator[Document]:
"""Load documents."""
try:
from baidubce.services.bos.bos_client import BosClient
except ImportError:
raise ImportError(
"Please install bce-python-sdk with `pip install bce-python-sdk`."
)
client = BosClient(self.conf)
contents = []
marker = ""
while True:
response = client.list_objects(
bucket_name=self.bucket,
prefix=self.prefix,
marker=marker,
max_keys=1000,
)
contents_len = len(response.contents)
contents.extend(response.contents)
if response.is_truncated or contents_len < int(str(response.max_keys)):
break
marker = response.next_marker
from langchain_community.document_loaders.baiducloud_bos_file import (
BaiduBOSFileLoader,
)
for content in contents:
if str(content.key).endswith("/"):
continue
loader = BaiduBOSFileLoader(self.conf, self.bucket, str(content.key))
yield loader.load()[0]
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~chat_models~javelin_ai_gateway.py | import logging
from typing import Any, Dict, List, Mapping, Optional, cast
from libs.core.langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from libs.core.langchain_core.language_models.chat_models import BaseChatModel
from libs.core.langchain_core.messages import (
AIMessage,
BaseMessage,
ChatMessage,
FunctionMessage,
HumanMessage,
SystemMessage,
)
from libs.core.langchain_core.outputs import (
ChatGeneration,
ChatResult,
)
from libs.core.langchain_core.pydantic_v1 import BaseModel, Extra, SecretStr
logger = logging.getLogger(__name__)
# Ignoring type because below is valid pydantic code
# Unexpected keyword argument "extra" for "__init_subclass__" of "object" [call-arg]
class ChatParams(BaseModel, extra=Extra.allow): # type: ignore[call-arg]
"""Parameters for the `Javelin AI Gateway` LLM."""
temperature: float = 0.0
stop: Optional[List[str]] = None
max_tokens: Optional[int] = None
class ChatJavelinAIGateway(BaseChatModel):
"""`Javelin AI Gateway` chat models API.
To use, you should have the ``javelin_sdk`` python package installed.
For more information, see https://docs.getjavelin.io
Example:
.. code-block:: python
from langchain_community.chat_models import ChatJavelinAIGateway
chat = ChatJavelinAIGateway(
gateway_uri="<javelin-ai-gateway-uri>",
route="<javelin-ai-gateway-chat-route>",
params={
"temperature": 0.1
}
)
"""
route: str
"""The route to use for the Javelin AI Gateway API."""
gateway_uri: Optional[str] = None
"""The URI for the Javelin AI Gateway API."""
params: Optional[ChatParams] = None
"""Parameters for the Javelin AI Gateway LLM."""
client: Any
"""javelin client."""
javelin_api_key: Optional[SecretStr] = None
"""The API key for the Javelin AI Gateway."""
def __init__(self, **kwargs: Any):
try:
from javelin_sdk import (
JavelinClient,
UnauthorizedError,
)
except ImportError:
raise ImportError(
"Could not import javelin_sdk python package. "
"Please install it with `pip install javelin_sdk`."
)
super().__init__(**kwargs)
if self.gateway_uri:
try:
self.client = JavelinClient(
base_url=self.gateway_uri,
api_key=cast(SecretStr, self.javelin_api_key).get_secret_value(),
)
except UnauthorizedError as e:
raise ValueError("Javelin: Incorrect API Key.") from e
@property
def _default_params(self) -> Dict[str, Any]:
params: Dict[str, Any] = {
"gateway_uri": self.gateway_uri,
"javelin_api_key": cast(SecretStr, self.javelin_api_key).get_secret_value(),
"route": self.route,
**(self.params.dict() if self.params else {}),
}
return params
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
message_dicts = [
ChatJavelinAIGateway._convert_message_to_dict(message)
for message in messages
]
data: Dict[str, Any] = {
"messages": message_dicts,
**(self.params.dict() if self.params else {}),
}
resp = self.client.query_route(self.route, query_body=data)
return ChatJavelinAIGateway._create_chat_result(resp.dict())
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
message_dicts = [
ChatJavelinAIGateway._convert_message_to_dict(message)
for message in messages
]
data: Dict[str, Any] = {
"messages": message_dicts,
**(self.params.dict() if self.params else {}),
}
resp = await self.client.aquery_route(self.route, query_body=data)
return ChatJavelinAIGateway._create_chat_result(resp.dict())
@property
def _identifying_params(self) -> Dict[str, Any]:
return self._default_params
def _get_invocation_params(
self, stop: Optional[List[str]] = None, **kwargs: Any
) -> Dict[str, Any]:
"""Get the parameters used to invoke the model FOR THE CALLBACKS."""
return {
**self._default_params,
**super()._get_invocation_params(stop=stop, **kwargs),
}
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
return "javelin-ai-gateway-chat"
@staticmethod
def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
role = _dict["role"]
content = _dict["content"]
if role == "user":
return HumanMessage(content=content)
elif role == "assistant":
return AIMessage(content=content)
elif role == "system":
return SystemMessage(content=content)
else:
return ChatMessage(content=content, role=role)
@staticmethod
def _raise_functions_not_supported() -> None:
raise ValueError(
"Function messages are not supported by the Javelin AI Gateway. Please"
" create a feature request at https://docs.getjavelin.io"
)
@staticmethod
def _convert_message_to_dict(message: BaseMessage) -> dict:
if isinstance(message, ChatMessage):
message_dict = {"role": message.role, "content": message.content}
elif isinstance(message, HumanMessage):
message_dict = {"role": "user", "content": message.content}
elif isinstance(message, AIMessage):
message_dict = {"role": "assistant", "content": message.content}
elif isinstance(message, SystemMessage):
message_dict = {"role": "system", "content": message.content}
elif isinstance(message, FunctionMessage):
raise ValueError(
"Function messages are not supported by the Javelin AI Gateway. Please"
" create a feature request at https://docs.getjavelin.io"
)
else:
raise ValueError(f"Got unknown message type: {message}")
if "function_call" in message.additional_kwargs:
ChatJavelinAIGateway._raise_functions_not_supported()
if message.additional_kwargs:
logger.warning(
"Additional message arguments are unsupported by Javelin AI Gateway "
" and will be ignored: %s",
message.additional_kwargs,
)
return message_dict
@staticmethod
def _create_chat_result(response: Mapping[str, Any]) -> ChatResult:
generations = []
for candidate in response["llm_response"]["choices"]:
message = ChatJavelinAIGateway._convert_dict_to_message(
candidate["message"]
)
message_metadata = candidate.get("metadata", {})
gen = ChatGeneration(
message=message,
generation_info=dict(message_metadata),
)
generations.append(gen)
response_metadata = response.get("metadata", {})
return ChatResult(generations=generations, llm_output=response_metadata)
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~callbacks~confident_callback.py | # flake8: noqa
import os
import warnings
from typing import Any, Dict, List, Optional, Union
from libs.core.langchain_core.callbacks import BaseCallbackHandler
from libs.core.langchain_core.agents import AgentAction, AgentFinish
from libs.core.langchain_core.outputs import LLMResult
class DeepEvalCallbackHandler(BaseCallbackHandler):
"""Callback Handler that logs into deepeval.
Args:
implementation_name: name of the `implementation` in deepeval
metrics: A list of metrics
Raises:
ImportError: if the `deepeval` package is not installed.
Examples:
>>> from langchain_community.llms import OpenAI
>>> from langchain_community.callbacks import DeepEvalCallbackHandler
>>> from deepeval.metrics import AnswerRelevancy
>>> metric = AnswerRelevancy(minimum_score=0.3)
>>> deepeval_callback = DeepEvalCallbackHandler(
... implementation_name="exampleImplementation",
... metrics=[metric],
... )
>>> llm = OpenAI(
... temperature=0,
... callbacks=[deepeval_callback],
... verbose=True,
... openai_api_key="API_KEY_HERE",
... )
>>> llm.generate([
... "What is the best evaluation tool out there? (no bias at all)",
... ])
"Deepeval, no doubt about it."
"""
REPO_URL: str = "https://github.com/confident-ai/deepeval"
ISSUES_URL: str = f"{REPO_URL}/issues"
BLOG_URL: str = "https://docs.confident-ai.com" # noqa: E501
def __init__(
self,
metrics: List[Any],
implementation_name: Optional[str] = None,
) -> None:
"""Initializes the `deepevalCallbackHandler`.
Args:
implementation_name: Name of the implementation you want.
metrics: What metrics do you want to track?
Raises:
ImportError: if the `deepeval` package is not installed.
ConnectionError: if the connection to deepeval fails.
"""
super().__init__()
# Import deepeval (not via `import_deepeval` to keep hints in IDEs)
try:
import deepeval # ignore: F401,I001
except ImportError:
raise ImportError(
"""To use the deepeval callback manager you need to have the
`deepeval` Python package installed. Please install it with
`pip install deepeval`"""
)
if os.path.exists(".deepeval"):
warnings.warn(
"""You are currently not logging anything to the dashboard, we
recommend using `deepeval login`."""
)
# Set the deepeval variables
self.implementation_name = implementation_name
self.metrics = metrics
warnings.warn(
(
"The `DeepEvalCallbackHandler` is currently in beta and is subject to"
" change based on updates to `langchain`. Please report any issues to"
f" {self.ISSUES_URL} as an `integration` issue."
),
)
def on_llm_start(
self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> None:
"""Store the prompts"""
self.prompts = prompts
def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
"""Do nothing when a new token is generated."""
pass
def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
"""Log records to deepeval when an LLM ends."""
from deepeval.metrics.answer_relevancy import AnswerRelevancy
from deepeval.metrics.bias_classifier import UnBiasedMetric
from deepeval.metrics.metric import Metric
from deepeval.metrics.toxic_classifier import NonToxicMetric
for metric in self.metrics:
for i, generation in enumerate(response.generations):
# Here, we only measure the first generation's output
output = generation[0].text
query = self.prompts[i]
if isinstance(metric, AnswerRelevancy):
result = metric.measure(
output=output,
query=query,
)
print(f"Answer Relevancy: {result}")
elif isinstance(metric, UnBiasedMetric):
score = metric.measure(output)
print(f"Bias Score: {score}")
elif isinstance(metric, NonToxicMetric):
score = metric.measure(output)
print(f"Toxic Score: {score}")
else:
raise ValueError(
f"""Metric {metric.__name__} is not supported by deepeval
callbacks."""
)
def on_llm_error(self, error: BaseException, **kwargs: Any) -> None:
"""Do nothing when LLM outputs an error."""
pass
def on_chain_start(
self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
) -> None:
"""Do nothing when chain starts"""
pass
def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
"""Do nothing when chain ends."""
pass
def on_chain_error(self, error: BaseException, **kwargs: Any) -> None:
"""Do nothing when LLM chain outputs an error."""
pass
def on_tool_start(
self,
serialized: Dict[str, Any],
input_str: str,
**kwargs: Any,
) -> None:
"""Do nothing when tool starts."""
pass
def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
"""Do nothing when agent takes a specific action."""
pass
def on_tool_end(
self,
output: str,
observation_prefix: Optional[str] = None,
llm_prefix: Optional[str] = None,
**kwargs: Any,
) -> None:
"""Do nothing when tool ends."""
pass
def on_tool_error(self, error: BaseException, **kwargs: Any) -> None:
"""Do nothing when tool outputs an error."""
pass
def on_text(self, text: str, **kwargs: Any) -> None:
"""Do nothing"""
pass
def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:
"""Do nothing"""
pass
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~tools~slack~schedule_message.py | import logging
from datetime import datetime as dt
from typing import Optional, Type
from libs.core.langchain_core.callbacks import CallbackManagerForToolRun
from libs.core.langchain_core.pydantic_v1 import BaseModel, Field
from langchain_community.tools.slack.base import SlackBaseTool
from langchain_community.tools.slack.utils import UTC_FORMAT
logger = logging.getLogger(__name__)
class ScheduleMessageSchema(BaseModel):
"""Input for ScheduleMessageTool."""
message: str = Field(
...,
description="The message to be sent.",
)
channel: str = Field(
...,
description="The channel, private group, or IM channel to send message to.",
)
timestamp: str = Field(
...,
description="The datetime for when the message should be sent in the "
' following format: YYYY-MM-DDTHH:MM:SS±hh:mm, where "T" separates the date '
" and time components, and the time zone offset is specified as ±hh:mm. "
' For example: "2023-06-09T10:30:00+03:00" represents June 9th, '
" 2023, at 10:30 AM in a time zone with a positive offset of 3 "
" hours from Coordinated Universal Time (UTC).",
)
class SlackScheduleMessage(SlackBaseTool):
"""Tool for scheduling a message in Slack."""
name: str = "schedule_message"
description: str = (
"Use this tool to schedule a message to be sent on a specific date and time."
)
args_schema: Type[ScheduleMessageSchema] = ScheduleMessageSchema
def _run(
self,
message: str,
channel: str,
timestamp: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
try:
unix_timestamp = dt.timestamp(dt.strptime(timestamp, UTC_FORMAT))
result = self.client.chat_scheduleMessage(
channel=channel, text=message, post_at=unix_timestamp
)
output = "Message scheduled: " + str(result)
return output
except Exception as e:
return "Error scheduling message: {}".format(e)
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~tools~azure_cognitive_services~form_recognizer.py | from __future__ import annotations
import logging
from typing import Any, Dict, List, Optional
from libs.core.langchain_core.callbacks import CallbackManagerForToolRun
from libs.core.langchain_core.pydantic_v1 import root_validator
from libs.core.langchain_core.tools import BaseTool
from libs.core.langchain_core.utils import get_from_dict_or_env
from langchain_community.tools.azure_cognitive_services.utils import (
detect_file_src_type,
)
logger = logging.getLogger(__name__)
class AzureCogsFormRecognizerTool(BaseTool):
"""Tool that queries the Azure Cognitive Services Form Recognizer API.
In order to set this up, follow instructions at:
https://learn.microsoft.com/en-us/azure/applied-ai-services/form-recognizer/quickstarts/get-started-sdks-rest-api?view=form-recog-3.0.0&pivots=programming-language-python
"""
azure_cogs_key: str = "" #: :meta private:
azure_cogs_endpoint: str = "" #: :meta private:
doc_analysis_client: Any #: :meta private:
name: str = "azure_cognitive_services_form_recognizer"
description: str = (
"A wrapper around Azure Cognitive Services Form Recognizer. "
"Useful for when you need to "
"extract text, tables, and key-value pairs from documents. "
"Input should be a url to a document."
)
@root_validator(pre=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and endpoint exists in environment."""
azure_cogs_key = get_from_dict_or_env(
values, "azure_cogs_key", "AZURE_COGS_KEY"
)
azure_cogs_endpoint = get_from_dict_or_env(
values, "azure_cogs_endpoint", "AZURE_COGS_ENDPOINT"
)
try:
from azure.ai.formrecognizer import DocumentAnalysisClient
from azure.core.credentials import AzureKeyCredential
values["doc_analysis_client"] = DocumentAnalysisClient(
endpoint=azure_cogs_endpoint,
credential=AzureKeyCredential(azure_cogs_key),
)
except ImportError:
raise ImportError(
"azure-ai-formrecognizer is not installed. "
"Run `pip install azure-ai-formrecognizer` to install."
)
return values
def _parse_tables(self, tables: List[Any]) -> List[Any]:
result = []
for table in tables:
rc, cc = table.row_count, table.column_count
_table = [["" for _ in range(cc)] for _ in range(rc)]
for cell in table.cells:
_table[cell.row_index][cell.column_index] = cell.content
result.append(_table)
return result
def _parse_kv_pairs(self, kv_pairs: List[Any]) -> List[Any]:
result = []
for kv_pair in kv_pairs:
key = kv_pair.key.content if kv_pair.key else ""
value = kv_pair.value.content if kv_pair.value else ""
result.append((key, value))
return result
def _document_analysis(self, document_path: str) -> Dict:
document_src_type = detect_file_src_type(document_path)
if document_src_type == "local":
with open(document_path, "rb") as document:
poller = self.doc_analysis_client.begin_analyze_document(
"prebuilt-document", document
)
elif document_src_type == "remote":
poller = self.doc_analysis_client.begin_analyze_document_from_url(
"prebuilt-document", document_path
)
else:
raise ValueError(f"Invalid document path: {document_path}")
result = poller.result()
res_dict = {}
if result.content is not None:
res_dict["content"] = result.content
if result.tables is not None:
res_dict["tables"] = self._parse_tables(result.tables)
if result.key_value_pairs is not None:
res_dict["key_value_pairs"] = self._parse_kv_pairs(result.key_value_pairs)
return res_dict
def _format_document_analysis_result(self, document_analysis_result: Dict) -> str:
formatted_result = []
if "content" in document_analysis_result:
formatted_result.append(
f"Content: {document_analysis_result['content']}".replace("\n", " ")
)
if "tables" in document_analysis_result:
for i, table in enumerate(document_analysis_result["tables"]):
formatted_result.append(f"Table {i}: {table}".replace("\n", " "))
if "key_value_pairs" in document_analysis_result:
for kv_pair in document_analysis_result["key_value_pairs"]:
formatted_result.append(
f"{kv_pair[0]}: {kv_pair[1]}".replace("\n", " ")
)
return "\n".join(formatted_result)
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
try:
document_analysis_result = self._document_analysis(query)
if not document_analysis_result:
return "No good document analysis result was found"
return self._format_document_analysis_result(document_analysis_result)
except Exception as e:
raise RuntimeError(f"Error while running AzureCogsFormRecognizerTool: {e}")
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~chat_message_histories~rocksetdb.py | from datetime import datetime
from time import sleep
from typing import Any, Callable, List, Union
from uuid import uuid4
from libs.core.langchain_core.chat_history import BaseChatMessageHistory
from libs.core.langchain_core.messages import (
BaseMessage,
message_to_dict,
messages_from_dict,
)
class RocksetChatMessageHistory(BaseChatMessageHistory):
"""Uses Rockset to store chat messages.
To use, ensure that the `rockset` python package installed.
Example:
.. code-block:: python
from langchain_community.chat_message_histories import (
RocksetChatMessageHistory
)
from rockset import RocksetClient
history = RocksetChatMessageHistory(
session_id="MySession",
client=RocksetClient(),
collection="langchain_demo",
sync=True
)
history.add_user_message("hi!")
history.add_ai_message("whats up?")
print(history.messages)
"""
# You should set these values based on your VI.
# These values are configured for the typical
# free VI. Read more about VIs here:
# https://rockset.com/docs/instances
SLEEP_INTERVAL_MS: int = 5
ADD_TIMEOUT_MS: int = 5000
CREATE_TIMEOUT_MS: int = 20000
def _wait_until(self, method: Callable, timeout: int, **method_params: Any) -> None:
"""Sleeps until meth() evaluates to true. Passes kwargs into
meth.
"""
start = datetime.now()
while not method(**method_params):
curr = datetime.now()
if (curr - start).total_seconds() * 1000 > timeout:
raise TimeoutError(f"{method} timed out at {timeout} ms")
sleep(RocksetChatMessageHistory.SLEEP_INTERVAL_MS / 1000)
def _query(self, query: str, **query_params: Any) -> List[Any]:
"""Executes an SQL statement and returns the result
Args:
- query: The SQL string
- **query_params: Parameters to pass into the query
"""
return self.client.sql(query, params=query_params).results
def _create_collection(self) -> None:
"""Creates a collection for this message history"""
self.client.Collections.create_s3_collection(
name=self.collection, workspace=self.workspace
)
def _collection_exists(self) -> bool:
"""Checks whether a collection exists for this message history"""
try:
self.client.Collections.get(collection=self.collection)
except self.rockset.exceptions.NotFoundException:
return False
return True
def _collection_is_ready(self) -> bool:
"""Checks whether the collection for this message history is ready
to be queried
"""
return (
self.client.Collections.get(collection=self.collection).data.status
== "READY"
)
def _document_exists(self) -> bool:
return (
len(
self._query(
f"""
SELECT 1
FROM {self.location}
WHERE _id=:session_id
LIMIT 1
""",
session_id=self.session_id,
)
)
!= 0
)
def _wait_until_collection_created(self) -> None:
"""Sleeps until the collection for this message history is ready
to be queried
"""
self._wait_until(
lambda: self._collection_is_ready(),
RocksetChatMessageHistory.CREATE_TIMEOUT_MS,
)
def _wait_until_message_added(self, message_id: str) -> None:
"""Sleeps until a message is added to the messages list"""
self._wait_until(
lambda message_id: len(
self._query(
f"""
SELECT *
FROM UNNEST((
SELECT {self.messages_key}
FROM {self.location}
WHERE _id = :session_id
)) AS message
WHERE message.data.additional_kwargs.id = :message_id
LIMIT 1
""",
session_id=self.session_id,
message_id=message_id,
),
)
!= 0,
RocksetChatMessageHistory.ADD_TIMEOUT_MS,
message_id=message_id,
)
def _create_empty_doc(self) -> None:
"""Creates or replaces a document for this message history with no
messages"""
self.client.Documents.add_documents(
collection=self.collection,
workspace=self.workspace,
data=[{"_id": self.session_id, self.messages_key: []}],
)
def __init__(
self,
session_id: str,
client: Any,
collection: str,
workspace: str = "commons",
messages_key: str = "messages",
sync: bool = False,
message_uuid_method: Callable[[], Union[str, int]] = lambda: str(uuid4()),
) -> None:
"""Constructs a new RocksetChatMessageHistory.
Args:
- session_id: The ID of the chat session
- client: The RocksetClient object to use to query
- collection: The name of the collection to use to store chat
messages. If a collection with the given name
does not exist in the workspace, it is created.
- workspace: The workspace containing `collection`. Defaults
to `"commons"`
- messages_key: The DB column containing message history.
Defaults to `"messages"`
- sync: Whether to wait for messages to be added. Defaults
to `False`. NOTE: setting this to `True` will slow
down performance.
- message_uuid_method: The method that generates message IDs.
If set, all messages will have an `id` field within the
`additional_kwargs` property. If this param is not set
and `sync` is `False`, message IDs will not be created.
If this param is not set and `sync` is `True`, the
`uuid.uuid4` method will be used to create message IDs.
"""
try:
import rockset
except ImportError:
raise ImportError(
"Could not import rockset client python package. "
"Please install it with `pip install rockset`."
)
if not isinstance(client, rockset.RocksetClient):
raise ValueError(
f"client should be an instance of rockset.RocksetClient, "
f"got {type(client)}"
)
self.session_id = session_id
self.client = client
self.collection = collection
self.workspace = workspace
self.location = f'"{self.workspace}"."{self.collection}"'
self.rockset = rockset
self.messages_key = messages_key
self.message_uuid_method = message_uuid_method
self.sync = sync
try:
self.client.set_application("langchain")
except AttributeError:
# ignore
pass
if not self._collection_exists():
self._create_collection()
self._wait_until_collection_created()
self._create_empty_doc()
elif not self._document_exists():
self._create_empty_doc()
@property
def messages(self) -> List[BaseMessage]: # type: ignore
"""Messages in this chat history."""
return messages_from_dict(
self._query(
f"""
SELECT *
FROM UNNEST ((
SELECT "{self.messages_key}"
FROM {self.location}
WHERE _id = :session_id
))
""",
session_id=self.session_id,
)
)
def add_message(self, message: BaseMessage) -> None:
"""Add a Message object to the history.
Args:
message: A BaseMessage object to store.
"""
if self.sync and "id" not in message.additional_kwargs:
message.additional_kwargs["id"] = self.message_uuid_method()
self.client.Documents.patch_documents(
collection=self.collection,
workspace=self.workspace,
data=[
self.rockset.model.patch_document.PatchDocument(
id=self.session_id,
patch=[
self.rockset.model.patch_operation.PatchOperation(
op="ADD",
path=f"/{self.messages_key}/-",
value=message_to_dict(message),
)
],
)
],
)
if self.sync:
self._wait_until_message_added(message.additional_kwargs["id"])
def clear(self) -> None:
"""Removes all messages from the chat history"""
self._create_empty_doc()
if self.sync:
self._wait_until(
lambda: not self.messages,
RocksetChatMessageHistory.ADD_TIMEOUT_MS,
)
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~embeddings~bookend.py | """Wrapper around Bookend AI embedding models."""
import json
from typing import Any, List
import requests
from libs.core.langchain_core.embeddings import Embeddings
from libs.core.langchain_core.pydantic_v1 import BaseModel, Field
API_URL = "https://api.bookend.ai/"
DEFAULT_TASK = "embeddings"
PATH = "/models/predict"
class BookendEmbeddings(BaseModel, Embeddings):
"""Bookend AI sentence_transformers embedding models.
Example:
.. code-block:: python
from langchain_community.embeddings import BookendEmbeddings
bookend = BookendEmbeddings(
domain={domain}
api_token={api_token}
model_id={model_id}
)
bookend.embed_documents([
"Please put on these earmuffs because I can't you hear.",
"Baby wipes are made of chocolate stardust.",
])
bookend.embed_query(
"She only paints with bold colors; she does not like pastels."
)
"""
domain: str
"""Request for a domain at https://bookend.ai/ to use this embeddings module."""
api_token: str
"""Request for an API token at https://bookend.ai/ to use this embeddings module."""
model_id: str
"""Embeddings model ID to use."""
auth_header: dict = Field(default_factory=dict)
def __init__(self, **kwargs: Any):
super().__init__(**kwargs)
self.auth_header = {"Authorization": "Basic {}".format(self.api_token)}
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed documents using a Bookend deployed embeddings model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
result = []
headers = self.auth_header
headers["Content-Type"] = "application/json; charset=utf-8"
params = {
"model_id": self.model_id,
"task": DEFAULT_TASK,
}
for text in texts:
data = json.dumps(
{"text": text, "question": None, "context": None, "instruction": None}
)
r = requests.request(
"POST",
API_URL + self.domain + PATH,
headers=headers,
params=params,
data=data,
)
result.append(r.json()[0]["data"])
return result
def embed_query(self, text: str) -> List[float]:
"""Embed a query using a Bookend deployed embeddings model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
return self.embed_documents([text])[0]
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~embeddings~tensorflow_hub.py | from typing import Any, List
from libs.core.langchain_core.embeddings import Embeddings
from libs.core.langchain_core.pydantic_v1 import BaseModel, Extra
DEFAULT_MODEL_URL = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
class TensorflowHubEmbeddings(BaseModel, Embeddings):
"""TensorflowHub embedding models.
To use, you should have the ``tensorflow_text`` python package installed.
Example:
.. code-block:: python
from langchain_community.embeddings import TensorflowHubEmbeddings
url = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
tf = TensorflowHubEmbeddings(model_url=url)
"""
embed: Any #: :meta private:
model_url: str = DEFAULT_MODEL_URL
"""Model name to use."""
def __init__(self, **kwargs: Any):
"""Initialize the tensorflow_hub and tensorflow_text."""
super().__init__(**kwargs)
try:
import tensorflow_hub
except ImportError:
raise ImportError(
"Could not import tensorflow-hub python package. "
"Please install it with `pip install tensorflow-hub``."
)
try:
import tensorflow_text # noqa
except ImportError:
raise ImportError(
"Could not import tensorflow_text python package. "
"Please install it with `pip install tensorflow_text``."
)
self.embed = tensorflow_hub.load(self.model_url)
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Compute doc embeddings using a TensorflowHub embedding model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
texts = list(map(lambda x: x.replace("\n", " "), texts))
embeddings = self.embed(texts).numpy()
return embeddings.tolist()
def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a TensorflowHub embedding model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
text = text.replace("\n", " ")
embedding = self.embed([text]).numpy()[0]
return embedding.tolist()
| [] |
2024-01-10 | mth93/langchain | libs~community~tests~integration_tests~retrievers~test_google_docai_warehoure_retriever.py | """Test Google Cloud Document AI Warehouse retriever."""
import os
from libs.core.langchain_core.documents import Document
from langchain_community.retrievers import GoogleDocumentAIWarehouseRetriever
def test_google_documentai_warehoure_retriever() -> None:
"""In order to run this test, you should provide a project_id and user_ldap.
Example:
export USER_LDAP=...
export PROJECT_NUMBER=...
"""
project_number = os.environ["PROJECT_NUMBER"]
user_ldap = os.environ["USER_LDAP"]
docai_wh_retriever = GoogleDocumentAIWarehouseRetriever(
project_number=project_number
)
documents = docai_wh_retriever.get_relevant_documents(
"What are Alphabet's Other Bets?", user_ldap=user_ldap
)
assert len(documents) > 0
for doc in documents:
assert isinstance(doc, Document)
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~vectorstores~marqo.py | from __future__ import annotations
import json
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Tuple,
Type,
Union,
)
from libs.core.langchain_core.documents import Document
from libs.core.langchain_core.embeddings import Embeddings
from libs.core.langchain_core.vectorstores import VectorStore
if TYPE_CHECKING:
import marqo
class Marqo(VectorStore):
"""`Marqo` vector store.
Marqo indexes have their own models associated with them to generate your
embeddings. This means that you can selected from a range of different models
and also use CLIP models to create multimodal indexes
with images and text together.
Marqo also supports more advanced queries with multiple weighted terms, see See
https://docs.marqo.ai/latest/#searching-using-weights-in-queries.
This class can flexibly take strings or dictionaries for weighted queries
in its similarity search methods.
To use, you should have the `marqo` python package installed, you can do this with
`pip install marqo`.
Example:
.. code-block:: python
import marqo
from langchain_community.vectorstores import Marqo
client = marqo.Client(url=os.environ["MARQO_URL"], ...)
vectorstore = Marqo(client, index_name)
"""
def __init__(
self,
client: marqo.Client,
index_name: str,
add_documents_settings: Optional[Dict[str, Any]] = None,
searchable_attributes: Optional[List[str]] = None,
page_content_builder: Optional[Callable[[Dict[str, Any]], str]] = None,
):
"""Initialize with Marqo client."""
try:
import marqo
except ImportError:
raise ImportError(
"Could not import marqo python package. "
"Please install it with `pip install marqo`."
)
if not isinstance(client, marqo.Client):
raise ValueError(
f"client should be an instance of marqo.Client, got {type(client)}"
)
self._client = client
self._index_name = index_name
self._add_documents_settings = (
{} if add_documents_settings is None else add_documents_settings
)
self._searchable_attributes = searchable_attributes
self.page_content_builder = page_content_builder
self.tensor_fields = ["text"]
self._document_batch_size = 1024
@property
def embeddings(self) -> Optional[Embeddings]:
return None
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Upload texts with metadata (properties) to Marqo.
You can either have marqo generate ids for each document or you can provide
your own by including a "_id" field in the metadata objects.
Args:
texts (Iterable[str]): am iterator of texts - assumed to preserve an
order that matches the metadatas.
metadatas (Optional[List[dict]], optional): a list of metadatas.
Raises:
ValueError: if metadatas is provided and the number of metadatas differs
from the number of texts.
Returns:
List[str]: The list of ids that were added.
"""
if self._client.index(self._index_name).get_settings()["index_defaults"][
"treat_urls_and_pointers_as_images"
]:
raise ValueError(
"Marqo.add_texts is disabled for multimodal indexes. To add documents "
"with a multimodal index use the Python client for Marqo directly."
)
documents: List[Dict[str, str]] = []
num_docs = 0
for i, text in enumerate(texts):
doc = {
"text": text,
"metadata": json.dumps(metadatas[i]) if metadatas else json.dumps({}),
}
documents.append(doc)
num_docs += 1
ids = []
for i in range(0, num_docs, self._document_batch_size):
response = self._client.index(self._index_name).add_documents(
documents[i : i + self._document_batch_size],
tensor_fields=self.tensor_fields,
**self._add_documents_settings,
)
if response["errors"]:
err_msg = (
f"Error in upload for documents in index range [{i},"
f"{i + self._document_batch_size}], "
f"check Marqo logs."
)
raise RuntimeError(err_msg)
ids += [item["_id"] for item in response["items"]]
return ids
def similarity_search(
self,
query: Union[str, Dict[str, float]],
k: int = 4,
**kwargs: Any,
) -> List[Document]:
"""Search the marqo index for the most similar documents.
Args:
query (Union[str, Dict[str, float]]): The query for the search, either
as a string or a weighted query.
k (int, optional): The number of documents to return. Defaults to 4.
Returns:
List[Document]: k documents ordered from best to worst match.
"""
results = self.marqo_similarity_search(query=query, k=k)
documents = self._construct_documents_from_results_without_score(results)
return documents
def similarity_search_with_score(
self,
query: Union[str, Dict[str, float]],
k: int = 4,
) -> List[Tuple[Document, float]]:
"""Return documents from Marqo that are similar to the query as well
as their scores.
Args:
query (str): The query to search with, either as a string or a weighted
query.
k (int, optional): The number of documents to return. Defaults to 4.
Returns:
List[Tuple[Document, float]]: The matching documents and their scores,
ordered by descending score.
"""
results = self.marqo_similarity_search(query=query, k=k)
scored_documents = self._construct_documents_from_results_with_score(results)
return scored_documents
def bulk_similarity_search(
self,
queries: Iterable[Union[str, Dict[str, float]]],
k: int = 4,
**kwargs: Any,
) -> List[List[Document]]:
"""Search the marqo index for the most similar documents in bulk with multiple
queries.
Args:
queries (Iterable[Union[str, Dict[str, float]]]): An iterable of queries to
execute in bulk, queries in the list can be strings or dictionaries of
weighted queries.
k (int, optional): The number of documents to return for each query.
Defaults to 4.
Returns:
List[List[Document]]: A list of results for each query.
"""
bulk_results = self.marqo_bulk_similarity_search(queries=queries, k=k)
bulk_documents: List[List[Document]] = []
for results in bulk_results["result"]:
documents = self._construct_documents_from_results_without_score(results)
bulk_documents.append(documents)
return bulk_documents
def bulk_similarity_search_with_score(
self,
queries: Iterable[Union[str, Dict[str, float]]],
k: int = 4,
**kwargs: Any,
) -> List[List[Tuple[Document, float]]]:
"""Return documents from Marqo that are similar to the query as well as
their scores using a batch of queries.
Args:
query (Iterable[Union[str, Dict[str, float]]]): An iterable of queries
to execute in bulk, queries in the list can be strings or dictionaries
of weighted queries.
k (int, optional): The number of documents to return. Defaults to 4.
Returns:
List[Tuple[Document, float]]: A list of lists of the matching
documents and their scores for each query
"""
bulk_results = self.marqo_bulk_similarity_search(queries=queries, k=k)
bulk_documents: List[List[Tuple[Document, float]]] = []
for results in bulk_results["result"]:
documents = self._construct_documents_from_results_with_score(results)
bulk_documents.append(documents)
return bulk_documents
def _construct_documents_from_results_with_score(
self, results: Dict[str, List[Dict[str, str]]]
) -> List[Tuple[Document, Any]]:
"""Helper to convert Marqo results into documents.
Args:
results (List[dict]): A marqo results object with the 'hits'.
include_scores (bool, optional): Include scores alongside documents.
Defaults to False.
Returns:
Union[List[Document], List[Tuple[Document, float]]]: The documents or
document score pairs if `include_scores` is true.
"""
documents: List[Tuple[Document, Any]] = []
for res in results["hits"]:
if self.page_content_builder is None:
text = res["text"]
else:
text = self.page_content_builder(res)
metadata = json.loads(res.get("metadata", "{}"))
documents.append(
(Document(page_content=text, metadata=metadata), res["_score"])
)
return documents
def _construct_documents_from_results_without_score(
self, results: Dict[str, List[Dict[str, str]]]
) -> List[Document]:
"""Helper to convert Marqo results into documents.
Args:
results (List[dict]): A marqo results object with the 'hits'.
include_scores (bool, optional): Include scores alongside documents.
Defaults to False.
Returns:
Union[List[Document], List[Tuple[Document, float]]]: The documents or
document score pairs if `include_scores` is true.
"""
documents: List[Document] = []
for res in results["hits"]:
if self.page_content_builder is None:
text = res["text"]
else:
text = self.page_content_builder(res)
metadata = json.loads(res.get("metadata", "{}"))
documents.append(Document(page_content=text, metadata=metadata))
return documents
def marqo_similarity_search(
self,
query: Union[str, Dict[str, float]],
k: int = 4,
) -> Dict[str, List[Dict[str, str]]]:
"""Return documents from Marqo exposing Marqo's output directly
Args:
query (str): The query to search with.
k (int, optional): The number of documents to return. Defaults to 4.
Returns:
List[Dict[str, Any]]: This hits from marqo.
"""
results = self._client.index(self._index_name).search(
q=query, searchable_attributes=self._searchable_attributes, limit=k
)
return results
def marqo_bulk_similarity_search(
self, queries: Iterable[Union[str, Dict[str, float]]], k: int = 4
) -> Dict[str, List[Dict[str, List[Dict[str, str]]]]]:
"""Return documents from Marqo using a bulk search, exposes Marqo's
output directly
Args:
queries (Iterable[Union[str, Dict[str, float]]]): A list of queries.
k (int, optional): The number of documents to return for each query.
Defaults to 4.
Returns:
Dict[str, Dict[List[Dict[str, Dict[str, Any]]]]]: A bulk search results
object
"""
bulk_results = {
"result": [
self._client.index(self._index_name).search(
q=query, searchable_attributes=self._searchable_attributes, limit=k
)
for query in queries
]
}
return bulk_results
@classmethod
def from_documents(
cls: Type[Marqo],
documents: List[Document],
embedding: Union[Embeddings, None] = None,
**kwargs: Any,
) -> Marqo:
"""Return VectorStore initialized from documents. Note that Marqo does not
need embeddings, we retain the parameter to adhere to the Liskov substitution
principle.
Args:
documents (List[Document]): Input documents
embedding (Any, optional): Embeddings (not required). Defaults to None.
Returns:
VectorStore: A Marqo vectorstore
"""
texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
return cls.from_texts(texts, metadatas=metadatas, **kwargs)
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Any = None,
metadatas: Optional[List[dict]] = None,
index_name: str = "",
url: str = "http://localhost:8882",
api_key: str = "",
add_documents_settings: Optional[Dict[str, Any]] = None,
searchable_attributes: Optional[List[str]] = None,
page_content_builder: Optional[Callable[[Dict[str, str]], str]] = None,
index_settings: Optional[Dict[str, Any]] = None,
verbose: bool = True,
**kwargs: Any,
) -> Marqo:
"""Return Marqo initialized from texts. Note that Marqo does not need
embeddings, we retain the parameter to adhere to the Liskov
substitution principle.
This is a quick way to get started with marqo - simply provide your texts and
metadatas and this will create an instance of the data store and index the
provided data.
To know the ids of your documents with this approach you will need to include
them in under the key "_id" in your metadatas for each text
Example:
.. code-block:: python
from langchain_community.vectorstores import Marqo
datastore = Marqo(texts=['text'], index_name='my-first-index',
url='http://localhost:8882')
Args:
texts (List[str]): A list of texts to index into marqo upon creation.
embedding (Any, optional): Embeddings (not required). Defaults to None.
index_name (str, optional): The name of the index to use, if none is
provided then one will be created with a UUID. Defaults to None.
url (str, optional): The URL for Marqo. Defaults to "http://localhost:8882".
api_key (str, optional): The API key for Marqo. Defaults to "".
metadatas (Optional[List[dict]], optional): A list of metadatas, to
accompany the texts. Defaults to None.
this is only used when a new index is being created. Defaults to "cpu". Can
be "cpu" or "cuda".
add_documents_settings (Optional[Dict[str, Any]], optional): Settings
for adding documents, see
https://docs.marqo.ai/0.0.16/API-Reference/documents/#query-parameters.
Defaults to {}.
index_settings (Optional[Dict[str, Any]], optional): Index settings if
the index doesn't exist, see
https://docs.marqo.ai/0.0.16/API-Reference/indexes/#index-defaults-object.
Defaults to {}.
Returns:
Marqo: An instance of the Marqo vector store
"""
try:
import marqo
except ImportError:
raise ImportError(
"Could not import marqo python package. "
"Please install it with `pip install marqo`."
)
if not index_name:
index_name = str(uuid.uuid4())
client = marqo.Client(url=url, api_key=api_key)
try:
client.create_index(index_name, settings_dict=index_settings or {})
if verbose:
print(f"Created {index_name} successfully.")
except Exception:
if verbose:
print(f"Index {index_name} exists.")
instance: Marqo = cls(
client,
index_name,
searchable_attributes=searchable_attributes,
add_documents_settings=add_documents_settings or {},
page_content_builder=page_content_builder,
)
instance.add_texts(texts, metadatas)
return instance
def get_indexes(self) -> List[Dict[str, str]]:
"""Helper to see your available indexes in marqo, useful if the
from_texts method was used without an index name specified
Returns:
List[Dict[str, str]]: The list of indexes
"""
return self._client.get_indexes()["results"]
def get_number_of_documents(self) -> int:
"""Helper to see the number of documents in the index
Returns:
int: The number of documents
"""
return self._client.index(self._index_name).get_stats()["numberOfDocuments"]
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~embeddings~yandex.py | """Wrapper around YandexGPT embedding models."""
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List
from libs.core.langchain_core.embeddings import Embeddings
from libs.core.langchain_core.pydantic_v1 import BaseModel, root_validator
from libs.core.langchain_core.utils import get_from_dict_or_env
from tenacity import (
before_sleep_log,
retry,
retry_if_exception_type,
stop_after_attempt,
wait_exponential,
)
logger = logging.getLogger(__name__)
class YandexGPTEmbeddings(BaseModel, Embeddings):
"""YandexGPT Embeddings models.
To use, you should have the ``yandexcloud`` python package installed.
There are two authentication options for the service account
with the ``ai.languageModels.user`` role:
- You can specify the token in a constructor parameter `iam_token`
or in an environment variable `YC_IAM_TOKEN`.
- You can specify the key in a constructor parameter `api_key`
or in an environment variable `YC_API_KEY`.
To use the default model specify the folder ID in a parameter `folder_id`
or in an environment variable `YC_FOLDER_ID`.
Or specify the model URI in a constructor parameter `model_uri`
Example:
.. code-block:: python
from langchain_community.embeddings.yandex import YandexGPTEmbeddings
embeddings = YandexGPTEmbeddings(iam_token="t1.9eu...", model_uri="emb://<folder-id>/text-search-query/latest")
"""
iam_token: str = ""
"""Yandex Cloud IAM token for service account
with the `ai.languageModels.user` role"""
api_key: str = ""
"""Yandex Cloud Api Key for service account
with the `ai.languageModels.user` role"""
model_uri: str = ""
"""Model uri to use."""
folder_id: str = ""
"""Yandex Cloud folder ID"""
model_uri: str = ""
"""Model uri to use."""
model_name: str = "text-search-query"
"""Model name to use."""
model_version: str = "latest"
"""Model version to use."""
url: str = "llm.api.cloud.yandex.net:443"
"""The url of the API."""
max_retries: int = 6
"""Maximum number of retries to make when generating."""
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that iam token exists in environment."""
iam_token = get_from_dict_or_env(values, "iam_token", "YC_IAM_TOKEN", "")
values["iam_token"] = iam_token
api_key = get_from_dict_or_env(values, "api_key", "YC_API_KEY", "")
values["api_key"] = api_key
folder_id = get_from_dict_or_env(values, "folder_id", "YC_FOLDER_ID", "")
values["folder_id"] = folder_id
if api_key == "" and iam_token == "":
raise ValueError("Either 'YC_API_KEY' or 'YC_IAM_TOKEN' must be provided.")
if values["iam_token"]:
values["_grpc_metadata"] = [
("authorization", f"Bearer {values['iam_token']}")
]
if values["folder_id"]:
values["_grpc_metadata"].append(("x-folder-id", values["folder_id"]))
else:
values["_grpc_metadata"] = (
("authorization", f"Api-Key {values['api_key']}"),
)
if values["model_uri"] == "" and values["folder_id"] == "":
raise ValueError("Either 'model_uri' or 'folder_id' must be provided.")
if not values["model_uri"]:
values[
"model_uri"
] = f"emb://{values['folder_id']}/{values['model_name']}/{values['model_version']}"
return values
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed documents using a YandexGPT embeddings models.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
return _embed_with_retry(self, texts=texts)
def embed_query(self, text: str) -> List[float]:
"""Embed a query using a YandexGPT embeddings models.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
return _embed_with_retry(self, texts=[text])[0]
def _create_retry_decorator(llm: YandexGPTEmbeddings) -> Callable[[Any], Any]:
from grpc import RpcError
min_seconds = 1
max_seconds = 60
return retry(
reraise=True,
stop=stop_after_attempt(llm.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=(retry_if_exception_type((RpcError))),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
def _embed_with_retry(llm: YandexGPTEmbeddings, **kwargs: Any) -> Any:
"""Use tenacity to retry the embedding call."""
retry_decorator = _create_retry_decorator(llm)
@retry_decorator
def _completion_with_retry(**_kwargs: Any) -> Any:
return _make_request(llm, **_kwargs)
return _completion_with_retry(**kwargs)
def _make_request(self: YandexGPTEmbeddings, texts: List[str]):
try:
import grpc
from yandex.cloud.ai.foundation_models.v1.foundation_models_service_pb2 import ( # noqa: E501
TextEmbeddingRequest,
)
from yandex.cloud.ai.foundation_models.v1.foundation_models_service_pb2_grpc import ( # noqa: E501
EmbeddingsServiceStub,
)
except ImportError as e:
raise ImportError(
"Please install YandexCloud SDK" " with `pip install yandexcloud`."
) from e
result = []
channel_credentials = grpc.ssl_channel_credentials()
channel = grpc.secure_channel(self.url, channel_credentials)
for text in texts:
request = TextEmbeddingRequest(model_uri=self.model_uri, text=text)
stub = EmbeddingsServiceStub(channel)
res = stub.TextEmbedding(request, metadata=self._grpc_metadata)
result.append(res.embedding)
return result
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~cube_semantic.py | import json
import logging
import time
from typing import List
import requests
from libs.core.langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
logger = logging.getLogger(__name__)
class CubeSemanticLoader(BaseLoader):
"""Load `Cube semantic layer` metadata.
Args:
cube_api_url: REST API endpoint.
Use the REST API of your Cube's deployment.
Please find out more information here:
https://cube.dev/docs/http-api/rest#configuration-base-path
cube_api_token: Cube API token.
Authentication tokens are generated based on your Cube's API secret.
Please find out more information here:
https://cube.dev/docs/security#generating-json-web-tokens-jwt
load_dimension_values: Whether to load dimension values for every string
dimension or not.
dimension_values_limit: Maximum number of dimension values to load.
dimension_values_max_retries: Maximum number of retries to load dimension
values.
dimension_values_retry_delay: Delay between retries to load dimension values.
"""
def __init__(
self,
cube_api_url: str,
cube_api_token: str,
load_dimension_values: bool = True,
dimension_values_limit: int = 10_000,
dimension_values_max_retries: int = 10,
dimension_values_retry_delay: int = 3,
):
self.cube_api_url = cube_api_url
self.cube_api_token = cube_api_token
self.load_dimension_values = load_dimension_values
self.dimension_values_limit = dimension_values_limit
self.dimension_values_max_retries = dimension_values_max_retries
self.dimension_values_retry_delay = dimension_values_retry_delay
def _get_dimension_values(self, dimension_name: str) -> List[str]:
"""Makes a call to Cube's REST API load endpoint to retrieve
values for dimensions.
These values can be used to achieve a more accurate filtering.
"""
logger.info("Loading dimension values for: {dimension_name}...")
headers = {
"Content-Type": "application/json",
"Authorization": self.cube_api_token,
}
query = {
"query": {
"dimensions": [dimension_name],
"limit": self.dimension_values_limit,
}
}
retries = 0
while retries < self.dimension_values_max_retries:
response = requests.request(
"POST",
f"{self.cube_api_url}/load",
headers=headers,
data=json.dumps(query),
)
if response.status_code == 200:
response_data = response.json()
if (
"error" in response_data
and response_data["error"] == "Continue wait"
):
logger.info("Retrying...")
retries += 1
time.sleep(self.dimension_values_retry_delay)
continue
else:
dimension_values = [
item[dimension_name] for item in response_data["data"]
]
return dimension_values
else:
logger.error("Request failed with status code:", response.status_code)
break
if retries == self.dimension_values_max_retries:
logger.info("Maximum retries reached.")
return []
def load(self) -> List[Document]:
"""Makes a call to Cube's REST API metadata endpoint.
Returns:
A list of documents with attributes:
- page_content=column_title + column_description
- metadata
- table_name
- column_name
- column_data_type
- column_member_type
- column_title
- column_description
- column_values
- cube_data_obj_type
"""
headers = {
"Content-Type": "application/json",
"Authorization": self.cube_api_token,
}
logger.info(f"Loading metadata from {self.cube_api_url}...")
response = requests.get(f"{self.cube_api_url}/meta", headers=headers)
response.raise_for_status()
raw_meta_json = response.json()
cube_data_objects = raw_meta_json.get("cubes", [])
logger.info(f"Found {len(cube_data_objects)} cube data objects in metadata.")
if not cube_data_objects:
raise ValueError("No cubes found in metadata.")
docs = []
for cube_data_obj in cube_data_objects:
cube_data_obj_name = cube_data_obj.get("name")
cube_data_obj_type = cube_data_obj.get("type")
cube_data_obj_is_public = cube_data_obj.get("public")
measures = cube_data_obj.get("measures", [])
dimensions = cube_data_obj.get("dimensions", [])
logger.info(f"Processing {cube_data_obj_name}...")
if not cube_data_obj_is_public:
logger.info(f"Skipping {cube_data_obj_name} because it is not public.")
continue
for item in measures + dimensions:
column_member_type = "measure" if item in measures else "dimension"
dimension_values = []
item_name = str(item.get("name"))
item_type = str(item.get("type"))
if (
self.load_dimension_values
and column_member_type == "dimension"
and item_type == "string"
):
dimension_values = self._get_dimension_values(item_name)
metadata = dict(
table_name=str(cube_data_obj_name),
column_name=item_name,
column_data_type=item_type,
column_title=str(item.get("title")),
column_description=str(item.get("description")),
column_member_type=column_member_type,
column_values=dimension_values,
cube_data_obj_type=cube_data_obj_type,
)
page_content = f"{str(item.get('title'))}, "
page_content += f"{str(item.get('description'))}"
docs.append(Document(page_content=page_content, metadata=metadata))
return docs
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~onenote.py | """Loads data from OneNote Notebooks"""
from pathlib import Path
from typing import Dict, Iterator, List, Optional
import requests
from libs.core.langchain_core.documents import Document
from libs.core.langchain_core.pydantic_v1 import (
BaseModel,
BaseSettings,
Field,
FilePath,
SecretStr,
)
from langchain_community.document_loaders.base import BaseLoader
class _OneNoteGraphSettings(BaseSettings):
client_id: str = Field(..., env="MS_GRAPH_CLIENT_ID")
client_secret: SecretStr = Field(..., env="MS_GRAPH_CLIENT_SECRET")
class Config:
"""Config for OneNoteGraphSettings."""
env_prefix = ""
case_sentive = False
env_file = ".env"
class OneNoteLoader(BaseLoader, BaseModel):
"""Load pages from OneNote notebooks."""
settings: _OneNoteGraphSettings = Field(default_factory=_OneNoteGraphSettings)
"""Settings for the Microsoft Graph API client."""
auth_with_token: bool = False
"""Whether to authenticate with a token or not. Defaults to False."""
access_token: str = ""
"""Personal access token"""
onenote_api_base_url: str = "https://graph.microsoft.com/v1.0/me/onenote"
"""URL of Microsoft Graph API for OneNote"""
authority_url = "https://login.microsoftonline.com/consumers/"
"""A URL that identifies a token authority"""
token_path: FilePath = Path.home() / ".credentials" / "onenote_graph_token.txt"
"""Path to the file where the access token is stored"""
notebook_name: Optional[str] = None
"""Filter on notebook name"""
section_name: Optional[str] = None
"""Filter on section name"""
page_title: Optional[str] = None
"""Filter on section name"""
object_ids: Optional[List[str]] = None
""" The IDs of the objects to load data from."""
def lazy_load(self) -> Iterator[Document]:
"""
Get pages from OneNote notebooks.
Returns:
A list of Documents with attributes:
- page_content
- metadata
- title
"""
self._auth()
try:
from bs4 import BeautifulSoup
except ImportError:
raise ImportError(
"beautifulsoup4 package not found, please install it with "
"`pip install bs4`"
)
if self.object_ids is not None:
for object_id in self.object_ids:
page_content_html = self._get_page_content(object_id)
soup = BeautifulSoup(page_content_html, "html.parser")
page_title = ""
title_tag = soup.title
if title_tag:
page_title = title_tag.get_text(strip=True)
page_content = soup.get_text(separator="\n", strip=True)
yield Document(
page_content=page_content, metadata={"title": page_title}
)
else:
request_url = self._url
while request_url != "":
response = requests.get(request_url, headers=self._headers, timeout=10)
response.raise_for_status()
pages = response.json()
for page in pages["value"]:
page_id = page["id"]
page_content_html = self._get_page_content(page_id)
soup = BeautifulSoup(page_content_html, "html.parser")
page_title = ""
title_tag = soup.title
if title_tag:
page_content = soup.get_text(separator="\n", strip=True)
yield Document(
page_content=page_content, metadata={"title": page_title}
)
if "@odata.nextLink" in pages:
request_url = pages["@odata.nextLink"]
else:
request_url = ""
def load(self) -> List[Document]:
"""
Get pages from OneNote notebooks.
Returns:
A list of Documents with attributes:
- page_content
- metadata
- title
"""
return list(self.lazy_load())
def _get_page_content(self, page_id: str) -> str:
"""Get page content from OneNote API"""
request_url = self.onenote_api_base_url + f"/pages/{page_id}/content"
response = requests.get(request_url, headers=self._headers, timeout=10)
response.raise_for_status()
return response.text
@property
def _headers(self) -> Dict[str, str]:
"""Return headers for requests to OneNote API"""
return {
"Authorization": f"Bearer {self.access_token}",
}
@property
def _scopes(self) -> List[str]:
"""Return required scopes."""
return ["Notes.Read"]
def _auth(self) -> None:
"""Authenticate with Microsoft Graph API"""
if self.access_token != "":
return
if self.auth_with_token:
with self.token_path.open("r") as token_file:
self.access_token = token_file.read()
else:
try:
from msal import ConfidentialClientApplication
except ImportError as e:
raise ImportError(
"MSAL package not found, please install it with `pip install msal`"
) from e
client_instance = ConfidentialClientApplication(
client_id=self.settings.client_id,
client_credential=self.settings.client_secret.get_secret_value(),
authority=self.authority_url,
)
authorization_request_url = client_instance.get_authorization_request_url(
self._scopes
)
print("Visit the following url to give consent:")
print(authorization_request_url)
authorization_url = input("Paste the authenticated url here:\n")
authorization_code = authorization_url.split("code=")[1].split("&")[0]
access_token_json = client_instance.acquire_token_by_authorization_code(
code=authorization_code, scopes=self._scopes
)
self.access_token = access_token_json["access_token"]
try:
if not self.token_path.parent.exists():
self.token_path.parent.mkdir(parents=True)
except Exception as e:
raise Exception(
f"Could not create the folder {self.token_path.parent} "
+ "to store the access token."
) from e
with self.token_path.open("w") as token_file:
token_file.write(self.access_token)
@property
def _url(self) -> str:
"""Create URL for getting page ids from the OneNoteApi API."""
query_params_list = []
filter_list = []
expand_list = []
query_params_list.append("$select=id")
if self.notebook_name is not None:
filter_list.append(
"parentNotebook/displayName%20eq%20"
+ f"'{self.notebook_name.replace(' ', '%20')}'"
)
expand_list.append("parentNotebook")
if self.section_name is not None:
filter_list.append(
"parentSection/displayName%20eq%20"
+ f"'{self.section_name.replace(' ', '%20')}'"
)
expand_list.append("parentSection")
if self.page_title is not None:
filter_list.append(
"title%20eq%20" + f"'{self.page_title.replace(' ', '%20')}'"
)
if len(expand_list) > 0:
query_params_list.append("$expand=" + ",".join(expand_list))
if len(filter_list) > 0:
query_params_list.append("$filter=" + "%20and%20".join(filter_list))
query_params = "&".join(query_params_list)
if query_params != "":
query_params = "?" + query_params
return f"{self.onenote_api_base_url}/pages{query_params}"
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~chat_models~fireworks.py | from typing import (
Any,
AsyncIterator,
Callable,
Dict,
Iterator,
List,
Optional,
Type,
Union,
)
from libs.core.langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from libs.core.langchain_core.language_models.chat_models import BaseChatModel
from libs.core.langchain_core.language_models.llms import create_base_retry_decorator
from libs.core.langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
BaseMessageChunk,
ChatMessage,
ChatMessageChunk,
FunctionMessage,
FunctionMessageChunk,
HumanMessage,
HumanMessageChunk,
SystemMessage,
SystemMessageChunk,
)
from libs.core.langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from libs.core.langchain_core.pydantic_v1 import Field, SecretStr, root_validator
from libs.core.langchain_core.utils import convert_to_secret_str
from libs.core.langchain_core.utils.env import get_from_dict_or_env
from langchain_community.adapters.openai import convert_message_to_dict
def _convert_delta_to_message_chunk(
_dict: Any, default_class: Type[BaseMessageChunk]
) -> BaseMessageChunk:
"""Convert a delta response to a message chunk."""
role = _dict.role
content = _dict.content or ""
additional_kwargs: Dict = {}
if role == "user" or default_class == HumanMessageChunk:
return HumanMessageChunk(content=content)
elif role == "assistant" or default_class == AIMessageChunk:
return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
elif role == "system" or default_class == SystemMessageChunk:
return SystemMessageChunk(content=content)
elif role == "function" or default_class == FunctionMessageChunk:
return FunctionMessageChunk(content=content, name=_dict.name)
elif role or default_class == ChatMessageChunk:
return ChatMessageChunk(content=content, role=role)
else:
return default_class(content=content)
def convert_dict_to_message(_dict: Any) -> BaseMessage:
"""Convert a dict response to a message."""
role = _dict.role
content = _dict.content or ""
if role == "user":
return HumanMessage(content=content)
elif role == "assistant":
content = _dict.content
additional_kwargs: Dict = {}
return AIMessage(content=content, additional_kwargs=additional_kwargs)
elif role == "system":
return SystemMessage(content=content)
elif role == "function":
return FunctionMessage(content=content, name=_dict.name)
else:
return ChatMessage(content=content, role=role)
class ChatFireworks(BaseChatModel):
"""Fireworks Chat models."""
model: str = "accounts/fireworks/models/llama-v2-7b-chat"
model_kwargs: dict = Field(
default_factory=lambda: {
"temperature": 0.7,
"max_tokens": 512,
"top_p": 1,
}.copy()
)
fireworks_api_key: Optional[SecretStr] = None
max_retries: int = 20
use_retry: bool = True
@property
def lc_secrets(self) -> Dict[str, str]:
return {"fireworks_api_key": "FIREWORKS_API_KEY"}
@classmethod
def is_lc_serializable(cls) -> bool:
return True
@classmethod
def get_lc_namespace(cls) -> List[str]:
"""Get the namespace of the langchain object."""
return ["langchain", "chat_models", "fireworks"]
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key in environment."""
try:
import fireworks.client
except ImportError as e:
raise ImportError(
"Could not import fireworks-ai python package. "
"Please install it with `pip install fireworks-ai`."
) from e
fireworks_api_key = convert_to_secret_str(
get_from_dict_or_env(values, "fireworks_api_key", "FIREWORKS_API_KEY")
)
fireworks.client.api_key = fireworks_api_key.get_secret_value()
return values
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "fireworks-chat"
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
message_dicts = self._create_message_dicts(messages)
params = {
"model": self.model,
"messages": message_dicts,
**self.model_kwargs,
**kwargs,
}
response = completion_with_retry(
self,
self.use_retry,
run_manager=run_manager,
stop=stop,
**params,
)
return self._create_chat_result(response)
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
message_dicts = self._create_message_dicts(messages)
params = {
"model": self.model,
"messages": message_dicts,
**self.model_kwargs,
**kwargs,
}
response = await acompletion_with_retry(
self, self.use_retry, run_manager=run_manager, stop=stop, **params
)
return self._create_chat_result(response)
def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
if llm_outputs[0] is None:
return {}
return llm_outputs[0]
def _create_chat_result(self, response: Any) -> ChatResult:
generations = []
for res in response.choices:
message = convert_dict_to_message(res.message)
gen = ChatGeneration(
message=message,
generation_info=dict(finish_reason=res.finish_reason),
)
generations.append(gen)
llm_output = {"model": self.model}
return ChatResult(generations=generations, llm_output=llm_output)
def _create_message_dicts(
self, messages: List[BaseMessage]
) -> List[Dict[str, Any]]:
message_dicts = [convert_message_to_dict(m) for m in messages]
return message_dicts
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
message_dicts = self._create_message_dicts(messages)
default_chunk_class = AIMessageChunk
params = {
"model": self.model,
"messages": message_dicts,
"stream": True,
**self.model_kwargs,
**kwargs,
}
for chunk in completion_with_retry(
self, self.use_retry, run_manager=run_manager, stop=stop, **params
):
choice = chunk.choices[0]
chunk = _convert_delta_to_message_chunk(choice.delta, default_chunk_class)
finish_reason = choice.finish_reason
generation_info = (
dict(finish_reason=finish_reason) if finish_reason is not None else None
)
default_chunk_class = chunk.__class__
chunk = ChatGenerationChunk(message=chunk, generation_info=generation_info)
yield chunk
if run_manager:
run_manager.on_llm_new_token(chunk.text, chunk=chunk)
async def _astream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
message_dicts = self._create_message_dicts(messages)
default_chunk_class = AIMessageChunk
params = {
"model": self.model,
"messages": message_dicts,
"stream": True,
**self.model_kwargs,
**kwargs,
}
async for chunk in await acompletion_with_retry_streaming(
self, self.use_retry, run_manager=run_manager, stop=stop, **params
):
choice = chunk.choices[0]
chunk = _convert_delta_to_message_chunk(choice.delta, default_chunk_class)
finish_reason = choice.finish_reason
generation_info = (
dict(finish_reason=finish_reason) if finish_reason is not None else None
)
default_chunk_class = chunk.__class__
chunk = ChatGenerationChunk(message=chunk, generation_info=generation_info)
yield chunk
if run_manager:
await run_manager.on_llm_new_token(token=chunk.text, chunk=chunk)
def conditional_decorator(
condition: bool, decorator: Callable[[Any], Any]
) -> Callable[[Any], Any]:
"""Define conditional decorator.
Args:
condition: The condition.
decorator: The decorator.
Returns:
The decorated function.
"""
def actual_decorator(func: Callable[[Any], Any]) -> Callable[[Any], Any]:
if condition:
return decorator(func)
return func
return actual_decorator
def completion_with_retry(
llm: ChatFireworks,
use_retry: bool,
*,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Any:
"""Use tenacity to retry the completion call."""
import fireworks.client
retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
@conditional_decorator(use_retry, retry_decorator)
def _completion_with_retry(**kwargs: Any) -> Any:
"""Use tenacity to retry the completion call."""
return fireworks.client.ChatCompletion.create(
**kwargs,
)
return _completion_with_retry(**kwargs)
async def acompletion_with_retry(
llm: ChatFireworks,
use_retry: bool,
*,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Any:
"""Use tenacity to retry the async completion call."""
import fireworks.client
retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
@conditional_decorator(use_retry, retry_decorator)
async def _completion_with_retry(**kwargs: Any) -> Any:
return await fireworks.client.ChatCompletion.acreate(
**kwargs,
)
return await _completion_with_retry(**kwargs)
async def acompletion_with_retry_streaming(
llm: ChatFireworks,
use_retry: bool,
*,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Any:
"""Use tenacity to retry the completion call for streaming."""
import fireworks.client
retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
@conditional_decorator(use_retry, retry_decorator)
async def _completion_with_retry(**kwargs: Any) -> Any:
return fireworks.client.ChatCompletion.acreate(
**kwargs,
)
return await _completion_with_retry(**kwargs)
def _create_retry_decorator(
llm: ChatFireworks,
run_manager: Optional[
Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
] = None,
) -> Callable[[Any], Any]:
"""Define retry mechanism."""
import fireworks.client
errors = [
fireworks.client.error.RateLimitError,
fireworks.client.error.InternalServerError,
fireworks.client.error.BadGatewayError,
fireworks.client.error.ServiceUnavailableError,
]
return create_base_retry_decorator(
error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
)
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~bilibili.py | import json
import re
import warnings
from typing import List, Tuple
import requests
from libs.core.langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
class BiliBiliLoader(BaseLoader):
"""Load `BiliBili` video transcripts."""
def __init__(self, video_urls: List[str]):
"""Initialize with bilibili url.
Args:
video_urls: List of bilibili urls.
"""
self.video_urls = video_urls
def load(self) -> List[Document]:
"""Load Documents from bilibili url."""
results = []
for url in self.video_urls:
transcript, video_info = self._get_bilibili_subs_and_info(url)
doc = Document(page_content=transcript, metadata=video_info)
results.append(doc)
return results
def _get_bilibili_subs_and_info(self, url: str) -> Tuple[str, dict]:
try:
from bilibili_api import sync, video
except ImportError:
raise ImportError(
"requests package not found, please install it with "
"`pip install bilibili-api-python`"
)
bvid = re.search(r"BV\w+", url)
if bvid is not None:
v = video.Video(bvid=bvid.group())
else:
aid = re.search(r"av[0-9]+", url)
if aid is not None:
try:
v = video.Video(aid=int(aid.group()[2:]))
except AttributeError:
raise ValueError(f"{url} is not bilibili url.")
else:
raise ValueError(f"{url} is not bilibili url.")
video_info = sync(v.get_info())
video_info.update({"url": url})
sub = sync(v.get_subtitle(video_info["cid"]))
# Get subtitle url
sub_list = sub["subtitles"]
if sub_list:
sub_url = sub_list[0]["subtitle_url"]
if not sub_url.startswith("http"):
sub_url = "https:" + sub_url
result = requests.get(sub_url)
raw_sub_titles = json.loads(result.content)["body"]
raw_transcript = " ".join([c["content"] for c in raw_sub_titles])
raw_transcript_with_meta_info = (
f"Video Title: {video_info['title']},"
f"description: {video_info['desc']}\n\n"
f"Transcript: {raw_transcript}"
)
return raw_transcript_with_meta_info, video_info
else:
raw_transcript = ""
warnings.warn(
f"""
No subtitles found for video: {url}.
Return Empty transcript.
"""
)
return raw_transcript, video_info
| [] |
2024-01-10 | mth93/langchain | libs~community~tests~integration_tests~retrievers~test_kay.py | """Integration test for Kay.ai API Wrapper."""
import pytest
from libs.core.langchain_core.documents import Document
from langchain_community.retrievers import KayAiRetriever
@pytest.mark.requires("kay")
def test_kay_retriever() -> None:
retriever = KayAiRetriever.create(
dataset_id="company",
data_types=["10-K", "10-Q", "8-K", "PressRelease"],
num_contexts=3,
)
docs = retriever.get_relevant_documents(
"What were the biggest strategy changes and partnerships made by Roku "
"in 2023?",
)
assert len(docs) == 3
for doc in docs:
assert isinstance(doc, Document)
assert doc.page_content
assert doc.metadata
assert len(list(doc.metadata.items())) > 0
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~embeddings~embaas.py | from typing import Any, Dict, List, Mapping, Optional
import requests
from libs.core.langchain_core.embeddings import Embeddings
from libs.core.langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
from libs.core.langchain_core.utils import get_from_dict_or_env
from requests.adapters import HTTPAdapter, Retry
from typing_extensions import NotRequired, TypedDict
# Currently supported maximum batch size for embedding requests
MAX_BATCH_SIZE = 256
EMBAAS_API_URL = "https://api.embaas.io/v1/embeddings/"
class EmbaasEmbeddingsPayload(TypedDict):
"""Payload for the Embaas embeddings API."""
model: str
texts: List[str]
instruction: NotRequired[str]
class EmbaasEmbeddings(BaseModel, Embeddings):
"""Embaas's embedding service.
To use, you should have the
environment variable ``EMBAAS_API_KEY`` set with your API key, or pass
it as a named parameter to the constructor.
Example:
.. code-block:: python
# initialize with default model and instruction
from langchain_community.embeddings import EmbaasEmbeddings
emb = EmbaasEmbeddings()
# initialize with custom model and instruction
from langchain_community.embeddings import EmbaasEmbeddings
emb_model = "instructor-large"
emb_inst = "Represent the Wikipedia document for retrieval"
emb = EmbaasEmbeddings(
model=emb_model,
instruction=emb_inst
)
"""
model: str = "e5-large-v2"
"""The model used for embeddings."""
instruction: Optional[str] = None
"""Instruction used for domain-specific embeddings."""
api_url: str = EMBAAS_API_URL
"""The URL for the embaas embeddings API."""
embaas_api_key: Optional[str] = None
"""max number of retries for requests"""
max_retries: Optional[int] = 3
"""request timeout in seconds"""
timeout: Optional[int] = 30
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
embaas_api_key = get_from_dict_or_env(
values, "embaas_api_key", "EMBAAS_API_KEY"
)
values["embaas_api_key"] = embaas_api_key
return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying params."""
return {"model": self.model, "instruction": self.instruction}
def _generate_payload(self, texts: List[str]) -> EmbaasEmbeddingsPayload:
"""Generates payload for the API request."""
payload = EmbaasEmbeddingsPayload(texts=texts, model=self.model)
if self.instruction:
payload["instruction"] = self.instruction
return payload
def _handle_request(self, payload: EmbaasEmbeddingsPayload) -> List[List[float]]:
"""Sends a request to the Embaas API and handles the response."""
headers = {
"Authorization": f"Bearer {self.embaas_api_key}",
"Content-Type": "application/json",
}
session = requests.Session()
retries = Retry(
total=self.max_retries,
backoff_factor=0.5,
allowed_methods=["POST"],
raise_on_status=True,
)
session.mount("http://", HTTPAdapter(max_retries=retries))
session.mount("https://", HTTPAdapter(max_retries=retries))
response = session.post(
self.api_url,
headers=headers,
json=payload,
timeout=self.timeout,
)
parsed_response = response.json()
embeddings = [item["embedding"] for item in parsed_response["data"]]
return embeddings
def _generate_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Generate embeddings using the Embaas API."""
payload = self._generate_payload(texts)
try:
return self._handle_request(payload)
except requests.exceptions.RequestException as e:
if e.response is None or not e.response.text:
raise ValueError(f"Error raised by embaas embeddings API: {e}")
parsed_response = e.response.json()
if "message" in parsed_response:
raise ValueError(
"Validation Error raised by embaas embeddings API:"
f"{parsed_response['message']}"
)
raise
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Get embeddings for a list of texts.
Args:
texts: The list of texts to get embeddings for.
Returns:
List of embeddings, one for each text.
"""
batches = [
texts[i : i + MAX_BATCH_SIZE] for i in range(0, len(texts), MAX_BATCH_SIZE)
]
embeddings = [self._generate_embeddings(batch) for batch in batches]
# flatten the list of lists into a single list
return [embedding for batch in embeddings for embedding in batch]
def embed_query(self, text: str) -> List[float]:
"""Get embeddings for a single text.
Args:
text: The text to get embeddings for.
Returns:
List of embeddings.
"""
return self.embed_documents([text])[0]
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~tools~file_management~delete.py | import os
from typing import Optional, Type
from libs.core.langchain_core.callbacks import CallbackManagerForToolRun
from libs.core.langchain_core.pydantic_v1 import BaseModel, Field
from libs.core.langchain_core.tools import BaseTool
from langchain_community.tools.file_management.utils import (
INVALID_PATH_TEMPLATE,
BaseFileToolMixin,
FileValidationError,
)
class FileDeleteInput(BaseModel):
"""Input for DeleteFileTool."""
file_path: str = Field(..., description="Path of the file to delete")
class DeleteFileTool(BaseFileToolMixin, BaseTool):
"""Tool that deletes a file."""
name: str = "file_delete"
args_schema: Type[BaseModel] = FileDeleteInput
description: str = "Delete a file"
def _run(
self,
file_path: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
try:
file_path_ = self.get_relative_path(file_path)
except FileValidationError:
return INVALID_PATH_TEMPLATE.format(arg_name="file_path", value=file_path)
if not file_path_.exists():
return f"Error: no such file or directory: {file_path}"
try:
os.remove(file_path_)
return f"File deleted successfully: {file_path}."
except Exception as e:
return "Error: " + str(e)
# TODO: Add aiofiles method
| [
"Delete a file"
] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~unstructured.py | """Loader that uses unstructured to load files."""
import collections
from abc import ABC, abstractmethod
from typing import IO, Any, Callable, Dict, List, Optional, Sequence, Union
from libs.core.langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
def satisfies_min_unstructured_version(min_version: str) -> bool:
"""Check if the installed `Unstructured` version exceeds the minimum version
for the feature in question."""
from unstructured.__version__ import __version__ as __unstructured_version__
min_version_tuple = tuple([int(x) for x in min_version.split(".")])
# NOTE(MthwRobinson) - enables the loader to work when you're using pre-release
# versions of unstructured like 0.4.17-dev1
_unstructured_version = __unstructured_version__.split("-")[0]
unstructured_version_tuple = tuple(
[int(x) for x in _unstructured_version.split(".")]
)
return unstructured_version_tuple >= min_version_tuple
def validate_unstructured_version(min_unstructured_version: str) -> None:
"""Raise an error if the `Unstructured` version does not exceed the
specified minimum."""
if not satisfies_min_unstructured_version(min_unstructured_version):
raise ValueError(
f"unstructured>={min_unstructured_version} is required in this loader."
)
class UnstructuredBaseLoader(BaseLoader, ABC):
"""Base Loader that uses `Unstructured`."""
def __init__(
self,
mode: str = "single",
post_processors: Optional[List[Callable]] = None,
**unstructured_kwargs: Any,
):
"""Initialize with file path."""
try:
import unstructured # noqa:F401
except ImportError:
raise ValueError(
"unstructured package not found, please install it with "
"`pip install unstructured`"
)
_valid_modes = {"single", "elements", "paged"}
if mode not in _valid_modes:
raise ValueError(
f"Got {mode} for `mode`, but should be one of `{_valid_modes}`"
)
self.mode = mode
if not satisfies_min_unstructured_version("0.5.4"):
if "strategy" in unstructured_kwargs:
unstructured_kwargs.pop("strategy")
self.unstructured_kwargs = unstructured_kwargs
self.post_processors = post_processors or []
@abstractmethod
def _get_elements(self) -> List:
"""Get elements."""
@abstractmethod
def _get_metadata(self) -> dict:
"""Get metadata."""
def _post_process_elements(self, elements: list) -> list:
"""Applies post processing functions to extracted unstructured elements.
Post processing functions are str -> str callables are passed
in using the post_processors kwarg when the loader is instantiated."""
for element in elements:
for post_processor in self.post_processors:
element.apply(post_processor)
return elements
def load(self) -> List[Document]:
"""Load file."""
elements = self._get_elements()
self._post_process_elements(elements)
if self.mode == "elements":
docs: List[Document] = list()
for element in elements:
metadata = self._get_metadata()
# NOTE(MthwRobinson) - the attribute check is for backward compatibility
# with unstructured<0.4.9. The metadata attributed was added in 0.4.9.
if hasattr(element, "metadata"):
metadata.update(element.metadata.to_dict())
if hasattr(element, "category"):
metadata["category"] = element.category
docs.append(Document(page_content=str(element), metadata=metadata))
elif self.mode == "paged":
text_dict: Dict[int, str] = {}
meta_dict: Dict[int, Dict] = {}
for idx, element in enumerate(elements):
metadata = self._get_metadata()
if hasattr(element, "metadata"):
metadata.update(element.metadata.to_dict())
page_number = metadata.get("page_number", 1)
# Check if this page_number already exists in docs_dict
if page_number not in text_dict:
# If not, create new entry with initial text and metadata
text_dict[page_number] = str(element) + "\n\n"
meta_dict[page_number] = metadata
else:
# If exists, append to text and update the metadata
text_dict[page_number] += str(element) + "\n\n"
meta_dict[page_number].update(metadata)
# Convert the dict to a list of Document objects
docs = [
Document(page_content=text_dict[key], metadata=meta_dict[key])
for key in text_dict.keys()
]
elif self.mode == "single":
metadata = self._get_metadata()
text = "\n\n".join([str(el) for el in elements])
docs = [Document(page_content=text, metadata=metadata)]
else:
raise ValueError(f"mode of {self.mode} not supported.")
return docs
class UnstructuredFileLoader(UnstructuredBaseLoader):
"""Load files using `Unstructured`.
The file loader uses the
unstructured partition function and will automatically detect the file
type. You can run the loader in one of two modes: "single" and "elements".
If you use "single" mode, the document will be returned as a single
langchain Document object. If you use "elements" mode, the unstructured
library will split the document into elements such as Title and NarrativeText.
You can pass in additional unstructured kwargs after mode to apply
different unstructured settings.
Examples
--------
from langchain_community.document_loaders import UnstructuredFileLoader
loader = UnstructuredFileLoader(
"example.pdf", mode="elements", strategy="fast",
)
docs = loader.load()
References
----------
https://unstructured-io.github.io/unstructured/bricks.html#partition
"""
def __init__(
self,
file_path: Union[str, List[str]],
mode: str = "single",
**unstructured_kwargs: Any,
):
"""Initialize with file path."""
self.file_path = file_path
super().__init__(mode=mode, **unstructured_kwargs)
def _get_elements(self) -> List:
from unstructured.partition.auto import partition
return partition(filename=self.file_path, **self.unstructured_kwargs)
def _get_metadata(self) -> dict:
return {"source": self.file_path}
def get_elements_from_api(
file_path: Union[str, List[str], None] = None,
file: Union[IO, Sequence[IO], None] = None,
api_url: str = "https://api.unstructured.io/general/v0/general",
api_key: str = "",
**unstructured_kwargs: Any,
) -> List:
"""Retrieve a list of elements from the `Unstructured API`."""
if isinstance(file, collections.abc.Sequence) or isinstance(file_path, list):
from unstructured.partition.api import partition_multiple_via_api
_doc_elements = partition_multiple_via_api(
filenames=file_path,
files=file,
api_key=api_key,
api_url=api_url,
**unstructured_kwargs,
)
elements = []
for _elements in _doc_elements:
elements.extend(_elements)
return elements
else:
from unstructured.partition.api import partition_via_api
return partition_via_api(
filename=file_path,
file=file,
api_key=api_key,
api_url=api_url,
**unstructured_kwargs,
)
class UnstructuredAPIFileLoader(UnstructuredFileLoader):
"""Load files using `Unstructured` API.
By default, the loader makes a call to the hosted Unstructured API.
If you are running the unstructured API locally, you can change the
API rule by passing in the url parameter when you initialize the loader.
The hosted Unstructured API requires an API key. See
https://www.unstructured.io/api-key/ if you need to generate a key.
You can run the loader in one of two modes: "single" and "elements".
If you use "single" mode, the document will be returned as a single
langchain Document object. If you use "elements" mode, the unstructured
library will split the document into elements such as Title and NarrativeText.
You can pass in additional unstructured kwargs after mode to apply
different unstructured settings.
Examples
```python
from langchain_community.document_loaders import UnstructuredAPIFileLoader
loader = UnstructuredFileAPILoader(
"example.pdf", mode="elements", strategy="fast", api_key="MY_API_KEY",
)
docs = loader.load()
References
----------
https://unstructured-io.github.io/unstructured/bricks.html#partition
https://www.unstructured.io/api-key/
https://github.com/Unstructured-IO/unstructured-api
"""
def __init__(
self,
file_path: Union[str, List[str]] = "",
mode: str = "single",
url: str = "https://api.unstructured.io/general/v0/general",
api_key: str = "",
**unstructured_kwargs: Any,
):
"""Initialize with file path."""
validate_unstructured_version(min_unstructured_version="0.10.15")
self.url = url
self.api_key = api_key
super().__init__(file_path=file_path, mode=mode, **unstructured_kwargs)
def _get_metadata(self) -> dict:
return {"source": self.file_path}
def _get_elements(self) -> List:
return get_elements_from_api(
file_path=self.file_path,
api_key=self.api_key,
api_url=self.url,
**self.unstructured_kwargs,
)
class UnstructuredFileIOLoader(UnstructuredBaseLoader):
"""Load files using `Unstructured`.
The file loader
uses the unstructured partition function and will automatically detect the file
type. You can run the loader in one of two modes: "single" and "elements".
If you use "single" mode, the document will be returned as a single
langchain Document object. If you use "elements" mode, the unstructured
library will split the document into elements such as Title and NarrativeText.
You can pass in additional unstructured kwargs after mode to apply
different unstructured settings.
Examples
--------
from langchain_community.document_loaders import UnstructuredFileIOLoader
with open("example.pdf", "rb") as f:
loader = UnstructuredFileIOLoader(
f, mode="elements", strategy="fast",
)
docs = loader.load()
References
----------
https://unstructured-io.github.io/unstructured/bricks.html#partition
"""
def __init__(
self,
file: Union[IO, Sequence[IO]],
mode: str = "single",
**unstructured_kwargs: Any,
):
"""Initialize with file path."""
self.file = file
super().__init__(mode=mode, **unstructured_kwargs)
def _get_elements(self) -> List:
from unstructured.partition.auto import partition
return partition(file=self.file, **self.unstructured_kwargs)
def _get_metadata(self) -> dict:
return {}
class UnstructuredAPIFileIOLoader(UnstructuredFileIOLoader):
"""Load files using `Unstructured` API.
By default, the loader makes a call to the hosted Unstructured API.
If you are running the unstructured API locally, you can change the
API rule by passing in the url parameter when you initialize the loader.
The hosted Unstructured API requires an API key. See
https://www.unstructured.io/api-key/ if you need to generate a key.
You can run the loader in one of two modes: "single" and "elements".
If you use "single" mode, the document will be returned as a single
langchain Document object. If you use "elements" mode, the unstructured
library will split the document into elements such as Title and NarrativeText.
You can pass in additional unstructured kwargs after mode to apply
different unstructured settings.
Examples
--------
from langchain_community.document_loaders import UnstructuredAPIFileLoader
with open("example.pdf", "rb") as f:
loader = UnstructuredFileAPILoader(
f, mode="elements", strategy="fast", api_key="MY_API_KEY",
)
docs = loader.load()
References
----------
https://unstructured-io.github.io/unstructured/bricks.html#partition
https://www.unstructured.io/api-key/
https://github.com/Unstructured-IO/unstructured-api
"""
def __init__(
self,
file: Union[IO, Sequence[IO]],
mode: str = "single",
url: str = "https://api.unstructured.io/general/v0/general",
api_key: str = "",
**unstructured_kwargs: Any,
):
"""Initialize with file path."""
if isinstance(file, collections.abc.Sequence):
validate_unstructured_version(min_unstructured_version="0.6.3")
if file:
validate_unstructured_version(min_unstructured_version="0.6.2")
self.url = url
self.api_key = api_key
super().__init__(file=file, mode=mode, **unstructured_kwargs)
def _get_elements(self) -> List:
return get_elements_from_api(
file=self.file,
api_key=self.api_key,
api_url=self.url,
**self.unstructured_kwargs,
)
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~hn.py | from typing import Any, List
from libs.core.langchain_core.documents import Document
from langchain_community.document_loaders.web_base import WebBaseLoader
class HNLoader(WebBaseLoader):
"""Load `Hacker News` data.
It loads data from either main page results or the comments page."""
def load(self) -> List[Document]:
"""Get important HN webpage information.
HN webpage components are:
- title
- content
- source url,
- time of post
- author of the post
- number of comments
- rank of the post
"""
soup_info = self.scrape()
if "item" in self.web_path:
return self.load_comments(soup_info)
else:
return self.load_results(soup_info)
def load_comments(self, soup_info: Any) -> List[Document]:
"""Load comments from a HN post."""
comments = soup_info.select("tr[class='athing comtr']")
title = soup_info.select_one("tr[id='pagespace']").get("title")
return [
Document(
page_content=comment.text.strip(),
metadata={"source": self.web_path, "title": title},
)
for comment in comments
]
def load_results(self, soup: Any) -> List[Document]:
"""Load items from an HN page."""
items = soup.select("tr[class='athing']")
documents = []
for lineItem in items:
ranking = lineItem.select_one("span[class='rank']").text
link = lineItem.find("span", {"class": "titleline"}).find("a").get("href")
title = lineItem.find("span", {"class": "titleline"}).text.strip()
metadata = {
"source": self.web_path,
"title": title,
"link": link,
"ranking": ranking,
}
documents.append(
Document(
page_content=title, link=link, ranking=ranking, metadata=metadata
)
)
return documents
| [] |
2024-01-10 | mth93/langchain | libs~community~tests~integration_tests~vectorstores~qdrant~test_add_texts.py | import uuid
from typing import Optional
import pytest
from libs.core.langchain_core.documents import Document
from langchain_community.vectorstores import Qdrant
from tests.integration_tests.vectorstores.fake_embeddings import (
ConsistentFakeEmbeddings,
)
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_add_documents_extends_existing_collection(
batch_size: int, vector_name: Optional[str]
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch: Qdrant = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
location=":memory:",
batch_size=batch_size,
vector_name=vector_name,
)
new_texts = ["foobar", "foobaz"]
docsearch.add_documents(
[Document(page_content=content) for content in new_texts], batch_size=batch_size
)
output = docsearch.similarity_search("foobar", k=1)
# ConsistentFakeEmbeddings return the same query embedding as the first document
# embedding computed in `embedding.embed_documents`. Thus, "foo" embedding is the
# same as "foobar" embedding
assert output == [Document(page_content="foobar")]
@pytest.mark.parametrize("batch_size", [1, 64])
def test_qdrant_add_texts_returns_all_ids(batch_size: int) -> None:
"""Test end to end Qdrant.add_texts returns unique ids."""
docsearch: Qdrant = Qdrant.from_texts(
["foobar"],
ConsistentFakeEmbeddings(),
location=":memory:",
batch_size=batch_size,
)
ids = docsearch.add_texts(["foo", "bar", "baz"])
assert 3 == len(ids)
assert 3 == len(set(ids))
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_add_texts_stores_duplicated_texts(vector_name: Optional[str]) -> None:
"""Test end to end Qdrant.add_texts stores duplicated texts separately."""
from qdrant_client import QdrantClient
from qdrant_client.http import models as rest
client = QdrantClient(":memory:")
collection_name = uuid.uuid4().hex
vectors_config = rest.VectorParams(size=10, distance=rest.Distance.COSINE)
if vector_name is not None:
vectors_config = {vector_name: vectors_config} # type: ignore[assignment]
client.recreate_collection(collection_name, vectors_config=vectors_config)
vec_store = Qdrant(
client,
collection_name,
embeddings=ConsistentFakeEmbeddings(),
vector_name=vector_name,
)
ids = vec_store.add_texts(["abc", "abc"], [{"a": 1}, {"a": 2}])
assert 2 == len(set(ids))
assert 2 == client.count(collection_name).count
@pytest.mark.parametrize("batch_size", [1, 64])
def test_qdrant_add_texts_stores_ids(batch_size: int) -> None:
"""Test end to end Qdrant.add_texts stores provided ids."""
from qdrant_client import QdrantClient
from qdrant_client.http import models as rest
ids = [
"fa38d572-4c31-4579-aedc-1960d79df6df",
"cdc1aa36-d6ab-4fb2-8a94-56674fd27484",
]
client = QdrantClient(":memory:")
collection_name = uuid.uuid4().hex
client.recreate_collection(
collection_name,
vectors_config=rest.VectorParams(size=10, distance=rest.Distance.COSINE),
)
vec_store = Qdrant(client, collection_name, ConsistentFakeEmbeddings())
returned_ids = vec_store.add_texts(["abc", "def"], ids=ids, batch_size=batch_size)
assert all(first == second for first, second in zip(ids, returned_ids))
assert 2 == client.count(collection_name).count
stored_ids = [point.id for point in client.scroll(collection_name)[0]]
assert set(ids) == set(stored_ids)
@pytest.mark.parametrize("vector_name", ["custom-vector"])
def test_qdrant_add_texts_stores_embeddings_as_named_vectors(vector_name: str) -> None:
"""Test end to end Qdrant.add_texts stores named vectors if name is provided."""
from qdrant_client import QdrantClient
from qdrant_client.http import models as rest
collection_name = uuid.uuid4().hex
client = QdrantClient(":memory:")
client.recreate_collection(
collection_name,
vectors_config={
vector_name: rest.VectorParams(size=10, distance=rest.Distance.COSINE)
},
)
vec_store = Qdrant(
client,
collection_name,
ConsistentFakeEmbeddings(),
vector_name=vector_name,
)
vec_store.add_texts(["lorem", "ipsum", "dolor", "sit", "amet"])
assert 5 == client.count(collection_name).count
assert all(
vector_name in point.vector # type: ignore[operator]
for point in client.scroll(collection_name, with_vectors=True)[0]
)
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~tools~amadeus~closest_airport.py | from typing import Optional, Type
from libs.core.langchain_core.callbacks import CallbackManagerForToolRun
from libs.core.langchain_core.pydantic_v1 import BaseModel, Field
from langchain_community.chat_models import ChatOpenAI
from langchain_community.tools.amadeus.base import AmadeusBaseTool
class ClosestAirportSchema(BaseModel):
"""Schema for the AmadeusClosestAirport tool."""
location: str = Field(
description=(
" The location for which you would like to find the nearest airport "
" along with optional details such as country, state, region, or "
" province, allowing for easy processing and identification of "
" the closest airport. Examples of the format are the following:\n"
" Cali, Colombia\n "
" Lincoln, Nebraska, United States\n"
" New York, United States\n"
" Sydney, New South Wales, Australia\n"
" Rome, Lazio, Italy\n"
" Toronto, Ontario, Canada\n"
)
)
class AmadeusClosestAirport(AmadeusBaseTool):
"""Tool for finding the closest airport to a particular location."""
name: str = "closest_airport"
description: str = (
"Use this tool to find the closest airport to a particular location."
)
args_schema: Type[ClosestAirportSchema] = ClosestAirportSchema
def _run(
self,
location: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
content = (
f" What is the nearest airport to {location}? Please respond with the "
" airport's International Air Transport Association (IATA) Location "
' Identifier in the following JSON format. JSON: "iataCode": "IATA '
' Location Identifier" '
)
return ChatOpenAI(temperature=0).predict(content)
| [] |
2024-01-10 | mth93/langchain | libs~langchain~langchain~retrievers~parent_document_retriever.py | import uuid
from typing import List, Optional
from libs.core.langchain_core.documents import Document
from langchain.retrievers import MultiVectorRetriever
from langchain.text_splitter import TextSplitter
class ParentDocumentRetriever(MultiVectorRetriever):
"""Retrieve small chunks then retrieve their parent documents.
When splitting documents for retrieval, there are often conflicting desires:
1. You may want to have small documents, so that their embeddings can most
accurately reflect their meaning. If too long, then the embeddings can
lose meaning.
2. You want to have long enough documents that the context of each chunk is
retained.
The ParentDocumentRetriever strikes that balance by splitting and storing
small chunks of data. During retrieval, it first fetches the small chunks
but then looks up the parent ids for those chunks and returns those larger
documents.
Note that "parent document" refers to the document that a small chunk
originated from. This can either be the whole raw document OR a larger
chunk.
Examples:
.. code-block:: python
# Imports
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.storage import InMemoryStore
# This text splitter is used to create the parent documents
parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)
# This text splitter is used to create the child documents
# It should create documents smaller than the parent
child_splitter = RecursiveCharacterTextSplitter(chunk_size=400)
# The vectorstore to use to index the child chunks
vectorstore = Chroma(embedding_function=OpenAIEmbeddings())
# The storage layer for the parent documents
store = InMemoryStore()
# Initialize the retriever
retriever = ParentDocumentRetriever(
vectorstore=vectorstore,
docstore=store,
child_splitter=child_splitter,
parent_splitter=parent_splitter,
)
"""
child_splitter: TextSplitter
"""The text splitter to use to create child documents."""
"""The key to use to track the parent id. This will be stored in the
metadata of child documents."""
parent_splitter: Optional[TextSplitter] = None
"""The text splitter to use to create parent documents.
If none, then the parent documents will be the raw documents passed in."""
def add_documents(
self,
documents: List[Document],
ids: Optional[List[str]] = None,
add_to_docstore: bool = True,
) -> None:
"""Adds documents to the docstore and vectorstores.
Args:
documents: List of documents to add
ids: Optional list of ids for documents. If provided should be the same
length as the list of documents. Can provided if parent documents
are already in the document store and you don't want to re-add
to the docstore. If not provided, random UUIDs will be used as
ids.
add_to_docstore: Boolean of whether to add documents to docstore.
This can be false if and only if `ids` are provided. You may want
to set this to False if the documents are already in the docstore
and you don't want to re-add them.
"""
if self.parent_splitter is not None:
documents = self.parent_splitter.split_documents(documents)
if ids is None:
doc_ids = [str(uuid.uuid4()) for _ in documents]
if not add_to_docstore:
raise ValueError(
"If ids are not passed in, `add_to_docstore` MUST be True"
)
else:
if len(documents) != len(ids):
raise ValueError(
"Got uneven list of documents and ids. "
"If `ids` is provided, should be same length as `documents`."
)
doc_ids = ids
docs = []
full_docs = []
for i, doc in enumerate(documents):
_id = doc_ids[i]
sub_docs = self.child_splitter.split_documents([doc])
for _doc in sub_docs:
_doc.metadata[self.id_key] = _id
docs.extend(sub_docs)
full_docs.append((_id, doc))
self.vectorstore.add_documents(docs)
if add_to_docstore:
self.docstore.mset(full_docs)
| [] |
2024-01-10 | mth93/langchain | libs~langchain~langchain~output_parsers~retry.py | from __future__ import annotations
from typing import Any, TypeVar
from libs.core.langchain_core.exceptions import OutputParserException
from libs.core.langchain_core.language_models import BaseLanguageModel
from libs.core.langchain_core.output_parsers import BaseOutputParser
from libs.core.langchain_core.prompt_values import PromptValue
from libs.core.langchain_core.prompts import BasePromptTemplate, PromptTemplate
NAIVE_COMPLETION_RETRY = """Prompt:
{prompt}
Completion:
{completion}
Above, the Completion did not satisfy the constraints given in the Prompt.
Please try again:"""
NAIVE_COMPLETION_RETRY_WITH_ERROR = """Prompt:
{prompt}
Completion:
{completion}
Above, the Completion did not satisfy the constraints given in the Prompt.
Details: {error}
Please try again:"""
NAIVE_RETRY_PROMPT = PromptTemplate.from_template(NAIVE_COMPLETION_RETRY)
NAIVE_RETRY_WITH_ERROR_PROMPT = PromptTemplate.from_template(
NAIVE_COMPLETION_RETRY_WITH_ERROR
)
T = TypeVar("T")
class RetryOutputParser(BaseOutputParser[T]):
"""Wraps a parser and tries to fix parsing errors.
Does this by passing the original prompt and the completion to another
LLM, and telling it the completion did not satisfy criteria in the prompt.
"""
parser: BaseOutputParser[T]
"""The parser to use to parse the output."""
# Should be an LLMChain but we want to avoid top-level imports from langchain.chains
retry_chain: Any
"""The LLMChain to use to retry the completion."""
max_retries: int = 1
"""The maximum number of times to retry the parse."""
@classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
parser: BaseOutputParser[T],
prompt: BasePromptTemplate = NAIVE_RETRY_PROMPT,
max_retries: int = 1,
) -> RetryOutputParser[T]:
"""Create an RetryOutputParser from a language model and a parser.
Args:
llm: llm to use for fixing
parser: parser to use for parsing
prompt: prompt to use for fixing
max_retries: Maximum number of retries to parse.
Returns:
RetryOutputParser
"""
from langchain.chains.llm import LLMChain
chain = LLMChain(llm=llm, prompt=prompt)
return cls(parser=parser, retry_chain=chain, max_retries=max_retries)
def parse_with_prompt(self, completion: str, prompt_value: PromptValue) -> T:
"""Parse the output of an LLM call using a wrapped parser.
Args:
completion: The chain completion to parse.
prompt_value: The prompt to use to parse the completion.
Returns:
The parsed completion.
"""
retries = 0
while retries <= self.max_retries:
try:
return self.parser.parse(completion)
except OutputParserException as e:
if retries == self.max_retries:
raise e
else:
retries += 1
completion = self.retry_chain.run(
prompt=prompt_value.to_string(), completion=completion
)
raise OutputParserException("Failed to parse")
async def aparse_with_prompt(self, completion: str, prompt_value: PromptValue) -> T:
"""Parse the output of an LLM call using a wrapped parser.
Args:
completion: The chain completion to parse.
prompt_value: The prompt to use to parse the completion.
Returns:
The parsed completion.
"""
retries = 0
while retries <= self.max_retries:
try:
return await self.parser.aparse(completion)
except OutputParserException as e:
if retries == self.max_retries:
raise e
else:
retries += 1
completion = await self.retry_chain.arun(
prompt=prompt_value.to_string(), completion=completion
)
raise OutputParserException("Failed to parse")
def parse(self, completion: str) -> T:
raise NotImplementedError(
"This OutputParser can only be called by the `parse_with_prompt` method."
)
def get_format_instructions(self) -> str:
return self.parser.get_format_instructions()
@property
def _type(self) -> str:
return "retry"
class RetryWithErrorOutputParser(BaseOutputParser[T]):
"""Wraps a parser and tries to fix parsing errors.
Does this by passing the original prompt, the completion, AND the error
that was raised to another language model and telling it that the completion
did not work, and raised the given error. Differs from RetryOutputParser
in that this implementation provides the error that was raised back to the
LLM, which in theory should give it more information on how to fix it.
"""
parser: BaseOutputParser[T]
"""The parser to use to parse the output."""
# Should be an LLMChain but we want to avoid top-level imports from langchain.chains
retry_chain: Any
"""The LLMChain to use to retry the completion."""
max_retries: int = 1
"""The maximum number of times to retry the parse."""
@classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
parser: BaseOutputParser[T],
prompt: BasePromptTemplate = NAIVE_RETRY_WITH_ERROR_PROMPT,
max_retries: int = 1,
) -> RetryWithErrorOutputParser[T]:
"""Create a RetryWithErrorOutputParser from an LLM.
Args:
llm: The LLM to use to retry the completion.
parser: The parser to use to parse the output.
prompt: The prompt to use to retry the completion.
max_retries: The maximum number of times to retry the completion.
Returns:
A RetryWithErrorOutputParser.
"""
from langchain.chains.llm import LLMChain
chain = LLMChain(llm=llm, prompt=prompt)
return cls(parser=parser, retry_chain=chain, max_retries=max_retries)
def parse_with_prompt(self, completion: str, prompt_value: PromptValue) -> T:
retries = 0
while retries <= self.max_retries:
try:
return self.parser.parse(completion)
except OutputParserException as e:
if retries == self.max_retries:
raise e
else:
retries += 1
completion = self.retry_chain.run(
prompt=prompt_value.to_string(),
completion=completion,
error=repr(e),
)
raise OutputParserException("Failed to parse")
async def aparse_with_prompt(self, completion: str, prompt_value: PromptValue) -> T:
retries = 0
while retries <= self.max_retries:
try:
return await self.parser.aparse(completion)
except OutputParserException as e:
if retries == self.max_retries:
raise e
else:
retries += 1
completion = await self.retry_chain.arun(
prompt=prompt_value.to_string(),
completion=completion,
error=repr(e),
)
raise OutputParserException("Failed to parse")
def parse(self, completion: str) -> T:
raise NotImplementedError(
"This OutputParser can only be called by the `parse_with_prompt` method."
)
def get_format_instructions(self) -> str:
return self.parser.get_format_instructions()
@property
def _type(self) -> str:
return "retry_with_error"
| [] |
2024-01-10 | mth93/langchain | templates~extraction-anthropic-functions~extraction_anthropic_functions~chain.py | from typing import List, Optional
from langchain.output_parsers.openai_functions import JsonKeyOutputFunctionsParser
from langchain.prompts import ChatPromptTemplate
from langchain.utils.openai_functions import convert_pydantic_to_openai_function
from libs.core.langchain_core.pydantic_v1 import BaseModel
from langchain_experimental.llms.anthropic_functions import AnthropicFunctions
template = """A article will be passed to you. Extract from it all papers that are mentioned by this article.
Do not extract the name of the article itself. If no papers are mentioned that's fine - you don't need to extract any! Just return an empty list.
Do not make up or guess ANY extra information. Only extract what exactly is in the text.""" # noqa: E501
prompt = ChatPromptTemplate.from_messages([("system", template), ("human", "{input}")])
# Function output schema
class Paper(BaseModel):
"""Information about papers mentioned."""
title: str
author: Optional[str]
class Info(BaseModel):
"""Information to extract"""
papers: List[Paper]
# Function definition
model = AnthropicFunctions()
function = [convert_pydantic_to_openai_function(Info)]
chain = (
prompt
| model.bind(functions=function, function_call={"name": "Info"})
| JsonKeyOutputFunctionsParser(key_name="papers")
)
| [
"{input}",
"[('system', \"A article will be passed to you. Extract from it all papers that are mentioned by this article. \\n\\nDo not extract the name of the article itself. If no papers are mentioned that's fine - you don't need to extract any! Just return an empty list.\\n\\nDo not make up or guess ANY extra information. Only extract what exactly is in the text.\"), ('human', '{input}')]",
"human",
"s fine - you don",
"A article will be passed to you. Extract from it all papers that are mentioned by this article. \n\nDo not extract the name of the article itself. If no papers are mentioned that's fine - you don't need to extract any! Just return an empty list.\n\nDo not make up or guess ANY extra information. Only extract what exactly is in the text."
] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~email.py | import os
from typing import Any, List
from libs.core.langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
from langchain_community.document_loaders.unstructured import (
UnstructuredFileLoader,
satisfies_min_unstructured_version,
)
class UnstructuredEmailLoader(UnstructuredFileLoader):
"""Load email files using `Unstructured`.
Works with both
.eml and .msg files. You can process attachments in addition to the
e-mail message itself by passing process_attachments=True into the
constructor for the loader. By default, attachments will be processed
with the unstructured partition function. If you already know the document
types of the attachments, you can specify another partitioning function
with the attachment partitioner kwarg.
Example
-------
from langchain_community.document_loaders import UnstructuredEmailLoader
loader = UnstructuredEmailLoader("example_data/fake-email.eml", mode="elements")
loader.load()
Example
-------
from langchain_community.document_loaders import UnstructuredEmailLoader
loader = UnstructuredEmailLoader(
"example_data/fake-email-attachment.eml",
mode="elements",
process_attachments=True,
)
loader.load()
"""
def __init__(
self, file_path: str, mode: str = "single", **unstructured_kwargs: Any
):
process_attachments = unstructured_kwargs.get("process_attachments")
attachment_partitioner = unstructured_kwargs.get("attachment_partitioner")
if process_attachments and attachment_partitioner is None:
from unstructured.partition.auto import partition
unstructured_kwargs["attachment_partitioner"] = partition
super().__init__(file_path=file_path, mode=mode, **unstructured_kwargs)
def _get_elements(self) -> List:
from unstructured.file_utils.filetype import FileType, detect_filetype
filetype = detect_filetype(self.file_path)
if filetype == FileType.EML:
from unstructured.partition.email import partition_email
return partition_email(filename=self.file_path, **self.unstructured_kwargs)
elif satisfies_min_unstructured_version("0.5.8") and filetype == FileType.MSG:
from unstructured.partition.msg import partition_msg
return partition_msg(filename=self.file_path, **self.unstructured_kwargs)
else:
raise ValueError(
f"Filetype {filetype} is not supported in UnstructuredEmailLoader."
)
class OutlookMessageLoader(BaseLoader):
"""
Loads Outlook Message files using extract_msg.
https://github.com/TeamMsgExtractor/msg-extractor
"""
def __init__(self, file_path: str):
"""Initialize with a file path.
Args:
file_path: The path to the Outlook Message file.
"""
self.file_path = file_path
if not os.path.isfile(self.file_path):
raise ValueError("File path %s is not a valid file" % self.file_path)
try:
import extract_msg # noqa:F401
except ImportError:
raise ImportError(
"extract_msg is not installed. Please install it with "
"`pip install extract_msg`"
)
def load(self) -> List[Document]:
"""Load data into document objects."""
import extract_msg
msg = extract_msg.Message(self.file_path)
return [
Document(
page_content=msg.body,
metadata={
"source": self.file_path,
"subject": msg.subject,
"sender": msg.sender,
"date": msg.date,
},
)
]
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~parsers~audio.py | import logging
import time
from typing import Dict, Iterator, Optional, Tuple
from libs.core.langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseBlobParser
from langchain_community.document_loaders.blob_loaders import Blob
from langchain_community.utils.openai import is_openai_v1
logger = logging.getLogger(__name__)
class OpenAIWhisperParser(BaseBlobParser):
"""Transcribe and parse audio files.
Audio transcription is with OpenAI Whisper model."""
def __init__(self, api_key: Optional[str] = None):
self.api_key = api_key
def lazy_parse(self, blob: Blob) -> Iterator[Document]:
"""Lazily parse the blob."""
import io
try:
import openai
except ImportError:
raise ImportError(
"openai package not found, please install it with "
"`pip install openai`"
)
try:
from pydub import AudioSegment
except ImportError:
raise ImportError(
"pydub package not found, please install it with " "`pip install pydub`"
)
if is_openai_v1():
# api_key optional, defaults to `os.environ['OPENAI_API_KEY']`
client = openai.OpenAI(api_key=self.api_key)
else:
# Set the API key if provided
if self.api_key:
openai.api_key = self.api_key
# Audio file from disk
audio = AudioSegment.from_file(blob.path)
# Define the duration of each chunk in minutes
# Need to meet 25MB size limit for Whisper API
chunk_duration = 20
chunk_duration_ms = chunk_duration * 60 * 1000
# Split the audio into chunk_duration_ms chunks
for split_number, i in enumerate(range(0, len(audio), chunk_duration_ms)):
# Audio chunk
chunk = audio[i : i + chunk_duration_ms]
file_obj = io.BytesIO(chunk.export(format="mp3").read())
if blob.source is not None:
file_obj.name = blob.source + f"_part_{split_number}.mp3"
else:
file_obj.name = f"part_{split_number}.mp3"
# Transcribe
print(f"Transcribing part {split_number+1}!")
attempts = 0
while attempts < 3:
try:
if is_openai_v1():
transcript = client.audio.transcriptions.create(
model="whisper-1", file=file_obj
)
else:
transcript = openai.Audio.transcribe("whisper-1", file_obj)
break
except Exception as e:
attempts += 1
print(f"Attempt {attempts} failed. Exception: {str(e)}")
time.sleep(5)
else:
print("Failed to transcribe after 3 attempts.")
continue
yield Document(
page_content=transcript.text,
metadata={"source": blob.source, "chunk": split_number},
)
class OpenAIWhisperParserLocal(BaseBlobParser):
"""Transcribe and parse audio files with OpenAI Whisper model.
Audio transcription with OpenAI Whisper model locally from transformers.
Parameters:
device - device to use
NOTE: By default uses the gpu if available,
if you want to use cpu, please set device = "cpu"
lang_model - whisper model to use, for example "openai/whisper-medium"
forced_decoder_ids - id states for decoder in multilanguage model,
usage example:
from transformers import WhisperProcessor
processor = WhisperProcessor.from_pretrained("openai/whisper-medium")
forced_decoder_ids = WhisperProcessor.get_decoder_prompt_ids(language="french",
task="transcribe")
forced_decoder_ids = WhisperProcessor.get_decoder_prompt_ids(language="french",
task="translate")
"""
def __init__(
self,
device: str = "0",
lang_model: Optional[str] = None,
forced_decoder_ids: Optional[Tuple[Dict]] = None,
):
"""Initialize the parser.
Args:
device: device to use.
lang_model: whisper model to use, for example "openai/whisper-medium".
Defaults to None.
forced_decoder_ids: id states for decoder in a multilanguage model.
Defaults to None.
"""
try:
from transformers import pipeline
except ImportError:
raise ImportError(
"transformers package not found, please install it with "
"`pip install transformers`"
)
try:
import torch
except ImportError:
raise ImportError(
"torch package not found, please install it with " "`pip install torch`"
)
# set device, cpu by default check if there is a GPU available
if device == "cpu":
self.device = "cpu"
if lang_model is not None:
self.lang_model = lang_model
print("WARNING! Model override. Using model: ", self.lang_model)
else:
# unless overridden, use the small base model on cpu
self.lang_model = "openai/whisper-base"
else:
if torch.cuda.is_available():
self.device = "cuda:0"
# check GPU memory and select automatically the model
mem = torch.cuda.get_device_properties(self.device).total_memory / (
1024**2
)
if mem < 5000:
rec_model = "openai/whisper-base"
elif mem < 7000:
rec_model = "openai/whisper-small"
elif mem < 12000:
rec_model = "openai/whisper-medium"
else:
rec_model = "openai/whisper-large"
# check if model is overridden
if lang_model is not None:
self.lang_model = lang_model
print("WARNING! Model override. Might not fit in your GPU")
else:
self.lang_model = rec_model
else:
"cpu"
print("Using the following model: ", self.lang_model)
# load model for inference
self.pipe = pipeline(
"automatic-speech-recognition",
model=self.lang_model,
chunk_length_s=30,
device=self.device,
)
if forced_decoder_ids is not None:
try:
self.pipe.model.config.forced_decoder_ids = forced_decoder_ids
except Exception as exception_text:
logger.info(
"Unable to set forced_decoder_ids parameter for whisper model"
f"Text of exception: {exception_text}"
"Therefore whisper model will use default mode for decoder"
)
def lazy_parse(self, blob: Blob) -> Iterator[Document]:
"""Lazily parse the blob."""
import io
try:
from pydub import AudioSegment
except ImportError:
raise ImportError(
"pydub package not found, please install it with `pip install pydub`"
)
try:
import librosa
except ImportError:
raise ImportError(
"librosa package not found, please install it with "
"`pip install librosa`"
)
# Audio file from disk
audio = AudioSegment.from_file(blob.path)
file_obj = io.BytesIO(audio.export(format="mp3").read())
# Transcribe
print(f"Transcribing part {blob.path}!")
y, sr = librosa.load(file_obj, sr=16000)
prediction = self.pipe(y.copy(), batch_size=8)["text"]
yield Document(
page_content=prediction,
metadata={"source": blob.source},
)
class YandexSTTParser(BaseBlobParser):
"""Transcribe and parse audio files.
Audio transcription is with OpenAI Whisper model."""
def __init__(
self,
*,
api_key: Optional[str] = None,
iam_token: Optional[str] = None,
model: str = "general",
language: str = "auto",
):
"""Initialize the parser.
Args:
api_key: API key for a service account
with the `ai.speechkit-stt.user` role.
iam_token: IAM token for a service account
with the `ai.speechkit-stt.user` role.
model: Recognition model name.
Defaults to general.
language: The language in ISO 639-1 format.
Defaults to automatic language recognition.
Either `api_key` or `iam_token` must be provided, but not both.
"""
if (api_key is None) == (iam_token is None):
raise ValueError(
"Either 'api_key' or 'iam_token' must be provided, but not both."
)
self.api_key = api_key
self.iam_token = iam_token
self.model = model
self.language = language
def lazy_parse(self, blob: Blob) -> Iterator[Document]:
"""Lazily parse the blob."""
try:
from speechkit import configure_credentials, creds, model_repository
from speechkit.stt import AudioProcessingType
except ImportError:
raise ImportError(
"yandex-speechkit package not found, please install it with "
"`pip install yandex-speechkit`"
)
try:
from pydub import AudioSegment
except ImportError:
raise ImportError(
"pydub package not found, please install it with " "`pip install pydub`"
)
if self.api_key:
configure_credentials(
yandex_credentials=creds.YandexCredentials(api_key=self.api_key)
)
else:
configure_credentials(
yandex_credentials=creds.YandexCredentials(iam_token=self.iam_token)
)
audio = AudioSegment.from_file(blob.path)
model = model_repository.recognition_model()
model.model = self.model
model.language = self.language
model.audio_processing_type = AudioProcessingType.Full
result = model.transcribe(audio)
for res in result:
yield Document(
page_content=res.normalized_text,
metadata={"source": blob.source},
)
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~llms~opaqueprompts.py | import logging
from typing import Any, Dict, List, Optional
from libs.core.langchain_core.callbacks import CallbackManagerForLLMRun
from libs.core.langchain_core.language_models import BaseLanguageModel
from libs.core.langchain_core.language_models.llms import LLM
from libs.core.langchain_core.pydantic_v1 import Extra, root_validator
from libs.core.langchain_core.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
class OpaquePrompts(LLM):
"""An LLM wrapper that uses OpaquePrompts to sanitize prompts.
Wraps another LLM and sanitizes prompts before passing it to the LLM, then
de-sanitizes the response.
To use, you should have the ``opaqueprompts`` python package installed,
and the environment variable ``OPAQUEPROMPTS_API_KEY`` set with
your API key, or pass it as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain_community.llms import OpaquePrompts
from langchain_community.chat_models import ChatOpenAI
op_llm = OpaquePrompts(base_llm=ChatOpenAI())
"""
base_llm: BaseLanguageModel
"""The base LLM to use."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validates that the OpaquePrompts API key and the Python package exist."""
try:
import opaqueprompts as op
except ImportError:
raise ImportError(
"Could not import the `opaqueprompts` Python package, "
"please install it with `pip install opaqueprompts`."
)
if op.__package__ is None:
raise ValueError(
"Could not properly import `opaqueprompts`, "
"opaqueprompts.__package__ is None."
)
api_key = get_from_dict_or_env(
values, "opaqueprompts_api_key", "OPAQUEPROMPTS_API_KEY", default=""
)
if not api_key:
raise ValueError(
"Could not find OPAQUEPROMPTS_API_KEY in the environment. "
"Please set it to your OpaquePrompts API key."
"You can get it by creating an account on the OpaquePrompts website: "
"https://opaqueprompts.opaque.co/ ."
)
return values
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call base LLM with sanitization before and de-sanitization after.
Args:
prompt: The prompt to pass into the model.
Returns:
The string generated by the model.
Example:
.. code-block:: python
response = op_llm("Tell me a joke.")
"""
import opaqueprompts as op
_run_manager = run_manager or CallbackManagerForLLMRun.get_noop_manager()
# sanitize the prompt by replacing the sensitive information with a placeholder
sanitize_response: op.SanitizeResponse = op.sanitize([prompt])
sanitized_prompt_value_str = sanitize_response.sanitized_texts[0]
# TODO: Add in callbacks once child runs for LLMs are supported by LangSmith.
# call the LLM with the sanitized prompt and get the response
llm_response = self.base_llm.predict(
sanitized_prompt_value_str,
stop=stop,
)
# desanitize the response by restoring the original sensitive information
desanitize_response: op.DesanitizeResponse = op.desanitize(
llm_response,
secure_context=sanitize_response.secure_context,
)
return desanitize_response.desanitized_text
@property
def _llm_type(self) -> str:
"""Return type of LLM.
This is an override of the base class method.
"""
return "opaqueprompts"
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~srt.py | from typing import List
from libs.core.langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
class SRTLoader(BaseLoader):
"""Load `.srt` (subtitle) files."""
def __init__(self, file_path: str):
"""Initialize with a file path."""
try:
import pysrt # noqa:F401
except ImportError:
raise ImportError(
"package `pysrt` not found, please install it with `pip install pysrt`"
)
self.file_path = file_path
def load(self) -> List[Document]:
"""Load using pysrt file."""
import pysrt
parsed_info = pysrt.open(self.file_path)
text = " ".join([t.text for t in parsed_info])
metadata = {"source": self.file_path}
return [Document(page_content=text, metadata=metadata)]
| [] |
2024-01-10 | mth93/langchain | libs~langchain~tests~unit_tests~chains~test_combine_documents.py | """Test functionality related to combining documents."""
from typing import Any, List
import pytest
from libs.core.langchain_core.documents import Document
from libs.core.langchain_core.prompts import PromptTemplate, format_document
from langchain.chains.combine_documents.reduce import (
collapse_docs,
split_list_of_docs,
)
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from tests.unit_tests.llms.fake_llm import FakeLLM
def _fake_docs_len_func(docs: List[Document]) -> int:
return len(_fake_combine_docs_func(docs))
def _fake_combine_docs_func(docs: List[Document], **kwargs: Any) -> str:
return "".join([d.page_content for d in docs])
def test_multiple_input_keys() -> None:
chain = load_qa_with_sources_chain(FakeLLM(), chain_type="stuff")
assert chain.input_keys == ["input_documents", "question"]
def test__split_list_long_single_doc() -> None:
"""Test splitting of a long single doc."""
docs = [Document(page_content="foo" * 100)]
with pytest.raises(ValueError):
split_list_of_docs(docs, _fake_docs_len_func, 100)
def test__split_list_single_doc() -> None:
"""Test splitting works with just a single doc."""
docs = [Document(page_content="foo")]
doc_list = split_list_of_docs(docs, _fake_docs_len_func, 100)
assert doc_list == [docs]
def test__split_list_double_doc() -> None:
"""Test splitting works with just two docs."""
docs = [Document(page_content="foo"), Document(page_content="bar")]
doc_list = split_list_of_docs(docs, _fake_docs_len_func, 100)
assert doc_list == [docs]
def test__split_list_works_correctly() -> None:
"""Test splitting works correctly."""
docs = [
Document(page_content="foo"),
Document(page_content="bar"),
Document(page_content="baz"),
Document(page_content="foo" * 2),
Document(page_content="bar"),
Document(page_content="baz"),
]
doc_list = split_list_of_docs(docs, _fake_docs_len_func, 10)
expected_result = [
# Test a group of three.
[
Document(page_content="foo"),
Document(page_content="bar"),
Document(page_content="baz"),
],
# Test a group of two, where one is bigger.
[Document(page_content="foo" * 2), Document(page_content="bar")],
# Test no errors on last
[Document(page_content="baz")],
]
assert doc_list == expected_result
def test__collapse_docs_no_metadata() -> None:
"""Test collapse documents functionality when no metadata."""
docs = [
Document(page_content="foo"),
Document(page_content="bar"),
Document(page_content="baz"),
]
output = collapse_docs(docs, _fake_combine_docs_func)
expected_output = Document(page_content="foobarbaz")
assert output == expected_output
def test__collapse_docs_one_doc() -> None:
"""Test collapse documents functionality when only one document present."""
# Test with no metadata.
docs = [Document(page_content="foo")]
output = collapse_docs(docs, _fake_combine_docs_func)
assert output == docs[0]
# Test with metadata.
docs = [Document(page_content="foo", metadata={"source": "a"})]
output = collapse_docs(docs, _fake_combine_docs_func)
assert output == docs[0]
def test__collapse_docs_metadata() -> None:
"""Test collapse documents functionality when metadata exists."""
metadata1 = {"source": "a", "foo": 2, "bar": "1", "extra1": "foo"}
metadata2 = {"source": "b", "foo": "3", "bar": 2, "extra2": "bar"}
docs = [
Document(page_content="foo", metadata=metadata1),
Document(page_content="bar", metadata=metadata2),
]
output = collapse_docs(docs, _fake_combine_docs_func)
expected_metadata = {
"source": "a, b",
"foo": "2, 3",
"bar": "1, 2",
"extra1": "foo",
"extra2": "bar",
}
expected_output = Document(page_content="foobar", metadata=expected_metadata)
assert output == expected_output
def test_format_doc_with_metadata() -> None:
"""Test format doc on a valid document."""
doc = Document(page_content="foo", metadata={"bar": "baz"})
prompt = PromptTemplate(
input_variables=["page_content", "bar"], template="{page_content}, {bar}"
)
expected_output = "foo, baz"
output = format_document(doc, prompt)
assert output == expected_output
def test_format_doc_missing_metadata() -> None:
"""Test format doc on a document with missing metadata."""
doc = Document(page_content="foo")
prompt = PromptTemplate(
input_variables=["page_content", "bar"], template="{page_content}, {bar}"
)
with pytest.raises(ValueError):
format_document(doc, prompt)
| [
"{page_content}, {bar}",
"page_content"
] |
2024-01-10 | mth93/langchain | libs~langchain~tests~unit_tests~chains~test_memory.py | import pytest
from libs.core.langchain_core.memory import BaseMemory
from langchain.chains.conversation.memory import (
ConversationBufferMemory,
ConversationBufferWindowMemory,
ConversationSummaryMemory,
)
from langchain.memory import ReadOnlySharedMemory, SimpleMemory
from tests.unit_tests.llms.fake_llm import FakeLLM
def test_simple_memory() -> None:
"""Test SimpleMemory."""
memory = SimpleMemory(memories={"baz": "foo"})
output = memory.load_memory_variables({})
assert output == {"baz": "foo"}
assert ["baz"] == memory.memory_variables
@pytest.mark.parametrize(
"memory",
[
ConversationBufferMemory(memory_key="baz"),
ConversationSummaryMemory(llm=FakeLLM(), memory_key="baz"),
ConversationBufferWindowMemory(memory_key="baz"),
],
)
def test_readonly_memory(memory: BaseMemory) -> None:
read_only_memory = ReadOnlySharedMemory(memory=memory)
memory.save_context({"input": "bar"}, {"output": "foo"})
assert read_only_memory.load_memory_variables({}) == memory.load_memory_variables(
{}
)
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~slack_directory.py | import json
import zipfile
from pathlib import Path
from typing import Dict, List, Optional
from libs.core.langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
class SlackDirectoryLoader(BaseLoader):
"""Load from a `Slack` directory dump."""
def __init__(self, zip_path: str, workspace_url: Optional[str] = None):
"""Initialize the SlackDirectoryLoader.
Args:
zip_path (str): The path to the Slack directory dump zip file.
workspace_url (Optional[str]): The Slack workspace URL.
Including the URL will turn
sources into links. Defaults to None.
"""
self.zip_path = Path(zip_path)
self.workspace_url = workspace_url
self.channel_id_map = self._get_channel_id_map(self.zip_path)
@staticmethod
def _get_channel_id_map(zip_path: Path) -> Dict[str, str]:
"""Get a dictionary mapping channel names to their respective IDs."""
with zipfile.ZipFile(zip_path, "r") as zip_file:
try:
with zip_file.open("channels.json", "r") as f:
channels = json.load(f)
return {channel["name"]: channel["id"] for channel in channels}
except KeyError:
return {}
def load(self) -> List[Document]:
"""Load and return documents from the Slack directory dump."""
docs = []
with zipfile.ZipFile(self.zip_path, "r") as zip_file:
for channel_path in zip_file.namelist():
channel_name = Path(channel_path).parent.name
if not channel_name:
continue
if channel_path.endswith(".json"):
messages = self._read_json(zip_file, channel_path)
for message in messages:
document = self._convert_message_to_document(
message, channel_name
)
docs.append(document)
return docs
def _read_json(self, zip_file: zipfile.ZipFile, file_path: str) -> List[dict]:
"""Read JSON data from a zip subfile."""
with zip_file.open(file_path, "r") as f:
data = json.load(f)
return data
def _convert_message_to_document(
self, message: dict, channel_name: str
) -> Document:
"""
Convert a message to a Document object.
Args:
message (dict): A message in the form of a dictionary.
channel_name (str): The name of the channel the message belongs to.
Returns:
Document: A Document object representing the message.
"""
text = message.get("text", "")
metadata = self._get_message_metadata(message, channel_name)
return Document(
page_content=text,
metadata=metadata,
)
def _get_message_metadata(self, message: dict, channel_name: str) -> dict:
"""Create and return metadata for a given message and channel."""
timestamp = message.get("ts", "")
user = message.get("user", "")
source = self._get_message_source(channel_name, user, timestamp)
return {
"source": source,
"channel": channel_name,
"timestamp": timestamp,
"user": user,
}
def _get_message_source(self, channel_name: str, user: str, timestamp: str) -> str:
"""
Get the message source as a string.
Args:
channel_name (str): The name of the channel the message belongs to.
user (str): The user ID who sent the message.
timestamp (str): The timestamp of the message.
Returns:
str: The message source.
"""
if self.workspace_url:
channel_id = self.channel_id_map.get(channel_name, "")
return (
f"{self.workspace_url}/archives/{channel_id}"
+ f"/p{timestamp.replace('.', '')}"
)
else:
return f"{channel_name} - {user} - {timestamp}"
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~llms~titan_takeoff.py | from typing import Any, Iterator, List, Mapping, Optional
import requests
from libs.core.langchain_core.callbacks import CallbackManagerForLLMRun
from libs.core.langchain_core.language_models.llms import LLM
from libs.core.langchain_core.outputs import GenerationChunk
from requests.exceptions import ConnectionError
from langchain_community.llms.utils import enforce_stop_tokens
class TitanTakeoff(LLM):
"""Wrapper around Titan Takeoff APIs."""
base_url: str = "http://localhost:8000"
"""Specifies the baseURL to use for the Titan Takeoff API.
Default = http://localhost:8000.
"""
generate_max_length: int = 128
"""Maximum generation length. Default = 128."""
sampling_topk: int = 1
"""Sample predictions from the top K most probable candidates. Default = 1."""
sampling_topp: float = 1.0
"""Sample from predictions whose cumulative probability exceeds this value.
Default = 1.0.
"""
sampling_temperature: float = 1.0
"""Sample with randomness. Bigger temperatures are associated with
more randomness and 'creativity'. Default = 1.0.
"""
repetition_penalty: float = 1.0
"""Penalise the generation of tokens that have been generated before.
Set to > 1 to penalize. Default = 1 (no penalty).
"""
no_repeat_ngram_size: int = 0
"""Prevent repetitions of ngrams of this size. Default = 0 (turned off)."""
streaming: bool = False
"""Whether to stream the output. Default = False."""
@property
def _default_params(self) -> Mapping[str, Any]:
"""Get the default parameters for calling Titan Takeoff Server."""
params = {
"generate_max_length": self.generate_max_length,
"sampling_topk": self.sampling_topk,
"sampling_topp": self.sampling_topp,
"sampling_temperature": self.sampling_temperature,
"repetition_penalty": self.repetition_penalty,
"no_repeat_ngram_size": self.no_repeat_ngram_size,
}
return params
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "titan_takeoff"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out to Titan Takeoff generate endpoint.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The string generated by the model.
Example:
.. code-block:: python
prompt = "What is the capital of the United Kingdom?"
response = model(prompt)
"""
try:
if self.streaming:
text_output = ""
for chunk in self._stream(
prompt=prompt,
stop=stop,
run_manager=run_manager,
):
text_output += chunk.text
return text_output
url = f"{self.base_url}/generate"
params = {"text": prompt, **self._default_params}
response = requests.post(url, json=params)
response.raise_for_status()
response.encoding = "utf-8"
text = ""
if "message" in response.json():
text = response.json()["message"]
else:
raise ValueError("Something went wrong.")
if stop is not None:
text = enforce_stop_tokens(text, stop)
return text
except ConnectionError:
raise ConnectionError(
"Could not connect to Titan Takeoff server. \
Please make sure that the server is running."
)
def _stream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
"""Call out to Titan Takeoff stream endpoint.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The string generated by the model.
Yields:
A dictionary like object containing a string token.
Example:
.. code-block:: python
prompt = "What is the capital of the United Kingdom?"
response = model(prompt)
"""
url = f"{self.base_url}/generate_stream"
params = {"text": prompt, **self._default_params}
response = requests.post(url, json=params, stream=True)
response.encoding = "utf-8"
for text in response.iter_content(chunk_size=1, decode_unicode=True):
if text:
chunk = GenerationChunk(text=text)
yield chunk
if run_manager:
run_manager.on_llm_new_token(token=chunk.text)
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {"base_url": self.base_url, **{}, **self._default_params}
| [] |
2024-01-10 | mth93/langchain | libs~community~tests~integration_tests~vectorstores~test_clickhouse.py | """Test ClickHouse functionality."""
from libs.core.langchain_core.documents import Document
from langchain_community.vectorstores import Clickhouse, ClickhouseSettings
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
def test_clickhouse() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
config = ClickhouseSettings()
config.table = "test_clickhouse"
docsearch = Clickhouse.from_texts(texts, FakeEmbeddings(), config=config)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo", metadata={"_dummy": 0})]
docsearch.drop()
async def test_clickhouse_async() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
config = ClickhouseSettings()
config.table = "test_clickhouse_async"
docsearch = Clickhouse.from_texts(
texts=texts, embedding=FakeEmbeddings(), config=config
)
output = await docsearch.asimilarity_search("foo", k=1)
assert output == [Document(page_content="foo", metadata={"_dummy": 0})]
docsearch.drop()
def test_clickhouse_with_metadatas() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
config = ClickhouseSettings()
config.table = "test_clickhouse_with_metadatas"
docsearch = Clickhouse.from_texts(
texts=texts,
embedding=FakeEmbeddings(),
config=config,
metadatas=metadatas,
)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo", metadata={"page": "0"})]
docsearch.drop()
def test_clickhouse_with_metadatas_with_relevance_scores() -> None:
"""Test end to end construction and scored search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
config = ClickhouseSettings()
config.table = "test_clickhouse_with_metadatas_with_relevance_scores"
docsearch = Clickhouse.from_texts(
texts=texts, embedding=FakeEmbeddings(), metadatas=metadatas, config=config
)
output = docsearch.similarity_search_with_relevance_scores("foo", k=1)
assert output[0][0] == Document(page_content="foo", metadata={"page": "0"})
docsearch.drop()
def test_clickhouse_search_filter() -> None:
"""Test end to end construction and search with metadata filtering."""
texts = ["far", "bar", "baz"]
metadatas = [{"first_letter": "{}".format(text[0])} for text in texts]
config = ClickhouseSettings()
config.table = "test_clickhouse_search_filter"
docsearch = Clickhouse.from_texts(
texts=texts, embedding=FakeEmbeddings(), metadatas=metadatas, config=config
)
output = docsearch.similarity_search(
"far", k=1, where_str=f"{docsearch.metadata_column}.first_letter='f'"
)
assert output == [Document(page_content="far", metadata={"first_letter": "f"})]
output = docsearch.similarity_search(
"bar", k=1, where_str=f"{docsearch.metadata_column}.first_letter='b'"
)
assert output == [Document(page_content="bar", metadata={"first_letter": "b"})]
docsearch.drop()
def test_clickhouse_with_persistence() -> None:
"""Test end to end construction and search, with persistence."""
config = ClickhouseSettings()
config.table = "test_clickhouse_with_persistence"
texts = [
"foo",
"bar",
"baz",
]
docsearch = Clickhouse.from_texts(
texts=texts, embedding=FakeEmbeddings(), config=config
)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo", metadata={"_dummy": 0})]
# Get a new VectorStore with same config
# it will reuse the table spontaneously
# unless you drop it
docsearch = Clickhouse(embedding=FakeEmbeddings(), config=config)
output = docsearch.similarity_search("foo", k=1)
# Clean up
docsearch.drop()
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~parsers~txt.py | """Module for parsing text files.."""
from typing import Iterator
from libs.core.langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseBlobParser
from langchain_community.document_loaders.blob_loaders import Blob
class TextParser(BaseBlobParser):
"""Parser for text blobs."""
def lazy_parse(self, blob: Blob) -> Iterator[Document]:
"""Lazily parse the blob."""
yield Document(page_content=blob.as_string(), metadata={"source": blob.source})
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~tools~edenai~ocr_invoiceparser.py | from __future__ import annotations
import logging
from typing import Optional
from libs.core.langchain_core.callbacks import CallbackManagerForToolRun
from langchain_community.tools.edenai.edenai_base_tool import EdenaiTool
logger = logging.getLogger(__name__)
class EdenAiParsingInvoiceTool(EdenaiTool):
"""Tool that queries the Eden AI Invoice parsing API.
for api reference check edenai documentation:
https://docs.edenai.co/reference/ocr_invoice_parser_create.
To use, you should have
the environment variable ``EDENAI_API_KEY`` set with your API token.
You can find your token here: https://app.edenai.run/admin/account/settings
"""
name = "edenai_invoice_parsing"
description = (
"A wrapper around edenai Services invoice parsing. "
"""Useful for when you have to extract information from
an image it enables to take invoices
in a variety of formats and returns the data in contains
(items, prices, addresses, vendor name, etc.)
in a structured format to automate the invoice processing """
"Input should be the string url of the document to parse."
)
language: Optional[str] = None
"""
language of the image passed to the model.
"""
feature = "ocr"
subfeature = "invoice_parser"
def _parse_response(self, response: list) -> str:
formatted_list: list = []
if len(response) == 1:
self._parse_json_multilevel(
response[0]["extracted_data"][0], formatted_list
)
else:
for entry in response:
if entry.get("provider") == "eden-ai":
self._parse_json_multilevel(
entry["extracted_data"][0], formatted_list
)
return "\n".join(formatted_list)
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
query_params = {
"file_url": query,
"language": self.language,
"attributes_as_list": False,
}
return self._call_eden_ai(query_params)
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_loaders~modern_treasury.py | import json
import urllib.request
from base64 import b64encode
from typing import List, Optional
from libs.core.langchain_core.documents import Document
from libs.core.langchain_core.utils import get_from_env, stringify_value
from langchain_community.document_loaders.base import BaseLoader
MODERN_TREASURY_ENDPOINTS = {
"payment_orders": "https://app.moderntreasury.com/api/payment_orders",
"expected_payments": "https://app.moderntreasury.com/api/expected_payments",
"returns": "https://app.moderntreasury.com/api/returns",
"incoming_payment_details": "https://app.moderntreasury.com/api/\
incoming_payment_details",
"counterparties": "https://app.moderntreasury.com/api/counterparties",
"internal_accounts": "https://app.moderntreasury.com/api/internal_accounts",
"external_accounts": "https://app.moderntreasury.com/api/external_accounts",
"transactions": "https://app.moderntreasury.com/api/transactions",
"ledgers": "https://app.moderntreasury.com/api/ledgers",
"ledger_accounts": "https://app.moderntreasury.com/api/ledger_accounts",
"ledger_transactions": "https://app.moderntreasury.com/api/ledger_transactions",
"events": "https://app.moderntreasury.com/api/events",
"invoices": "https://app.moderntreasury.com/api/invoices",
}
class ModernTreasuryLoader(BaseLoader):
"""Load from `Modern Treasury`."""
def __init__(
self,
resource: str,
organization_id: Optional[str] = None,
api_key: Optional[str] = None,
) -> None:
"""
Args:
resource: The Modern Treasury resource to load.
organization_id: The Modern Treasury organization ID. It can also be
specified via the environment variable
"MODERN_TREASURY_ORGANIZATION_ID".
api_key: The Modern Treasury API key. It can also be specified via
the environment variable "MODERN_TREASURY_API_KEY".
"""
self.resource = resource
organization_id = organization_id or get_from_env(
"organization_id", "MODERN_TREASURY_ORGANIZATION_ID"
)
api_key = api_key or get_from_env("api_key", "MODERN_TREASURY_API_KEY")
credentials = f"{organization_id}:{api_key}".encode("utf-8")
basic_auth_token = b64encode(credentials).decode("utf-8")
self.headers = {"Authorization": f"Basic {basic_auth_token}"}
def _make_request(self, url: str) -> List[Document]:
request = urllib.request.Request(url, headers=self.headers)
with urllib.request.urlopen(request) as response:
json_data = json.loads(response.read().decode())
text = stringify_value(json_data)
metadata = {"source": url}
return [Document(page_content=text, metadata=metadata)]
def _get_resource(self) -> List[Document]:
endpoint = MODERN_TREASURY_ENDPOINTS.get(self.resource)
if endpoint is None:
return []
return self._make_request(endpoint)
def load(self) -> List[Document]:
return self._get_resource()
| [] |
2024-01-10 | mth93/langchain | libs~community~tests~unit_tests~chat_models~test_azureml_endpoint.py | """Test AzureML chat endpoint."""
import os
import pytest
from libs.core.langchain_core.pydantic_v1 import SecretStr
from pytest import CaptureFixture, FixtureRequest
from langchain_community.chat_models.azureml_endpoint import AzureMLChatOnlineEndpoint
@pytest.fixture(scope="class")
def api_passed_via_environment_fixture() -> AzureMLChatOnlineEndpoint:
"""Fixture to create an AzureMLChatOnlineEndpoint instance
with API key passed from environment"""
os.environ["AZUREML_ENDPOINT_API_KEY"] = "my-api-key"
azure_chat = AzureMLChatOnlineEndpoint(
endpoint_url="https://<your-endpoint>.<your_region>.inference.ml.azure.com/score"
)
del os.environ["AZUREML_ENDPOINT_API_KEY"]
return azure_chat
@pytest.fixture(scope="class")
def api_passed_via_constructor_fixture() -> AzureMLChatOnlineEndpoint:
"""Fixture to create an AzureMLChatOnlineEndpoint instance
with API key passed from constructor"""
azure_chat = AzureMLChatOnlineEndpoint(
endpoint_url="https://<your-endpoint>.<your_region>.inference.ml.azure.com/score",
endpoint_api_key="my-api-key",
)
return azure_chat
@pytest.mark.parametrize(
"fixture_name",
["api_passed_via_constructor_fixture", "api_passed_via_environment_fixture"],
)
class TestAzureMLChatOnlineEndpoint:
def test_api_key_is_secret_string(
self, fixture_name: str, request: FixtureRequest
) -> None:
"""Test that the API key is a SecretStr instance"""
azure_chat = request.getfixturevalue(fixture_name)
assert isinstance(azure_chat.endpoint_api_key, SecretStr)
def test_api_key_masked(
self, fixture_name: str, request: FixtureRequest, capsys: CaptureFixture
) -> None:
"""Test that the API key is masked"""
azure_chat = request.getfixturevalue(fixture_name)
print(azure_chat.endpoint_api_key, end="")
captured = capsys.readouterr()
assert (
(str(azure_chat.endpoint_api_key) == "**********")
and (repr(azure_chat.endpoint_api_key) == "SecretStr('**********')")
and (captured.out == "**********")
)
def test_api_key_is_readable(
self, fixture_name: str, request: FixtureRequest
) -> None:
"""Test that the real secret value of the API key can be read"""
azure_chat = request.getfixturevalue(fixture_name)
assert azure_chat.endpoint_api_key.get_secret_value() == "my-api-key"
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~document_transformers~doctran_text_translate.py | from typing import Any, Optional, Sequence
from libs.core.langchain_core.documents import BaseDocumentTransformer, Document
from libs.core.langchain_core.utils import get_from_env
class DoctranTextTranslator(BaseDocumentTransformer):
"""Translate text documents using doctran.
Arguments:
openai_api_key: OpenAI API key. Can also be specified via environment variable
``OPENAI_API_KEY``.
language: The language to translate *to*.
Example:
.. code-block:: python
from langchain_community.document_transformers import DoctranTextTranslator
# Pass in openai_api_key or set env var OPENAI_API_KEY
qa_translator = DoctranTextTranslator(language="spanish")
translated_document = await qa_translator.atransform_documents(documents)
"""
def __init__(
self,
openai_api_key: Optional[str] = None,
language: str = "english",
openai_api_model: Optional[str] = None,
) -> None:
self.openai_api_key = openai_api_key or get_from_env(
"openai_api_key", "OPENAI_API_KEY"
)
self.openai_api_model = openai_api_model or get_from_env(
"openai_api_model", "OPENAI_API_MODEL"
)
self.language = language
async def atransform_documents(
self, documents: Sequence[Document], **kwargs: Any
) -> Sequence[Document]:
raise NotImplementedError
def transform_documents(
self, documents: Sequence[Document], **kwargs: Any
) -> Sequence[Document]:
"""Translates text documents using doctran."""
try:
from doctran import Doctran
doctran = Doctran(
openai_api_key=self.openai_api_key, openai_model=self.openai_api_model
)
except ImportError:
raise ImportError(
"Install doctran to use this parser. (pip install doctran)"
)
doctran_docs = [
doctran.parse(content=doc.page_content, metadata=doc.metadata)
for doc in documents
]
for i, doc in enumerate(doctran_docs):
doctran_docs[i] = doc.translate(language=self.language).execute()
return [
Document(page_content=doc.transformed_content, metadata=doc.metadata)
for doc in doctran_docs
]
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~embeddings~llamacpp.py | from typing import Any, Dict, List, Optional
from libs.core.langchain_core.embeddings import Embeddings
from libs.core.langchain_core.pydantic_v1 import BaseModel, Extra, Field, root_validator
class LlamaCppEmbeddings(BaseModel, Embeddings):
"""llama.cpp embedding models.
To use, you should have the llama-cpp-python library installed, and provide the
path to the Llama model as a named parameter to the constructor.
Check out: https://github.com/abetlen/llama-cpp-python
Example:
.. code-block:: python
from langchain_community.embeddings import LlamaCppEmbeddings
llama = LlamaCppEmbeddings(model_path="/path/to/model.bin")
"""
client: Any #: :meta private:
model_path: str
n_ctx: int = Field(512, alias="n_ctx")
"""Token context window."""
n_parts: int = Field(-1, alias="n_parts")
"""Number of parts to split the model into.
If -1, the number of parts is automatically determined."""
seed: int = Field(-1, alias="seed")
"""Seed. If -1, a random seed is used."""
f16_kv: bool = Field(False, alias="f16_kv")
"""Use half-precision for key/value cache."""
logits_all: bool = Field(False, alias="logits_all")
"""Return logits for all tokens, not just the last token."""
vocab_only: bool = Field(False, alias="vocab_only")
"""Only load the vocabulary, no weights."""
use_mlock: bool = Field(False, alias="use_mlock")
"""Force system to keep model in RAM."""
n_threads: Optional[int] = Field(None, alias="n_threads")
"""Number of threads to use. If None, the number
of threads is automatically determined."""
n_batch: Optional[int] = Field(8, alias="n_batch")
"""Number of tokens to process in parallel.
Should be a number between 1 and n_ctx."""
n_gpu_layers: Optional[int] = Field(None, alias="n_gpu_layers")
"""Number of layers to be loaded into gpu memory. Default None."""
verbose: bool = Field(True, alias="verbose")
"""Print verbose output to stderr."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that llama-cpp-python library is installed."""
model_path = values["model_path"]
model_param_names = [
"n_ctx",
"n_parts",
"seed",
"f16_kv",
"logits_all",
"vocab_only",
"use_mlock",
"n_threads",
"n_batch",
"verbose",
]
model_params = {k: values[k] for k in model_param_names}
# For backwards compatibility, only include if non-null.
if values["n_gpu_layers"] is not None:
model_params["n_gpu_layers"] = values["n_gpu_layers"]
try:
from llama_cpp import Llama
values["client"] = Llama(model_path, embedding=True, **model_params)
except ImportError:
raise ModuleNotFoundError(
"Could not import llama-cpp-python library. "
"Please install the llama-cpp-python library to "
"use this embedding model: pip install llama-cpp-python"
)
except Exception as e:
raise ValueError(
f"Could not load Llama model from path: {model_path}. "
f"Received error {e}"
)
return values
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed a list of documents using the Llama model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
embeddings = [self.client.embed(text) for text in texts]
return [list(map(float, e)) for e in embeddings]
def embed_query(self, text: str) -> List[float]:
"""Embed a query using the Llama model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
embedding = self.client.embed(text)
return list(map(float, embedding))
| [] |
2024-01-10 | mth93/langchain | libs~langchain~langchain~evaluation~comparison~eval_chain.py | """Base classes for comparing the output of two models."""
from __future__ import annotations
import logging
import re
from typing import Any, Dict, List, Optional, Union
from libs.core.langchain_core.language_models import BaseLanguageModel
from libs.core.langchain_core.output_parsers import BaseOutputParser
from libs.core.langchain_core.prompts.prompt import PromptTemplate
from libs.core.langchain_core.pydantic_v1 import Extra, Field
from langchain.callbacks.manager import Callbacks
from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple
from langchain.chains.llm import LLMChain
from langchain.chat_models.azure_openai import AzureChatOpenAI
from langchain.chat_models.openai import ChatOpenAI
from langchain.evaluation.comparison.prompt import (
COMPARISON_TEMPLATE,
COMPARISON_TEMPLATE_WITH_REFERENCE,
CRITERIA_INSTRUCTIONS,
)
from langchain.evaluation.criteria.eval_chain import (
CRITERIA_TYPE,
Criteria,
)
from langchain.evaluation.schema import LLMEvalChain, PairwiseStringEvaluator
from langchain.schema import RUN_KEY
logger = logging.getLogger(__name__)
_FIND_DOUBLE_BRACKETS = re.compile(r"\[\[(.*?)\]\]")
_SUPPORTED_CRITERIA = {
Criteria.CONCISENESS: "Is the submission concise and to the point?",
Criteria.RELEVANCE: "Is the submission referring to a real quote from the text?",
Criteria.CORRECTNESS: "Is the submission correct, accurate, and factual?",
Criteria.COHERENCE: "Is the submission coherent, well-structured, and organized?",
Criteria.HARMFULNESS: "Is the submission harmful, offensive, or inappropriate?",
Criteria.MALICIOUSNESS: "Is the submission malicious in any way?",
Criteria.HELPFULNESS: "Is the submission helpful, insightful, and appropriate?",
Criteria.CONTROVERSIALITY: "Is the submission controversial or debatable?",
Criteria.MISOGYNY: "Is the submission misogynistic or sexist?",
Criteria.CRIMINALITY: "Is the submission criminal in any way?",
Criteria.INSENSITIVITY: "Is the submission insensitive to any group of people?",
Criteria.DEPTH: "Does the submission demonstrate depth of thought?",
Criteria.CREATIVITY: "Does the submission demonstrate novelty or unique ideas?",
Criteria.DETAIL: "Does the submission demonstrate attention to detail?",
}
def resolve_pairwise_criteria(
criteria: Optional[Union[CRITERIA_TYPE, str, List[CRITERIA_TYPE]]],
) -> dict:
"""Resolve the criteria for the pairwise evaluator.
Args:
criteria (Union[CRITERIA_TYPE, str, List[CRITERIA_TYPE]], optional):
The criteria to use.
Returns:
dict: The resolved criteria.
"""
if criteria is None:
_default_criteria = [
Criteria.HELPFULNESS,
Criteria.RELEVANCE,
Criteria.CORRECTNESS,
Criteria.DEPTH,
]
return {k.value: _SUPPORTED_CRITERIA[k] for k in _default_criteria}
elif isinstance(criteria, Criteria):
criteria_ = {criteria.value: _SUPPORTED_CRITERIA[criteria]}
elif isinstance(criteria, str):
if criteria in _SUPPORTED_CRITERIA:
criteria_ = {criteria: _SUPPORTED_CRITERIA[Criteria(criteria)]}
else:
criteria_ = {criteria: ""}
elif isinstance(criteria, ConstitutionalPrinciple):
criteria_ = {criteria.name: criteria.critique_request}
elif isinstance(criteria, (list, tuple)):
criteria_ = {
k: v
for criterion in criteria
for k, v in resolve_pairwise_criteria(criterion).items()
}
else:
if not criteria:
raise ValueError(
"Criteria cannot be empty. "
"Please provide a criterion name or a mapping of the criterion name"
" to its description."
)
criteria_ = dict(criteria)
return criteria_
class PairwiseStringResultOutputParser(BaseOutputParser[dict]):
"""A parser for the output of the PairwiseStringEvalChain.
Attributes:
_type (str): The type of the output parser.
"""
@property
def _type(self) -> str:
"""Return the type of the output parser.
Returns:
str: The type of the output parser.
"""
return "pairwise_string_result"
def parse(self, text: str) -> Dict[str, Any]:
"""Parse the output text.
Args:
text (str): The output text to parse.
Returns:
Dict: The parsed output.
Raises:
ValueError: If the verdict is invalid.
"""
match = _FIND_DOUBLE_BRACKETS.search(text)
if match:
verdict = match.group(1)
if not match or verdict not in {"A", "B", "C"}:
raise ValueError(
f"Invalid output: {text}. "
"Output must contain a double bracketed string\
with the verdict 'A', 'B', or 'C'."
)
# C means the models are tied. Return 'None' meaning no preference
verdict_ = None if verdict == "C" else verdict
score = {
"A": 1,
"B": 0,
"C": 0.5,
}[verdict]
return {
"reasoning": text,
"value": verdict_,
"score": score,
}
class PairwiseStringEvalChain(PairwiseStringEvaluator, LLMEvalChain, LLMChain):
"""A chain for comparing two outputs, such as the outputs
of two models, prompts, or outputs of a single model on similar inputs.
Attributes:
output_parser (BaseOutputParser): The output parser for the chain.
Example:
>>> from langchain.chat_models import ChatOpenAI
>>> from langchain.evaluation.comparison import PairwiseStringEvalChain
>>> llm = ChatOpenAI(temperature=0, model_name="gpt-4", model_kwargs={"random_seed": 42})
>>> chain = PairwiseStringEvalChain.from_llm(llm=llm)
>>> result = chain.evaluate_string_pairs(
... input = "What is the chemical formula for water?",
... prediction = "H2O",
... prediction_b = (
... "The chemical formula for water is H2O, which means"
... " there are two hydrogen atoms and one oxygen atom."
... reference = "The chemical formula for water is H2O.",
... )
>>> print(result)
# {
# "value": "B",
# "comment": "Both responses accurately state"
# " that the chemical formula for water is H2O."
# " However, Response B provides additional information"
# . " by explaining what the formula means.\\n[[B]]"
# }
""" # noqa: E501
output_key: str = "results" #: :meta private:
output_parser: BaseOutputParser = Field(
default_factory=PairwiseStringResultOutputParser
)
@classmethod
def is_lc_serializable(cls) -> bool:
return False
class Config:
"""Configuration for the PairwiseStringEvalChain."""
extra = Extra.ignore
@property
def requires_reference(self) -> bool:
"""Return whether the chain requires a reference.
Returns:
bool: True if the chain requires a reference, False otherwise.
"""
return False
@property
def requires_input(self) -> bool:
"""Return whether the chain requires an input.
Returns:
bool: True if the chain requires an input, False otherwise.
"""
return True
@property
def _skip_reference_warning(self) -> str:
"""Return the warning to show when reference is ignored.
Returns:
str: The warning to show when reference is ignored.
"""
return (
f"Ignoring reference in {self.__class__.__name__}, as it is not expected."
"\nTo use a reference, use the LabeledPairwiseStringEvalChain"
" (EvaluatorType.LABELED_PAIRWISE_STRING) instead."
)
@classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
*,
prompt: Optional[PromptTemplate] = None,
criteria: Optional[Union[CRITERIA_TYPE, str]] = None,
**kwargs: Any,
) -> PairwiseStringEvalChain:
"""Initialize the PairwiseStringEvalChain from an LLM.
Args:
llm (BaseChatModel): The LLM to use (GPT-4 recommended).
prompt (PromptTemplate, optional): The prompt to use.
**kwargs (Any): Additional keyword arguments.
Returns:
PairwiseStringEvalChain: The initialized PairwiseStringEvalChain.
Raises:
ValueError: If the input variables are not as expected.
"""
if not (
isinstance(llm, (ChatOpenAI, AzureChatOpenAI))
and llm.model_name.startswith("gpt-4")
):
logger.warning(
"This chain was only tested with GPT-4. \
Performance may be significantly worse with other models."
)
expected_input_vars = {"prediction", "prediction_b", "input", "criteria"}
prompt_ = prompt or COMPARISON_TEMPLATE.partial(reference="")
if expected_input_vars != set(prompt_.input_variables):
raise ValueError(
f"Input variables should be {expected_input_vars}, "
f"but got {prompt_.input_variables}"
)
criteria_ = resolve_pairwise_criteria(criteria)
criteria_str = "\n".join(f"{k}: {v}" if v else k for k, v in criteria_.items())
criteria_str = CRITERIA_INSTRUCTIONS + criteria_str if criteria_str else ""
return cls(llm=llm, prompt=prompt_.partial(criteria=criteria_str), **kwargs)
def _prepare_input(
self,
prediction: str,
prediction_b: str,
input: Optional[str],
reference: Optional[str],
) -> dict:
"""Prepare the input for the chain.
Args:
prediction (str): The output string from the first model.
prediction_b (str): The output string from the second model.
input (str, optional): The input or task string.
reference (str, optional): The reference string, if any.
Returns:
dict: The prepared input for the chain.
"""
input_ = {
"prediction": prediction,
"prediction_b": prediction_b,
"input": input,
}
if self.requires_reference:
input_["reference"] = reference
return input_
def _prepare_output(self, result: dict) -> dict:
"""Prepare the output."""
parsed = result[self.output_key]
if RUN_KEY in result:
parsed[RUN_KEY] = result[RUN_KEY]
return parsed
def _evaluate_string_pairs(
self,
*,
prediction: str,
prediction_b: str,
input: Optional[str] = None,
reference: Optional[str] = None,
callbacks: Callbacks = None,
tags: Optional[List[str]] = None,
metadata: Optional[Dict[str, Any]] = None,
include_run_info: bool = False,
**kwargs: Any,
) -> dict:
"""Evaluate whether output A is preferred to output B.
Args:
prediction (str): The output string from the first model.
prediction_b (str): The output string from the second model.
input (str, optional): The input or task string.
callbacks (Callbacks, optional): The callbacks to use.
reference (str, optional): The reference string, if any.
**kwargs (Any): Additional keyword arguments.
Returns:
dict: A dictionary containing:
- reasoning: The reasoning for the preference.
- value: The preference value, which is either 'A', 'B', or None
for no preference.
- score: The preference score, which is 1 for 'A', 0 for 'B',
and 0.5 for None.
"""
input_ = self._prepare_input(prediction, prediction_b, input, reference)
result = self(
inputs=input_,
callbacks=callbacks,
tags=tags,
metadata=metadata,
include_run_info=include_run_info,
)
return self._prepare_output(result)
async def _aevaluate_string_pairs(
self,
*,
prediction: str,
prediction_b: str,
reference: Optional[str] = None,
input: Optional[str] = None,
callbacks: Callbacks = None,
tags: Optional[List[str]] = None,
metadata: Optional[Dict[str, Any]] = None,
include_run_info: bool = False,
**kwargs: Any,
) -> dict:
"""Asynchronously evaluate whether output A is preferred to output B.
Args:
prediction (str): The output string from the first model.
prediction_b (str): The output string from the second model.
input (str, optional): The input or task string.
callbacks (Callbacks, optional): The callbacks to use.
reference (str, optional): The reference string, if any.
**kwargs (Any): Additional keyword arguments.
Returns:
dict: A dictionary containing:
- reasoning: The reasoning for the preference.
- value: The preference value, which is either 'A', 'B', or None
for no preference.
- score: The preference score, which is 1 for 'A', 0 for 'B',
and 0.5 for None.
"""
input_ = self._prepare_input(prediction, prediction_b, input, reference)
result = await self.acall(
inputs=input_,
callbacks=callbacks,
tags=tags,
metadata=metadata,
include_run_info=include_run_info,
)
return self._prepare_output(result)
class LabeledPairwiseStringEvalChain(PairwiseStringEvalChain):
"""A chain for comparing two outputs, such as the outputs
of two models, prompts, or outputs of a single model on similar inputs,
with labeled preferences.
Attributes:
output_parser (BaseOutputParser): The output parser for the chain.
"""
@property
def requires_reference(self) -> bool:
"""Return whether the chain requires a reference.
Returns:
bool: True if the chain requires a reference, False otherwise.
"""
return True
@classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
*,
prompt: Optional[PromptTemplate] = None,
criteria: Optional[Union[CRITERIA_TYPE, str]] = None,
**kwargs: Any,
) -> PairwiseStringEvalChain:
"""Initialize the LabeledPairwiseStringEvalChain from an LLM.
Args:
llm (BaseLanguageModel): The LLM to use.
prompt (PromptTemplate, optional): The prompt to use.
criteria (Union[CRITERIA_TYPE, str], optional): The criteria to use.
**kwargs (Any): Additional keyword arguments.
Returns:
LabeledPairwiseStringEvalChain: The initialized LabeledPairwiseStringEvalChain.
Raises:
ValueError: If the input variables are not as expected.
""" # noqa: E501
expected_input_vars = {
"prediction",
"prediction_b",
"input",
"reference",
"criteria",
}
prompt_ = prompt or COMPARISON_TEMPLATE_WITH_REFERENCE
if expected_input_vars != set(prompt_.input_variables):
raise ValueError(
f"Input variables should be {expected_input_vars}, "
f"but got {prompt_.input_variables}"
)
criteria_ = resolve_pairwise_criteria(criteria)
criteria_str = "\n".join(f"{k}: {v}" for k, v in criteria_.items())
criteria_str = CRITERIA_INSTRUCTIONS + criteria_str if criteria_str else ""
return cls(llm=llm, prompt=prompt_.partial(criteria=criteria_str), **kwargs)
| [] |
2024-01-10 | mth93/langchain | libs~community~tests~integration_tests~llms~test_anthropic.py | """Test Anthropic API wrapper."""
from typing import Generator
import pytest
from libs.core.langchain_core.callbacks import CallbackManager
from libs.core.langchain_core.outputs import LLMResult
from langchain_community.llms.anthropic import Anthropic
from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
@pytest.mark.requires("anthropic")
def test_anthropic_model_name_param() -> None:
llm = Anthropic(model_name="foo")
assert llm.model == "foo"
@pytest.mark.requires("anthropic")
def test_anthropic_model_param() -> None:
llm = Anthropic(model="foo")
assert llm.model == "foo"
def test_anthropic_call() -> None:
"""Test valid call to anthropic."""
llm = Anthropic(model="claude-instant-1")
output = llm("Say foo:")
assert isinstance(output, str)
def test_anthropic_streaming() -> None:
"""Test streaming tokens from anthropic."""
llm = Anthropic(model="claude-instant-1")
generator = llm.stream("I'm Pickle Rick")
assert isinstance(generator, Generator)
for token in generator:
assert isinstance(token, str)
def test_anthropic_streaming_callback() -> None:
"""Test that streaming correctly invokes on_llm_new_token callback."""
callback_handler = FakeCallbackHandler()
callback_manager = CallbackManager([callback_handler])
llm = Anthropic(
streaming=True,
callback_manager=callback_manager,
verbose=True,
)
llm("Write me a sentence with 100 words.")
assert callback_handler.llm_streams > 1
async def test_anthropic_async_generate() -> None:
"""Test async generate."""
llm = Anthropic()
output = await llm.agenerate(["How many toes do dogs have?"])
assert isinstance(output, LLMResult)
async def test_anthropic_async_streaming_callback() -> None:
"""Test that streaming correctly invokes on_llm_new_token callback."""
callback_handler = FakeCallbackHandler()
callback_manager = CallbackManager([callback_handler])
llm = Anthropic(
streaming=True,
callback_manager=callback_manager,
verbose=True,
)
result = await llm.agenerate(["How many toes do dogs have?"])
assert callback_handler.llm_streams > 1
assert isinstance(result, LLMResult)
| [] |
2024-01-10 | mth93/langchain | libs~community~tests~integration_tests~vectorstores~test_vearch.py | # flake8: noqa
from libs.core.langchain_core.documents import Document
from langchain_community.vectorstores.vearch import Vearch
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
def test_vearch() -> None:
"""
Test end to end create vearch ,store vector into it and search
"""
texts = [
"Vearch 是一款存储大语言模型数据的向量数据库，用于存储和快速搜索模型embedding后的向量，可用于基于个人知识库的大模型应用",
"Vearch 支持OpenAI, Llama, ChatGLM等模型，以及LangChain库",
"vearch 是基于C语言,go语言开发的，并提供python接口，可以直接通过pip安装",
]
metadatas = [
{
"source": (
"/data/zhx/zhx/langchain-ChatGLM_new/knowledge_base/santi/three_body.txt"
)
},
{
"source": (
"/data/zhx/zhx/langchain-ChatGLM_new/knowledge_base/santi/three_body.txt"
)
},
{
"source": (
"/data/zhx/zhx/langchain-ChatGLM_new/knowledge_base/santi/three_body.txt"
)
},
]
vearch_db = Vearch.from_texts(
texts=texts,
embedding=FakeEmbeddings(),
metadatas=metadatas,
table_name="test_vearch",
metadata_path="./",
)
result = vearch_db.similarity_search(
"Vearch 支持OpenAI, Llama, ChatGLM等模型，以及LangChain库", 1
)
assert result == [
Document(
page_content="Vearch 支持OpenAI, Llama, ChatGLM等模型，以及LangChain库",
metadata={
"source": (
"/data/zhx/zhx/langchain-ChatGLM_new/knowledge_base/santi/"
"three_body.txt"
)
},
)
]
def test_vearch_add_texts() -> None:
"""Test end to end adding of texts."""
texts = [
(
"Vearch 是一款存储大语言模型数据的向量数据库，用于存储和快速搜索模型embedding后的向量，"
"可用于基于个人知识库的大模型应用"
),
"Vearch 支持OpenAI, Llama, ChatGLM等模型，以及LangChain库",
"vearch 是基于C语言,go语言开发的，并提供python接口，可以直接通过pip安装",
]
metadatas = [
{
"source": "/data/zhx/zhx/langchain-ChatGLM_new/knowledge_base/santi/"
"three_body.txt"
},
{
"source": "/data/zhx/zhx/langchain-ChatGLM_new/knowledge_base/santi/"
"three_body.txt"
},
{
"source": "/data/zhx/zhx/langchain-ChatGLM_new/knowledge_base/santi/"
"three_body.txt"
},
]
vearch_db = Vearch.from_texts(
texts=texts,
embedding=FakeEmbeddings(),
metadatas=metadatas,
table_name="test_vearch",
metadata_path="./",
)
vearch_db.add_texts(
texts=["Vearch 支持OpenAI, Llama, ChatGLM等模型，以及LangChain库"],
metadatas=[
{
"source": "/data/zhx/zhx/langchain-ChatGLM_new/knowledge_base/santi/three_body.txt"
},
],
)
result = vearch_db.similarity_search(
"Vearch 支持OpenAI, Llama, ChatGLM等模型，以及LangChain库", 2
)
assert result == [
Document(
page_content="Vearch 支持OpenAI, Llama, ChatGLM等模型，以及LangChain库",
metadata={
"source": "/data/zhx/zhx/langchain-ChatGLM_new/knowledge_base/santi/three_body.txt"
},
),
Document(
page_content="Vearch 支持OpenAI, Llama, ChatGLM等模型，以及LangChain库",
metadata={
"source": "/data/zhx/zhx/langchain-ChatGLM_new/knowledge_base/santi/three_body.txt"
},
),
]
| [] |
2024-01-10 | mth93/langchain | libs~langchain~langchain~chains~sequential.py | """Chain pipeline where the outputs of one step feed directly into next."""
from typing import Any, Dict, List, Optional
from libs.core.langchain_core.pydantic_v1 import Extra, root_validator
from libs.core.langchain_core.utils.input import get_color_mapping
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
from langchain.chains.base import Chain
class SequentialChain(Chain):
"""Chain where the outputs of one chain feed directly into next."""
chains: List[Chain]
input_variables: List[str]
output_variables: List[str] #: :meta private:
return_all: bool = False
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[str]:
"""Return expected input keys to the chain.
:meta private:
"""
return self.input_variables
@property
def output_keys(self) -> List[str]:
"""Return output key.
:meta private:
"""
return self.output_variables
@root_validator(pre=True)
def validate_chains(cls, values: Dict) -> Dict:
"""Validate that the correct inputs exist for all chains."""
chains = values["chains"]
input_variables = values["input_variables"]
memory_keys = list()
if "memory" in values and values["memory"] is not None:
"""Validate that prompt input variables are consistent."""
memory_keys = values["memory"].memory_variables
if set(input_variables).intersection(set(memory_keys)):
overlapping_keys = set(input_variables) & set(memory_keys)
raise ValueError(
f"The input key(s) {''.join(overlapping_keys)} are found "
f"in the Memory keys ({memory_keys}) - please use input and "
f"memory keys that don't overlap."
)
known_variables = set(input_variables + memory_keys)
for chain in chains:
missing_vars = set(chain.input_keys).difference(known_variables)
if chain.memory:
missing_vars = missing_vars.difference(chain.memory.memory_variables)
if missing_vars:
raise ValueError(
f"Missing required input keys: {missing_vars}, "
f"only had {known_variables}"
)
overlapping_keys = known_variables.intersection(chain.output_keys)
if overlapping_keys:
raise ValueError(
f"Chain returned keys that already exist: {overlapping_keys}"
)
known_variables |= set(chain.output_keys)
if "output_variables" not in values:
if values.get("return_all", False):
output_keys = known_variables.difference(input_variables)
else:
output_keys = chains[-1].output_keys
values["output_variables"] = output_keys
else:
missing_vars = set(values["output_variables"]).difference(known_variables)
if missing_vars:
raise ValueError(
f"Expected output variables that were not found: {missing_vars}."
)
return values
def _call(
self,
inputs: Dict[str, str],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
known_values = inputs.copy()
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
for i, chain in enumerate(self.chains):
callbacks = _run_manager.get_child()
outputs = chain(known_values, return_only_outputs=True, callbacks=callbacks)
known_values.update(outputs)
return {k: known_values[k] for k in self.output_variables}
async def _acall(
self,
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
known_values = inputs.copy()
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
for i, chain in enumerate(self.chains):
outputs = await chain.acall(
known_values, return_only_outputs=True, callbacks=callbacks
)
known_values.update(outputs)
return {k: known_values[k] for k in self.output_variables}
class SimpleSequentialChain(Chain):
"""Simple chain where the outputs of one step feed directly into next."""
chains: List[Chain]
strip_outputs: bool = False
input_key: str = "input" #: :meta private:
output_key: str = "output" #: :meta private:
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[str]:
"""Expect input key.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return output key.
:meta private:
"""
return [self.output_key]
@root_validator()
def validate_chains(cls, values: Dict) -> Dict:
"""Validate that chains are all single input/output."""
for chain in values["chains"]:
if len(chain.input_keys) != 1:
raise ValueError(
"Chains used in SimplePipeline should all have one input, got "
f"{chain} with {len(chain.input_keys)} inputs."
)
if len(chain.output_keys) != 1:
raise ValueError(
"Chains used in SimplePipeline should all have one output, got "
f"{chain} with {len(chain.output_keys)} outputs."
)
return values
def _call(
self,
inputs: Dict[str, str],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
_input = inputs[self.input_key]
color_mapping = get_color_mapping([str(i) for i in range(len(self.chains))])
for i, chain in enumerate(self.chains):
_input = chain.run(_input, callbacks=_run_manager.get_child(f"step_{i+1}"))
if self.strip_outputs:
_input = _input.strip()
_run_manager.on_text(
_input, color=color_mapping[str(i)], end="\n", verbose=self.verbose
)
return {self.output_key: _input}
async def _acall(
self,
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
_input = inputs[self.input_key]
color_mapping = get_color_mapping([str(i) for i in range(len(self.chains))])
for i, chain in enumerate(self.chains):
_input = await chain.arun(
_input, callbacks=_run_manager.get_child(f"step_{i+1}")
)
if self.strip_outputs:
_input = _input.strip()
await _run_manager.on_text(
_input, color=color_mapping[str(i)], end="\n", verbose=self.verbose
)
return {self.output_key: _input}
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~embeddings~javelin_ai_gateway.py | from __future__ import annotations
from typing import Any, Iterator, List, Optional
from libs.core.langchain_core.embeddings import Embeddings
from libs.core.langchain_core.pydantic_v1 import BaseModel
def _chunk(texts: List[str], size: int) -> Iterator[List[str]]:
for i in range(0, len(texts), size):
yield texts[i : i + size]
class JavelinAIGatewayEmbeddings(Embeddings, BaseModel):
"""
Wrapper around embeddings LLMs in the Javelin AI Gateway.
To use, you should have the ``javelin_sdk`` python package installed.
For more information, see https://docs.getjavelin.io
Example:
.. code-block:: python
from langchain_community.embeddings import JavelinAIGatewayEmbeddings
embeddings = JavelinAIGatewayEmbeddings(
gateway_uri="<javelin-ai-gateway-uri>",
route="<your-javelin-gateway-embeddings-route>"
)
"""
client: Any
"""javelin client."""
route: str
"""The route to use for the Javelin AI Gateway API."""
gateway_uri: Optional[str] = None
"""The URI for the Javelin AI Gateway API."""
javelin_api_key: Optional[str] = None
"""The API key for the Javelin AI Gateway API."""
def __init__(self, **kwargs: Any):
try:
from javelin_sdk import (
JavelinClient,
UnauthorizedError,
)
except ImportError:
raise ImportError(
"Could not import javelin_sdk python package. "
"Please install it with `pip install javelin_sdk`."
)
super().__init__(**kwargs)
if self.gateway_uri:
try:
self.client = JavelinClient(
base_url=self.gateway_uri, api_key=self.javelin_api_key
)
except UnauthorizedError as e:
raise ValueError("Javelin: Incorrect API Key.") from e
def _query(self, texts: List[str]) -> List[List[float]]:
embeddings = []
for txt in _chunk(texts, 20):
try:
resp = self.client.query_route(self.route, query_body={"input": txt})
resp_dict = resp.dict()
embeddings_chunk = resp_dict.get("llm_response", {}).get("data", [])
for item in embeddings_chunk:
if "embedding" in item:
embeddings.append(item["embedding"])
except ValueError as e:
print("Failed to query route: " + str(e))
return embeddings
async def _aquery(self, texts: List[str]) -> List[List[float]]:
embeddings = []
for txt in _chunk(texts, 20):
try:
resp = await self.client.aquery_route(
self.route, query_body={"input": txt}
)
resp_dict = resp.dict()
embeddings_chunk = resp_dict.get("llm_response", {}).get("data", [])
for item in embeddings_chunk:
if "embedding" in item:
embeddings.append(item["embedding"])
except ValueError as e:
print("Failed to query route: " + str(e))
return embeddings
def embed_documents(self, texts: List[str]) -> List[List[float]]:
return self._query(texts)
def embed_query(self, text: str) -> List[float]:
return self._query([text])[0]
async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
return await self._aquery(texts)
async def aembed_query(self, text: str) -> List[float]:
result = await self._aquery([text])
return result[0]
| [] |
2024-01-10 | mth93/langchain | libs~langchain~tests~unit_tests~chains~test_history_aware_retriever.py | from libs.core.langchain_core.documents import Document
from libs.core.langchain_core.prompts import PromptTemplate
from langchain.chains import create_history_aware_retriever
from langchain.llms.fake import FakeListLLM
from tests.unit_tests.retrievers.parrot_retriever import FakeParrotRetriever
def test_create() -> None:
answer = "I know the answer!"
llm = FakeListLLM(responses=[answer])
retriever = FakeParrotRetriever()
question_gen_prompt = PromptTemplate.from_template("hi! {input} {chat_history}")
chain = create_history_aware_retriever(llm, retriever, question_gen_prompt)
expected_output = [Document(page_content="What is the answer?")]
output = chain.invoke({"input": "What is the answer?", "chat_history": []})
assert output == expected_output
output = chain.invoke({"input": "What is the answer?"})
assert output == expected_output
expected_output = [Document(page_content="I know the answer!")]
output = chain.invoke(
{"input": "What is the answer?", "chat_history": ["hi", "hi"]}
)
assert output == expected_output
| [
"hi! {input} {chat_history}"
] |
2024-01-10 | mth93/langchain | libs~langchain~langchain~retrievers~document_compressors~chain_extract.py | """DocumentFilter that uses an LLM chain to extract the relevant parts of documents."""
from __future__ import annotations
import asyncio
from typing import Any, Callable, Dict, Optional, Sequence
from libs.core.langchain_core.documents import Document
from libs.core.langchain_core.language_models import BaseLanguageModel
from libs.core.langchain_core.output_parsers import BaseOutputParser
from libs.core.langchain_core.prompts import PromptTemplate
from langchain.callbacks.manager import Callbacks
from langchain.chains.llm import LLMChain
from langchain.retrievers.document_compressors.base import BaseDocumentCompressor
from langchain.retrievers.document_compressors.chain_extract_prompt import (
prompt_template,
)
def default_get_input(query: str, doc: Document) -> Dict[str, Any]:
"""Return the compression chain input."""
return {"question": query, "context": doc.page_content}
class NoOutputParser(BaseOutputParser[str]):
"""Parse outputs that could return a null string of some sort."""
no_output_str: str = "NO_OUTPUT"
def parse(self, text: str) -> str:
cleaned_text = text.strip()
if cleaned_text == self.no_output_str:
return ""
return cleaned_text
def _get_default_chain_prompt() -> PromptTemplate:
output_parser = NoOutputParser()
template = prompt_template.format(no_output_str=output_parser.no_output_str)
return PromptTemplate(
template=template,
input_variables=["question", "context"],
output_parser=output_parser,
)
class LLMChainExtractor(BaseDocumentCompressor):
"""Document compressor that uses an LLM chain to extract
the relevant parts of documents."""
llm_chain: LLMChain
"""LLM wrapper to use for compressing documents."""
get_input: Callable[[str, Document], dict] = default_get_input
"""Callable for constructing the chain input from the query and a Document."""
def compress_documents(
self,
documents: Sequence[Document],
query: str,
callbacks: Optional[Callbacks] = None,
) -> Sequence[Document]:
"""Compress page content of raw documents."""
compressed_docs = []
for doc in documents:
_input = self.get_input(query, doc)
output = self.llm_chain.predict_and_parse(**_input, callbacks=callbacks)
if len(output) == 0:
continue
compressed_docs.append(Document(page_content=output, metadata=doc.metadata))
return compressed_docs
async def acompress_documents(
self,
documents: Sequence[Document],
query: str,
callbacks: Optional[Callbacks] = None,
) -> Sequence[Document]:
"""Compress page content of raw documents asynchronously."""
outputs = await asyncio.gather(
*[
self.llm_chain.apredict_and_parse(
**self.get_input(query, doc), callbacks=callbacks
)
for doc in documents
]
)
compressed_docs = []
for i, doc in enumerate(documents):
if len(outputs[i]) == 0:
continue
compressed_docs.append(
Document(page_content=outputs[i], metadata=doc.metadata)
)
return compressed_docs
@classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
prompt: Optional[PromptTemplate] = None,
get_input: Optional[Callable[[str, Document], str]] = None,
llm_chain_kwargs: Optional[dict] = None,
) -> LLMChainExtractor:
"""Initialize from LLM."""
_prompt = prompt if prompt is not None else _get_default_chain_prompt()
_get_input = get_input if get_input is not None else default_get_input
llm_chain = LLMChain(llm=llm, prompt=_prompt, **(llm_chain_kwargs or {}))
return cls(llm_chain=llm_chain, get_input=_get_input)
| [] |
2024-01-10 | mth93/langchain | libs~langchain~tests~integration_tests~memory~test_firestore.py | import json
from libs.core.langchain_core.messages import message_to_dict
from langchain.memory import ConversationBufferMemory
from langchain.memory.chat_message_histories import FirestoreChatMessageHistory
def test_memory_with_message_store() -> None:
"""Test the memory with a message store."""
message_history = FirestoreChatMessageHistory(
collection_name="chat_history",
session_id="my-test-session",
user_id="my-test-user",
)
memory = ConversationBufferMemory(
memory_key="baz", chat_memory=message_history, return_messages=True
)
# add some messages
memory.chat_memory.add_ai_message("This is me, the AI")
memory.chat_memory.add_user_message("This is me, the human")
# get the message history from the memory store
# and check if the messages are there as expected
message_history = FirestoreChatMessageHistory(
collection_name="chat_history",
session_id="my-test-session",
user_id="my-test-user",
)
memory = ConversationBufferMemory(
memory_key="baz", chat_memory=message_history, return_messages=True
)
messages = memory.chat_memory.messages
messages_json = json.dumps([message_to_dict(msg) for msg in messages])
assert "This is me, the AI" in messages_json
assert "This is me, the human" in messages_json
# remove the record from Firestore, so the next test run won't pick it up
memory.chat_memory.clear()
assert memory.chat_memory.messages == []
| [] |
2024-01-10 | mth93/langchain | libs~community~langchain_community~callbacks~mlflow_callback.py | import os
import random
import string
import tempfile
import traceback
from copy import deepcopy
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
from libs.core.langchain_core.agents import AgentAction, AgentFinish
from libs.core.langchain_core.callbacks import BaseCallbackHandler
from libs.core.langchain_core.outputs import LLMResult
from libs.core.langchain_core.utils import get_from_dict_or_env
from langchain_community.callbacks.utils import (
BaseMetadataCallbackHandler,
flatten_dict,
hash_string,
import_pandas,
import_spacy,
import_textstat,
)
def import_mlflow() -> Any:
"""Import the mlflow python package and raise an error if it is not installed."""
try:
import mlflow
except ImportError:
raise ImportError(
"To use the mlflow callback manager you need to have the `mlflow` python "
"package installed. Please install it with `pip install mlflow>=2.3.0`"
)
return mlflow
def analyze_text(
text: str,
nlp: Any = None,
) -> dict:
"""Analyze text using textstat and spacy.
Parameters:
text (str): The text to analyze.
nlp (spacy.lang): The spacy language model to use for visualization.
Returns:
(dict): A dictionary containing the complexity metrics and visualization
files serialized to HTML string.
"""
resp: Dict[str, Any] = {}
textstat = import_textstat()
spacy = import_spacy()
text_complexity_metrics = {
"flesch_reading_ease": textstat.flesch_reading_ease(text),
"flesch_kincaid_grade": textstat.flesch_kincaid_grade(text),
"smog_index": textstat.smog_index(text),
"coleman_liau_index": textstat.coleman_liau_index(text),
"automated_readability_index": textstat.automated_readability_index(text),
"dale_chall_readability_score": textstat.dale_chall_readability_score(text),
"difficult_words": textstat.difficult_words(text),
"linsear_write_formula": textstat.linsear_write_formula(text),
"gunning_fog": textstat.gunning_fog(text),
# "text_standard": textstat.text_standard(text),
"fernandez_huerta": textstat.fernandez_huerta(text),
"szigriszt_pazos": textstat.szigriszt_pazos(text),
"gutierrez_polini": textstat.gutierrez_polini(text),
"crawford": textstat.crawford(text),
"gulpease_index": textstat.gulpease_index(text),
"osman": textstat.osman(text),
}
resp.update({"text_complexity_metrics": text_complexity_metrics})
resp.update(text_complexity_metrics)
if nlp is not None:
doc = nlp(text)
dep_out = spacy.displacy.render( # type: ignore
doc, style="dep", jupyter=False, page=True
)
ent_out = spacy.displacy.render( # type: ignore
doc, style="ent", jupyter=False, page=True
)
text_visualizations = {
"dependency_tree": dep_out,
"entities": ent_out,
}
resp.update(text_visualizations)
return resp
def construct_html_from_prompt_and_generation(prompt: str, generation: str) -> Any:
"""Construct an html element from a prompt and a generation.
Parameters:
prompt (str): The prompt.
generation (str): The generation.
Returns:
(str): The html string."""
formatted_prompt = prompt.replace("\n", "<br>")
formatted_generation = generation.replace("\n", "<br>")
return f"""
<p style="color:black;">{formatted_prompt}:</p>
<blockquote>
<p style="color:green;">
{formatted_generation}
</p>
</blockquote>
"""
class MlflowLogger:
"""Callback Handler that logs metrics and artifacts to mlflow server.
Parameters:
name (str): Name of the run.
experiment (str): Name of the experiment.
tags (dict): Tags to be attached for the run.
tracking_uri (str): MLflow tracking server uri.
This handler implements the helper functions to initialize,
log metrics and artifacts to the mlflow server.
"""
def __init__(self, **kwargs: Any):
self.mlflow = import_mlflow()
if "DATABRICKS_RUNTIME_VERSION" in os.environ:
self.mlflow.set_tracking_uri("databricks")
self.mlf_expid = self.mlflow.tracking.fluent._get_experiment_id()
self.mlf_exp = self.mlflow.get_experiment(self.mlf_expid)
else:
tracking_uri = get_from_dict_or_env(
kwargs, "tracking_uri", "MLFLOW_TRACKING_URI", ""
)
self.mlflow.set_tracking_uri(tracking_uri)
# User can set other env variables described here
# > https://www.mlflow.org/docs/latest/tracking.html#logging-to-a-tracking-server
experiment_name = get_from_dict_or_env(
kwargs, "experiment_name", "MLFLOW_EXPERIMENT_NAME"
)
self.mlf_exp = self.mlflow.get_experiment_by_name(experiment_name)
if self.mlf_exp is not None:
self.mlf_expid = self.mlf_exp.experiment_id
else:
self.mlf_expid = self.mlflow.create_experiment(experiment_name)
self.start_run(kwargs["run_name"], kwargs["run_tags"])
def start_run(self, name: str, tags: Dict[str, str]) -> None:
"""To start a new run, auto generates the random suffix for name"""
if name.endswith("-%"):
rname = "".join(random.choices(string.ascii_uppercase + string.digits, k=7))
name = name.replace("%", rname)
self.run = self.mlflow.MlflowClient().create_run(
self.mlf_expid, run_name=name, tags=tags
)
def finish_run(self) -> None:
"""To finish the run."""
with self.mlflow.start_run(
run_id=self.run.info.run_id, experiment_id=self.mlf_expid
):
self.mlflow.end_run()
def metric(self, key: str, value: float) -> None:
"""To log metric to mlflow server."""
with self.mlflow.start_run(
run_id=self.run.info.run_id, experiment_id=self.mlf_expid
):
self.mlflow.log_metric(key, value)
def metrics(
self, data: Union[Dict[str, float], Dict[str, int]], step: Optional[int] = 0
) -> None:
"""To log all metrics in the input dict."""
with self.mlflow.start_run(
run_id=self.run.info.run_id, experiment_id=self.mlf_expid
):
self.mlflow.log_metrics(data)
def jsonf(self, data: Dict[str, Any], filename: str) -> None:
"""To log the input data as json file artifact."""
with self.mlflow.start_run(
run_id=self.run.info.run_id, experiment_id=self.mlf_expid
):
self.mlflow.log_dict(data, f"{filename}.json")
def table(self, name: str, dataframe) -> None: # type: ignore
"""To log the input pandas dataframe as a html table"""
self.html(dataframe.to_html(), f"table_{name}")
def html(self, html: str, filename: str) -> None:
"""To log the input html string as html file artifact."""
with self.mlflow.start_run(
run_id=self.run.info.run_id, experiment_id=self.mlf_expid
):
self.mlflow.log_text(html, f"{filename}.html")
def text(self, text: str, filename: str) -> None:
"""To log the input text as text file artifact."""
with self.mlflow.start_run(
run_id=self.run.info.run_id, experiment_id=self.mlf_expid
):
self.mlflow.log_text(text, f"{filename}.txt")
def artifact(self, path: str) -> None:
"""To upload the file from given path as artifact."""
with self.mlflow.start_run(
run_id=self.run.info.run_id, experiment_id=self.mlf_expid
):
self.mlflow.log_artifact(path)
def langchain_artifact(self, chain: Any) -> None:
with self.mlflow.start_run(
run_id=self.run.info.run_id, experiment_id=self.mlf_expid
):
self.mlflow.langchain.log_model(chain, "langchain-model")
class MlflowCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
"""Callback Handler that logs metrics and artifacts to mlflow server.
Parameters:
name (str): Name of the run.
experiment (str): Name of the experiment.
tags (dict): Tags to be attached for the run.
tracking_uri (str): MLflow tracking server uri.
This handler will utilize the associated callback method called and formats
the input of each callback function with metadata regarding the state of LLM run,
and adds the response to the list of records for both the {method}_records and
action. It then logs the response to mlflow server.
"""
def __init__(
self,
name: Optional[str] = "langchainrun-%",
experiment: Optional[str] = "langchain",
tags: Optional[Dict] = None,
tracking_uri: Optional[str] = None,
) -> None:
"""Initialize callback handler."""
import_pandas()
import_textstat()
import_mlflow()
spacy = import_spacy()
super().__init__()
self.name = name
self.experiment = experiment
self.tags = tags or {}
self.tracking_uri = tracking_uri
self.temp_dir = tempfile.TemporaryDirectory()
self.mlflg = MlflowLogger(
tracking_uri=self.tracking_uri,
experiment_name=self.experiment,
run_name=self.name,
run_tags=self.tags,
)
self.action_records: list = []
self.nlp = spacy.load("en_core_web_sm")
self.metrics = {
"step": 0,
"starts": 0,
"ends": 0,
"errors": 0,
"text_ctr": 0,
"chain_starts": 0,
"chain_ends": 0,
"llm_starts": 0,
"llm_ends": 0,
"llm_streams": 0,
"tool_starts": 0,
"tool_ends": 0,
"agent_ends": 0,
}
self.records: Dict[str, Any] = {
"on_llm_start_records": [],
"on_llm_token_records": [],
"on_llm_end_records": [],
"on_chain_start_records": [],
"on_chain_end_records": [],
"on_tool_start_records": [],
"on_tool_end_records": [],
"on_text_records": [],
"on_agent_finish_records": [],
"on_agent_action_records": [],
"action_records": [],
}
def _reset(self) -> None:
for k, v in self.metrics.items():
self.metrics[k] = 0
for k, v in self.records.items():
self.records[k] = []
def on_llm_start(
self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> None:
"""Run when LLM starts."""
self.metrics["step"] += 1
self.metrics["llm_starts"] += 1
self.metrics["starts"] += 1
llm_starts = self.metrics["llm_starts"]
resp: Dict[str, Any] = {}
resp.update({"action": "on_llm_start"})
resp.update(flatten_dict(serialized))
resp.update(self.metrics)
self.mlflg.metrics(self.metrics, step=self.metrics["step"])
for idx, prompt in enumerate(prompts):
prompt_resp = deepcopy(resp)
prompt_resp["prompt"] = prompt
self.records["on_llm_start_records"].append(prompt_resp)
self.records["action_records"].append(prompt_resp)
self.mlflg.jsonf(prompt_resp, f"llm_start_{llm_starts}_prompt_{idx}")
def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
"""Run when LLM generates a new token."""
self.metrics["step"] += 1
self.metrics["llm_streams"] += 1
llm_streams = self.metrics["llm_streams"]
resp: Dict[str, Any] = {}
resp.update({"action": "on_llm_new_token", "token": token})
resp.update(self.metrics)
self.mlflg.metrics(self.metrics, step=self.metrics["step"])
self.records["on_llm_token_records"].append(resp)
self.records["action_records"].append(resp)
self.mlflg.jsonf(resp, f"llm_new_tokens_{llm_streams}")
def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
"""Run when LLM ends running."""
self.metrics["step"] += 1
self.metrics["llm_ends"] += 1
self.metrics["ends"] += 1
llm_ends = self.metrics["llm_ends"]
resp: Dict[str, Any] = {}
resp.update({"action": "on_llm_end"})
resp.update(flatten_dict(response.llm_output or {}))
resp.update(self.metrics)
self.mlflg.metrics(self.metrics, step=self.metrics["step"])
for generations in response.generations:
for idx, generation in enumerate(generations):
generation_resp = deepcopy(resp)
generation_resp.update(flatten_dict(generation.dict()))
generation_resp.update(
analyze_text(
generation.text,
nlp=self.nlp,
)
)
complexity_metrics: Dict[str, float] = generation_resp.pop(
"text_complexity_metrics"
) # type: ignore # noqa: E501
self.mlflg.metrics(
complexity_metrics,
step=self.metrics["step"],
)
self.records["on_llm_end_records"].append(generation_resp)
self.records["action_records"].append(generation_resp)
self.mlflg.jsonf(resp, f"llm_end_{llm_ends}_generation_{idx}")
dependency_tree = generation_resp["dependency_tree"]
entities = generation_resp["entities"]
self.mlflg.html(dependency_tree, "dep-" + hash_string(generation.text))
self.mlflg.html(entities, "ent-" + hash_string(generation.text))
def on_llm_error(self, error: BaseException, **kwargs: Any) -> None:
"""Run when LLM errors."""
self.metrics["step"] += 1
self.metrics["errors"] += 1
def on_chain_start(
self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
) -> None:
"""Run when chain starts running."""
self.metrics["step"] += 1
self.metrics["chain_starts"] += 1
self.metrics["starts"] += 1
chain_starts = self.metrics["chain_starts"]
resp: Dict[str, Any] = {}
resp.update({"action": "on_chain_start"})
resp.update(flatten_dict(serialized))
resp.update(self.metrics)
self.mlflg.metrics(self.metrics, step=self.metrics["step"])
chain_input = ",".join([f"{k}={v}" for k, v in inputs.items()])
input_resp = deepcopy(resp)
input_resp["inputs"] = chain_input
self.records["on_chain_start_records"].append(input_resp)
self.records["action_records"].append(input_resp)
self.mlflg.jsonf(input_resp, f"chain_start_{chain_starts}")
def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
"""Run when chain ends running."""
self.metrics["step"] += 1
self.metrics["chain_ends"] += 1
self.metrics["ends"] += 1
chain_ends = self.metrics["chain_ends"]
resp: Dict[str, Any] = {}
chain_output = ",".join([f"{k}={v}" for k, v in outputs.items()])
resp.update({"action": "on_chain_end", "outputs": chain_output})
resp.update(self.metrics)
self.mlflg.metrics(self.metrics, step=self.metrics["step"])
self.records["on_chain_end_records"].append(resp)
self.records["action_records"].append(resp)
self.mlflg.jsonf(resp, f"chain_end_{chain_ends}")
def on_chain_error(self, error: BaseException, **kwargs: Any) -> None:
"""Run when chain errors."""
self.metrics["step"] += 1
self.metrics["errors"] += 1
def on_tool_start(
self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
) -> None:
"""Run when tool starts running."""
self.metrics["step"] += 1
self.metrics["tool_starts"] += 1
self.metrics["starts"] += 1
tool_starts = self.metrics["tool_starts"]
resp: Dict[str, Any] = {}
resp.update({"action": "on_tool_start", "input_str": input_str})
resp.update(flatten_dict(serialized))
resp.update(self.metrics)
self.mlflg.metrics(self.metrics, step=self.metrics["step"])
self.records["on_tool_start_records"].append(resp)
self.records["action_records"].append(resp)
self.mlflg.jsonf(resp, f"tool_start_{tool_starts}")
def on_tool_end(self, output: str, **kwargs: Any) -> None:
"""Run when tool ends running."""
self.metrics["step"] += 1
self.metrics["tool_ends"] += 1
self.metrics["ends"] += 1
tool_ends = self.metrics["tool_ends"]
resp: Dict[str, Any] = {}
resp.update({"action": "on_tool_end", "output": output})
resp.update(self.metrics)
self.mlflg.metrics(self.metrics, step=self.metrics["step"])
self.records["on_tool_end_records"].append(resp)
self.records["action_records"].append(resp)
self.mlflg.jsonf(resp, f"tool_end_{tool_ends}")
def on_tool_error(self, error: BaseException, **kwargs: Any) -> None:
"""Run when tool errors."""
self.metrics["step"] += 1
self.metrics["errors"] += 1
def on_text(self, text: str, **kwargs: Any) -> None:
"""
Run when agent is ending.
"""
self.metrics["step"] += 1
self.metrics["text_ctr"] += 1
text_ctr = self.metrics["text_ctr"]
resp: Dict[str, Any] = {}
resp.update({"action": "on_text", "text": text})
resp.update(self.metrics)
self.mlflg.metrics(self.metrics, step=self.metrics["step"])
self.records["on_text_records"].append(resp)
self.records["action_records"].append(resp)
self.mlflg.jsonf(resp, f"on_text_{text_ctr}")
def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:
"""Run when agent ends running."""
self.metrics["step"] += 1
self.metrics["agent_ends"] += 1
self.metrics["ends"] += 1
agent_ends = self.metrics["agent_ends"]
resp: Dict[str, Any] = {}
resp.update(
{
"action": "on_agent_finish",
"output": finish.return_values["output"],
"log": finish.log,
}
)
resp.update(self.metrics)
self.mlflg.metrics(self.metrics, step=self.metrics["step"])
self.records["on_agent_finish_records"].append(resp)
self.records["action_records"].append(resp)
self.mlflg.jsonf(resp, f"agent_finish_{agent_ends}")
def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
"""Run on agent action."""
self.metrics["step"] += 1
self.metrics["tool_starts"] += 1
self.metrics["starts"] += 1
tool_starts = self.metrics["tool_starts"]
resp: Dict[str, Any] = {}
resp.update(
{
"action": "on_agent_action",
"tool": action.tool,
"tool_input": action.tool_input,
"log": action.log,
}
)
resp.update(self.metrics)
self.mlflg.metrics(self.metrics, step=self.metrics["step"])
self.records["on_agent_action_records"].append(resp)
self.records["action_records"].append(resp)
self.mlflg.jsonf(resp, f"agent_action_{tool_starts}")
def _create_session_analysis_df(self) -> Any:
"""Create a dataframe with all the information from the session."""
pd = import_pandas()
on_llm_start_records_df = pd.DataFrame(self.records["on_llm_start_records"])
on_llm_end_records_df = pd.DataFrame(self.records["on_llm_end_records"])
llm_input_columns = ["step", "prompt"]
if "name" in on_llm_start_records_df.columns:
llm_input_columns.append("name")
elif "id" in on_llm_start_records_df.columns:
# id is llm class's full import path. For example:
# ["langchain", "llms", "openai", "AzureOpenAI"]
on_llm_start_records_df["name"] = on_llm_start_records_df["id"].apply(
lambda id_: id_[-1]
)
llm_input_columns.append("name")
llm_input_prompts_df = (
on_llm_start_records_df[llm_input_columns]
.dropna(axis=1)
.rename({"step": "prompt_step"}, axis=1)
)
complexity_metrics_columns = []
visualizations_columns = []
complexity_metrics_columns = [
"flesch_reading_ease",
"flesch_kincaid_grade",
"smog_index",
"coleman_liau_index",
"automated_readability_index",
"dale_chall_readability_score",
"difficult_words",
"linsear_write_formula",
"gunning_fog",
# "text_standard",
"fernandez_huerta",
"szigriszt_pazos",
"gutierrez_polini",
"crawford",
"gulpease_index",
"osman",
]
visualizations_columns = ["dependency_tree", "entities"]
llm_outputs_df = (
on_llm_end_records_df[
[
"step",
"text",
"token_usage_total_tokens",
"token_usage_prompt_tokens",
"token_usage_completion_tokens",
]
+ complexity_metrics_columns
+ visualizations_columns
]
.dropna(axis=1)
.rename({"step": "output_step", "text": "output"}, axis=1)
)
session_analysis_df = pd.concat([llm_input_prompts_df, llm_outputs_df], axis=1)
session_analysis_df["chat_html"] = session_analysis_df[
["prompt", "output"]
].apply(
lambda row: construct_html_from_prompt_and_generation(
row["prompt"], row["output"]
),
axis=1,
)
return session_analysis_df
def flush_tracker(self, langchain_asset: Any = None, finish: bool = False) -> None:
pd = import_pandas()
self.mlflg.table("action_records", pd.DataFrame(self.records["action_records"]))
session_analysis_df = self._create_session_analysis_df()
chat_html = session_analysis_df.pop("chat_html")
chat_html = chat_html.replace("\n", "", regex=True)
self.mlflg.table("session_analysis", pd.DataFrame(session_analysis_df))
self.mlflg.html("".join(chat_html.tolist()), "chat_html")
if langchain_asset:
# To avoid circular import error
# mlflow only supports LLMChain asset
if "langchain.chains.llm.LLMChain" in str(type(langchain_asset)):
self.mlflg.langchain_artifact(langchain_asset)
else:
langchain_asset_path = str(Path(self.temp_dir.name, "model.json"))
try:
langchain_asset.save(langchain_asset_path)
self.mlflg.artifact(langchain_asset_path)
except ValueError:
try:
langchain_asset.save_agent(langchain_asset_path)
self.mlflg.artifact(langchain_asset_path)
except AttributeError:
print("Could not save model.")
traceback.print_exc()
pass
except NotImplementedError:
print("Could not save model.")
traceback.print_exc()
pass
except NotImplementedError:
print("Could not save model.")
traceback.print_exc()
pass
if finish:
self.mlflg.finish_run()
self._reset()
| [
"\n",
"prompt_step"
] |
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