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closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,889 | When inialztion VertexAI() all passed parameters got ignored | ### System Info
langchain=0.0.194
python=3.11.3
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Run:
`VertexAI(project="my_project_name")`
### Expected behavior
The client will connect to the supplied project_id | https://github.com/langchain-ai/langchain/issues/5889 | https://github.com/langchain-ai/langchain/pull/5891 | 63fcf41bea5222f64b1c9a822f08cec9e55aa619 | 0eb1bc1a0245547316fe96ac8f86b0e67acb524f | "2023-06-08T16:06:31Z" | python | "2023-06-09T06:15:22Z" | langchain/llms/vertexai.py | """Wrapper around Google VertexAI models."""
from typing import TYPE_CHECKING, Any, Dict, List, Optional
from pydantic import BaseModel, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utilities.vertexai import (
init_vertexai,
raise_vertex_import_error,
)
if TYPE_CHECKING:
from vertexai.language_models._language_models import _LanguageModel
class _VertexAICommon(BaseModel): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,889 | When inialztion VertexAI() all passed parameters got ignored | ### System Info
langchain=0.0.194
python=3.11.3
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Run:
`VertexAI(project="my_project_name")`
### Expected behavior
The client will connect to the supplied project_id | https://github.com/langchain-ai/langchain/issues/5889 | https://github.com/langchain-ai/langchain/pull/5891 | 63fcf41bea5222f64b1c9a822f08cec9e55aa619 | 0eb1bc1a0245547316fe96ac8f86b0e67acb524f | "2023-06-08T16:06:31Z" | python | "2023-06-09T06:15:22Z" | langchain/llms/vertexai.py | client: "_LanguageModel" = None
model_name: str
"Model name to use."
temperature: float = 0.0
"Sampling temperature, it controls the degree of randomness in token selection."
max_output_tokens: int = 128
"Token limit determines the maximum amount of text output from one prompt."
top_p: float = 0.95
"Tokens are selected from most probable to least until the sum of their "
"probabilities equals the top-p value."
top_k: int = 40
"How the model selects tokens for output, the next token is selected from "
"among the top-k most probable tokens."
stop: Optional[List[str]] = None
"Optional list of stop words to use when generating."
project: Optional[str] = None
"The default GCP project to use when making Vertex API calls."
location: str = "us-central1"
"The default location to use when making API calls."
credentials: Any = None
"The default custom credentials (google.auth.credentials.Credentials) to use "
"when making API calls. If not provided, credentials will be ascertained from "
"the environment."
@property
def _default_params(self) -> Dict[str, Any]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,889 | When inialztion VertexAI() all passed parameters got ignored | ### System Info
langchain=0.0.194
python=3.11.3
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Run:
`VertexAI(project="my_project_name")`
### Expected behavior
The client will connect to the supplied project_id | https://github.com/langchain-ai/langchain/issues/5889 | https://github.com/langchain-ai/langchain/pull/5891 | 63fcf41bea5222f64b1c9a822f08cec9e55aa619 | 0eb1bc1a0245547316fe96ac8f86b0e67acb524f | "2023-06-08T16:06:31Z" | python | "2023-06-09T06:15:22Z" | langchain/llms/vertexai.py | base_params = {
"temperature": self.temperature,
"max_output_tokens": self.max_output_tokens,
"top_k": self.top_k,
"top_p": self.top_p,
}
return {**base_params}
def _predict(self, prompt: str, stop: Optional[List[str]] = None) -> str:
res = self.client.predict(prompt, **self._default_params)
return self._enforce_stop_words(res.text, stop)
def _enforce_stop_words(self, text: str, stop: Optional[List[str]] = None) -> str:
if stop is None and self.stop is not None:
stop = self.stop
if stop:
return enforce_stop_tokens(text, stop)
return text
@property
def _llm_type(self) -> str:
return "vertexai"
@classmethod
def _try_init_vertexai(cls, values: Dict) -> None:
allowed_params = ["project", "location", "credentials"]
params = {k: v for k, v in values.items() if v in allowed_params}
init_vertexai(**params)
return None
class VertexAI(_VertexAICommon, LLM): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,889 | When inialztion VertexAI() all passed parameters got ignored | ### System Info
langchain=0.0.194
python=3.11.3
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Run:
`VertexAI(project="my_project_name")`
### Expected behavior
The client will connect to the supplied project_id | https://github.com/langchain-ai/langchain/issues/5889 | https://github.com/langchain-ai/langchain/pull/5891 | 63fcf41bea5222f64b1c9a822f08cec9e55aa619 | 0eb1bc1a0245547316fe96ac8f86b0e67acb524f | "2023-06-08T16:06:31Z" | python | "2023-06-09T06:15:22Z" | langchain/llms/vertexai.py | """Wrapper around Google Vertex AI large language models."""
model_name: str = "text-bison"
tuned_model_name: Optional[str] = None
"The name of a tuned model, if it's provided, model_name is ignored."
@root_validator()
def validate_environment(cls, values: Dict) -> Dict: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,889 | When inialztion VertexAI() all passed parameters got ignored | ### System Info
langchain=0.0.194
python=3.11.3
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Run:
`VertexAI(project="my_project_name")`
### Expected behavior
The client will connect to the supplied project_id | https://github.com/langchain-ai/langchain/issues/5889 | https://github.com/langchain-ai/langchain/pull/5891 | 63fcf41bea5222f64b1c9a822f08cec9e55aa619 | 0eb1bc1a0245547316fe96ac8f86b0e67acb524f | "2023-06-08T16:06:31Z" | python | "2023-06-09T06:15:22Z" | langchain/llms/vertexai.py | """Validate that the python package exists in environment."""
cls._try_init_vertexai(values)
try:
from vertexai.preview.language_models import TextGenerationModel
except ImportError:
raise_vertex_import_error()
tuned_model_name = values.get("tuned_model_name")
if tuned_model_name:
values["client"] = TextGenerationModel.get_tuned_model(tuned_model_name)
else:
values["client"] = TextGenerationModel.from_pretrained(values["model_name"])
return values
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
) -> str:
"""Call Vertex model to get predictions based on the prompt.
Args:
prompt: The prompt to pass into the model.
stop: A list of stop words (optional).
run_manager: A Callbackmanager for LLM run, optional.
Returns:
The string generated by the model.
"""
return self._predict(prompt, stop) |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,889 | When inialztion VertexAI() all passed parameters got ignored | ### System Info
langchain=0.0.194
python=3.11.3
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Run:
`VertexAI(project="my_project_name")`
### Expected behavior
The client will connect to the supplied project_id | https://github.com/langchain-ai/langchain/issues/5889 | https://github.com/langchain-ai/langchain/pull/5891 | 63fcf41bea5222f64b1c9a822f08cec9e55aa619 | 0eb1bc1a0245547316fe96ac8f86b0e67acb524f | "2023-06-08T16:06:31Z" | python | "2023-06-09T06:15:22Z" | langchain/utilities/vertexai.py | """Utilities to init Vertex AI."""
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from google.auth.credentials import Credentials
def raise_vertex_import_error() -> None:
"""Raise ImportError related to Vertex SDK being not available.
Raises:
ImportError: an ImportError that mentions a required version of the SDK.
"""
sdk = "'google-cloud-aiplatform>=1.25.0'"
raise ImportError(
"Could not import VertexAI. Please, install it with " f"pip install {sdk}"
)
def init_vertexai( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,889 | When inialztion VertexAI() all passed parameters got ignored | ### System Info
langchain=0.0.194
python=3.11.3
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Run:
`VertexAI(project="my_project_name")`
### Expected behavior
The client will connect to the supplied project_id | https://github.com/langchain-ai/langchain/issues/5889 | https://github.com/langchain-ai/langchain/pull/5891 | 63fcf41bea5222f64b1c9a822f08cec9e55aa619 | 0eb1bc1a0245547316fe96ac8f86b0e67acb524f | "2023-06-08T16:06:31Z" | python | "2023-06-09T06:15:22Z" | langchain/utilities/vertexai.py | project_id: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional["Credentials"] = None,
) -> None:
"""Init vertexai.
Args:
project: The default GCP project to use when making Vertex API calls.
location: The default location to use when making API calls.
credentials: The default custom
credentials to use when making API calls. If not provided credentials
will be ascertained from the environment.
Raises:
ImportError: If importing vertexai SDK didn't not succeed.
"""
try:
import vertexai
except ImportError:
raise_vertex_import_error()
vertexai.init(
project=project_id,
location=location,
credentials=credentials,
) |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,835 | Support for the AWS endpoint URL in the DynamoDBChatMessageHistory | ### Feature request
I propose having the possibility of specifying the endpoint URL to AWS in the DynamoDBChatMessageHistory, so that it is possible to target not only the AWS cloud services, but also a local installation.
### Motivation
Specifying the endpoint URL, which is normally not done when addressing the cloud services, is very helpful when targeting a local instance (like [Localstack](https://localstack.cloud/)) when running local tests.
### Your contribution
I am providing this PR for the implementation: https://github.com/hwchase17/langchain/pull/5836/files | https://github.com/langchain-ai/langchain/issues/5835 | https://github.com/langchain-ai/langchain/pull/5836 | 0eb1bc1a0245547316fe96ac8f86b0e67acb524f | db7ef635c0e061fcbab2f608ccc60af15fc5585d | "2023-06-07T14:01:56Z" | python | "2023-06-09T06:21:11Z" | langchain/memory/chat_message_histories/dynamodb.py | import logging
from typing import List
from langchain.schema import (
BaseChatMessageHistory,
BaseMessage,
_message_to_dict,
messages_from_dict,
messages_to_dict,
)
logger = logging.getLogger(__name__)
class DynamoDBChatMessageHistory(BaseChatMessageHistory):
"""Chat message history that stores history in AWS DynamoDB.
This class expects that a DynamoDB table with name `table_name`
and a partition Key of `SessionId` is present.
Args:
table_name: name of the DynamoDB table
session_id: arbitrary key that is used to store the messages
of a single chat session.
"""
def __init__(self, table_name: str, session_id: str): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,835 | Support for the AWS endpoint URL in the DynamoDBChatMessageHistory | ### Feature request
I propose having the possibility of specifying the endpoint URL to AWS in the DynamoDBChatMessageHistory, so that it is possible to target not only the AWS cloud services, but also a local installation.
### Motivation
Specifying the endpoint URL, which is normally not done when addressing the cloud services, is very helpful when targeting a local instance (like [Localstack](https://localstack.cloud/)) when running local tests.
### Your contribution
I am providing this PR for the implementation: https://github.com/hwchase17/langchain/pull/5836/files | https://github.com/langchain-ai/langchain/issues/5835 | https://github.com/langchain-ai/langchain/pull/5836 | 0eb1bc1a0245547316fe96ac8f86b0e67acb524f | db7ef635c0e061fcbab2f608ccc60af15fc5585d | "2023-06-07T14:01:56Z" | python | "2023-06-09T06:21:11Z" | langchain/memory/chat_message_histories/dynamodb.py | import boto3
client = boto3.resource("dynamodb")
self.table = client.Table(table_name)
self.session_id = session_id
@property
def messages(self) -> List[BaseMessage]:
"""Retrieve the messages from DynamoDB"""
from botocore.exceptions import ClientError
try:
response = self.table.get_item(Key={"SessionId": self.session_id})
except ClientError as error:
if error.response["Error"]["Code"] == "ResourceNotFoundException":
logger.warning("No record found with session id: %s", self.session_id)
else:
logger.error(error)
if response and "Item" in response:
items = response["Item"]["History"]
else:
items = []
messages = messages_from_dict(items)
return messages
def add_message(self, message: BaseMessage) -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,835 | Support for the AWS endpoint URL in the DynamoDBChatMessageHistory | ### Feature request
I propose having the possibility of specifying the endpoint URL to AWS in the DynamoDBChatMessageHistory, so that it is possible to target not only the AWS cloud services, but also a local installation.
### Motivation
Specifying the endpoint URL, which is normally not done when addressing the cloud services, is very helpful when targeting a local instance (like [Localstack](https://localstack.cloud/)) when running local tests.
### Your contribution
I am providing this PR for the implementation: https://github.com/hwchase17/langchain/pull/5836/files | https://github.com/langchain-ai/langchain/issues/5835 | https://github.com/langchain-ai/langchain/pull/5836 | 0eb1bc1a0245547316fe96ac8f86b0e67acb524f | db7ef635c0e061fcbab2f608ccc60af15fc5585d | "2023-06-07T14:01:56Z" | python | "2023-06-09T06:21:11Z" | langchain/memory/chat_message_histories/dynamodb.py | """Append the message to the record in DynamoDB"""
from botocore.exceptions import ClientError
messages = messages_to_dict(self.messages)
_message = _message_to_dict(message)
messages.append(_message)
try:
self.table.put_item(
Item={"SessionId": self.session_id, "History": messages}
)
except ClientError as err:
logger.error(err)
def clear(self) -> None:
"""Clear session memory from DynamoDB"""
from botocore.exceptions import ClientError
try:
self.table.delete_item(Key={"SessionId": self.session_id})
except ClientError as err:
logger.error(err) |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,027 | ArxivAPIWrapper | The documentation says:
> It limits the Document content by doc_content_chars_max.
> Set doc_content_chars_max=None if you don't want to limit the content size.
But the claim type of int prevents this to be set as None:
https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/utilities/arxiv.py#LL41C5-L41C38
> ValidationError: 1 validation error for ArxivAPIWrapper
> doc_content_chars_max
> none is not an allowed value (type=type_error.none.not_allowed)
Can you change that?
In addition, can you also expose this parameter to the `ArxivLoader`?
Thank you! | https://github.com/langchain-ai/langchain/issues/6027 | https://github.com/langchain-ai/langchain/pull/6063 | a9b97aa6f4f0039804014192345f93612fef93be | b01cf0dd54bf078e348471a038842b82db370d66 | "2023-06-12T05:30:46Z" | python | "2023-06-16T05:16:42Z" | langchain/utilities/arxiv.py | """Util that calls Arxiv."""
import logging
import os
from typing import Any, Dict, List
from pydantic import BaseModel, Extra, root_validator
from langchain.schema import Document
logger = logging.getLogger(__name__)
class ArxivAPIWrapper(BaseModel): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,027 | ArxivAPIWrapper | The documentation says:
> It limits the Document content by doc_content_chars_max.
> Set doc_content_chars_max=None if you don't want to limit the content size.
But the claim type of int prevents this to be set as None:
https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/utilities/arxiv.py#LL41C5-L41C38
> ValidationError: 1 validation error for ArxivAPIWrapper
> doc_content_chars_max
> none is not an allowed value (type=type_error.none.not_allowed)
Can you change that?
In addition, can you also expose this parameter to the `ArxivLoader`?
Thank you! | https://github.com/langchain-ai/langchain/issues/6027 | https://github.com/langchain-ai/langchain/pull/6063 | a9b97aa6f4f0039804014192345f93612fef93be | b01cf0dd54bf078e348471a038842b82db370d66 | "2023-06-12T05:30:46Z" | python | "2023-06-16T05:16:42Z" | langchain/utilities/arxiv.py | """Wrapper around ArxivAPI.
To use, you should have the ``arxiv`` python package installed.
https://lukasschwab.me/arxiv.py/index.html
This wrapper will use the Arxiv API to conduct searches and
fetch document summaries. By default, it will return the document summaries
of the top-k results.
It limits the Document content by doc_content_chars_max.
Set doc_content_chars_max=None if you don't want to limit the content size.
Parameters:
top_k_results: number of the top-scored document used for the arxiv tool
ARXIV_MAX_QUERY_LENGTH: the cut limit on the query used for the arxiv tool.
load_max_docs: a limit to the number of loaded documents
load_all_available_meta:
if True: the `metadata` of the loaded Documents gets all available meta info
(see https://lukasschwab.me/arxiv.py/index.html#Result),
if False: the `metadata` gets only the most informative fields.
"""
arxiv_search: Any
arxiv_exceptions: Any
top_k_results: int = 3
ARXIV_MAX_QUERY_LENGTH = 300
load_max_docs: int = 100
load_all_available_meta: bool = False
doc_content_chars_max: int = 4000
class Config: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,027 | ArxivAPIWrapper | The documentation says:
> It limits the Document content by doc_content_chars_max.
> Set doc_content_chars_max=None if you don't want to limit the content size.
But the claim type of int prevents this to be set as None:
https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/utilities/arxiv.py#LL41C5-L41C38
> ValidationError: 1 validation error for ArxivAPIWrapper
> doc_content_chars_max
> none is not an allowed value (type=type_error.none.not_allowed)
Can you change that?
In addition, can you also expose this parameter to the `ArxivLoader`?
Thank you! | https://github.com/langchain-ai/langchain/issues/6027 | https://github.com/langchain-ai/langchain/pull/6063 | a9b97aa6f4f0039804014192345f93612fef93be | b01cf0dd54bf078e348471a038842b82db370d66 | "2023-06-12T05:30:46Z" | python | "2023-06-16T05:16:42Z" | langchain/utilities/arxiv.py | """Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that the python package exists in environment."""
try:
import arxiv
values["arxiv_search"] = arxiv.Search
values["arxiv_exceptions"] = (
arxiv.ArxivError,
arxiv.UnexpectedEmptyPageError,
arxiv.HTTPError,
)
values["arxiv_result"] = arxiv.Result
except ImportError:
raise ImportError(
"Could not import arxiv python package. "
"Please install it with `pip install arxiv`."
)
return values
def run(self, query: str) -> str: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,027 | ArxivAPIWrapper | The documentation says:
> It limits the Document content by doc_content_chars_max.
> Set doc_content_chars_max=None if you don't want to limit the content size.
But the claim type of int prevents this to be set as None:
https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/utilities/arxiv.py#LL41C5-L41C38
> ValidationError: 1 validation error for ArxivAPIWrapper
> doc_content_chars_max
> none is not an allowed value (type=type_error.none.not_allowed)
Can you change that?
In addition, can you also expose this parameter to the `ArxivLoader`?
Thank you! | https://github.com/langchain-ai/langchain/issues/6027 | https://github.com/langchain-ai/langchain/pull/6063 | a9b97aa6f4f0039804014192345f93612fef93be | b01cf0dd54bf078e348471a038842b82db370d66 | "2023-06-12T05:30:46Z" | python | "2023-06-16T05:16:42Z" | langchain/utilities/arxiv.py | """
Run Arxiv search and get the article meta information.
See https://lukasschwab.me/arxiv.py/index.html#Search
See https://lukasschwab.me/arxiv.py/index.html#Result
It uses only the most informative fields of article meta information.
"""
try:
results = self.arxiv_search(
query[: self.ARXIV_MAX_QUERY_LENGTH], max_results=self.top_k_results
).results()
except self.arxiv_exceptions as ex:
return f"Arxiv exception: {ex}"
docs = [
f"Published: {result.updated.date()}\nTitle: {result.title}\n"
f"Authors: {', '.join(a.name for a in result.authors)}\n"
f"Summary: {result.summary}"
for result in results
]
if docs:
return "\n\n".join(docs)[: self.doc_content_chars_max]
else:
return "No good Arxiv Result was found"
def load(self, query: str) -> List[Document]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,027 | ArxivAPIWrapper | The documentation says:
> It limits the Document content by doc_content_chars_max.
> Set doc_content_chars_max=None if you don't want to limit the content size.
But the claim type of int prevents this to be set as None:
https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/utilities/arxiv.py#LL41C5-L41C38
> ValidationError: 1 validation error for ArxivAPIWrapper
> doc_content_chars_max
> none is not an allowed value (type=type_error.none.not_allowed)
Can you change that?
In addition, can you also expose this parameter to the `ArxivLoader`?
Thank you! | https://github.com/langchain-ai/langchain/issues/6027 | https://github.com/langchain-ai/langchain/pull/6063 | a9b97aa6f4f0039804014192345f93612fef93be | b01cf0dd54bf078e348471a038842b82db370d66 | "2023-06-12T05:30:46Z" | python | "2023-06-16T05:16:42Z" | langchain/utilities/arxiv.py | """
Run Arxiv search and get the article texts plus the article meta information.
See https://lukasschwab.me/arxiv.py/index.html#Search
Returns: a list of documents with the document.page_content in text format
"""
try:
import fitz
except ImportError:
raise ImportError(
"PyMuPDF package not found, please install it with "
"`pip install pymupdf`"
)
try:
results = self.arxiv_search(
query[: self.ARXIV_MAX_QUERY_LENGTH], max_results=self.load_max_docs
).results()
except self.arxiv_exceptions as ex:
logger.debug("Error on arxiv: %s", ex)
return []
docs: List[Document] = []
for result in results:
try:
doc_file_name: str = result.download_pdf()
with fitz.open(doc_file_name) as doc_file:
text: str = "".join(page.get_text() for page in doc_file) |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,027 | ArxivAPIWrapper | The documentation says:
> It limits the Document content by doc_content_chars_max.
> Set doc_content_chars_max=None if you don't want to limit the content size.
But the claim type of int prevents this to be set as None:
https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/utilities/arxiv.py#LL41C5-L41C38
> ValidationError: 1 validation error for ArxivAPIWrapper
> doc_content_chars_max
> none is not an allowed value (type=type_error.none.not_allowed)
Can you change that?
In addition, can you also expose this parameter to the `ArxivLoader`?
Thank you! | https://github.com/langchain-ai/langchain/issues/6027 | https://github.com/langchain-ai/langchain/pull/6063 | a9b97aa6f4f0039804014192345f93612fef93be | b01cf0dd54bf078e348471a038842b82db370d66 | "2023-06-12T05:30:46Z" | python | "2023-06-16T05:16:42Z" | langchain/utilities/arxiv.py | except FileNotFoundError as f_ex:
logger.debug(f_ex)
continue
if self.load_all_available_meta:
extra_metadata = {
"entry_id": result.entry_id,
"published_first_time": str(result.published.date()),
"comment": result.comment,
"journal_ref": result.journal_ref,
"doi": result.doi,
"primary_category": result.primary_category,
"categories": result.categories,
"links": [link.href for link in result.links],
}
else:
extra_metadata = {}
metadata = {
"Published": str(result.updated.date()),
"Title": result.title,
"Authors": ", ".join(a.name for a in result.authors),
"Summary": result.summary,
**extra_metadata,
}
doc = Document(
page_content=text[: self.doc_content_chars_max], metadata=metadata
)
docs.append(doc)
os.remove(doc_file_name)
return docs |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,027 | ArxivAPIWrapper | The documentation says:
> It limits the Document content by doc_content_chars_max.
> Set doc_content_chars_max=None if you don't want to limit the content size.
But the claim type of int prevents this to be set as None:
https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/utilities/arxiv.py#LL41C5-L41C38
> ValidationError: 1 validation error for ArxivAPIWrapper
> doc_content_chars_max
> none is not an allowed value (type=type_error.none.not_allowed)
Can you change that?
In addition, can you also expose this parameter to the `ArxivLoader`?
Thank you! | https://github.com/langchain-ai/langchain/issues/6027 | https://github.com/langchain-ai/langchain/pull/6063 | a9b97aa6f4f0039804014192345f93612fef93be | b01cf0dd54bf078e348471a038842b82db370d66 | "2023-06-12T05:30:46Z" | python | "2023-06-16T05:16:42Z" | tests/integration_tests/utilities/test_arxiv.py | """Integration test for Arxiv API Wrapper."""
from typing import Any, List
import pytest
from langchain.agents.load_tools import load_tools
from langchain.schema import Document
from langchain.tools.base import BaseTool
from langchain.utilities import ArxivAPIWrapper
@pytest.fixture
def api_client() -> ArxivAPIWrapper:
return ArxivAPIWrapper()
def test_run_success(api_client: ArxivAPIWrapper) -> None:
"""Test that returns the correct answer"""
output = api_client.run("1605.08386")
assert "Heat-bath random walks with Markov bases" in output
def test_run_returns_several_docs(api_client: ArxivAPIWrapper) -> None:
"""Test that returns several docs"""
output = api_client.run("Caprice Stanley")
assert "On Mixing Behavior of a Family of Random Walks" in output
def test_run_returns_no_result(api_client: ArxivAPIWrapper) -> None:
"""Test that gives no result."""
output = api_client.run("1605.08386WWW")
assert "No good Arxiv Result was found" == output
def assert_docs(docs: List[Document]) -> None:
for doc in docs:
assert doc.page_content
assert doc.metadata
assert set(doc.metadata) == {"Published", "Title", "Authors", "Summary"}
def test_load_success(api_client: ArxivAPIWrapper) -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,027 | ArxivAPIWrapper | The documentation says:
> It limits the Document content by doc_content_chars_max.
> Set doc_content_chars_max=None if you don't want to limit the content size.
But the claim type of int prevents this to be set as None:
https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/utilities/arxiv.py#LL41C5-L41C38
> ValidationError: 1 validation error for ArxivAPIWrapper
> doc_content_chars_max
> none is not an allowed value (type=type_error.none.not_allowed)
Can you change that?
In addition, can you also expose this parameter to the `ArxivLoader`?
Thank you! | https://github.com/langchain-ai/langchain/issues/6027 | https://github.com/langchain-ai/langchain/pull/6063 | a9b97aa6f4f0039804014192345f93612fef93be | b01cf0dd54bf078e348471a038842b82db370d66 | "2023-06-12T05:30:46Z" | python | "2023-06-16T05:16:42Z" | tests/integration_tests/utilities/test_arxiv.py | """Test that returns one document"""
docs = api_client.load("1605.08386")
assert len(docs) == 1
assert_docs(docs)
def test_load_returns_no_result(api_client: ArxivAPIWrapper) -> None:
"""Test that returns no docs"""
docs = api_client.load("1605.08386WWW")
assert len(docs) == 0
def test_load_returns_limited_docs() -> None:
"""Test that returns several docs"""
expected_docs = 2
api_client = ArxivAPIWrapper(load_max_docs=expected_docs)
docs = api_client.load("ChatGPT")
assert len(docs) == expected_docs
assert_docs(docs)
def test_load_returns_full_set_of_metadata() -> None:
"""Test that returns several docs"""
api_client = ArxivAPIWrapper(load_max_docs=1, load_all_available_meta=True)
docs = api_client.load("ChatGPT")
assert len(docs) == 1
for doc in docs:
assert doc.page_content
assert doc.metadata
assert set(doc.metadata).issuperset(
{"Published", "Title", "Authors", "Summary"}
)
print(doc.metadata)
assert len(set(doc.metadata)) > 4
def _load_arxiv_from_universal_entry(**kwargs: Any) -> BaseTool: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,027 | ArxivAPIWrapper | The documentation says:
> It limits the Document content by doc_content_chars_max.
> Set doc_content_chars_max=None if you don't want to limit the content size.
But the claim type of int prevents this to be set as None:
https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/utilities/arxiv.py#LL41C5-L41C38
> ValidationError: 1 validation error for ArxivAPIWrapper
> doc_content_chars_max
> none is not an allowed value (type=type_error.none.not_allowed)
Can you change that?
In addition, can you also expose this parameter to the `ArxivLoader`?
Thank you! | https://github.com/langchain-ai/langchain/issues/6027 | https://github.com/langchain-ai/langchain/pull/6063 | a9b97aa6f4f0039804014192345f93612fef93be | b01cf0dd54bf078e348471a038842b82db370d66 | "2023-06-12T05:30:46Z" | python | "2023-06-16T05:16:42Z" | tests/integration_tests/utilities/test_arxiv.py | tools = load_tools(["arxiv"], **kwargs)
assert len(tools) == 1, "loaded more than 1 tool"
return tools[0]
def test_load_arxiv_from_universal_entry() -> None:
arxiv_tool = _load_arxiv_from_universal_entry()
output = arxiv_tool("Caprice Stanley")
assert (
"On Mixing Behavior of a Family of Random Walks" in output
), "failed to fetch a valid result"
def test_load_arxiv_from_universal_entry_with_params() -> None:
params = {
"top_k_results": 1,
"load_max_docs": 10,
"load_all_available_meta": True,
}
arxiv_tool = _load_arxiv_from_universal_entry(**params)
assert isinstance(arxiv_tool, ArxivAPIWrapper)
wp = arxiv_tool.api_wrapper
assert wp.top_k_results == 1, "failed to assert top_k_results"
assert wp.load_max_docs == 10, "failed to assert load_max_docs"
assert (
wp.load_all_available_meta is True
), "failed to assert load_all_available_meta" |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,282 | LLMRequestsChain not enforcing headers when making http requests | ### System Info
LangChain version 0.0.201
### Who can help?
@hwchase17 @agola
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Based on the documentation example, run the following script:
```python
from langchain.llms import OpenAI
from langchain.chains import LLMRequestsChain, LLMChain
from langchain.prompts import PromptTemplate
template = """Here is a company website content :
----
{requests_result}
----
We want to learn more about a company's activity and the kind of
clients they target. Perform an analysis and write a short summary.
"""
PROMPT = PromptTemplate(
input_variables=["requests_result"],
template=template,
)
chain = LLMRequestsChain(llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=PROMPT))
print(chain.requests_wrapper)
```
Gives
```bash
python3 bug-langchain-requests.py
headers=None aiosession=None
```
### Expected behavior
Provided headers should be enforced
```bash
python3 bug-langchain-requests.py
headers={'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36'} aiosession=None
``` | https://github.com/langchain-ai/langchain/issues/6282 | https://github.com/langchain-ai/langchain/pull/6283 | 23cdebddc446d14b22003819fbe66884b600c998 | 9ca11c06b73f225ff431500e174bf21fa8eb9a33 | "2023-06-16T12:44:22Z" | python | "2023-06-16T23:21:01Z" | langchain/chains/llm_requests.py | """Chain that hits a URL and then uses an LLM to parse results."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains import LLMChain
from langchain.chains.base import Chain
from langchain.requests import TextRequestsWrapper
DEFAULT_HEADERS = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36"
}
class LLMRequestsChain(Chain):
"""Chain that hits a URL and then uses an LLM to parse results."""
llm_chain: LLMChain
requests_wrapper: TextRequestsWrapper = Field(
default_factory=TextRequestsWrapper, exclude=True
)
text_length: int = 8000
requests_key: str = "requests_result"
input_key: str = "url"
output_key: str = "output"
class Config: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,282 | LLMRequestsChain not enforcing headers when making http requests | ### System Info
LangChain version 0.0.201
### Who can help?
@hwchase17 @agola
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Based on the documentation example, run the following script:
```python
from langchain.llms import OpenAI
from langchain.chains import LLMRequestsChain, LLMChain
from langchain.prompts import PromptTemplate
template = """Here is a company website content :
----
{requests_result}
----
We want to learn more about a company's activity and the kind of
clients they target. Perform an analysis and write a short summary.
"""
PROMPT = PromptTemplate(
input_variables=["requests_result"],
template=template,
)
chain = LLMRequestsChain(llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=PROMPT))
print(chain.requests_wrapper)
```
Gives
```bash
python3 bug-langchain-requests.py
headers=None aiosession=None
```
### Expected behavior
Provided headers should be enforced
```bash
python3 bug-langchain-requests.py
headers={'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36'} aiosession=None
``` | https://github.com/langchain-ai/langchain/issues/6282 | https://github.com/langchain-ai/langchain/pull/6283 | 23cdebddc446d14b22003819fbe66884b600c998 | 9ca11c06b73f225ff431500e174bf21fa8eb9a33 | "2023-06-16T12:44:22Z" | python | "2023-06-16T23:21:01Z" | langchain/chains/llm_requests.py | """Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[str]:
"""Will be whatever keys the prompt expects.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Will always return text key.
:meta private:
"""
return [self.output_key]
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
try:
from bs4 import BeautifulSoup
except ImportError:
raise ValueError(
"Could not import bs4 python package. "
"Please install it with `pip install bs4`."
)
return values
def _call( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,282 | LLMRequestsChain not enforcing headers when making http requests | ### System Info
LangChain version 0.0.201
### Who can help?
@hwchase17 @agola
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Based on the documentation example, run the following script:
```python
from langchain.llms import OpenAI
from langchain.chains import LLMRequestsChain, LLMChain
from langchain.prompts import PromptTemplate
template = """Here is a company website content :
----
{requests_result}
----
We want to learn more about a company's activity and the kind of
clients they target. Perform an analysis and write a short summary.
"""
PROMPT = PromptTemplate(
input_variables=["requests_result"],
template=template,
)
chain = LLMRequestsChain(llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=PROMPT))
print(chain.requests_wrapper)
```
Gives
```bash
python3 bug-langchain-requests.py
headers=None aiosession=None
```
### Expected behavior
Provided headers should be enforced
```bash
python3 bug-langchain-requests.py
headers={'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36'} aiosession=None
``` | https://github.com/langchain-ai/langchain/issues/6282 | https://github.com/langchain-ai/langchain/pull/6283 | 23cdebddc446d14b22003819fbe66884b600c998 | 9ca11c06b73f225ff431500e174bf21fa8eb9a33 | "2023-06-16T12:44:22Z" | python | "2023-06-16T23:21:01Z" | langchain/chains/llm_requests.py | self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
from bs4 import BeautifulSoup
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
url = inputs[self.input_key]
res = self.requests_wrapper.get(url)
soup = BeautifulSoup(res, "html.parser")
other_keys[self.requests_key] = soup.get_text()[: self.text_length]
result = self.llm_chain.predict(
callbacks=_run_manager.get_child(), **other_keys
)
return {self.output_key: result}
@property
def _chain_type(self) -> str:
return "llm_requests_chain" |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | """Wrapper around OpenAI APIs."""
from __future__ import annotations
import logging
import sys
import warnings
from typing import (
AbstractSet,
Any,
Callable,
Collection,
Dict,
Generator,
List,
Literal,
Mapping,
Optional,
Set,
Tuple,
Union,
)
from pydantic import Extra, Field, root_validator
from tenacity import (
before_sleep_log,
retry, |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | retry_if_exception_type,
stop_after_attempt,
wait_exponential,
)
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.base import BaseLLM
from langchain.schema import Generation, LLMResult
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
def update_token_usage(
keys: Set[str], response: Dict[str, Any], token_usage: Dict[str, Any]
) -> None:
"""Update token usage."""
_keys_to_use = keys.intersection(response["usage"])
for _key in _keys_to_use:
if _key not in token_usage:
token_usage[_key] = response["usage"][_key]
else:
token_usage[_key] += response["usage"][_key]
def _update_response(response: Dict[str, Any], stream_response: Dict[str, Any]) -> None:
"""Update response from the stream response."""
response["choices"][0]["text"] += stream_response["choices"][0]["text"]
response["choices"][0]["finish_reason"] = stream_response["choices"][0][
"finish_reason"
]
response["choices"][0]["logprobs"] = stream_response["choices"][0]["logprobs"]
def _streaming_response_template() -> Dict[str, Any]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | return {
"choices": [
{
"text": "",
"finish_reason": None,
"logprobs": None,
}
]
}
def _create_retry_decorator(llm: Union[BaseOpenAI, OpenAIChat]) -> Callable[[Any], Any]:
import openai
min_seconds = 4
max_seconds = 10
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(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
def completion_with_retry(llm: Union[BaseOpenAI, OpenAIChat], **kwargs: Any) -> Any: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | """Use tenacity to retry the completion call."""
retry_decorator = _create_retry_decorator(llm)
@retry_decorator
def _completion_with_retry(**kwargs: Any) -> Any:
return llm.client.create(**kwargs)
return _completion_with_retry(**kwargs)
async def acompletion_with_retry(
llm: Union[BaseOpenAI, OpenAIChat], **kwargs: Any
) -> Any:
"""Use tenacity to retry the async completion call."""
retry_decorator = _create_retry_decorator(llm)
@retry_decorator
async def _completion_with_retry(**kwargs: Any) -> Any:
return await llm.client.acreate(**kwargs)
return await _completion_with_retry(**kwargs)
class BaseOpenAI(BaseLLM): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | """Wrapper around OpenAI large language models."""
@property
def lc_secrets(self) -> Dict[str, str]:
return {"openai_api_key": "OPENAI_API_KEY"}
@property
def lc_serializable(self) -> bool:
return True
client: Any
model_name: str = Field("text-davinci-003", alias="model")
"""Model name to use."""
temperature: float = 0.7
"""What sampling temperature to use."""
max_tokens: int = 256
"""The maximum number of tokens to generate in the completion.
-1 returns as many tokens as possible given the prompt and
the models maximal context size."""
top_p: float = 1
"""Total probability mass of tokens to consider at each step."""
frequency_penalty: float = 0
"""Penalizes repeated tokens according to frequency."""
presence_penalty: float = 0
"""Penalizes repeated tokens."""
n: int = 1
"""How many completions to generate for each prompt."""
best_of: int = 1
"""Generates best_of completions server-side and returns the "best"."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
openai_api_key: Optional[str] = None |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | openai_api_base: Optional[str] = None
openai_organization: Optional[str] = None
openai_proxy: Optional[str] = None
batch_size: int = 20
"""Batch size to use when passing multiple documents to generate."""
request_timeout: Optional[Union[float, Tuple[float, float]]] = None
"""Timeout for requests to OpenAI completion API. Default is 600 seconds."""
logit_bias: Optional[Dict[str, float]] = Field(default_factory=dict)
"""Adjust the probability of specific tokens being generated."""
max_retries: int = 6
"""Maximum number of retries to make when generating."""
streaming: bool = False
"""Whether to stream the results or not."""
allowed_special: Union[Literal["all"], AbstractSet[str]] = set()
"""Set of special tokens that are allowed。"""
disallowed_special: Union[Literal["all"], Collection[str]] = "all"
"""Set of special tokens that are not allowed。"""
def __new__(cls, **data: Any) -> Union[OpenAIChat, BaseOpenAI]: # ty
"""Initialize the OpenAI object."""
model_name = data.get("model_name", "")
if model_name.startswith("gpt-3.5-turbo") or model_name.startswith("gpt-4"):
warnings.warn(
"You are trying to use a chat model. This way of initializing it is "
"no longer supported. Instead, please use: "
"`from langchain.chat_models import ChatOpenAI`"
)
return OpenAIChat(**data)
return super().__new__(cls)
class Config: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | """Configuration for this pydantic object."""
extra = Extra.ignore
allow_population_by_field_name = True
@root_validator(pre=True)
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = cls.all_required_field_names()
extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name in extra:
raise ValueError(f"Found {field_name} supplied twice.")
if field_name not in all_required_field_names:
logger.warning(
f"""WARNING! {field_name} is not default parameter.
{field_name} was transferred to model_kwargs.
Please confirm that {field_name} is what you intended."""
)
extra[field_name] = values.pop(field_name)
invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
if invalid_model_kwargs:
raise ValueError(
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
f"Instead they were passed in as part of `model_kwargs` parameter."
)
values["model_kwargs"] = extra
return values
@root_validator()
def validate_environment(cls, values: Dict) -> Dict: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | """Validate that api key and python package exists in environment."""
values["openai_api_key"] = get_from_dict_or_env(
values, "openai_api_key", "OPENAI_API_KEY"
)
values["openai_api_base"] = get_from_dict_or_env(
values,
"openai_api_base",
"OPENAI_API_BASE",
default="",
)
values["openai_proxy"] = get_from_dict_or_env(
values,
"openai_proxy",
"OPENAI_PROXY",
default="",
)
values["openai_organization"] = get_from_dict_or_env(
values,
"openai_organization",
"OPENAI_ORGANIZATION",
default="",
)
try:
import openai |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | values["client"] = openai.Completion
except ImportError:
raise ImportError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
if values["streaming"] and values["n"] > 1:
raise ValueError("Cannot stream results when n > 1.")
if values["streaming"] and values["best_of"] > 1:
raise ValueError("Cannot stream results when best_of > 1.")
return values
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling OpenAI API."""
normal_params = {
"temperature": self.temperature,
"max_tokens": self.max_tokens,
"top_p": self.top_p,
"frequency_penalty": self.frequency_penalty,
"presence_penalty": self.presence_penalty,
"n": self.n,
"request_timeout": self.request_timeout,
"logit_bias": self.logit_bias,
}
# Az
# do
if self.best_of > 1:
normal_params["best_of"] = self.best_of
return {**normal_params, **self.model_kwargs}
def _generate( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
"""Call out to OpenAI's endpoint with k unique prompts.
Args:
prompts: The prompts to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The full LLM output.
Example:
.. code-block:: python
response = openai.generate(["Tell me a joke."])
"""
# TO
params = self._invocation_params
params = {**params, **kwargs}
sub_prompts = self.get_sub_prompts(params, prompts, stop)
choices = []
token_usage: Dict[str, int] = {}
# Ge
# In
_keys = {"completion_tokens", "prompt_tokens", "total_tokens"}
for _prompts in sub_prompts:
if self.streaming:
if len(_prompts) > 1:
raise ValueError("Cannot stream results with multiple prompts.")
params["stream"] = True |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | response = _streaming_response_template()
for stream_resp in completion_with_retry(
self, prompt=_prompts, **params
):
if run_manager:
run_manager.on_llm_new_token(
stream_resp["choices"][0]["text"],
verbose=self.verbose,
logprobs=stream_resp["choices"][0]["logprobs"],
)
_update_response(response, stream_resp)
choices.extend(response["choices"])
else:
response = completion_with_retry(self, prompt=_prompts, **params)
choices.extend(response["choices"])
if not self.streaming:
# Ca
update_token_usage(_keys, response, token_usage)
return self.create_llm_result(choices, prompts, token_usage)
async def _agenerate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
"""Call out to OpenAI's endpoint async with k unique prompts."""
params = self._invocation_params
params = {**params, **kwargs}
sub_prompts = self.get_sub_prompts(params, prompts, stop) |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | choices = []
token_usage: Dict[str, int] = {}
# Ge
# In
_keys = {"completion_tokens", "prompt_tokens", "total_tokens"}
for _prompts in sub_prompts:
if self.streaming:
if len(_prompts) > 1:
raise ValueError("Cannot stream results with multiple prompts.")
params["stream"] = True
response = _streaming_response_template()
async for stream_resp in await acompletion_with_retry(
self, prompt=_prompts, **params
):
if run_manager:
await run_manager.on_llm_new_token(
stream_resp["choices"][0]["text"],
verbose=self.verbose,
logprobs=stream_resp["choices"][0]["logprobs"],
)
_update_response(response, stream_resp)
choices.extend(response["choices"])
else:
response = await acompletion_with_retry(self, prompt=_prompts, **params)
choices.extend(response["choices"])
if not self.streaming:
# Ca
update_token_usage(_keys, response, token_usage)
return self.create_llm_result(choices, prompts, token_usage)
def get_sub_prompts( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | self,
params: Dict[str, Any],
prompts: List[str],
stop: Optional[List[str]] = None,
) -> List[List[str]]:
"""Get the sub prompts for llm call."""
if stop is not None:
if "stop" in params:
raise ValueError("`stop` found in both the input and default params.")
params["stop"] = stop
if params["max_tokens"] == -1:
if len(prompts) != 1:
raise ValueError(
"max_tokens set to -1 not supported for multiple inputs."
)
params["max_tokens"] = self.max_tokens_for_prompt(prompts[0])
sub_prompts = [
prompts[i : i + self.batch_size]
for i in range(0, len(prompts), self.batch_size)
]
return sub_prompts
def create_llm_result( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | self, choices: Any, prompts: List[str], token_usage: Dict[str, int]
) -> LLMResult:
"""Create the LLMResult from the choices and prompts."""
generations = []
for i, _ in enumerate(prompts):
sub_choices = choices[i * self.n : (i + 1) * self.n]
generations.append(
[
Generation(
text=choice["text"],
generation_info=dict(
finish_reason=choice.get("finish_reason"),
logprobs=choice.get("logprobs"),
),
)
for choice in sub_choices
]
)
llm_output = {"token_usage": token_usage, "model_name": self.model_name}
return LLMResult(generations=generations, llm_output=llm_output)
def stream(self, prompt: str, stop: Optional[List[str]] = None) -> Generator: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | """Call OpenAI with streaming flag and return the resulting generator.
BETA: this is a beta feature while we figure out the right abstraction.
Once that happens, this interface could change.
Args:
prompt: The prompts to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
A generator representing the stream of tokens from OpenAI.
Example:
.. code-block:: python
generator = openai.stream("Tell me a joke.")
for token in generator:
yield token
"""
params = self.prep_streaming_params(stop)
generator = self.client.create(prompt=prompt, **params)
return generator
def prep_streaming_params(self, stop: Optional[List[str]] = None) -> Dict[str, Any]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | """Prepare the params for streaming."""
params = self._invocation_params
if "best_of" in params and params["best_of"] != 1:
raise ValueError("OpenAI only supports best_of == 1 for streaming")
if stop is not None:
if "stop" in params:
raise ValueError("`stop` found in both the input and default params.")
params["stop"] = stop
params["stream"] = True
return params
@property
def _invocation_params(self) -> Dict[str, Any]:
"""Get the parameters used to invoke the model."""
openai_creds: Dict[str, Any] = {
"api_key": self.openai_api_key,
"api_base": self.openai_api_base,
"organization": self.openai_organization,
}
if self.openai_proxy:
import openai
openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy} # ty
return {**openai_creds, **self._default_params}
@property
def _identifying_params(self) -> Mapping[str, Any]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | """Get the identifying parameters."""
return {**{"model_name": self.model_name}, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "openai"
def get_token_ids(self, text: str) -> List[int]:
"""Get the token IDs using the tiktoken package."""
# ti
if sys.version_info[1] < 8:
return super().get_num_tokens(text)
try:
import tiktoken
except ImportError:
raise ImportError(
"Could not import tiktoken python package. "
"This is needed in order to calculate get_num_tokens. "
"Please install it with `pip install tiktoken`."
)
enc = tiktoken.encoding_for_model(self.model_name)
return enc.encode(
text,
allowed_special=self.allowed_special,
disallowed_special=self.disallowed_special,
)
def modelname_to_contextsize(self, modelname: str) -> int: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | """Calculate the maximum number of tokens possible to generate for a model.
Args:
modelname: The modelname we want to know the context size for.
Returns:
The maximum context size
Example:
.. code-block:: python
max_tokens = openai.modelname_to_contextsize("text-davinci-003")
"""
model_token_mapping = {
"gpt-4": 8192,
"gpt-4-0314": 8192,
"gpt-4-32k": 32768,
"gpt-4-32k-0314": 32768,
"gpt-3.5-turbo": 4096,
"gpt-3.5-turbo-0301": 4096,
"text-ada-001": 2049,
"ada": 2049,
"text-babbage-001": 2040,
"babbage": 2049,
"text-curie-001": 2049,
"curie": 2049,
"davinci": 2049,
"text-davinci-003": 4097,
"text-davinci-002": 4097, |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | "code-davinci-002": 8001,
"code-davinci-001": 8001,
"code-cushman-002": 2048,
"code-cushman-001": 2048,
}
# ha
if "ft-" in modelname:
modelname = modelname.split(":")[0]
context_size = model_token_mapping.get(modelname, None)
if context_size is None:
raise ValueError(
f"Unknown model: {modelname}. Please provide a valid OpenAI model name."
"Known models are: " + ", ".join(model_token_mapping.keys())
)
return context_size
def max_tokens_for_prompt(self, prompt: str) -> int:
"""Calculate the maximum number of tokens possible to generate for a prompt.
Args:
prompt: The prompt to pass into the model.
Returns:
The maximum number of tokens to generate for a prompt.
Example:
.. code-block:: python
max_tokens = openai.max_token_for_prompt("Tell me a joke.")
"""
num_tokens = self.get_num_tokens(prompt)
# ge
max_size = self.modelname_to_contextsize(self.model_name)
return max_size - num_tokens
class OpenAI(BaseOpenAI): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | """Wrapper around OpenAI large language models.
To use, you should have the ``openai`` python package installed, and the
environment variable ``OPENAI_API_KEY`` set with your API key.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example:
.. code-block:: python
from langchain.llms import OpenAI
openai = OpenAI(model_name="text-davinci-003")
"""
@property
def _invocation_params(self) -> Dict[str, Any]:
return {**{"model": self.model_name}, **super()._invocation_params}
class AzureOpenAI(BaseOpenAI):
"""Wrapper around Azure-specific OpenAI large language models.
To use, you should have the ``openai`` python package installed, and the
environment variable ``OPENAI_API_KEY`` set with your API key.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example:
.. code-block:: python
from langchain.llms import AzureOpenAI
openai = AzureOpenAI(model_name="text-davinci-003")
"""
deployment_name: str = ""
"""Deployment name to use."""
openai_api_type: str = "azure"
openai_api_version: str = ""
@root_validator()
def validate_azure_settings(cls, values: Dict) -> Dict: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | values["openai_api_version"] = get_from_dict_or_env(
values,
"openai_api_version",
"OPENAI_API_VERSION",
)
values["openai_api_type"] = get_from_dict_or_env(
values,
"openai_api_type",
"OPENAI_API_TYPE",
)
return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
return {
**{"deployment_name": self.deployment_name},
**super()._identifying_params,
}
@property
def _invocation_params(self) -> Dict[str, Any]:
openai_params = {
"engine": self.deployment_name,
"api_type": self.openai_api_type,
"api_version": self.openai_api_version,
}
return {**openai_params, **super()._invocation_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "azure"
class OpenAIChat(BaseLLM): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | """Wrapper around OpenAI Chat large language models.
To use, you should have the ``openai`` python package installed, and the
environment variable ``OPENAI_API_KEY`` set with your API key.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example:
.. code-block:: python
from langchain.llms import OpenAIChat
openaichat = OpenAIChat(model_name="gpt-3.5-turbo")
"""
client: Any
model_name: str = "gpt-3.5-turbo"
"""Model name to use."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
openai_api_key: Optional[str] = None
openai_api_base: Optional[str] = None
openai_proxy: Optional[str] = None
max_retries: int = 6
"""Maximum number of retries to make when generating."""
prefix_messages: List = Field(default_factory=list)
"""Series of messages for Chat input."""
streaming: bool = False
"""Whether to stream the results or not."""
allowed_special: Union[Literal["all"], AbstractSet[str]] = set()
"""Set of special tokens that are allowed。"""
disallowed_special: Union[Literal["all"], Collection[str]] = "all"
"""Set of special tokens that are not allowed。"""
class Config: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | """Configuration for this pydantic object."""
extra = Extra.ignore
@root_validator(pre=True)
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = {field.alias for field in cls.__fields__.values()}
extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name not in all_required_field_names:
if field_name in extra:
raise ValueError(f"Found {field_name} supplied twice.")
extra[field_name] = values.pop(field_name)
values["model_kwargs"] = extra
return values
@root_validator()
def validate_environment(cls, values: Dict) -> Dict: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | """Validate that api key and python package exists in environment."""
openai_api_key = get_from_dict_or_env(
values, "openai_api_key", "OPENAI_API_KEY"
)
openai_api_base = get_from_dict_or_env(
values,
"openai_api_base",
"OPENAI_API_BASE",
default="",
)
openai_proxy = get_from_dict_or_env(
values,
"openai_proxy",
"OPENAI_PROXY",
default="",
)
openai_organization = get_from_dict_or_env(
values, "openai_organization", "OPENAI_ORGANIZATION", default=""
)
try:
import openai
openai.api_key = openai_api_key |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | if openai_api_base:
openai.api_base = openai_api_base
if openai_organization:
openai.organization = openai_organization
if openai_proxy:
openai.proxy = {"http": openai_proxy, "https": openai_proxy} # ty
except ImportError:
raise ImportError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
try:
values["client"] = openai.ChatCompletion
except AttributeError:
raise ValueError(
"`openai` has no `ChatCompletion` attribute, this is likely "
"due to an old version of the openai package. Try upgrading it "
"with `pip install --upgrade openai`."
)
warnings.warn(
"You are trying to use a chat model. This way of initializing it is "
"no longer supported. Instead, please use: "
"`from langchain.chat_models import ChatOpenAI`"
)
return values
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling OpenAI API."""
return self.model_kwargs
def _get_chat_params( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | self, prompts: List[str], stop: Optional[List[str]] = None
) -> Tuple:
if len(prompts) > 1:
raise ValueError(
f"OpenAIChat currently only supports single prompt, got {prompts}"
)
messages = self.prefix_messages + [{"role": "user", "content": prompts[0]}]
params: Dict[str, Any] = {**{"model": self.model_name}, **self._default_params}
if stop is not None:
if "stop" in params:
raise ValueError("`stop` found in both the input and default params.")
params["stop"] = stop
if params.get("max_tokens") == -1:
# for Ch
del params["max_tokens"]
return messages, params
def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
messages, params = self._get_chat_params(prompts, stop)
params = {**params, **kwargs}
if self.streaming:
response = ""
params["stream"] = True
for stream_resp in completion_with_retry(self, messages=messages, **params):
token = stream_resp["choices"][0]["delta"].get("content", "") |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | response += token
if run_manager:
run_manager.on_llm_new_token(
token,
)
return LLMResult(
generations=[[Generation(text=response)]],
)
else:
full_response = completion_with_retry(self, messages=messages, **params)
llm_output = {
"token_usage": full_response["usage"],
"model_name": self.model_name,
}
return LLMResult(
generations=[
[Generation(text=full_response["choices"][0]["message"]["content"])]
],
llm_output=llm_output,
)
async def _agenerate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
messages, params = self._get_chat_params(prompts, stop)
params = {**params, **kwargs}
if self.streaming: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | response = ""
params["stream"] = True
async for stream_resp in await acompletion_with_retry(
self, messages=messages, **params
):
token = stream_resp["choices"][0]["delta"].get("content", "")
response += token
if run_manager:
await run_manager.on_llm_new_token(
token,
)
return LLMResult(
generations=[[Generation(text=response)]],
)
else:
full_response = await acompletion_with_retry(
self, messages=messages, **params
)
llm_output = {
"token_usage": full_response["usage"],
"model_name": self.model_name,
}
return LLMResult(
generations=[
[Generation(text=full_response["choices"][0]["message"]["content"])]
],
llm_output=llm_output,
)
@property
def _identifying_params(self) -> Mapping[str, Any]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,039 | Make modelname_to_contextsize as a staticmethod to use it without create an object | ### Feature request
Make [modelname_to_contextsize](https://github.com/hwchase17/langchain/blob/289e9aeb9d122d689d68b2e77236ce3dfcd606a7/langchain/llms/openai.py#L503) as staticmethod to use it without create an object.
### Motivation
While using ChatOpenAI or AzureChatOpenAI, to use modelname_to_contextsize we need to create OpenAI or AzureOpenAI object whether we don't use it.
For example, llama-index using [modelname_to_contextsize](https://github.com/jerryjliu/llama_index/blob/f614448a045788c9c5c9a774f407a992ae1f7743/llama_index/llm_predictor/base.py#L42) to get context size, but it raise an error if we using AzureOpenAI without setting OPENAI_API_TOKEN.
### Your contribution
#6040 | https://github.com/langchain-ai/langchain/issues/6039 | https://github.com/langchain-ai/langchain/pull/6040 | 427551eabf32e0c9fa4428dcfad5fed86f99bbdf | cdd1d78bf2a383972af15921611a06e7efe53f93 | "2023-06-12T10:23:07Z" | python | "2023-06-17T16:13:08Z" | langchain/llms/openai.py | """Get the identifying parameters."""
return {**{"model_name": self.model_name}, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "openai-chat"
def get_token_ids(self, text: str) -> List[int]:
"""Get the token IDs using the tiktoken package."""
# ti
if sys.version_info[1] < 8:
return super().get_token_ids(text)
try:
import tiktoken
except ImportError:
raise ImportError(
"Could not import tiktoken python package. "
"This is needed in order to calculate get_num_tokens. "
"Please install it with `pip install tiktoken`."
)
enc = tiktoken.encoding_for_model(self.model_name)
return enc.encode(
text,
allowed_special=self.allowed_special,
disallowed_special=self.disallowed_special,
) |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,698 | Permission Error with PDF loader | I was testing OnlinePDFLoader yesterday iirc and it was working fine. Today I tried experimenting and I keep getting this error
`PermissionError: [Errno 13] Permission denied: 'C:\\Users\\REALGL~1\\AppData\\Local\\Temp\\tmp3chr08y0`
it may be occurring because the `tempfile.NamedTemporaryFile()` in `pdf.py` is still open when the PDF partitioning function is trying to access it | https://github.com/langchain-ai/langchain/issues/2698 | https://github.com/langchain-ai/langchain/pull/6170 | 4fc7939848a600064dc20b44e86c19e2cfa01491 | 5be465bd86f940cf831e3a4d2841d92ce8699ffb | "2023-04-11T06:17:16Z" | python | "2023-06-18T23:39:57Z" | langchain/document_loaders/pdf.py | """Loader that loads PDF files."""
import json
import logging
import os
import tempfile
import time
from abc import ABC
from io import StringIO
from pathlib import Path
from typing import Any, Iterator, List, Mapping, Optional
from urllib.parse import urlparse
import requests
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.document_loaders.blob_loaders import Blob
from langchain.document_loaders.parsers.pdf import (
PDFMinerParser,
PDFPlumberParser,
PyMuPDFParser,
PyPDFium2Parser,
PyPDFParser,
)
from langchain.document_loaders.unstructured import UnstructuredFileLoader
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__file__)
class UnstructuredPDFLoader(UnstructuredFileLoader): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,698 | Permission Error with PDF loader | I was testing OnlinePDFLoader yesterday iirc and it was working fine. Today I tried experimenting and I keep getting this error
`PermissionError: [Errno 13] Permission denied: 'C:\\Users\\REALGL~1\\AppData\\Local\\Temp\\tmp3chr08y0`
it may be occurring because the `tempfile.NamedTemporaryFile()` in `pdf.py` is still open when the PDF partitioning function is trying to access it | https://github.com/langchain-ai/langchain/issues/2698 | https://github.com/langchain-ai/langchain/pull/6170 | 4fc7939848a600064dc20b44e86c19e2cfa01491 | 5be465bd86f940cf831e3a4d2841d92ce8699ffb | "2023-04-11T06:17:16Z" | python | "2023-06-18T23:39:57Z" | langchain/document_loaders/pdf.py | """Loader that uses unstructured to load PDF files."""
def _get_elements(self) -> List:
from unstructured.partition.pdf import partition_pdf
return partition_pdf(filename=self.file_path, **self.unstructured_kwargs)
class BasePDFLoader(BaseLoader, ABC):
"""Base loader class for PDF files.
Defaults to check for local file, but if the file is a web path, it will download it
to a temporary file, and use that, then clean up the temporary file after completion
"""
def __init__(self, file_path: str):
"""Initialize with file path."""
self.file_path = file_path
self.web_path = None
if "~" in self.file_path:
self.file_path = os.path.expanduser(self.file_path)
if not os.path.isfile(self.file_path) and self._is_valid_url(self.file_path):
r = requests.get(self.file_path)
if r.status_code != 200:
raise ValueError(
"Check the url of your file; returned status code %s"
% r.status_code
)
self.web_path = self.file_path
self.temp_file = tempfile.NamedTemporaryFile()
self.temp_file.write(r.content)
self.file_path = self.temp_file.name
elif not os.path.isfile(self.file_path):
raise ValueError("File path %s is not a valid file or url" % self.file_path)
def __del__(self) -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,698 | Permission Error with PDF loader | I was testing OnlinePDFLoader yesterday iirc and it was working fine. Today I tried experimenting and I keep getting this error
`PermissionError: [Errno 13] Permission denied: 'C:\\Users\\REALGL~1\\AppData\\Local\\Temp\\tmp3chr08y0`
it may be occurring because the `tempfile.NamedTemporaryFile()` in `pdf.py` is still open when the PDF partitioning function is trying to access it | https://github.com/langchain-ai/langchain/issues/2698 | https://github.com/langchain-ai/langchain/pull/6170 | 4fc7939848a600064dc20b44e86c19e2cfa01491 | 5be465bd86f940cf831e3a4d2841d92ce8699ffb | "2023-04-11T06:17:16Z" | python | "2023-06-18T23:39:57Z" | langchain/document_loaders/pdf.py | if hasattr(self, "temp_file"):
self.temp_file.close()
@staticmethod
def _is_valid_url(url: str) -> bool:
"""Check if the url is valid."""
parsed = urlparse(url)
return bool(parsed.netloc) and bool(parsed.scheme)
@property
def source(self) -> str:
return self.web_path if self.web_path is not None else self.file_path
class OnlinePDFLoader(BasePDFLoader):
"""Loader that loads online PDFs."""
def load(self) -> List[Document]:
"""Load documents."""
loader = UnstructuredPDFLoader(str(self.file_path))
return loader.load()
class PyPDFLoader(BasePDFLoader): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,698 | Permission Error with PDF loader | I was testing OnlinePDFLoader yesterday iirc and it was working fine. Today I tried experimenting and I keep getting this error
`PermissionError: [Errno 13] Permission denied: 'C:\\Users\\REALGL~1\\AppData\\Local\\Temp\\tmp3chr08y0`
it may be occurring because the `tempfile.NamedTemporaryFile()` in `pdf.py` is still open when the PDF partitioning function is trying to access it | https://github.com/langchain-ai/langchain/issues/2698 | https://github.com/langchain-ai/langchain/pull/6170 | 4fc7939848a600064dc20b44e86c19e2cfa01491 | 5be465bd86f940cf831e3a4d2841d92ce8699ffb | "2023-04-11T06:17:16Z" | python | "2023-06-18T23:39:57Z" | langchain/document_loaders/pdf.py | """Loads a PDF with pypdf and chunks at character level.
Loader also stores page numbers in metadatas.
"""
def __init__(self, file_path: str) -> None:
"""Initialize with file path."""
try:
import pypdf
except ImportError:
raise ImportError(
"pypdf package not found, please install it with " "`pip install pypdf`"
)
self.parser = PyPDFParser()
super().__init__(file_path)
def load(self) -> List[Document]:
"""Load given path as pages."""
return list(self.lazy_load())
def lazy_load(
self,
) -> Iterator[Document]:
"""Lazy load given path as pages."""
blob = Blob.from_path(self.file_path)
yield from self.parser.parse(blob)
class PyPDFium2Loader(BasePDFLoader):
"""Loads a PDF with pypdfium2 and chunks at character level."""
def __init__(self, file_path: str):
"""Initialize with file path."""
super().__init__(file_path)
self.parser = PyPDFium2Parser()
def load(self) -> List[Document]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,698 | Permission Error with PDF loader | I was testing OnlinePDFLoader yesterday iirc and it was working fine. Today I tried experimenting and I keep getting this error
`PermissionError: [Errno 13] Permission denied: 'C:\\Users\\REALGL~1\\AppData\\Local\\Temp\\tmp3chr08y0`
it may be occurring because the `tempfile.NamedTemporaryFile()` in `pdf.py` is still open when the PDF partitioning function is trying to access it | https://github.com/langchain-ai/langchain/issues/2698 | https://github.com/langchain-ai/langchain/pull/6170 | 4fc7939848a600064dc20b44e86c19e2cfa01491 | 5be465bd86f940cf831e3a4d2841d92ce8699ffb | "2023-04-11T06:17:16Z" | python | "2023-06-18T23:39:57Z" | langchain/document_loaders/pdf.py | """Load given path as pages."""
return list(self.lazy_load())
def lazy_load(
self,
) -> Iterator[Document]:
"""Lazy load given path as pages."""
blob = Blob.from_path(self.file_path)
yield from self.parser.parse(blob)
class PyPDFDirectoryLoader(BaseLoader):
"""Loads a directory with PDF files with pypdf and chunks at character level.
Loader also stores page numbers in metadatas.
"""
def __init__(
self,
path: str,
glob: str = "**/[!.]*.pdf",
silent_errors: bool = False,
load_hidden: bool = False,
recursive: bool = False,
):
self.path = path
self.glob = glob
self.load_hidden = load_hidden
self.recursive = recursive
self.silent_errors = silent_errors
@staticmethod
def _is_visible(path: Path) -> bool: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,698 | Permission Error with PDF loader | I was testing OnlinePDFLoader yesterday iirc and it was working fine. Today I tried experimenting and I keep getting this error
`PermissionError: [Errno 13] Permission denied: 'C:\\Users\\REALGL~1\\AppData\\Local\\Temp\\tmp3chr08y0`
it may be occurring because the `tempfile.NamedTemporaryFile()` in `pdf.py` is still open when the PDF partitioning function is trying to access it | https://github.com/langchain-ai/langchain/issues/2698 | https://github.com/langchain-ai/langchain/pull/6170 | 4fc7939848a600064dc20b44e86c19e2cfa01491 | 5be465bd86f940cf831e3a4d2841d92ce8699ffb | "2023-04-11T06:17:16Z" | python | "2023-06-18T23:39:57Z" | langchain/document_loaders/pdf.py | return not any(part.startswith(".") for part in path.parts)
def load(self) -> List[Document]:
p = Path(self.path)
docs = []
items = p.rglob(self.glob) if self.recursive else p.glob(self.glob)
for i in items:
if i.is_file():
if self._is_visible(i.relative_to(p)) or self.load_hidden:
try:
loader = PyPDFLoader(str(i))
sub_docs = loader.load()
for doc in sub_docs:
doc.metadata["source"] = str(i)
docs.extend(sub_docs)
except Exception as e:
if self.silent_errors:
logger.warning(e)
else:
raise e
return docs
class PDFMinerLoader(BasePDFLoader): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,698 | Permission Error with PDF loader | I was testing OnlinePDFLoader yesterday iirc and it was working fine. Today I tried experimenting and I keep getting this error
`PermissionError: [Errno 13] Permission denied: 'C:\\Users\\REALGL~1\\AppData\\Local\\Temp\\tmp3chr08y0`
it may be occurring because the `tempfile.NamedTemporaryFile()` in `pdf.py` is still open when the PDF partitioning function is trying to access it | https://github.com/langchain-ai/langchain/issues/2698 | https://github.com/langchain-ai/langchain/pull/6170 | 4fc7939848a600064dc20b44e86c19e2cfa01491 | 5be465bd86f940cf831e3a4d2841d92ce8699ffb | "2023-04-11T06:17:16Z" | python | "2023-06-18T23:39:57Z" | langchain/document_loaders/pdf.py | """Loader that uses PDFMiner to load PDF files."""
def __init__(self, file_path: str) -> None:
"""Initialize with file path."""
try:
from pdfminer.high_level import extract_text
except ImportError:
raise ImportError(
"`pdfminer` package not found, please install it with "
"`pip install pdfminer.six`"
)
super().__init__(file_path)
self.parser = PDFMinerParser()
def load(self) -> List[Document]:
"""Eagerly load the content."""
return list(self.lazy_load())
def lazy_load(
self,
) -> Iterator[Document]:
"""Lazily lod documents."""
blob = Blob.from_path(self.file_path)
yield from self.parser.parse(blob)
class PDFMinerPDFasHTMLLoader(BasePDFLoader): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,698 | Permission Error with PDF loader | I was testing OnlinePDFLoader yesterday iirc and it was working fine. Today I tried experimenting and I keep getting this error
`PermissionError: [Errno 13] Permission denied: 'C:\\Users\\REALGL~1\\AppData\\Local\\Temp\\tmp3chr08y0`
it may be occurring because the `tempfile.NamedTemporaryFile()` in `pdf.py` is still open when the PDF partitioning function is trying to access it | https://github.com/langchain-ai/langchain/issues/2698 | https://github.com/langchain-ai/langchain/pull/6170 | 4fc7939848a600064dc20b44e86c19e2cfa01491 | 5be465bd86f940cf831e3a4d2841d92ce8699ffb | "2023-04-11T06:17:16Z" | python | "2023-06-18T23:39:57Z" | langchain/document_loaders/pdf.py | """Loader that uses PDFMiner to load PDF files as HTML content."""
def __init__(self, file_path: str):
"""Initialize with file path."""
try:
from pdfminer.high_level import extract_text_to_fp
except ImportError:
raise ImportError(
"`pdfminer` package not found, please install it with "
"`pip install pdfminer.six`"
)
super().__init__(file_path)
def load(self) -> List[Document]:
"""Load file."""
from pdfminer.high_level import extract_text_to_fp
from pdfminer.layout import LAParams
from pdfminer.utils import open_filename
output_string = StringIO()
with open_filename(self.file_path, "rb") as fp:
extract_text_to_fp(
fp,
output_string,
codec="",
laparams=LAParams(),
output_type="html",
)
metadata = {"source": self.file_path}
return [Document(page_content=output_string.getvalue(), metadata=metadata)]
class PyMuPDFLoader(BasePDFLoader): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,698 | Permission Error with PDF loader | I was testing OnlinePDFLoader yesterday iirc and it was working fine. Today I tried experimenting and I keep getting this error
`PermissionError: [Errno 13] Permission denied: 'C:\\Users\\REALGL~1\\AppData\\Local\\Temp\\tmp3chr08y0`
it may be occurring because the `tempfile.NamedTemporaryFile()` in `pdf.py` is still open when the PDF partitioning function is trying to access it | https://github.com/langchain-ai/langchain/issues/2698 | https://github.com/langchain-ai/langchain/pull/6170 | 4fc7939848a600064dc20b44e86c19e2cfa01491 | 5be465bd86f940cf831e3a4d2841d92ce8699ffb | "2023-04-11T06:17:16Z" | python | "2023-06-18T23:39:57Z" | langchain/document_loaders/pdf.py | """Loader that uses PyMuPDF to load PDF files."""
def __init__(self, file_path: str) -> None:
"""Initialize with file path."""
try:
import fitz
except ImportError:
raise ImportError(
"`PyMuPDF` package not found, please install it with "
"`pip install pymupdf`"
)
super().__init__(file_path)
def load(self, **kwargs: Optional[Any]) -> List[Document]:
"""Load file."""
parser = PyMuPDFParser(text_kwargs=kwargs)
blob = Blob.from_path(self.file_path)
return parser.parse(blob)
class MathpixPDFLoader(BasePDFLoader): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,698 | Permission Error with PDF loader | I was testing OnlinePDFLoader yesterday iirc and it was working fine. Today I tried experimenting and I keep getting this error
`PermissionError: [Errno 13] Permission denied: 'C:\\Users\\REALGL~1\\AppData\\Local\\Temp\\tmp3chr08y0`
it may be occurring because the `tempfile.NamedTemporaryFile()` in `pdf.py` is still open when the PDF partitioning function is trying to access it | https://github.com/langchain-ai/langchain/issues/2698 | https://github.com/langchain-ai/langchain/pull/6170 | 4fc7939848a600064dc20b44e86c19e2cfa01491 | 5be465bd86f940cf831e3a4d2841d92ce8699ffb | "2023-04-11T06:17:16Z" | python | "2023-06-18T23:39:57Z" | langchain/document_loaders/pdf.py | def __init__(
self,
file_path: str,
processed_file_format: str = "mmd",
max_wait_time_seconds: int = 500,
should_clean_pdf: bool = False,
**kwargs: Any,
) -> None:
super().__init__(file_path)
self.mathpix_api_key = get_from_dict_or_env(
kwargs, "mathpix_api_key", "MATHPIX_API_KEY"
)
self.mathpix_api_id = get_from_dict_or_env(
kwargs, "mathpix_api_id", "MATHPIX_API_ID"
)
self.processed_file_format = processed_file_format
self.max_wait_time_seconds = max_wait_time_seconds
self.should_clean_pdf = should_clean_pdf
@property
def headers(self) -> dict:
return {"app_id": self.mathpix_api_id, "app_key": self.mathpix_api_key}
@property
def url(self) -> str:
return "https://api.mathpix.com/v3/pdf"
@property
def data(self) -> dict: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,698 | Permission Error with PDF loader | I was testing OnlinePDFLoader yesterday iirc and it was working fine. Today I tried experimenting and I keep getting this error
`PermissionError: [Errno 13] Permission denied: 'C:\\Users\\REALGL~1\\AppData\\Local\\Temp\\tmp3chr08y0`
it may be occurring because the `tempfile.NamedTemporaryFile()` in `pdf.py` is still open when the PDF partitioning function is trying to access it | https://github.com/langchain-ai/langchain/issues/2698 | https://github.com/langchain-ai/langchain/pull/6170 | 4fc7939848a600064dc20b44e86c19e2cfa01491 | 5be465bd86f940cf831e3a4d2841d92ce8699ffb | "2023-04-11T06:17:16Z" | python | "2023-06-18T23:39:57Z" | langchain/document_loaders/pdf.py | options = {"conversion_formats": {self.processed_file_format: True}}
return {"options_json": json.dumps(options)}
def send_pdf(self) -> str:
with open(self.file_path, "rb") as f:
files = {"file": f}
response = requests.post(
self.url, headers=self.headers, files=files, data=self.data
)
response_data = response.json()
if "pdf_id" in response_data:
pdf_id = response_data["pdf_id"]
return pdf_id
else:
raise ValueError("Unable to send PDF to Mathpix.")
def wait_for_processing(self, pdf_id: str) -> None:
url = self.url + "/" + pdf_id
for _ in range(0, self.max_wait_time_seconds, 5):
response = requests.get(url, headers=self.headers)
response_data = response.json()
status = response_data.get("status", None)
if status == "completed":
return
elif status == "error":
raise ValueError("Unable to retrieve PDF from Mathpix")
else:
print(f"Status: {status}, waiting for processing to complete")
time.sleep(5)
raise TimeoutError
def get_processed_pdf(self, pdf_id: str) -> str: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,698 | Permission Error with PDF loader | I was testing OnlinePDFLoader yesterday iirc and it was working fine. Today I tried experimenting and I keep getting this error
`PermissionError: [Errno 13] Permission denied: 'C:\\Users\\REALGL~1\\AppData\\Local\\Temp\\tmp3chr08y0`
it may be occurring because the `tempfile.NamedTemporaryFile()` in `pdf.py` is still open when the PDF partitioning function is trying to access it | https://github.com/langchain-ai/langchain/issues/2698 | https://github.com/langchain-ai/langchain/pull/6170 | 4fc7939848a600064dc20b44e86c19e2cfa01491 | 5be465bd86f940cf831e3a4d2841d92ce8699ffb | "2023-04-11T06:17:16Z" | python | "2023-06-18T23:39:57Z" | langchain/document_loaders/pdf.py | self.wait_for_processing(pdf_id)
url = f"{self.url}/{pdf_id}.{self.processed_file_format}"
response = requests.get(url, headers=self.headers)
return response.content.decode("utf-8")
def clean_pdf(self, contents: str) -> str:
contents = "\n".join(
[line for line in contents.split("\n") if not line.startswith("![]")]
)
contents = contents.replace("\\section{", "# ").replace("}", "")
contents = (
contents.replace(r"\$", "$")
.replace(r"\%", "%")
.replace(r"\(", "(")
.replace(r"\)", ")")
)
return contents
def load(self) -> List[Document]:
pdf_id = self.send_pdf()
contents = self.get_processed_pdf(pdf_id)
if self.should_clean_pdf:
contents = self.clean_pdf(contents)
metadata = {"source": self.source, "file_path": self.source}
return [Document(page_content=contents, metadata=metadata)]
class PDFPlumberLoader(BasePDFLoader): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,698 | Permission Error with PDF loader | I was testing OnlinePDFLoader yesterday iirc and it was working fine. Today I tried experimenting and I keep getting this error
`PermissionError: [Errno 13] Permission denied: 'C:\\Users\\REALGL~1\\AppData\\Local\\Temp\\tmp3chr08y0`
it may be occurring because the `tempfile.NamedTemporaryFile()` in `pdf.py` is still open when the PDF partitioning function is trying to access it | https://github.com/langchain-ai/langchain/issues/2698 | https://github.com/langchain-ai/langchain/pull/6170 | 4fc7939848a600064dc20b44e86c19e2cfa01491 | 5be465bd86f940cf831e3a4d2841d92ce8699ffb | "2023-04-11T06:17:16Z" | python | "2023-06-18T23:39:57Z" | langchain/document_loaders/pdf.py | """Loader that uses pdfplumber to load PDF files."""
def __init__(
self, file_path: str, text_kwargs: Optional[Mapping[str, Any]] = None
) -> None:
"""Initialize with file path."""
try:
import pdfplumber
except ImportError:
raise ImportError(
"pdfplumber package not found, please install it with "
"`pip install pdfplumber`"
)
super().__init__(file_path)
self.text_kwargs = text_kwargs or {}
def load(self) -> List[Document]:
"""Load file."""
parser = PDFPlumberParser(text_kwargs=self.text_kwargs)
blob = Blob.from_path(self.file_path)
return parser.parse(blob) |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,225 | OpenAI functions dont work with async streaming... | ### System Info
Version: 0.0.200
### Who can help?
@hwchase17 , @agola11
- I have a PR ready ... creating an issue so I can pair it
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
...
openai.py
async def _agenerate( ...
has different implementation than def generate...
when running the chain with `acall` >>
1. fails on
inner_completion += token # token is null, raises error
and after fix the function call was not captured...
### Expected behavior
the same as `generate` | https://github.com/langchain-ai/langchain/issues/6225 | https://github.com/langchain-ai/langchain/pull/6226 | ea6a5b03e077526896071da80530bebb94eb390b | e2f36ee6082506049419875fa4a374f8fa2a88fe | "2023-06-15T13:22:11Z" | python | "2023-06-19T00:05:16Z" | langchain/chat_models/openai.py | """OpenAI chat wrapper."""
from __future__ import annotations
import logging
import sys
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
List,
Mapping,
Optional,
Tuple, |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,225 | OpenAI functions dont work with async streaming... | ### System Info
Version: 0.0.200
### Who can help?
@hwchase17 , @agola11
- I have a PR ready ... creating an issue so I can pair it
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
...
openai.py
async def _agenerate( ...
has different implementation than def generate...
when running the chain with `acall` >>
1. fails on
inner_completion += token # token is null, raises error
and after fix the function call was not captured...
### Expected behavior
the same as `generate` | https://github.com/langchain-ai/langchain/issues/6225 | https://github.com/langchain-ai/langchain/pull/6226 | ea6a5b03e077526896071da80530bebb94eb390b | e2f36ee6082506049419875fa4a374f8fa2a88fe | "2023-06-15T13:22:11Z" | python | "2023-06-19T00:05:16Z" | langchain/chat_models/openai.py | Union,
)
from pydantic import Field, root_validator
from tenacity import (
before_sleep_log,
retry,
retry_if_exception_type,
stop_after_attempt,
wait_exponential,
)
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.chat_models.base import BaseChatModel
from langchain.schema import (
AIMessage,
BaseMessage,
ChatGeneration,
ChatMessage,
ChatResult,
FunctionMessage,
HumanMessage,
SystemMessage,
)
from langchain.utils import get_from_dict_or_env
if TYPE_CHECKING:
import tiktoken
logger = logging.getLogger(__name__)
def _import_tiktoken() -> Any: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,225 | OpenAI functions dont work with async streaming... | ### System Info
Version: 0.0.200
### Who can help?
@hwchase17 , @agola11
- I have a PR ready ... creating an issue so I can pair it
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
...
openai.py
async def _agenerate( ...
has different implementation than def generate...
when running the chain with `acall` >>
1. fails on
inner_completion += token # token is null, raises error
and after fix the function call was not captured...
### Expected behavior
the same as `generate` | https://github.com/langchain-ai/langchain/issues/6225 | https://github.com/langchain-ai/langchain/pull/6226 | ea6a5b03e077526896071da80530bebb94eb390b | e2f36ee6082506049419875fa4a374f8fa2a88fe | "2023-06-15T13:22:11Z" | python | "2023-06-19T00:05:16Z" | langchain/chat_models/openai.py | try:
import tiktoken
except ImportError:
raise ValueError(
"Could not import tiktoken python package. "
"This is needed in order to calculate get_token_ids. "
"Please install it with `pip install tiktoken`."
)
return tiktoken
def _create_retry_decorator(llm: ChatOpenAI) -> Callable[[Any], Any]:
import openai
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(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
async def acompletion_with_retry(llm: ChatOpenAI, **kwargs: Any) -> Any: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,225 | OpenAI functions dont work with async streaming... | ### System Info
Version: 0.0.200
### Who can help?
@hwchase17 , @agola11
- I have a PR ready ... creating an issue so I can pair it
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
...
openai.py
async def _agenerate( ...
has different implementation than def generate...
when running the chain with `acall` >>
1. fails on
inner_completion += token # token is null, raises error
and after fix the function call was not captured...
### Expected behavior
the same as `generate` | https://github.com/langchain-ai/langchain/issues/6225 | https://github.com/langchain-ai/langchain/pull/6226 | ea6a5b03e077526896071da80530bebb94eb390b | e2f36ee6082506049419875fa4a374f8fa2a88fe | "2023-06-15T13:22:11Z" | python | "2023-06-19T00:05:16Z" | langchain/chat_models/openai.py | """Use tenacity to retry the async completion call."""
retry_decorator = _create_retry_decorator(llm)
@retry_decorator
async def _completion_with_retry(**kwargs: Any) -> Any:
return await llm.client.acreate(**kwargs)
return await _completion_with_retry(**kwargs)
def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
role = _dict["role"]
if role == "user":
return HumanMessage(content=_dict["content"])
elif role == "assistant":
content = _dict["content"] or ""
if _dict.get("function_call"):
additional_kwargs = {"function_call": dict(_dict["function_call"])}
else:
additional_kwargs = {}
return AIMessage(content=content, additional_kwargs=additional_kwargs)
elif role == "system":
return SystemMessage(content=_dict["content"])
else:
return ChatMessage(content=_dict["content"], role=role)
def _convert_message_to_dict(message: BaseMessage) -> dict: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,225 | OpenAI functions dont work with async streaming... | ### System Info
Version: 0.0.200
### Who can help?
@hwchase17 , @agola11
- I have a PR ready ... creating an issue so I can pair it
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
...
openai.py
async def _agenerate( ...
has different implementation than def generate...
when running the chain with `acall` >>
1. fails on
inner_completion += token # token is null, raises error
and after fix the function call was not captured...
### Expected behavior
the same as `generate` | https://github.com/langchain-ai/langchain/issues/6225 | https://github.com/langchain-ai/langchain/pull/6226 | ea6a5b03e077526896071da80530bebb94eb390b | e2f36ee6082506049419875fa4a374f8fa2a88fe | "2023-06-15T13:22:11Z" | python | "2023-06-19T00:05:16Z" | langchain/chat_models/openai.py | 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}
if "function_call" in message.additional_kwargs:
message_dict["function_call"] = message.additional_kwargs["function_call"]
elif isinstance(message, SystemMessage):
message_dict = {"role": "system", "content": message.content}
elif isinstance(message, FunctionMessage):
message_dict = {
"role": "function",
"content": message.content,
"name": message.name,
}
else:
raise ValueError(f"Got unknown type {message}")
if "name" in message.additional_kwargs:
message_dict["name"] = message.additional_kwargs["name"]
return message_dict
class ChatOpenAI(BaseChatModel): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,225 | OpenAI functions dont work with async streaming... | ### System Info
Version: 0.0.200
### Who can help?
@hwchase17 , @agola11
- I have a PR ready ... creating an issue so I can pair it
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
...
openai.py
async def _agenerate( ...
has different implementation than def generate...
when running the chain with `acall` >>
1. fails on
inner_completion += token # token is null, raises error
and after fix the function call was not captured...
### Expected behavior
the same as `generate` | https://github.com/langchain-ai/langchain/issues/6225 | https://github.com/langchain-ai/langchain/pull/6226 | ea6a5b03e077526896071da80530bebb94eb390b | e2f36ee6082506049419875fa4a374f8fa2a88fe | "2023-06-15T13:22:11Z" | python | "2023-06-19T00:05:16Z" | langchain/chat_models/openai.py | """Wrapper around OpenAI Chat large language models.
To use, you should have the ``openai`` python package installed, and the
environment variable ``OPENAI_API_KEY`` set with your API key.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example:
.. code-block:: python
from langchain.chat_models import ChatOpenAI
openai = ChatOpenAI(model_name="gpt-3.5-turbo")
"""
@property
def lc_serializable(self) -> bool: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,225 | OpenAI functions dont work with async streaming... | ### System Info
Version: 0.0.200
### Who can help?
@hwchase17 , @agola11
- I have a PR ready ... creating an issue so I can pair it
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
...
openai.py
async def _agenerate( ...
has different implementation than def generate...
when running the chain with `acall` >>
1. fails on
inner_completion += token # token is null, raises error
and after fix the function call was not captured...
### Expected behavior
the same as `generate` | https://github.com/langchain-ai/langchain/issues/6225 | https://github.com/langchain-ai/langchain/pull/6226 | ea6a5b03e077526896071da80530bebb94eb390b | e2f36ee6082506049419875fa4a374f8fa2a88fe | "2023-06-15T13:22:11Z" | python | "2023-06-19T00:05:16Z" | langchain/chat_models/openai.py | return True
client: Any
model_name: str = Field(default="gpt-3.5-turbo", alias="model")
"""Model name to use."""
temperature: float = 0.7
"""What sampling temperature to use."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
openai_api_key: Optional[str] = None
"""Base URL path for API requests,
leave blank if not using a proxy or service emulator."""
openai_api_base: Optional[str] = None
openai_organization: Optional[str] = None
openai_proxy: Optional[str] = None
request_timeout: Optional[Union[float, Tuple[float, float]]] = None
"""Timeout for requests to OpenAI completion API. Default is 600 seconds."""
max_retries: int = 6
"""Maximum number of retries to make when generating."""
streaming: bool = False
"""Whether to stream the results or not."""
n: int = 1
"""Number of chat completions to generate for each prompt."""
max_tokens: Optional[int] = None
"""Maximum number of tokens to generate."""
class Config: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,225 | OpenAI functions dont work with async streaming... | ### System Info
Version: 0.0.200
### Who can help?
@hwchase17 , @agola11
- I have a PR ready ... creating an issue so I can pair it
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
...
openai.py
async def _agenerate( ...
has different implementation than def generate...
when running the chain with `acall` >>
1. fails on
inner_completion += token # token is null, raises error
and after fix the function call was not captured...
### Expected behavior
the same as `generate` | https://github.com/langchain-ai/langchain/issues/6225 | https://github.com/langchain-ai/langchain/pull/6226 | ea6a5b03e077526896071da80530bebb94eb390b | e2f36ee6082506049419875fa4a374f8fa2a88fe | "2023-06-15T13:22:11Z" | python | "2023-06-19T00:05:16Z" | langchain/chat_models/openai.py | """Configuration for this pydantic object."""
allow_population_by_field_name = True
@root_validator(pre=True)
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = cls.all_required_field_names()
extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name in extra:
raise ValueError(f"Found {field_name} supplied twice.")
if field_name not in all_required_field_names:
logger.warning(
f"""WARNING! {field_name} is not default parameter.
{field_name} was transferred to model_kwargs.
Please confirm that {field_name} is what you intended."""
)
extra[field_name] = values.pop(field_name)
invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
if invalid_model_kwargs:
raise ValueError(
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
f"Instead they were passed in as part of `model_kwargs` parameter."
)
values["model_kwargs"] = extra
return values
@root_validator()
def validate_environment(cls, values: Dict) -> Dict: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,225 | OpenAI functions dont work with async streaming... | ### System Info
Version: 0.0.200
### Who can help?
@hwchase17 , @agola11
- I have a PR ready ... creating an issue so I can pair it
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
...
openai.py
async def _agenerate( ...
has different implementation than def generate...
when running the chain with `acall` >>
1. fails on
inner_completion += token # token is null, raises error
and after fix the function call was not captured...
### Expected behavior
the same as `generate` | https://github.com/langchain-ai/langchain/issues/6225 | https://github.com/langchain-ai/langchain/pull/6226 | ea6a5b03e077526896071da80530bebb94eb390b | e2f36ee6082506049419875fa4a374f8fa2a88fe | "2023-06-15T13:22:11Z" | python | "2023-06-19T00:05:16Z" | langchain/chat_models/openai.py | """Validate that api key and python package exists in environment."""
values["openai_api_key"] = get_from_dict_or_env(
values, "openai_api_key", "OPENAI_API_KEY"
)
values["openai_organization"] = get_from_dict_or_env(
values,
"openai_organization",
"OPENAI_ORGANIZATION",
default="",
)
values["openai_api_base"] = get_from_dict_or_env(
values,
"openai_api_base",
"OPENAI_API_BASE", |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,225 | OpenAI functions dont work with async streaming... | ### System Info
Version: 0.0.200
### Who can help?
@hwchase17 , @agola11
- I have a PR ready ... creating an issue so I can pair it
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
...
openai.py
async def _agenerate( ...
has different implementation than def generate...
when running the chain with `acall` >>
1. fails on
inner_completion += token # token is null, raises error
and after fix the function call was not captured...
### Expected behavior
the same as `generate` | https://github.com/langchain-ai/langchain/issues/6225 | https://github.com/langchain-ai/langchain/pull/6226 | ea6a5b03e077526896071da80530bebb94eb390b | e2f36ee6082506049419875fa4a374f8fa2a88fe | "2023-06-15T13:22:11Z" | python | "2023-06-19T00:05:16Z" | langchain/chat_models/openai.py | default="",
)
values["openai_proxy"] = get_from_dict_or_env(
values,
"openai_proxy",
"OPENAI_PROXY",
default="",
)
try:
import openai
except ImportError:
raise ValueError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
try:
values["client"] = openai.ChatCompletion
except AttributeError:
raise ValueError(
"`openai` has no `ChatCompletion` attribute, this is likely "
"due to an old version of the openai package. Try upgrading it "
"with `pip install --upgrade openai`."
)
if values["n"] < 1:
raise ValueError("n must be at least 1.")
if values["n"] > 1 and values["streaming"]:
raise ValueError("n must be 1 when streaming.")
return values
@property
def _default_params(self) -> Dict[str, Any]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,225 | OpenAI functions dont work with async streaming... | ### System Info
Version: 0.0.200
### Who can help?
@hwchase17 , @agola11
- I have a PR ready ... creating an issue so I can pair it
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
...
openai.py
async def _agenerate( ...
has different implementation than def generate...
when running the chain with `acall` >>
1. fails on
inner_completion += token # token is null, raises error
and after fix the function call was not captured...
### Expected behavior
the same as `generate` | https://github.com/langchain-ai/langchain/issues/6225 | https://github.com/langchain-ai/langchain/pull/6226 | ea6a5b03e077526896071da80530bebb94eb390b | e2f36ee6082506049419875fa4a374f8fa2a88fe | "2023-06-15T13:22:11Z" | python | "2023-06-19T00:05:16Z" | langchain/chat_models/openai.py | """Get the default parameters for calling OpenAI API."""
return {
"model": self.model_name,
"request_timeout": self.request_timeout,
"max_tokens": self.max_tokens,
"stream": self.streaming,
"n": self.n,
"temperature": self.temperature,
**self.model_kwargs,
}
def _create_retry_decorator(self) -> Callable[[Any], Any]:
import openai
min_seconds = 1
max_seconds = 60
return retry(
reraise=True,
stop=stop_after_attempt(self.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=(
retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
def completion_with_retry(self, **kwargs: Any) -> Any: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,225 | OpenAI functions dont work with async streaming... | ### System Info
Version: 0.0.200
### Who can help?
@hwchase17 , @agola11
- I have a PR ready ... creating an issue so I can pair it
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
...
openai.py
async def _agenerate( ...
has different implementation than def generate...
when running the chain with `acall` >>
1. fails on
inner_completion += token # token is null, raises error
and after fix the function call was not captured...
### Expected behavior
the same as `generate` | https://github.com/langchain-ai/langchain/issues/6225 | https://github.com/langchain-ai/langchain/pull/6226 | ea6a5b03e077526896071da80530bebb94eb390b | e2f36ee6082506049419875fa4a374f8fa2a88fe | "2023-06-15T13:22:11Z" | python | "2023-06-19T00:05:16Z" | langchain/chat_models/openai.py | """Use tenacity to retry the completion call."""
retry_decorator = self._create_retry_decorator()
@retry_decorator
def _completion_with_retry(**kwargs: Any) -> Any:
return self.client.create(**kwargs)
return _completion_with_retry(**kwargs)
def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
overall_token_usage: dict = {}
for output in llm_outputs:
if output is None:
continue
token_usage = output["token_usage"]
for k, v in token_usage.items():
if k in overall_token_usage:
overall_token_usage[k] += v
else:
overall_token_usage[k] = v
return {"token_usage": overall_token_usage, "model_name": self.model_name}
def _generate( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,225 | OpenAI functions dont work with async streaming... | ### System Info
Version: 0.0.200
### Who can help?
@hwchase17 , @agola11
- I have a PR ready ... creating an issue so I can pair it
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
...
openai.py
async def _agenerate( ...
has different implementation than def generate...
when running the chain with `acall` >>
1. fails on
inner_completion += token # token is null, raises error
and after fix the function call was not captured...
### Expected behavior
the same as `generate` | https://github.com/langchain-ai/langchain/issues/6225 | https://github.com/langchain-ai/langchain/pull/6226 | ea6a5b03e077526896071da80530bebb94eb390b | e2f36ee6082506049419875fa4a374f8fa2a88fe | "2023-06-15T13:22:11Z" | python | "2023-06-19T00:05:16Z" | langchain/chat_models/openai.py | self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
message_dicts, params = self._create_message_dicts(messages, stop)
params = {**params, **kwargs}
if self.streaming:
inner_completion = ""
role = "assistant"
params["stream"] = True
function_call: Optional[dict] = None
for stream_resp in self.completion_with_retry(
messages=message_dicts, **params
):
role = stream_resp["choices"][0]["delta"].get("role", role)
token = stream_resp["choices"][0]["delta"].get("content") or "" |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,225 | OpenAI functions dont work with async streaming... | ### System Info
Version: 0.0.200
### Who can help?
@hwchase17 , @agola11
- I have a PR ready ... creating an issue so I can pair it
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
...
openai.py
async def _agenerate( ...
has different implementation than def generate...
when running the chain with `acall` >>
1. fails on
inner_completion += token # token is null, raises error
and after fix the function call was not captured...
### Expected behavior
the same as `generate` | https://github.com/langchain-ai/langchain/issues/6225 | https://github.com/langchain-ai/langchain/pull/6226 | ea6a5b03e077526896071da80530bebb94eb390b | e2f36ee6082506049419875fa4a374f8fa2a88fe | "2023-06-15T13:22:11Z" | python | "2023-06-19T00:05:16Z" | langchain/chat_models/openai.py | inner_completion += token
_function_call = stream_resp["choices"][0]["delta"].get("function_call")
if _function_call:
if function_call is None:
function_call = _function_call
else:
function_call["arguments"] += _function_call["arguments"]
if run_manager:
run_manager.on_llm_new_token(token)
message = _convert_dict_to_message(
{
"content": inner_completion,
"role": role,
"function_call": function_call,
}
)
return ChatResult(generations=[ChatGeneration(message=message)])
response = self.completion_with_retry(messages=message_dicts, **params)
return self._create_chat_result(response)
def _create_message_dicts(
self, messages: List[BaseMessage], stop: Optional[List[str]]
) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
params = dict(self._invocation_params)
if stop is not None:
if "stop" in params:
raise ValueError("`stop` found in both the input and default params.")
params["stop"] = stop
message_dicts = [_convert_message_to_dict(m) for m in messages]
return message_dicts, params
def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,225 | OpenAI functions dont work with async streaming... | ### System Info
Version: 0.0.200
### Who can help?
@hwchase17 , @agola11
- I have a PR ready ... creating an issue so I can pair it
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
...
openai.py
async def _agenerate( ...
has different implementation than def generate...
when running the chain with `acall` >>
1. fails on
inner_completion += token # token is null, raises error
and after fix the function call was not captured...
### Expected behavior
the same as `generate` | https://github.com/langchain-ai/langchain/issues/6225 | https://github.com/langchain-ai/langchain/pull/6226 | ea6a5b03e077526896071da80530bebb94eb390b | e2f36ee6082506049419875fa4a374f8fa2a88fe | "2023-06-15T13:22:11Z" | python | "2023-06-19T00:05:16Z" | langchain/chat_models/openai.py | generations = []
for res in response["choices"]:
message = _convert_dict_to_message(res["message"])
gen = ChatGeneration(message=message)
generations.append(gen)
llm_output = {"token_usage": response["usage"], "model_name": self.model_name}
return ChatResult(generations=generations, llm_output=llm_output)
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
message_dicts, params = self._create_message_dicts(messages, stop)
params = {**params, **kwargs}
if self.streaming:
inner_completion = ""
role = "assistant"
params["stream"] = True
async for stream_resp in await acompletion_with_retry(
self, messages=message_dicts, **params
):
role = stream_resp["choices"][0]["delta"].get("role", role)
token = stream_resp["choices"][0]["delta"].get("content", "")
inner_completion += token
if run_manager:
await run_manager.on_llm_new_token(token)
message = _convert_dict_to_message( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,225 | OpenAI functions dont work with async streaming... | ### System Info
Version: 0.0.200
### Who can help?
@hwchase17 , @agola11
- I have a PR ready ... creating an issue so I can pair it
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
...
openai.py
async def _agenerate( ...
has different implementation than def generate...
when running the chain with `acall` >>
1. fails on
inner_completion += token # token is null, raises error
and after fix the function call was not captured...
### Expected behavior
the same as `generate` | https://github.com/langchain-ai/langchain/issues/6225 | https://github.com/langchain-ai/langchain/pull/6226 | ea6a5b03e077526896071da80530bebb94eb390b | e2f36ee6082506049419875fa4a374f8fa2a88fe | "2023-06-15T13:22:11Z" | python | "2023-06-19T00:05:16Z" | langchain/chat_models/openai.py | {"content": inner_completion, "role": role}
)
return ChatResult(generations=[ChatGeneration(message=message)])
else:
response = await acompletion_with_retry(
self, messages=message_dicts, **params
)
return self._create_chat_result(response)
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {**{"model_name": self.model_name}, **self._default_params}
@property
def _invocation_params(self) -> Mapping[str, Any]:
"""Get the parameters used to invoke the model."""
openai_creds: Dict[str, Any] = {
"api_key": self.openai_api_key,
"api_base": self.openai_api_base,
"organization": self.openai_organization,
"model": self.model_name,
}
if self.openai_proxy:
import openai
openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy}
return {**openai_creds, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
return "openai-chat"
def _get_encoding_model(self) -> Tuple[str, tiktoken.Encoding]: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,225 | OpenAI functions dont work with async streaming... | ### System Info
Version: 0.0.200
### Who can help?
@hwchase17 , @agola11
- I have a PR ready ... creating an issue so I can pair it
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
...
openai.py
async def _agenerate( ...
has different implementation than def generate...
when running the chain with `acall` >>
1. fails on
inner_completion += token # token is null, raises error
and after fix the function call was not captured...
### Expected behavior
the same as `generate` | https://github.com/langchain-ai/langchain/issues/6225 | https://github.com/langchain-ai/langchain/pull/6226 | ea6a5b03e077526896071da80530bebb94eb390b | e2f36ee6082506049419875fa4a374f8fa2a88fe | "2023-06-15T13:22:11Z" | python | "2023-06-19T00:05:16Z" | langchain/chat_models/openai.py | tiktoken_ = _import_tiktoken()
model = self.model_name
if model == "gpt-3.5-turbo":
model = "gpt-3.5-turbo-0301"
elif model == "gpt-4":
model = "gpt-4-0314"
try:
encoding = tiktoken_.encoding_for_model(model)
except KeyError:
logger.warning("Warning: model not found. Using cl100k_base encoding.")
model = "cl100k_base"
encoding = tiktoken_.get_encoding(model)
return model, encoding
def get_token_ids(self, text: str) -> List[int]:
"""Get the tokens present in the text with tiktoken package."""
if sys.version_info[1] <= 7:
return super().get_token_ids(text)
_, encoding_model = self._get_encoding_model()
return encoding_model.encode(text)
def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:
"""Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
Official documentation: https://github.com/openai/openai-cookbook/blob/
main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb""" |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,225 | OpenAI functions dont work with async streaming... | ### System Info
Version: 0.0.200
### Who can help?
@hwchase17 , @agola11
- I have a PR ready ... creating an issue so I can pair it
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
...
openai.py
async def _agenerate( ...
has different implementation than def generate...
when running the chain with `acall` >>
1. fails on
inner_completion += token # token is null, raises error
and after fix the function call was not captured...
### Expected behavior
the same as `generate` | https://github.com/langchain-ai/langchain/issues/6225 | https://github.com/langchain-ai/langchain/pull/6226 | ea6a5b03e077526896071da80530bebb94eb390b | e2f36ee6082506049419875fa4a374f8fa2a88fe | "2023-06-15T13:22:11Z" | python | "2023-06-19T00:05:16Z" | langchain/chat_models/openai.py | if sys.version_info[1] <= 7:
return super().get_num_tokens_from_messages(messages)
model, encoding = self._get_encoding_model()
if model.startswith("gpt-3.5-turbo"):
tokens_per_message = 4
tokens_per_name = -1
elif model.startswith("gpt-4"):
tokens_per_message = 3
tokens_per_name = 1
else:
raise NotImplementedError(
f"get_num_tokens_from_messages() is not presently implemented "
f"for model {model}."
"See https://github.com/openai/openai-python/blob/main/chatml.md for "
"information on how messages are converted to tokens."
)
num_tokens = 0
messages_dict = [_convert_message_to_dict(m) for m in messages]
for message in messages_dict:
num_tokens += tokens_per_message
for key, value in message.items():
num_tokens += len(encoding.encode(value))
if key == "name":
num_tokens += tokens_per_name
num_tokens += 3
return num_tokens |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,807 | Issue: Integration tests fail for faiss vector store | ### Issue you'd like to raise.
Integration tests for faiss vector store fail when run.
It appears that the tests are not in sync with the module implementation.
command: poetry run pytest tests/integration_tests/vectorstores/test_faiss.py
Results summary:
======================================================= short test summary info =======================================================
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_faiss_local_save_load - FileExistsError: [Errno 17] File exists: '/var/folders/nm/q080zph50yz4mcc7_vcvdcy00000gp/T/tmpt6hov952'
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_faiss_similarity_search_with_relevance_scores - TypeError: __init__() got an unexpected keyword argument 'normalize_score_fn'
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_faiss_invalid_normalize_fn - TypeError: __init__() got an unexpected keyword argument 'normalize_score_fn'
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_missing_normalize_score_fn - Failed: DID NOT RAISE <class 'ValueError'>
=============================================== 4 failed, 6 passed, 2 warnings in 0.70s ===============================================
### Suggestion:
Correct tests/integration_tests/vectorstores/test_faiss.py to be in sync with langchain.vectorstores.faiss | https://github.com/langchain-ai/langchain/issues/5807 | https://github.com/langchain-ai/langchain/pull/6281 | ddd518a161f85a89f5c2dc0b8f262aba11cb3869 | 6aa7b04f7978e3783e386fd6714d9e1d44b3f5a2 | "2023-06-07T03:49:08Z" | python | "2023-06-19T00:25:49Z" | tests/integration_tests/vectorstores/test_faiss.py | """Test FAISS functionality."""
import datetime
import math
import tempfile
import pytest
from langchain.docstore.document import Document
from langchain.docstore.in_memory import InMemoryDocstore
from langchain.docstore.wikipedia import Wikipedia
from langchain.vectorstores.faiss import FAISS
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
def test_faiss() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = FAISS.from_texts(texts, FakeEmbeddings())
index_to_id = docsearch.index_to_docstore_id
expected_docstore = InMemoryDocstore(
{
index_to_id[0]: Document(page_content="foo"),
index_to_id[1]: Document(page_content="bar"),
index_to_id[2]: Document(page_content="baz"),
}
)
assert docsearch.docstore.__dict__ == expected_docstore.__dict__
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo")]
def test_faiss_vector_sim() -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,807 | Issue: Integration tests fail for faiss vector store | ### Issue you'd like to raise.
Integration tests for faiss vector store fail when run.
It appears that the tests are not in sync with the module implementation.
command: poetry run pytest tests/integration_tests/vectorstores/test_faiss.py
Results summary:
======================================================= short test summary info =======================================================
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_faiss_local_save_load - FileExistsError: [Errno 17] File exists: '/var/folders/nm/q080zph50yz4mcc7_vcvdcy00000gp/T/tmpt6hov952'
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_faiss_similarity_search_with_relevance_scores - TypeError: __init__() got an unexpected keyword argument 'normalize_score_fn'
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_faiss_invalid_normalize_fn - TypeError: __init__() got an unexpected keyword argument 'normalize_score_fn'
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_missing_normalize_score_fn - Failed: DID NOT RAISE <class 'ValueError'>
=============================================== 4 failed, 6 passed, 2 warnings in 0.70s ===============================================
### Suggestion:
Correct tests/integration_tests/vectorstores/test_faiss.py to be in sync with langchain.vectorstores.faiss | https://github.com/langchain-ai/langchain/issues/5807 | https://github.com/langchain-ai/langchain/pull/6281 | ddd518a161f85a89f5c2dc0b8f262aba11cb3869 | 6aa7b04f7978e3783e386fd6714d9e1d44b3f5a2 | "2023-06-07T03:49:08Z" | python | "2023-06-19T00:25:49Z" | tests/integration_tests/vectorstores/test_faiss.py | """Test vector similarity."""
texts = ["foo", "bar", "baz"]
docsearch = FAISS.from_texts(texts, FakeEmbeddings())
index_to_id = docsearch.index_to_docstore_id
expected_docstore = InMemoryDocstore(
{
index_to_id[0]: Document(page_content="foo"),
index_to_id[1]: Document(page_content="bar"),
index_to_id[2]: Document(page_content="baz"),
}
)
assert docsearch.docstore.__dict__ == expected_docstore.__dict__
query_vec = FakeEmbeddings().embed_query(text="foo")
output = docsearch.similarity_search_by_vector(query_vec, k=1)
assert output == [Document(page_content="foo")]
output = docsearch.max_marginal_relevance_search_by_vector(query_vec, k=10)
assert len(output) == len(texts)
def test_faiss_with_metadatas() -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,807 | Issue: Integration tests fail for faiss vector store | ### Issue you'd like to raise.
Integration tests for faiss vector store fail when run.
It appears that the tests are not in sync with the module implementation.
command: poetry run pytest tests/integration_tests/vectorstores/test_faiss.py
Results summary:
======================================================= short test summary info =======================================================
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_faiss_local_save_load - FileExistsError: [Errno 17] File exists: '/var/folders/nm/q080zph50yz4mcc7_vcvdcy00000gp/T/tmpt6hov952'
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_faiss_similarity_search_with_relevance_scores - TypeError: __init__() got an unexpected keyword argument 'normalize_score_fn'
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_faiss_invalid_normalize_fn - TypeError: __init__() got an unexpected keyword argument 'normalize_score_fn'
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_missing_normalize_score_fn - Failed: DID NOT RAISE <class 'ValueError'>
=============================================== 4 failed, 6 passed, 2 warnings in 0.70s ===============================================
### Suggestion:
Correct tests/integration_tests/vectorstores/test_faiss.py to be in sync with langchain.vectorstores.faiss | https://github.com/langchain-ai/langchain/issues/5807 | https://github.com/langchain-ai/langchain/pull/6281 | ddd518a161f85a89f5c2dc0b8f262aba11cb3869 | 6aa7b04f7978e3783e386fd6714d9e1d44b3f5a2 | "2023-06-07T03:49:08Z" | python | "2023-06-19T00:25:49Z" | tests/integration_tests/vectorstores/test_faiss.py | """Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = FAISS.from_texts(texts, FakeEmbeddings(), metadatas=metadatas)
expected_docstore = InMemoryDocstore(
{
docsearch.index_to_docstore_id[0]: Document(
page_content="foo", metadata={"page": 0}
),
docsearch.index_to_docstore_id[1]: Document(
page_content="bar", metadata={"page": 1}
),
docsearch.index_to_docstore_id[2]: Document(
page_content="baz", metadata={"page": 2}
),
}
)
assert docsearch.docstore.__dict__ == expected_docstore.__dict__
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo", metadata={"page": 0})]
def test_faiss_with_metadatas_and_filter() -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,807 | Issue: Integration tests fail for faiss vector store | ### Issue you'd like to raise.
Integration tests for faiss vector store fail when run.
It appears that the tests are not in sync with the module implementation.
command: poetry run pytest tests/integration_tests/vectorstores/test_faiss.py
Results summary:
======================================================= short test summary info =======================================================
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_faiss_local_save_load - FileExistsError: [Errno 17] File exists: '/var/folders/nm/q080zph50yz4mcc7_vcvdcy00000gp/T/tmpt6hov952'
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_faiss_similarity_search_with_relevance_scores - TypeError: __init__() got an unexpected keyword argument 'normalize_score_fn'
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_faiss_invalid_normalize_fn - TypeError: __init__() got an unexpected keyword argument 'normalize_score_fn'
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_missing_normalize_score_fn - Failed: DID NOT RAISE <class 'ValueError'>
=============================================== 4 failed, 6 passed, 2 warnings in 0.70s ===============================================
### Suggestion:
Correct tests/integration_tests/vectorstores/test_faiss.py to be in sync with langchain.vectorstores.faiss | https://github.com/langchain-ai/langchain/issues/5807 | https://github.com/langchain-ai/langchain/pull/6281 | ddd518a161f85a89f5c2dc0b8f262aba11cb3869 | 6aa7b04f7978e3783e386fd6714d9e1d44b3f5a2 | "2023-06-07T03:49:08Z" | python | "2023-06-19T00:25:49Z" | tests/integration_tests/vectorstores/test_faiss.py | texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = FAISS.from_texts(texts, FakeEmbeddings(), metadatas=metadatas)
expected_docstore = InMemoryDocstore(
{
docsearch.index_to_docstore_id[0]: Document(
page_content="foo", metadata={"page": 0}
),
docsearch.index_to_docstore_id[1]: Document(
page_content="bar", metadata={"page": 1}
),
docsearch.index_to_docstore_id[2]: Document(
page_content="baz", metadata={"page": 2}
),
}
)
assert docsearch.docstore.__dict__ == expected_docstore.__dict__
output = docsearch.similarity_search("foo", k=1, filter={"page": 1})
assert output == []
def test_faiss_search_not_found() -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,807 | Issue: Integration tests fail for faiss vector store | ### Issue you'd like to raise.
Integration tests for faiss vector store fail when run.
It appears that the tests are not in sync with the module implementation.
command: poetry run pytest tests/integration_tests/vectorstores/test_faiss.py
Results summary:
======================================================= short test summary info =======================================================
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_faiss_local_save_load - FileExistsError: [Errno 17] File exists: '/var/folders/nm/q080zph50yz4mcc7_vcvdcy00000gp/T/tmpt6hov952'
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_faiss_similarity_search_with_relevance_scores - TypeError: __init__() got an unexpected keyword argument 'normalize_score_fn'
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_faiss_invalid_normalize_fn - TypeError: __init__() got an unexpected keyword argument 'normalize_score_fn'
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_missing_normalize_score_fn - Failed: DID NOT RAISE <class 'ValueError'>
=============================================== 4 failed, 6 passed, 2 warnings in 0.70s ===============================================
### Suggestion:
Correct tests/integration_tests/vectorstores/test_faiss.py to be in sync with langchain.vectorstores.faiss | https://github.com/langchain-ai/langchain/issues/5807 | https://github.com/langchain-ai/langchain/pull/6281 | ddd518a161f85a89f5c2dc0b8f262aba11cb3869 | 6aa7b04f7978e3783e386fd6714d9e1d44b3f5a2 | "2023-06-07T03:49:08Z" | python | "2023-06-19T00:25:49Z" | tests/integration_tests/vectorstores/test_faiss.py | """Test what happens when document is not found."""
texts = ["foo", "bar", "baz"]
docsearch = FAISS.from_texts(texts, FakeEmbeddings())
docsearch.docstore = InMemoryDocstore({})
with pytest.raises(ValueError):
docsearch.similarity_search("foo")
def test_faiss_add_texts() -> None:
"""Test end to end adding of texts."""
texts = ["foo", "bar", "baz"]
docsearch = FAISS.from_texts(texts, FakeEmbeddings())
docsearch.add_texts(["foo"])
output = docsearch.similarity_search("foo", k=2)
assert output == [Document(page_content="foo"), Document(page_content="foo")]
def test_faiss_add_texts_not_supported() -> None:
"""Test adding of texts to a docstore that doesn't support it."""
docsearch = FAISS(FakeEmbeddings().embed_query, None, Wikipedia(), {})
with pytest.raises(ValueError):
docsearch.add_texts(["foo"])
def test_faiss_local_save_load() -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,807 | Issue: Integration tests fail for faiss vector store | ### Issue you'd like to raise.
Integration tests for faiss vector store fail when run.
It appears that the tests are not in sync with the module implementation.
command: poetry run pytest tests/integration_tests/vectorstores/test_faiss.py
Results summary:
======================================================= short test summary info =======================================================
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_faiss_local_save_load - FileExistsError: [Errno 17] File exists: '/var/folders/nm/q080zph50yz4mcc7_vcvdcy00000gp/T/tmpt6hov952'
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_faiss_similarity_search_with_relevance_scores - TypeError: __init__() got an unexpected keyword argument 'normalize_score_fn'
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_faiss_invalid_normalize_fn - TypeError: __init__() got an unexpected keyword argument 'normalize_score_fn'
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_missing_normalize_score_fn - Failed: DID NOT RAISE <class 'ValueError'>
=============================================== 4 failed, 6 passed, 2 warnings in 0.70s ===============================================
### Suggestion:
Correct tests/integration_tests/vectorstores/test_faiss.py to be in sync with langchain.vectorstores.faiss | https://github.com/langchain-ai/langchain/issues/5807 | https://github.com/langchain-ai/langchain/pull/6281 | ddd518a161f85a89f5c2dc0b8f262aba11cb3869 | 6aa7b04f7978e3783e386fd6714d9e1d44b3f5a2 | "2023-06-07T03:49:08Z" | python | "2023-06-19T00:25:49Z" | tests/integration_tests/vectorstores/test_faiss.py | """Test end to end serialization."""
texts = ["foo", "bar", "baz"]
docsearch = FAISS.from_texts(texts, FakeEmbeddings())
temp_timestamp = datetime.datetime.utcnow().strftime("%Y%m%d-%H%M%S")
with tempfile.TemporaryDirectory(suffix="_" + temp_timestamp + "/") as temp_folder:
docsearch.save_local(temp_folder)
new_docsearch = FAISS.load_local(temp_folder, FakeEmbeddings())
assert new_docsearch.index is not None
def test_faiss_similarity_search_with_relevance_scores() -> None:
"""Test the similarity search with normalized similarities."""
texts = ["foo", "bar", "baz"]
docsearch = FAISS.from_texts(
texts,
FakeEmbeddings(),
relevance_score_fn=lambda score: 1.0 - score / math.sqrt(2),
)
outputs = docsearch.similarity_search_with_relevance_scores("foo", k=1)
output, score = outputs[0]
assert output == Document(page_content="foo")
assert score == 1.0
def test_faiss_invalid_normalize_fn() -> None:
"""Test the similarity search with normalized similarities."""
texts = ["foo", "bar", "baz"]
docsearch = FAISS.from_texts(
texts, FakeEmbeddings(), relevance_score_fn=lambda _: 2.0
)
with pytest.warns(Warning, match="scores must be between"):
docsearch.similarity_search_with_relevance_scores("foo", k=1)
def test_missing_normalize_score_fn() -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,807 | Issue: Integration tests fail for faiss vector store | ### Issue you'd like to raise.
Integration tests for faiss vector store fail when run.
It appears that the tests are not in sync with the module implementation.
command: poetry run pytest tests/integration_tests/vectorstores/test_faiss.py
Results summary:
======================================================= short test summary info =======================================================
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_faiss_local_save_load - FileExistsError: [Errno 17] File exists: '/var/folders/nm/q080zph50yz4mcc7_vcvdcy00000gp/T/tmpt6hov952'
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_faiss_similarity_search_with_relevance_scores - TypeError: __init__() got an unexpected keyword argument 'normalize_score_fn'
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_faiss_invalid_normalize_fn - TypeError: __init__() got an unexpected keyword argument 'normalize_score_fn'
FAILED tests/integration_tests/vectorstores/test_faiss.py::test_missing_normalize_score_fn - Failed: DID NOT RAISE <class 'ValueError'>
=============================================== 4 failed, 6 passed, 2 warnings in 0.70s ===============================================
### Suggestion:
Correct tests/integration_tests/vectorstores/test_faiss.py to be in sync with langchain.vectorstores.faiss | https://github.com/langchain-ai/langchain/issues/5807 | https://github.com/langchain-ai/langchain/pull/6281 | ddd518a161f85a89f5c2dc0b8f262aba11cb3869 | 6aa7b04f7978e3783e386fd6714d9e1d44b3f5a2 | "2023-06-07T03:49:08Z" | python | "2023-06-19T00:25:49Z" | tests/integration_tests/vectorstores/test_faiss.py | """Test doesn't perform similarity search without a normalize score function."""
with pytest.raises(ValueError):
texts = ["foo", "bar", "baz"]
faiss_instance = FAISS.from_texts(texts, FakeEmbeddings())
faiss_instance.relevance_score_fn = None
faiss_instance.similarity_search_with_relevance_scores("foo", k=2) |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,131 | Azure Cognitive Search Vector Store doesn't apply search_kwargs when performing queries | ### System Info
Langchain 0.0.199
Python 3.10.11
Windows 11 (but will occur on any platform.
### Who can help?
@hwchase17
@ruoccofabrizio
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
To reproduce this issue create an AzureSearch Vector Store and a RetrievalQA with a search_kwargs, like in this sample code:
```
import os
cognitive_search_name = os.environ["AZURE_SEARCH_SERVICE_NAME"]
vector_store_address: str = f"https://{cognitive_search_name}.search.windows.net/"
index_name: str = os.environ["AZURE_SEARCH_SERVICE_INDEX_NAME"]
vector_store_password: str = os.environ["AZURE_SEARCH_SERVICE_ADMIN_KEY"]
from langchain.vectorstores.azuresearch import AzureSearch
embeddings = OpenAIEmbeddings(model="text-embedding-ada-002", chunk_size=1, client=any)
vector_store = AzureSearch(azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name=index_name,
embedding_function=embeddings.embed_query)
from langchain.chains import RetrievalQA
llm = AzureChatOpenAI(deployment_name="gpt35", model_name="gpt-3.5-turbo-0301", openai_api_version="2023-03-15-preview", temperature=temperature, client=None)
index = get_vector_store()
retriever = index.as_retriever()
retriever.search_kwargs = {'filters': "metadata eq 'something'"}
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever,
)
return qa
```
When you execute this code using ```qa``` the search_kwargs appear in the method ```similarity_search``` in ```azuresearch.py``` but are never passed to the methods ```vector_search```, ```hybrid_search```, and ```semantic_hybrid``` where they actually would be used.
### Expected behavior
In my example they should apply a filter to the azure cognitive search index before doing the vector search, but this is not happening because filters will always be empty when it gets to the functions where they are used. (```vector_search```, ```hybrid_search```, and ```semantic_hybrid```) | https://github.com/langchain-ai/langchain/issues/6131 | https://github.com/langchain-ai/langchain/pull/6132 | 395a2a3724507bafc7afe9e04ecbae60a7c66c7e | 22862043543e55fa0467c739714230eae3425512 | "2023-06-14T02:08:49Z" | python | "2023-06-19T00:39:06Z" | langchain/vectorstores/azuresearch.py | """Wrapper around Azure Cognitive Search."""
from __future__ import annotations
import base64
import json
import logging
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Tuple,
Type,
)
import numpy as np
from pydantic import BaseModel, root_validator
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema import BaseRetriever
from langchain.utils import get_from_env
from langchain.vectorstores.base import VectorStore
logger = logging.getLogger()
if TYPE_CHECKING:
from azure.search.documents import SearchClient
FIELDS_ID = get_from_env(
key="AZURESEARCH_FIELDS_ID", env_key="AZURESEARCH_FIELDS_ID", default="id"
) |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,131 | Azure Cognitive Search Vector Store doesn't apply search_kwargs when performing queries | ### System Info
Langchain 0.0.199
Python 3.10.11
Windows 11 (but will occur on any platform.
### Who can help?
@hwchase17
@ruoccofabrizio
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
To reproduce this issue create an AzureSearch Vector Store and a RetrievalQA with a search_kwargs, like in this sample code:
```
import os
cognitive_search_name = os.environ["AZURE_SEARCH_SERVICE_NAME"]
vector_store_address: str = f"https://{cognitive_search_name}.search.windows.net/"
index_name: str = os.environ["AZURE_SEARCH_SERVICE_INDEX_NAME"]
vector_store_password: str = os.environ["AZURE_SEARCH_SERVICE_ADMIN_KEY"]
from langchain.vectorstores.azuresearch import AzureSearch
embeddings = OpenAIEmbeddings(model="text-embedding-ada-002", chunk_size=1, client=any)
vector_store = AzureSearch(azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name=index_name,
embedding_function=embeddings.embed_query)
from langchain.chains import RetrievalQA
llm = AzureChatOpenAI(deployment_name="gpt35", model_name="gpt-3.5-turbo-0301", openai_api_version="2023-03-15-preview", temperature=temperature, client=None)
index = get_vector_store()
retriever = index.as_retriever()
retriever.search_kwargs = {'filters': "metadata eq 'something'"}
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever,
)
return qa
```
When you execute this code using ```qa``` the search_kwargs appear in the method ```similarity_search``` in ```azuresearch.py``` but are never passed to the methods ```vector_search```, ```hybrid_search```, and ```semantic_hybrid``` where they actually would be used.
### Expected behavior
In my example they should apply a filter to the azure cognitive search index before doing the vector search, but this is not happening because filters will always be empty when it gets to the functions where they are used. (```vector_search```, ```hybrid_search```, and ```semantic_hybrid```) | https://github.com/langchain-ai/langchain/issues/6131 | https://github.com/langchain-ai/langchain/pull/6132 | 395a2a3724507bafc7afe9e04ecbae60a7c66c7e | 22862043543e55fa0467c739714230eae3425512 | "2023-06-14T02:08:49Z" | python | "2023-06-19T00:39:06Z" | langchain/vectorstores/azuresearch.py | FIELDS_CONTENT = get_from_env(
key="AZURESEARCH_FIELDS_CONTENT",
env_key="AZURESEARCH_FIELDS_CONTENT",
default="content",
)
FIELDS_CONTENT_VECTOR = get_from_env(
key="AZURESEARCH_FIELDS_CONTENT_VECTOR",
env_key="AZURESEARCH_FIELDS_CONTENT_VECTOR",
default="content_vector",
)
FIELDS_METADATA = get_from_env(
key="AZURESEARCH_FIELDS_TAG", env_key="AZURESEARCH_FIELDS_TAG", default="metadata"
)
MAX_UPLOAD_BATCH_SIZE = 1000
def _get_search_client(
endpoint: str,
key: str,
index_name: str,
embedding_function: Callable,
semantic_configuration_name: Optional[str] = None,
) -> SearchClient:
from azure.core.credentials import AzureKeyCredential
from azure.core.exceptions import ResourceNotFoundError
from azure.identity import DefaultAzureCredential
from azure.search.documents import SearchClient
from azure.search.documents.indexes import SearchIndexClient
from azure.search.documents.indexes.models import (
PrioritizedFields,
SearchableField,
SearchField, |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,131 | Azure Cognitive Search Vector Store doesn't apply search_kwargs when performing queries | ### System Info
Langchain 0.0.199
Python 3.10.11
Windows 11 (but will occur on any platform.
### Who can help?
@hwchase17
@ruoccofabrizio
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
To reproduce this issue create an AzureSearch Vector Store and a RetrievalQA with a search_kwargs, like in this sample code:
```
import os
cognitive_search_name = os.environ["AZURE_SEARCH_SERVICE_NAME"]
vector_store_address: str = f"https://{cognitive_search_name}.search.windows.net/"
index_name: str = os.environ["AZURE_SEARCH_SERVICE_INDEX_NAME"]
vector_store_password: str = os.environ["AZURE_SEARCH_SERVICE_ADMIN_KEY"]
from langchain.vectorstores.azuresearch import AzureSearch
embeddings = OpenAIEmbeddings(model="text-embedding-ada-002", chunk_size=1, client=any)
vector_store = AzureSearch(azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name=index_name,
embedding_function=embeddings.embed_query)
from langchain.chains import RetrievalQA
llm = AzureChatOpenAI(deployment_name="gpt35", model_name="gpt-3.5-turbo-0301", openai_api_version="2023-03-15-preview", temperature=temperature, client=None)
index = get_vector_store()
retriever = index.as_retriever()
retriever.search_kwargs = {'filters': "metadata eq 'something'"}
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever,
)
return qa
```
When you execute this code using ```qa``` the search_kwargs appear in the method ```similarity_search``` in ```azuresearch.py``` but are never passed to the methods ```vector_search```, ```hybrid_search```, and ```semantic_hybrid``` where they actually would be used.
### Expected behavior
In my example they should apply a filter to the azure cognitive search index before doing the vector search, but this is not happening because filters will always be empty when it gets to the functions where they are used. (```vector_search```, ```hybrid_search```, and ```semantic_hybrid```) | https://github.com/langchain-ai/langchain/issues/6131 | https://github.com/langchain-ai/langchain/pull/6132 | 395a2a3724507bafc7afe9e04ecbae60a7c66c7e | 22862043543e55fa0467c739714230eae3425512 | "2023-06-14T02:08:49Z" | python | "2023-06-19T00:39:06Z" | langchain/vectorstores/azuresearch.py | SearchFieldDataType,
SearchIndex,
SemanticConfiguration,
SemanticField,
SemanticSettings,
SimpleField,
VectorSearch,
VectorSearchAlgorithmConfiguration,
)
if key is None:
credential = DefaultAzureCredential()
else:
credential = AzureKeyCredential(key)
index_client: SearchIndexClient = SearchIndexClient(
endpoint=endpoint, credential=credential
)
try:
index_client.get_index(name=index_name)
except ResourceNotFoundError:
fields = [
SimpleField(
name=FIELDS_ID,
type=SearchFieldDataType.String,
key=True,
filterable=True,
),
SearchableField(
name=FIELDS_CONTENT,
type=SearchFieldDataType.String, |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,131 | Azure Cognitive Search Vector Store doesn't apply search_kwargs when performing queries | ### System Info
Langchain 0.0.199
Python 3.10.11
Windows 11 (but will occur on any platform.
### Who can help?
@hwchase17
@ruoccofabrizio
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
To reproduce this issue create an AzureSearch Vector Store and a RetrievalQA with a search_kwargs, like in this sample code:
```
import os
cognitive_search_name = os.environ["AZURE_SEARCH_SERVICE_NAME"]
vector_store_address: str = f"https://{cognitive_search_name}.search.windows.net/"
index_name: str = os.environ["AZURE_SEARCH_SERVICE_INDEX_NAME"]
vector_store_password: str = os.environ["AZURE_SEARCH_SERVICE_ADMIN_KEY"]
from langchain.vectorstores.azuresearch import AzureSearch
embeddings = OpenAIEmbeddings(model="text-embedding-ada-002", chunk_size=1, client=any)
vector_store = AzureSearch(azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name=index_name,
embedding_function=embeddings.embed_query)
from langchain.chains import RetrievalQA
llm = AzureChatOpenAI(deployment_name="gpt35", model_name="gpt-3.5-turbo-0301", openai_api_version="2023-03-15-preview", temperature=temperature, client=None)
index = get_vector_store()
retriever = index.as_retriever()
retriever.search_kwargs = {'filters': "metadata eq 'something'"}
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever,
)
return qa
```
When you execute this code using ```qa``` the search_kwargs appear in the method ```similarity_search``` in ```azuresearch.py``` but are never passed to the methods ```vector_search```, ```hybrid_search```, and ```semantic_hybrid``` where they actually would be used.
### Expected behavior
In my example they should apply a filter to the azure cognitive search index before doing the vector search, but this is not happening because filters will always be empty when it gets to the functions where they are used. (```vector_search```, ```hybrid_search```, and ```semantic_hybrid```) | https://github.com/langchain-ai/langchain/issues/6131 | https://github.com/langchain-ai/langchain/pull/6132 | 395a2a3724507bafc7afe9e04ecbae60a7c66c7e | 22862043543e55fa0467c739714230eae3425512 | "2023-06-14T02:08:49Z" | python | "2023-06-19T00:39:06Z" | langchain/vectorstores/azuresearch.py | searchable=True,
retrievable=True,
),
SearchField(
name=FIELDS_CONTENT_VECTOR,
type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
searchable=True,
dimensions=len(embedding_function("Text")),
vector_search_configuration="default",
),
SearchableField(
name=FIELDS_METADATA,
type=SearchFieldDataType.String,
searchable=True,
retrievable=True,
),
]
vector_search = VectorSearch(
algorithm_configurations=[
VectorSearchAlgorithmConfiguration(
name="default",
kind="hnsw",
hnsw_parameters={
"m": 4,
"efConstruction": 400,
"efSearch": 500,
"metric": "cosine",
},
) |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,131 | Azure Cognitive Search Vector Store doesn't apply search_kwargs when performing queries | ### System Info
Langchain 0.0.199
Python 3.10.11
Windows 11 (but will occur on any platform.
### Who can help?
@hwchase17
@ruoccofabrizio
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
To reproduce this issue create an AzureSearch Vector Store and a RetrievalQA with a search_kwargs, like in this sample code:
```
import os
cognitive_search_name = os.environ["AZURE_SEARCH_SERVICE_NAME"]
vector_store_address: str = f"https://{cognitive_search_name}.search.windows.net/"
index_name: str = os.environ["AZURE_SEARCH_SERVICE_INDEX_NAME"]
vector_store_password: str = os.environ["AZURE_SEARCH_SERVICE_ADMIN_KEY"]
from langchain.vectorstores.azuresearch import AzureSearch
embeddings = OpenAIEmbeddings(model="text-embedding-ada-002", chunk_size=1, client=any)
vector_store = AzureSearch(azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name=index_name,
embedding_function=embeddings.embed_query)
from langchain.chains import RetrievalQA
llm = AzureChatOpenAI(deployment_name="gpt35", model_name="gpt-3.5-turbo-0301", openai_api_version="2023-03-15-preview", temperature=temperature, client=None)
index = get_vector_store()
retriever = index.as_retriever()
retriever.search_kwargs = {'filters': "metadata eq 'something'"}
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever,
)
return qa
```
When you execute this code using ```qa``` the search_kwargs appear in the method ```similarity_search``` in ```azuresearch.py``` but are never passed to the methods ```vector_search```, ```hybrid_search```, and ```semantic_hybrid``` where they actually would be used.
### Expected behavior
In my example they should apply a filter to the azure cognitive search index before doing the vector search, but this is not happening because filters will always be empty when it gets to the functions where they are used. (```vector_search```, ```hybrid_search```, and ```semantic_hybrid```) | https://github.com/langchain-ai/langchain/issues/6131 | https://github.com/langchain-ai/langchain/pull/6132 | 395a2a3724507bafc7afe9e04ecbae60a7c66c7e | 22862043543e55fa0467c739714230eae3425512 | "2023-06-14T02:08:49Z" | python | "2023-06-19T00:39:06Z" | langchain/vectorstores/azuresearch.py | ]
)
semantic_settings = (
None
if semantic_configuration_name is None
else SemanticSettings(
configurations=[
SemanticConfiguration(
name=semantic_configuration_name,
prioritized_fields=PrioritizedFields(
prioritized_content_fields=[
SemanticField(field_name=FIELDS_CONTENT)
],
),
)
]
)
)
index = SearchIndex(
name=index_name,
fields=fields,
vector_search=vector_search,
semantic_settings=semantic_settings,
)
index_client.create_index(index)
return SearchClient(endpoint=endpoint, index_name=index_name, credential=credential)
class AzureSearch(VectorStore): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,131 | Azure Cognitive Search Vector Store doesn't apply search_kwargs when performing queries | ### System Info
Langchain 0.0.199
Python 3.10.11
Windows 11 (but will occur on any platform.
### Who can help?
@hwchase17
@ruoccofabrizio
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
To reproduce this issue create an AzureSearch Vector Store and a RetrievalQA with a search_kwargs, like in this sample code:
```
import os
cognitive_search_name = os.environ["AZURE_SEARCH_SERVICE_NAME"]
vector_store_address: str = f"https://{cognitive_search_name}.search.windows.net/"
index_name: str = os.environ["AZURE_SEARCH_SERVICE_INDEX_NAME"]
vector_store_password: str = os.environ["AZURE_SEARCH_SERVICE_ADMIN_KEY"]
from langchain.vectorstores.azuresearch import AzureSearch
embeddings = OpenAIEmbeddings(model="text-embedding-ada-002", chunk_size=1, client=any)
vector_store = AzureSearch(azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name=index_name,
embedding_function=embeddings.embed_query)
from langchain.chains import RetrievalQA
llm = AzureChatOpenAI(deployment_name="gpt35", model_name="gpt-3.5-turbo-0301", openai_api_version="2023-03-15-preview", temperature=temperature, client=None)
index = get_vector_store()
retriever = index.as_retriever()
retriever.search_kwargs = {'filters': "metadata eq 'something'"}
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever,
)
return qa
```
When you execute this code using ```qa``` the search_kwargs appear in the method ```similarity_search``` in ```azuresearch.py``` but are never passed to the methods ```vector_search```, ```hybrid_search```, and ```semantic_hybrid``` where they actually would be used.
### Expected behavior
In my example they should apply a filter to the azure cognitive search index before doing the vector search, but this is not happening because filters will always be empty when it gets to the functions where they are used. (```vector_search```, ```hybrid_search```, and ```semantic_hybrid```) | https://github.com/langchain-ai/langchain/issues/6131 | https://github.com/langchain-ai/langchain/pull/6132 | 395a2a3724507bafc7afe9e04ecbae60a7c66c7e | 22862043543e55fa0467c739714230eae3425512 | "2023-06-14T02:08:49Z" | python | "2023-06-19T00:39:06Z" | langchain/vectorstores/azuresearch.py | def __init__(
self,
azure_search_endpoint: str,
azure_search_key: str,
index_name: str,
embedding_function: Callable,
search_type: str = "hybrid",
semantic_configuration_name: Optional[str] = None,
semantic_query_language: str = "en-us",
**kwargs: Any,
):
"""Initialize with necessary components."""
self.embedding_function = embedding_function
self.client = _get_search_client(
azure_search_endpoint,
azure_search_key,
index_name,
embedding_function,
semantic_configuration_name,
)
self.search_type = search_type
self.semantic_configuration_name = semantic_configuration_name
self.semantic_query_language = semantic_query_language
def add_texts( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,131 | Azure Cognitive Search Vector Store doesn't apply search_kwargs when performing queries | ### System Info
Langchain 0.0.199
Python 3.10.11
Windows 11 (but will occur on any platform.
### Who can help?
@hwchase17
@ruoccofabrizio
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
To reproduce this issue create an AzureSearch Vector Store and a RetrievalQA with a search_kwargs, like in this sample code:
```
import os
cognitive_search_name = os.environ["AZURE_SEARCH_SERVICE_NAME"]
vector_store_address: str = f"https://{cognitive_search_name}.search.windows.net/"
index_name: str = os.environ["AZURE_SEARCH_SERVICE_INDEX_NAME"]
vector_store_password: str = os.environ["AZURE_SEARCH_SERVICE_ADMIN_KEY"]
from langchain.vectorstores.azuresearch import AzureSearch
embeddings = OpenAIEmbeddings(model="text-embedding-ada-002", chunk_size=1, client=any)
vector_store = AzureSearch(azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name=index_name,
embedding_function=embeddings.embed_query)
from langchain.chains import RetrievalQA
llm = AzureChatOpenAI(deployment_name="gpt35", model_name="gpt-3.5-turbo-0301", openai_api_version="2023-03-15-preview", temperature=temperature, client=None)
index = get_vector_store()
retriever = index.as_retriever()
retriever.search_kwargs = {'filters': "metadata eq 'something'"}
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever,
)
return qa
```
When you execute this code using ```qa``` the search_kwargs appear in the method ```similarity_search``` in ```azuresearch.py``` but are never passed to the methods ```vector_search```, ```hybrid_search```, and ```semantic_hybrid``` where they actually would be used.
### Expected behavior
In my example they should apply a filter to the azure cognitive search index before doing the vector search, but this is not happening because filters will always be empty when it gets to the functions where they are used. (```vector_search```, ```hybrid_search```, and ```semantic_hybrid```) | https://github.com/langchain-ai/langchain/issues/6131 | https://github.com/langchain-ai/langchain/pull/6132 | 395a2a3724507bafc7afe9e04ecbae60a7c66c7e | 22862043543e55fa0467c739714230eae3425512 | "2023-06-14T02:08:49Z" | python | "2023-06-19T00:39:06Z" | langchain/vectorstores/azuresearch.py | self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Add texts data to an existing index."""
keys = kwargs.get("keys")
ids = []
data = []
for i, text in enumerate(texts):
key = keys[i] if keys else str(uuid.uuid4())
key = base64.urlsafe_b64encode(bytes(key, "utf-8")).decode("ascii")
metadata = metadatas[i] if metadatas else {}
data.append( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,131 | Azure Cognitive Search Vector Store doesn't apply search_kwargs when performing queries | ### System Info
Langchain 0.0.199
Python 3.10.11
Windows 11 (but will occur on any platform.
### Who can help?
@hwchase17
@ruoccofabrizio
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
To reproduce this issue create an AzureSearch Vector Store and a RetrievalQA with a search_kwargs, like in this sample code:
```
import os
cognitive_search_name = os.environ["AZURE_SEARCH_SERVICE_NAME"]
vector_store_address: str = f"https://{cognitive_search_name}.search.windows.net/"
index_name: str = os.environ["AZURE_SEARCH_SERVICE_INDEX_NAME"]
vector_store_password: str = os.environ["AZURE_SEARCH_SERVICE_ADMIN_KEY"]
from langchain.vectorstores.azuresearch import AzureSearch
embeddings = OpenAIEmbeddings(model="text-embedding-ada-002", chunk_size=1, client=any)
vector_store = AzureSearch(azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name=index_name,
embedding_function=embeddings.embed_query)
from langchain.chains import RetrievalQA
llm = AzureChatOpenAI(deployment_name="gpt35", model_name="gpt-3.5-turbo-0301", openai_api_version="2023-03-15-preview", temperature=temperature, client=None)
index = get_vector_store()
retriever = index.as_retriever()
retriever.search_kwargs = {'filters': "metadata eq 'something'"}
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever,
)
return qa
```
When you execute this code using ```qa``` the search_kwargs appear in the method ```similarity_search``` in ```azuresearch.py``` but are never passed to the methods ```vector_search```, ```hybrid_search```, and ```semantic_hybrid``` where they actually would be used.
### Expected behavior
In my example they should apply a filter to the azure cognitive search index before doing the vector search, but this is not happening because filters will always be empty when it gets to the functions where they are used. (```vector_search```, ```hybrid_search```, and ```semantic_hybrid```) | https://github.com/langchain-ai/langchain/issues/6131 | https://github.com/langchain-ai/langchain/pull/6132 | 395a2a3724507bafc7afe9e04ecbae60a7c66c7e | 22862043543e55fa0467c739714230eae3425512 | "2023-06-14T02:08:49Z" | python | "2023-06-19T00:39:06Z" | langchain/vectorstores/azuresearch.py | {
"@search.action": "upload",
FIELDS_ID: key,
FIELDS_CONTENT: text,
FIELDS_CONTENT_VECTOR: np.array(
self.embedding_function(text), dtype=np.float32
).tolist(),
FIELDS_METADATA: json.dumps(metadata),
}
)
ids.append(key)
if len(data) == MAX_UPLOAD_BATCH_SIZE:
response = self.client.upload_documents(documents=data)
if not all([r.succeeded for r in response]):
raise Exception(response)
data = []
if len(data) == 0:
return ids
response = self.client.upload_documents(documents=data)
if all([r.succeeded for r in response]):
return ids
else:
raise Exception(response)
def similarity_search( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,131 | Azure Cognitive Search Vector Store doesn't apply search_kwargs when performing queries | ### System Info
Langchain 0.0.199
Python 3.10.11
Windows 11 (but will occur on any platform.
### Who can help?
@hwchase17
@ruoccofabrizio
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
To reproduce this issue create an AzureSearch Vector Store and a RetrievalQA with a search_kwargs, like in this sample code:
```
import os
cognitive_search_name = os.environ["AZURE_SEARCH_SERVICE_NAME"]
vector_store_address: str = f"https://{cognitive_search_name}.search.windows.net/"
index_name: str = os.environ["AZURE_SEARCH_SERVICE_INDEX_NAME"]
vector_store_password: str = os.environ["AZURE_SEARCH_SERVICE_ADMIN_KEY"]
from langchain.vectorstores.azuresearch import AzureSearch
embeddings = OpenAIEmbeddings(model="text-embedding-ada-002", chunk_size=1, client=any)
vector_store = AzureSearch(azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name=index_name,
embedding_function=embeddings.embed_query)
from langchain.chains import RetrievalQA
llm = AzureChatOpenAI(deployment_name="gpt35", model_name="gpt-3.5-turbo-0301", openai_api_version="2023-03-15-preview", temperature=temperature, client=None)
index = get_vector_store()
retriever = index.as_retriever()
retriever.search_kwargs = {'filters': "metadata eq 'something'"}
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever,
)
return qa
```
When you execute this code using ```qa``` the search_kwargs appear in the method ```similarity_search``` in ```azuresearch.py``` but are never passed to the methods ```vector_search```, ```hybrid_search```, and ```semantic_hybrid``` where they actually would be used.
### Expected behavior
In my example they should apply a filter to the azure cognitive search index before doing the vector search, but this is not happening because filters will always be empty when it gets to the functions where they are used. (```vector_search```, ```hybrid_search```, and ```semantic_hybrid```) | https://github.com/langchain-ai/langchain/issues/6131 | https://github.com/langchain-ai/langchain/pull/6132 | 395a2a3724507bafc7afe9e04ecbae60a7c66c7e | 22862043543e55fa0467c739714230eae3425512 | "2023-06-14T02:08:49Z" | python | "2023-06-19T00:39:06Z" | langchain/vectorstores/azuresearch.py | self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
search_type = kwargs.get("search_type", self.search_type)
if search_type == "similarity":
docs = self.vector_search(query, k=k)
elif search_type == "hybrid":
docs = self.hybrid_search(query, k=k)
elif search_type == "semantic_hybrid":
docs = self.semantic_hybrid_search(query, k=k)
else:
raise ValueError(f"search_type of {search_type} not allowed.")
return docs
def vector_search(self, query: str, k: int = 4, **kwargs: Any) -> List[Document]:
"""
Returns the most similar indexed documents to the query text.
Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
Returns:
List[Document]: A list of documents that are most similar to the query text.
"""
docs_and_scores = self.vector_search_with_score(
query, k=k, filters=kwargs.get("filters", None)
)
return [doc for doc, _ in docs_and_scores]
def vector_search_with_score( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,131 | Azure Cognitive Search Vector Store doesn't apply search_kwargs when performing queries | ### System Info
Langchain 0.0.199
Python 3.10.11
Windows 11 (but will occur on any platform.
### Who can help?
@hwchase17
@ruoccofabrizio
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
To reproduce this issue create an AzureSearch Vector Store and a RetrievalQA with a search_kwargs, like in this sample code:
```
import os
cognitive_search_name = os.environ["AZURE_SEARCH_SERVICE_NAME"]
vector_store_address: str = f"https://{cognitive_search_name}.search.windows.net/"
index_name: str = os.environ["AZURE_SEARCH_SERVICE_INDEX_NAME"]
vector_store_password: str = os.environ["AZURE_SEARCH_SERVICE_ADMIN_KEY"]
from langchain.vectorstores.azuresearch import AzureSearch
embeddings = OpenAIEmbeddings(model="text-embedding-ada-002", chunk_size=1, client=any)
vector_store = AzureSearch(azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name=index_name,
embedding_function=embeddings.embed_query)
from langchain.chains import RetrievalQA
llm = AzureChatOpenAI(deployment_name="gpt35", model_name="gpt-3.5-turbo-0301", openai_api_version="2023-03-15-preview", temperature=temperature, client=None)
index = get_vector_store()
retriever = index.as_retriever()
retriever.search_kwargs = {'filters': "metadata eq 'something'"}
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever,
)
return qa
```
When you execute this code using ```qa``` the search_kwargs appear in the method ```similarity_search``` in ```azuresearch.py``` but are never passed to the methods ```vector_search```, ```hybrid_search```, and ```semantic_hybrid``` where they actually would be used.
### Expected behavior
In my example they should apply a filter to the azure cognitive search index before doing the vector search, but this is not happening because filters will always be empty when it gets to the functions where they are used. (```vector_search```, ```hybrid_search```, and ```semantic_hybrid```) | https://github.com/langchain-ai/langchain/issues/6131 | https://github.com/langchain-ai/langchain/pull/6132 | 395a2a3724507bafc7afe9e04ecbae60a7c66c7e | 22862043543e55fa0467c739714230eae3425512 | "2023-06-14T02:08:49Z" | python | "2023-06-19T00:39:06Z" | langchain/vectorstores/azuresearch.py | self, query: str, k: int = 4, filters: Optional[str] = None
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query and score for each
"""
from azure.search.documents.models import Vector
results = self.client.search(
search_text="",
vector=Vector(
value=np.array(
self.embedding_function(query), dtype=np.float32
).tolist(),
k=k,
fields=FIELDS_CONTENT_VECTOR, |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 6,131 | Azure Cognitive Search Vector Store doesn't apply search_kwargs when performing queries | ### System Info
Langchain 0.0.199
Python 3.10.11
Windows 11 (but will occur on any platform.
### Who can help?
@hwchase17
@ruoccofabrizio
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
To reproduce this issue create an AzureSearch Vector Store and a RetrievalQA with a search_kwargs, like in this sample code:
```
import os
cognitive_search_name = os.environ["AZURE_SEARCH_SERVICE_NAME"]
vector_store_address: str = f"https://{cognitive_search_name}.search.windows.net/"
index_name: str = os.environ["AZURE_SEARCH_SERVICE_INDEX_NAME"]
vector_store_password: str = os.environ["AZURE_SEARCH_SERVICE_ADMIN_KEY"]
from langchain.vectorstores.azuresearch import AzureSearch
embeddings = OpenAIEmbeddings(model="text-embedding-ada-002", chunk_size=1, client=any)
vector_store = AzureSearch(azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name=index_name,
embedding_function=embeddings.embed_query)
from langchain.chains import RetrievalQA
llm = AzureChatOpenAI(deployment_name="gpt35", model_name="gpt-3.5-turbo-0301", openai_api_version="2023-03-15-preview", temperature=temperature, client=None)
index = get_vector_store()
retriever = index.as_retriever()
retriever.search_kwargs = {'filters': "metadata eq 'something'"}
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever,
)
return qa
```
When you execute this code using ```qa``` the search_kwargs appear in the method ```similarity_search``` in ```azuresearch.py``` but are never passed to the methods ```vector_search```, ```hybrid_search```, and ```semantic_hybrid``` where they actually would be used.
### Expected behavior
In my example they should apply a filter to the azure cognitive search index before doing the vector search, but this is not happening because filters will always be empty when it gets to the functions where they are used. (```vector_search```, ```hybrid_search```, and ```semantic_hybrid```) | https://github.com/langchain-ai/langchain/issues/6131 | https://github.com/langchain-ai/langchain/pull/6132 | 395a2a3724507bafc7afe9e04ecbae60a7c66c7e | 22862043543e55fa0467c739714230eae3425512 | "2023-06-14T02:08:49Z" | python | "2023-06-19T00:39:06Z" | langchain/vectorstores/azuresearch.py | ),
select=[f"{FIELDS_ID},{FIELDS_CONTENT},{FIELDS_METADATA}"],
filter=filters,
)
docs = [
(
Document(
page_content=result[FIELDS_CONTENT],
metadata=json.loads(result[FIELDS_METADATA]),
),
float(result["@search.score"]),
)
for result in results
]
return docs
def hybrid_search(self, query: str, k: int = 4, **kwargs: Any) -> List[Document]:
"""
Returns the most similar indexed documents to the query text.
Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
Returns:
List[Document]: A list of documents that are most similar to the query text.
"""
docs_and_scores = self.hybrid_search_with_score(
query, k=k, filters=kwargs.get("filters", None)
)
return [doc for doc, _ in docs_and_scores]
def hybrid_search_with_score( |
Subsets and Splits