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ids = [md5(text.encode("utf-8")).hexdigest() for text in texts]
for i, doc in enumerate(texts):
doc_id = ids[i]
metadata = metadatas[i] if metadatas else {}
succeeded = self._index_doc(doc_id, doc, metadata)
if not succeeded:
self._delete_doc(doc_id)
self._index_doc(doc_id, doc, metadata)
return ids
[docs] def similarity_search_with_score(
self,
query: str,
k: int = 5,
alpha: float = 0.025,
filter: Optional[str] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return Vectara documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 5.
alpha: parameter for hybrid search (called "lambda" in Vectara
documentation).
filter: Dictionary of argument(s) to filter on metadata. For example a
filter can be "doc.rating > 3.0 and part.lang = 'deu'"} see
https://docs.vectara.com/docs/search-apis/sql/filter-overview
for more details.
Returns:
List of Documents most similar to the query and score for each.
"""
response = self._session.post(
headers=self._get_post_headers(),
url="https://api.vectara.io/v1/query",
data=json.dumps(
{
"query": [
{
"query": query,
"start": 0,
"num_results": k,
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"start": 0,
"num_results": k,
"context_config": {
"sentences_before": 3,
"sentences_after": 3,
},
"corpus_key": [
{
"customer_id": self._vectara_customer_id,
"corpus_id": self._vectara_corpus_id,
"metadataFilter": filter,
"lexical_interpolation_config": {"lambda": alpha},
}
],
}
]
}
),
timeout=10,
)
if response.status_code != 200:
logging.error(
"Query failed %s",
f"(code {response.status_code}, reason {response.reason}, details "
f"{response.text})",
)
return []
result = response.json()
responses = result["responseSet"][0]["response"]
vectara_default_metadata = ["lang", "len", "offset"]
docs = [
(
Document(
page_content=x["text"],
metadata={
m["name"]: m["value"]
for m in x["metadata"]
if m["name"] not in vectara_default_metadata
},
),
x["score"],
)
for x in responses
]
return docs
[docs] def similarity_search(
self,
query: str,
k: int = 5,
alpha: float = 0.025,
filter: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Return Vectara documents most similar to query, along with scores.
Args:
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"""Return Vectara documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 5.
filter: Dictionary of argument(s) to filter on metadata. For example a
filter can be "doc.rating > 3.0 and part.lang = 'deu'"} see
https://docs.vectara.com/docs/search-apis/sql/filter-overview for more
details.
Returns:
List of Documents most similar to the query
"""
docs_and_scores = self.similarity_search_with_score(
query, k=k, alpha=alpha, filter=filter, **kwargs
)
return [doc for doc, _ in docs_and_scores]
[docs] @classmethod
def from_texts(
cls: Type[Vectara],
texts: List[str],
embedding: Optional[Embeddings] = None,
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> Vectara:
"""Construct Vectara wrapper from raw documents.
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain import Vectara
vectara = Vectara.from_texts(
texts,
vectara_customer_id=customer_id,
vectara_corpus_id=corpus_id,
vectara_api_key=api_key,
)
"""
# Note: Vectara generates its own embeddings, so we ignore the provided
# embeddings (required by interface)
vectara = cls(**kwargs)
vectara.add_texts(texts, metadatas)
return vectara
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vectara.add_texts(texts, metadatas)
return vectara
[docs] def as_retriever(self, **kwargs: Any) -> VectaraRetriever:
return VectaraRetriever(vectorstore=self, **kwargs)
class VectaraRetriever(VectorStoreRetriever):
vectorstore: Vectara
search_kwargs: dict = Field(default_factory=lambda: {"alpha": 0.025, "k": 5})
"""Search params.
k: Number of Documents to return. Defaults to 5.
alpha: parameter for hybrid search (called "lambda" in Vectara
documentation).
filter: Dictionary of argument(s) to filter on metadata. For example a
filter can be "doc.rating > 3.0 and part.lang = 'deu'"} see
https://docs.vectara.com/docs/search-apis/sql/filter-overview
for more details.
"""
def add_texts(
self, texts: List[str], metadatas: Optional[List[dict]] = None
) -> None:
"""Add text to the Vectara vectorstore.
Args:
texts (List[str]): The text
metadatas (List[dict]): Metadata dicts, must line up with existing store
"""
self.vectorstore.add_texts(texts, metadatas)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html
|
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|
Source code for langchain.vectorstores.zilliz
from __future__ import annotations
import logging
from typing import Any, List, Optional
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.milvus import Milvus
logger = logging.getLogger(__name__)
[docs]class Zilliz(Milvus):
def _create_index(self) -> None:
"""Create a index on the collection"""
from pymilvus import Collection, MilvusException
if isinstance(self.col, Collection) and self._get_index() is None:
try:
# If no index params, use a default AutoIndex based one
if self.index_params is None:
self.index_params = {
"metric_type": "L2",
"index_type": "AUTOINDEX",
"params": {},
}
try:
self.col.create_index(
self._vector_field,
index_params=self.index_params,
using=self.alias,
)
# If default did not work, most likely Milvus self-hosted
except MilvusException:
# Use HNSW based index
self.index_params = {
"metric_type": "L2",
"index_type": "HNSW",
"params": {"M": 8, "efConstruction": 64},
}
self.col.create_index(
self._vector_field,
index_params=self.index_params,
using=self.alias,
)
logger.debug(
"Successfully created an index on collection: %s",
self.collection_name,
)
except MilvusException as e:
logger.error(
"Failed to create an index on collection: %s", self.collection_name
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|
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"Failed to create an index on collection: %s", self.collection_name
)
raise e
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
collection_name: str = "LangChainCollection",
connection_args: dict[str, Any] = {},
consistency_level: str = "Session",
index_params: Optional[dict] = None,
search_params: Optional[dict] = None,
drop_old: bool = False,
**kwargs: Any,
) -> Zilliz:
"""Create a Zilliz collection, indexes it with HNSW, and insert data.
Args:
texts (List[str]): Text data.
embedding (Embeddings): Embedding function.
metadatas (Optional[List[dict]]): Metadata for each text if it exists.
Defaults to None.
collection_name (str, optional): Collection name to use. Defaults to
"LangChainCollection".
connection_args (dict[str, Any], optional): Connection args to use. Defaults
to DEFAULT_MILVUS_CONNECTION.
consistency_level (str, optional): Which consistency level to use. Defaults
to "Session".
index_params (Optional[dict], optional): Which index_params to use.
Defaults to None.
search_params (Optional[dict], optional): Which search params to use.
Defaults to None.
drop_old (Optional[bool], optional): Whether to drop the collection with
that name if it exists. Defaults to False.
Returns:
Zilliz: Zilliz Vector Store
"""
vector_db = cls(
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|
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Zilliz: Zilliz Vector Store
"""
vector_db = cls(
embedding_function=embedding,
collection_name=collection_name,
connection_args=connection_args,
consistency_level=consistency_level,
index_params=index_params,
search_params=search_params,
drop_old=drop_old,
**kwargs,
)
vector_db.add_texts(texts=texts, metadatas=metadatas)
return vector_db
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/vectorstores/zilliz.html
|
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|
Source code for langchain.vectorstores.base
"""Interface for vector stores."""
from __future__ import annotations
import asyncio
import warnings
from abc import ABC, abstractmethod
from functools import partial
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type, TypeVar
from pydantic import BaseModel, Field, root_validator
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema import BaseRetriever
VST = TypeVar("VST", bound="VectorStore")
[docs]class VectorStore(ABC):
"""Interface for vector stores."""
[docs] @abstractmethod
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
kwargs: vectorstore specific parameters
Returns:
List of ids from adding the texts into the vectorstore.
"""
[docs] async def aadd_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore."""
raise NotImplementedError
[docs] def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
"""Run more documents through the embeddings and add to the vectorstore.
Args:
documents (List[Document]: Documents to add to the vectorstore.
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Args:
documents (List[Document]: Documents to add to the vectorstore.
Returns:
List[str]: List of IDs of the added texts.
"""
# TODO: Handle the case where the user doesn't provide ids on the Collection
texts = [doc.page_content for doc in documents]
metadatas = [doc.metadata for doc in documents]
return self.add_texts(texts, metadatas, **kwargs)
[docs] async def aadd_documents(
self, documents: List[Document], **kwargs: Any
) -> List[str]:
"""Run more documents through the embeddings and add to the vectorstore.
Args:
documents (List[Document]: Documents to add to the vectorstore.
Returns:
List[str]: List of IDs of the added texts.
"""
texts = [doc.page_content for doc in documents]
metadatas = [doc.metadata for doc in documents]
return await self.aadd_texts(texts, metadatas, **kwargs)
[docs] def search(self, query: str, search_type: str, **kwargs: Any) -> List[Document]:
"""Return docs most similar to query using specified search type."""
if search_type == "similarity":
return self.similarity_search(query, **kwargs)
elif search_type == "mmr":
return self.max_marginal_relevance_search(query, **kwargs)
else:
raise ValueError(
f"search_type of {search_type} not allowed. Expected "
"search_type to be 'similarity' or 'mmr'."
)
[docs] async def asearch(
self, query: str, search_type: str, **kwargs: Any
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self, query: str, search_type: str, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query using specified search type."""
if search_type == "similarity":
return await self.asimilarity_search(query, **kwargs)
elif search_type == "mmr":
return await self.amax_marginal_relevance_search(query, **kwargs)
else:
raise ValueError(
f"search_type of {search_type} not allowed. Expected "
"search_type to be 'similarity' or 'mmr'."
)
[docs] @abstractmethod
def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query."""
[docs] def similarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Args:
query: input text
k: Number of Documents to return. Defaults to 4.
**kwargs: kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns:
List of Tuples of (doc, similarity_score)
"""
docs_and_similarities = self._similarity_search_with_relevance_scores(
query, k=k, **kwargs
)
if any(
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|
query, k=k, **kwargs
)
if any(
similarity < 0.0 or similarity > 1.0
for _, similarity in docs_and_similarities
):
warnings.warn(
"Relevance scores must be between"
f" 0 and 1, got {docs_and_similarities}"
)
score_threshold = kwargs.get("score_threshold")
if score_threshold is not None:
docs_and_similarities = [
(doc, similarity)
for doc, similarity in docs_and_similarities
if similarity >= score_threshold
]
if len(docs_and_similarities) == 0:
warnings.warn(
f"No relevant docs were retrieved using the relevance score\
threshold {score_threshold}"
)
return docs_and_similarities
def _similarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and relevance scores, normalized on a scale from 0 to 1.
0 is dissimilar, 1 is most similar.
"""
raise NotImplementedError
[docs] async def asimilarity_search_with_relevance_scores(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query."""
# This is a temporary workaround to make the similarity search
# asynchronous. The proper solution is to make the similarity search
# asynchronous in the vector store implementations.
func = partial(self.similarity_search_with_relevance_scores, query, k, **kwargs)
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|
func = partial(self.similarity_search_with_relevance_scores, query, k, **kwargs)
return await asyncio.get_event_loop().run_in_executor(None, func)
[docs] async def asimilarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query."""
# This is a temporary workaround to make the similarity search
# asynchronous. The proper solution is to make the similarity search
# asynchronous in the vector store implementations.
func = partial(self.similarity_search, query, k, **kwargs)
return await asyncio.get_event_loop().run_in_executor(None, func)
[docs] def similarity_search_by_vector(
self, embedding: List[float], k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query vector.
"""
raise NotImplementedError
[docs] async def asimilarity_search_by_vector(
self, embedding: List[float], k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to embedding vector."""
# This is a temporary workaround to make the similarity search
# asynchronous. The proper solution is to make the similarity search
# asynchronous in the vector store implementations.
func = partial(self.similarity_search_by_vector, embedding, k, **kwargs)
return await asyncio.get_event_loop().run_in_executor(None, func)
[docs] def max_marginal_relevance_search(
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|
[docs] def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
raise NotImplementedError
[docs] async def amax_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance."""
# This is a temporary workaround to make the similarity search
# asynchronous. The proper solution is to make the similarity search
# asynchronous in the vector store implementations.
func = partial(
self.max_marginal_relevance_search, query, k, fetch_k, lambda_mult, **kwargs
)
return await asyncio.get_event_loop().run_in_executor(None, func)
|
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|
)
return await asyncio.get_event_loop().run_in_executor(None, func)
[docs] def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
raise NotImplementedError
[docs] async def amax_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance."""
raise NotImplementedError
[docs] @classmethod
def from_documents(
cls: Type[VST],
documents: List[Document],
embedding: Embeddings,
**kwargs: Any,
) -> VST:
"""Return VectorStore initialized from documents and embeddings."""
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|
) -> VST:
"""Return VectorStore initialized from documents and embeddings."""
texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
[docs] @classmethod
async def afrom_documents(
cls: Type[VST],
documents: List[Document],
embedding: Embeddings,
**kwargs: Any,
) -> VST:
"""Return VectorStore initialized from documents and embeddings."""
texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
return await cls.afrom_texts(texts, embedding, metadatas=metadatas, **kwargs)
[docs] @classmethod
@abstractmethod
def from_texts(
cls: Type[VST],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> VST:
"""Return VectorStore initialized from texts and embeddings."""
[docs] @classmethod
async def afrom_texts(
cls: Type[VST],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> VST:
"""Return VectorStore initialized from texts and embeddings."""
raise NotImplementedError
[docs] def as_retriever(self, **kwargs: Any) -> VectorStoreRetriever:
return VectorStoreRetriever(vectorstore=self, **kwargs)
class VectorStoreRetriever(BaseRetriever, BaseModel):
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class VectorStoreRetriever(BaseRetriever, BaseModel):
vectorstore: VectorStore
search_type: str = "similarity"
search_kwargs: dict = Field(default_factory=dict)
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
@root_validator()
def validate_search_type(cls, values: Dict) -> Dict:
"""Validate search type."""
if "search_type" in values:
search_type = values["search_type"]
if search_type not in ("similarity", "similarity_score_threshold", "mmr"):
raise ValueError(f"search_type of {search_type} not allowed.")
if search_type == "similarity_score_threshold":
score_threshold = values["search_kwargs"].get("score_threshold")
if (score_threshold is None) or (
not isinstance(score_threshold, float)
):
raise ValueError(
"`score_threshold` is not specified with a float value(0~1) "
"in `search_kwargs`."
)
return values
def get_relevant_documents(self, query: str) -> List[Document]:
if self.search_type == "similarity":
docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
elif self.search_type == "similarity_score_threshold":
docs_and_similarities = (
self.vectorstore.similarity_search_with_relevance_scores(
query, **self.search_kwargs
)
)
docs = [doc for doc, _ in docs_and_similarities]
elif self.search_type == "mmr":
docs = self.vectorstore.max_marginal_relevance_search(
query, **self.search_kwargs
)
else:
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query, **self.search_kwargs
)
else:
raise ValueError(f"search_type of {self.search_type} not allowed.")
return docs
async def aget_relevant_documents(self, query: str) -> List[Document]:
if self.search_type == "similarity":
docs = await self.vectorstore.asimilarity_search(
query, **self.search_kwargs
)
elif self.search_type == "similarity_score_threshold":
docs_and_similarities = (
await self.vectorstore.asimilarity_search_with_relevance_scores(
query, **self.search_kwargs
)
)
docs = [doc for doc, _ in docs_and_similarities]
elif self.search_type == "mmr":
docs = await self.vectorstore.amax_marginal_relevance_search(
query, **self.search_kwargs
)
else:
raise ValueError(f"search_type of {self.search_type} not allowed.")
return docs
def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
"""Add documents to vectorstore."""
return self.vectorstore.add_documents(documents, **kwargs)
async def aadd_documents(
self, documents: List[Document], **kwargs: Any
) -> List[str]:
"""Add documents to vectorstore."""
return await self.vectorstore.aadd_documents(documents, **kwargs)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html
|
bd9140eddc5a-0
|
Source code for langchain.vectorstores.lancedb
"""Wrapper around LanceDB vector database"""
from __future__ import annotations
import uuid
from typing import Any, Iterable, List, Optional
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.base import VectorStore
[docs]class LanceDB(VectorStore):
"""Wrapper around LanceDB vector database.
To use, you should have ``lancedb`` python package installed.
Example:
.. code-block:: python
db = lancedb.connect('./lancedb')
table = db.open_table('my_table')
vectorstore = LanceDB(table, embedding_function)
vectorstore.add_texts(['text1', 'text2'])
result = vectorstore.similarity_search('text1')
"""
def __init__(
self,
connection: Any,
embedding: Embeddings,
vector_key: Optional[str] = "vector",
id_key: Optional[str] = "id",
text_key: Optional[str] = "text",
):
"""Initialize with Lance DB connection"""
try:
import lancedb
except ImportError:
raise ValueError(
"Could not import lancedb python package. "
"Please install it with `pip install lancedb`."
)
if not isinstance(connection, lancedb.db.LanceTable):
raise ValueError(
"connection should be an instance of lancedb.db.LanceTable, ",
f"got {type(connection)}",
)
self._connection = connection
self._embedding = embedding
self._vector_key = vector_key
self._id_key = id_key
self._text_key = text_key
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self._id_key = id_key
self._text_key = text_key
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Turn texts into embedding and add it to the database
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids: Optional list of ids to associate with the texts.
Returns:
List of ids of the added texts.
"""
# Embed texts and create documents
docs = []
ids = ids or [str(uuid.uuid4()) for _ in texts]
embeddings = self._embedding.embed_documents(list(texts))
for idx, text in enumerate(texts):
embedding = embeddings[idx]
metadata = metadatas[idx] if metadatas else {}
docs.append(
{
self._vector_key: embedding,
self._id_key: ids[idx],
self._text_key: text,
**metadata,
}
)
self._connection.add(docs)
return ids
[docs] def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return documents most similar to the query
Args:
query: String to query the vectorstore with.
k: Number of documents to return.
Returns:
List of documents most similar to the query.
"""
embedding = self._embedding.embed_query(query)
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"""
embedding = self._embedding.embed_query(query)
docs = self._connection.search(embedding).limit(k).to_df()
return [
Document(
page_content=row[self._text_key],
metadata=row[docs.columns != self._text_key],
)
for _, row in docs.iterrows()
]
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
connection: Any = None,
vector_key: Optional[str] = "vector",
id_key: Optional[str] = "id",
text_key: Optional[str] = "text",
**kwargs: Any,
) -> LanceDB:
instance = LanceDB(
connection,
embedding,
vector_key,
id_key,
text_key,
)
instance.add_texts(texts, metadatas=metadatas, **kwargs)
return instance
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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Source code for langchain.vectorstores.annoy
"""Wrapper around Annoy vector database."""
from __future__ import annotations
import os
import pickle
import uuid
from configparser import ConfigParser
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
import numpy as np
from langchain.docstore.base import Docstore
from langchain.docstore.document import Document
from langchain.docstore.in_memory import InMemoryDocstore
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance
INDEX_METRICS = frozenset(["angular", "euclidean", "manhattan", "hamming", "dot"])
DEFAULT_METRIC = "angular"
def dependable_annoy_import() -> Any:
"""Import annoy if available, otherwise raise error."""
try:
import annoy
except ImportError:
raise ValueError(
"Could not import annoy python package. "
"Please install it with `pip install --user annoy` "
)
return annoy
[docs]class Annoy(VectorStore):
"""Wrapper around Annoy vector database.
To use, you should have the ``annoy`` python package installed.
Example:
.. code-block:: python
from langchain import Annoy
db = Annoy(embedding_function, index, docstore, index_to_docstore_id)
"""
def __init__(
self,
embedding_function: Callable,
index: Any,
metric: str,
docstore: Docstore,
index_to_docstore_id: Dict[int, str],
):
"""Initialize with necessary components."""
self.embedding_function = embedding_function
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):
"""Initialize with necessary components."""
self.embedding_function = embedding_function
self.index = index
self.metric = metric
self.docstore = docstore
self.index_to_docstore_id = index_to_docstore_id
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
raise NotImplementedError(
"Annoy does not allow to add new data once the index is build."
)
[docs] def process_index_results(
self, idxs: List[int], dists: List[float]
) -> List[Tuple[Document, float]]:
"""Turns annoy results into a list of documents and scores.
Args:
idxs: List of indices of the documents in the index.
dists: List of distances of the documents in the index.
Returns:
List of Documents and scores.
"""
docs = []
for idx, dist in zip(idxs, dists):
_id = self.index_to_docstore_id[idx]
doc = self.docstore.search(_id)
if not isinstance(doc, Document):
raise ValueError(f"Could not find document for id {_id}, got {doc}")
docs.append((doc, dist))
return docs
[docs] def similarity_search_with_score_by_vector(
self, embedding: List[float], k: int = 4, search_k: int = -1
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
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Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
search_k: inspect up to search_k nodes which defaults
to n_trees * n if not provided
Returns:
List of Documents most similar to the query and score for each
"""
idxs, dists = self.index.get_nns_by_vector(
embedding, k, search_k=search_k, include_distances=True
)
return self.process_index_results(idxs, dists)
[docs] def similarity_search_with_score_by_index(
self, docstore_index: int, k: int = 4, search_k: int = -1
) -> 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.
search_k: inspect up to search_k nodes which defaults
to n_trees * n if not provided
Returns:
List of Documents most similar to the query and score for each
"""
idxs, dists = self.index.get_nns_by_item(
docstore_index, k, search_k=search_k, include_distances=True
)
return self.process_index_results(idxs, dists)
[docs] def similarity_search_with_score(
self, query: str, k: int = 4, search_k: int = -1
) -> 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.
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k: Number of Documents to return. Defaults to 4.
search_k: inspect up to search_k nodes which defaults
to n_trees * n if not provided
Returns:
List of Documents most similar to the query and score for each
"""
embedding = self.embedding_function(query)
docs = self.similarity_search_with_score_by_vector(embedding, k, search_k)
return docs
[docs] def similarity_search_by_vector(
self, embedding: List[float], k: int = 4, search_k: int = -1, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
search_k: inspect up to search_k nodes which defaults
to n_trees * n if not provided
Returns:
List of Documents most similar to the embedding.
"""
docs_and_scores = self.similarity_search_with_score_by_vector(
embedding, k, search_k
)
return [doc for doc, _ in docs_and_scores]
[docs] def similarity_search_by_index(
self, docstore_index: int, k: int = 4, search_k: int = -1, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to docstore_index.
Args:
docstore_index: Index of document in docstore
k: Number of Documents to return. Defaults to 4.
search_k: inspect up to search_k nodes which defaults
to n_trees * n if not provided
Returns:
List of Documents most similar to the embedding.
"""
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Returns:
List of Documents most similar to the embedding.
"""
docs_and_scores = self.similarity_search_with_score_by_index(
docstore_index, k, search_k
)
return [doc for doc, _ in docs_and_scores]
[docs] def similarity_search(
self, query: str, k: int = 4, search_k: int = -1, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
search_k: inspect up to search_k nodes which defaults
to n_trees * n if not provided
Returns:
List of Documents most similar to the query.
"""
docs_and_scores = self.similarity_search_with_score(query, k, search_k)
return [doc for doc, _ in docs_and_scores]
[docs] def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
k: Number of Documents to return. Defaults to 4.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
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of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
idxs = self.index.get_nns_by_vector(
embedding, fetch_k, search_k=-1, include_distances=False
)
embeddings = [self.index.get_item_vector(i) for i in idxs]
mmr_selected = maximal_marginal_relevance(
np.array([embedding], dtype=np.float32),
embeddings,
k=k,
lambda_mult=lambda_mult,
)
# ignore the -1's if not enough docs are returned/indexed
selected_indices = [idxs[i] for i in mmr_selected if i != -1]
docs = []
for i in selected_indices:
_id = self.index_to_docstore_id[i]
doc = self.docstore.search(_id)
if not isinstance(doc, Document):
raise ValueError(f"Could not find document for id {_id}, got {doc}")
docs.append(doc)
return docs
[docs] def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
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k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
embedding = self.embedding_function(query)
docs = self.max_marginal_relevance_search_by_vector(
embedding, k, fetch_k, lambda_mult=lambda_mult
)
return docs
@classmethod
def __from(
cls,
texts: List[str],
embeddings: List[List[float]],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
metric: str = DEFAULT_METRIC,
trees: int = 100,
n_jobs: int = -1,
**kwargs: Any,
) -> Annoy:
if metric not in INDEX_METRICS:
raise ValueError(
(
f"Unsupported distance metric: {metric}. "
f"Expected one of {list(INDEX_METRICS)}"
)
)
annoy = dependable_annoy_import()
if not embeddings:
raise ValueError("embeddings must be provided to build AnnoyIndex")
f = len(embeddings[0])
index = annoy.AnnoyIndex(f, metric=metric)
for i, emb in enumerate(embeddings):
index.add_item(i, emb)
index.build(trees, n_jobs=n_jobs)
documents = []
for i, text in enumerate(texts):
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documents = []
for i, text in enumerate(texts):
metadata = metadatas[i] if metadatas else {}
documents.append(Document(page_content=text, metadata=metadata))
index_to_id = {i: str(uuid.uuid4()) for i in range(len(documents))}
docstore = InMemoryDocstore(
{index_to_id[i]: doc for i, doc in enumerate(documents)}
)
return cls(embedding.embed_query, index, metric, docstore, index_to_id)
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
metric: str = DEFAULT_METRIC,
trees: int = 100,
n_jobs: int = -1,
**kwargs: Any,
) -> Annoy:
"""Construct Annoy wrapper from raw documents.
Args:
texts: List of documents to index.
embedding: Embedding function to use.
metadatas: List of metadata dictionaries to associate with documents.
metric: Metric to use for indexing. Defaults to "angular".
trees: Number of trees to use for indexing. Defaults to 100.
n_jobs: Number of jobs to use for indexing. Defaults to -1.
This is a user friendly interface that:
1. Embeds documents.
2. Creates an in memory docstore
3. Initializes the Annoy database
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain import Annoy
from langchain.embeddings import OpenAIEmbeddings
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from langchain import Annoy
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
index = Annoy.from_texts(texts, embeddings)
"""
embeddings = embedding.embed_documents(texts)
return cls.__from(
texts, embeddings, embedding, metadatas, metric, trees, n_jobs, **kwargs
)
[docs] @classmethod
def from_embeddings(
cls,
text_embeddings: List[Tuple[str, List[float]]],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
metric: str = DEFAULT_METRIC,
trees: int = 100,
n_jobs: int = -1,
**kwargs: Any,
) -> Annoy:
"""Construct Annoy wrapper from embeddings.
Args:
text_embeddings: List of tuples of (text, embedding)
embedding: Embedding function to use.
metadatas: List of metadata dictionaries to associate with documents.
metric: Metric to use for indexing. Defaults to "angular".
trees: Number of trees to use for indexing. Defaults to 100.
n_jobs: Number of jobs to use for indexing. Defaults to -1
This is a user friendly interface that:
1. Creates an in memory docstore with provided embeddings
2. Initializes the Annoy database
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain import Annoy
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
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embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
db = Annoy.from_embeddings(text_embedding_pairs, embeddings)
"""
texts = [t[0] for t in text_embeddings]
embeddings = [t[1] for t in text_embeddings]
return cls.__from(
texts, embeddings, embedding, metadatas, metric, trees, n_jobs, **kwargs
)
[docs] def save_local(self, folder_path: str, prefault: bool = False) -> None:
"""Save Annoy index, docstore, and index_to_docstore_id to disk.
Args:
folder_path: folder path to save index, docstore,
and index_to_docstore_id to.
prefault: Whether to pre-load the index into memory.
"""
path = Path(folder_path)
os.makedirs(path, exist_ok=True)
# save index, index config, docstore and index_to_docstore_id
config_object = ConfigParser()
config_object["ANNOY"] = {
"f": self.index.f,
"metric": self.metric,
}
self.index.save(str(path / "index.annoy"), prefault=prefault)
with open(path / "index.pkl", "wb") as file:
pickle.dump((self.docstore, self.index_to_docstore_id, config_object), file)
[docs] @classmethod
def load_local(
cls,
folder_path: str,
embeddings: Embeddings,
) -> Annoy:
"""Load Annoy index, docstore, and index_to_docstore_id to disk.
Args:
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Args:
folder_path: folder path to load index, docstore,
and index_to_docstore_id from.
embeddings: Embeddings to use when generating queries.
"""
path = Path(folder_path)
# load index separately since it is not picklable
annoy = dependable_annoy_import()
# load docstore and index_to_docstore_id
with open(path / "index.pkl", "rb") as file:
docstore, index_to_docstore_id, config_object = pickle.load(file)
f = int(config_object["ANNOY"]["f"])
metric = config_object["ANNOY"]["metric"]
index = annoy.AnnoyIndex(f, metric=metric)
index.load(str(path / "index.annoy"))
return cls(
embeddings.embed_query, index, metric, docstore, index_to_docstore_id
)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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Source code for langchain.vectorstores.redis
"""Wrapper around Redis vector database."""
from __future__ import annotations
import json
import logging
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Literal,
Mapping,
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.utils import get_from_dict_or_env
from langchain.vectorstores.base import VectorStore, VectorStoreRetriever
logger = logging.getLogger(__name__)
if TYPE_CHECKING:
from redis.client import Redis as RedisType
from redis.commands.search.query import Query
# required modules
REDIS_REQUIRED_MODULES = [
{"name": "search", "ver": 20400},
{"name": "searchlight", "ver": 20400},
]
# distance mmetrics
REDIS_DISTANCE_METRICS = Literal["COSINE", "IP", "L2"]
def _check_redis_module_exist(client: RedisType, required_modules: List[dict]) -> None:
"""Check if the correct Redis modules are installed."""
installed_modules = client.module_list()
installed_modules = {
module[b"name"].decode("utf-8"): module for module in installed_modules
}
for module in required_modules:
if module["name"] in installed_modules and int(
installed_modules[module["name"]][b"ver"]
) >= int(module["ver"]):
return
# otherwise raise error
error_message = (
"Redis cannot be used as a vector database without RediSearch >=2.4"
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"Redis cannot be used as a vector database without RediSearch >=2.4"
"Please head to https://redis.io/docs/stack/search/quick_start/"
"to know more about installing the RediSearch module within Redis Stack."
)
logging.error(error_message)
raise ValueError(error_message)
def _check_index_exists(client: RedisType, index_name: str) -> bool:
"""Check if Redis index exists."""
try:
client.ft(index_name).info()
except: # noqa: E722
logger.info("Index does not exist")
return False
logger.info("Index already exists")
return True
def _redis_key(prefix: str) -> str:
"""Redis key schema for a given prefix."""
return f"{prefix}:{uuid.uuid4().hex}"
def _redis_prefix(index_name: str) -> str:
"""Redis key prefix for a given index."""
return f"doc:{index_name}"
def _default_relevance_score(val: float) -> float:
return 1 - val
[docs]class Redis(VectorStore):
"""Wrapper around Redis vector database.
To use, you should have the ``redis`` python package installed.
Example:
.. code-block:: python
from langchain.vectorstores import Redis
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = Redis(
redis_url="redis://username:password@localhost:6379"
index_name="my-index",
embedding_function=embeddings.embed_query,
)
"""
def __init__(
self,
redis_url: str,
index_name: str,
embedding_function: Callable,
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redis_url: str,
index_name: str,
embedding_function: Callable,
content_key: str = "content",
metadata_key: str = "metadata",
vector_key: str = "content_vector",
relevance_score_fn: Optional[
Callable[[float], float]
] = _default_relevance_score,
**kwargs: Any,
):
"""Initialize with necessary components."""
try:
import redis
except ImportError:
raise ValueError(
"Could not import redis python package. "
"Please install it with `pip install redis>=4.1.0`."
)
self.embedding_function = embedding_function
self.index_name = index_name
try:
# connect to redis from url
redis_client = redis.from_url(redis_url, **kwargs)
# check if redis has redisearch module installed
_check_redis_module_exist(redis_client, REDIS_REQUIRED_MODULES)
except ValueError as e:
raise ValueError(f"Redis failed to connect: {e}")
self.client = redis_client
self.content_key = content_key
self.metadata_key = metadata_key
self.vector_key = vector_key
self.relevance_score_fn = relevance_score_fn
def _create_index(
self, dim: int = 1536, distance_metric: REDIS_DISTANCE_METRICS = "COSINE"
) -> None:
try:
from redis.commands.search.field import TextField, VectorField
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
except ImportError:
raise ValueError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
)
# Check if index exists
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)
# Check if index exists
if not _check_index_exists(self.client, self.index_name):
# Define schema
schema = (
TextField(name=self.content_key),
TextField(name=self.metadata_key),
VectorField(
self.vector_key,
"FLAT",
{
"TYPE": "FLOAT32",
"DIM": dim,
"DISTANCE_METRIC": distance_metric,
},
),
)
prefix = _redis_prefix(self.index_name)
# Create Redis Index
self.client.ft(self.index_name).create_index(
fields=schema,
definition=IndexDefinition(prefix=[prefix], index_type=IndexType.HASH),
)
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
embeddings: Optional[List[List[float]]] = None,
keys: Optional[List[str]] = None,
batch_size: int = 1000,
**kwargs: Any,
) -> List[str]:
"""Add more texts to the vectorstore.
Args:
texts (Iterable[str]): Iterable of strings/text to add to the vectorstore.
metadatas (Optional[List[dict]], optional): Optional list of metadatas.
Defaults to None.
embeddings (Optional[List[List[float]]], optional): Optional pre-generated
embeddings. Defaults to None.
keys (Optional[List[str]], optional): Optional key values to use as ids.
Defaults to None.
batch_size (int, optional): Batch size to use for writes. Defaults to 1000.
Returns:
List[str]: List of ids added to the vectorstore
"""
ids = []
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List[str]: List of ids added to the vectorstore
"""
ids = []
prefix = _redis_prefix(self.index_name)
# Write data to redis
pipeline = self.client.pipeline(transaction=False)
for i, text in enumerate(texts):
# Use provided values by default or fallback
key = keys[i] if keys else _redis_key(prefix)
metadata = metadatas[i] if metadatas else {}
embedding = embeddings[i] if embeddings else self.embedding_function(text)
pipeline.hset(
key,
mapping={
self.content_key: text,
self.vector_key: np.array(embedding, dtype=np.float32).tobytes(),
self.metadata_key: json.dumps(metadata),
},
)
ids.append(key)
# Write batch
if i % batch_size == 0:
pipeline.execute()
# Cleanup final batch
pipeline.execute()
return ids
[docs] def similarity_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.similarity_search_with_score(query, k=k)
return [doc for doc, _ in docs_and_scores]
[docs] def similarity_search_limit_score(
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[docs] def similarity_search_limit_score(
self, query: str, k: int = 4, score_threshold: float = 0.2, **kwargs: Any
) -> List[Document]:
"""
Returns the most similar indexed documents to the query text within the
score_threshold range.
Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
score_threshold (float): The minimum matching score required for a document
to be considered a match. Defaults to 0.2.
Because the similarity calculation algorithm is based on cosine similarity,
the smaller the angle, the higher the similarity.
Returns:
List[Document]: A list of documents that are most similar to the query text,
including the match score for each document.
Note:
If there are no documents that satisfy the score_threshold value,
an empty list is returned.
"""
docs_and_scores = self.similarity_search_with_score(query, k=k)
return [doc for doc, score in docs_and_scores if score < score_threshold]
def _prepare_query(self, k: int) -> Query:
try:
from redis.commands.search.query import Query
except ImportError:
raise ValueError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
)
# Prepare the Query
hybrid_fields = "*"
base_query = (
f"{hybrid_fields}=>[KNN {k} @{self.vector_key} $vector AS vector_score]"
)
return_fields = [self.metadata_key, self.content_key, "vector_score"]
return (
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return (
Query(base_query)
.return_fields(*return_fields)
.sort_by("vector_score")
.paging(0, k)
.dialect(2)
)
[docs] def similarity_search_with_score(
self, query: str, k: int = 4
) -> 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
"""
# Creates embedding vector from user query
embedding = self.embedding_function(query)
# Creates Redis query
redis_query = self._prepare_query(k)
params_dict: Mapping[str, str] = {
"vector": np.array(embedding) # type: ignore
.astype(dtype=np.float32)
.tobytes()
}
# Perform vector search
results = self.client.ft(self.index_name).search(redis_query, params_dict)
# Prepare document results
docs = [
(
Document(
page_content=result.content, metadata=json.loads(result.metadata)
),
float(result.vector_score),
)
for result in results.docs
]
return docs
def _similarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and relevance scores, normalized on a scale from 0 to 1.
0 is dissimilar, 1 is most similar.
"""
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0 is dissimilar, 1 is most similar.
"""
if self.relevance_score_fn is None:
raise ValueError(
"relevance_score_fn must be provided to"
" Redis constructor to normalize scores"
)
docs_and_scores = self.similarity_search_with_score(query, k=k)
return [(doc, self.relevance_score_fn(score)) for doc, score in docs_and_scores]
[docs] @classmethod
def from_texts_return_keys(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
index_name: Optional[str] = None,
content_key: str = "content",
metadata_key: str = "metadata",
vector_key: str = "content_vector",
distance_metric: REDIS_DISTANCE_METRICS = "COSINE",
**kwargs: Any,
) -> Tuple[Redis, List[str]]:
"""Create a Redis vectorstore from raw documents.
This is a user-friendly interface that:
1. Embeds documents.
2. Creates a new index for the embeddings in Redis.
3. Adds the documents to the newly created Redis index.
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain.vectorstores import Redis
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
redisearch = RediSearch.from_texts(
texts,
embeddings,
redis_url="redis://username:password@localhost:6379"
)
"""
redis_url = get_from_dict_or_env(kwargs, "redis_url", "REDIS_URL")
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redis_url = get_from_dict_or_env(kwargs, "redis_url", "REDIS_URL")
if "redis_url" in kwargs:
kwargs.pop("redis_url")
# Name of the search index if not given
if not index_name:
index_name = uuid.uuid4().hex
# Create instance
instance = cls(
redis_url,
index_name,
embedding.embed_query,
content_key=content_key,
metadata_key=metadata_key,
vector_key=vector_key,
**kwargs,
)
# Create embeddings over documents
embeddings = embedding.embed_documents(texts)
# Create the search index
instance._create_index(dim=len(embeddings[0]), distance_metric=distance_metric)
# Add data to Redis
keys = instance.add_texts(texts, metadatas, embeddings)
return instance, keys
[docs] @classmethod
def from_texts(
cls: Type[Redis],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
index_name: Optional[str] = None,
content_key: str = "content",
metadata_key: str = "metadata",
vector_key: str = "content_vector",
**kwargs: Any,
) -> Redis:
"""Create a Redis vectorstore from raw documents.
This is a user-friendly interface that:
1. Embeds documents.
2. Creates a new index for the embeddings in Redis.
3. Adds the documents to the newly created Redis index.
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain.vectorstores import Redis
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Example:
.. code-block:: python
from langchain.vectorstores import Redis
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
redisearch = RediSearch.from_texts(
texts,
embeddings,
redis_url="redis://username:password@localhost:6379"
)
"""
instance, _ = cls.from_texts_return_keys(
texts,
embedding,
metadatas=metadatas,
index_name=index_name,
content_key=content_key,
metadata_key=metadata_key,
vector_key=vector_key,
**kwargs,
)
return instance
[docs] @staticmethod
def drop_index(
index_name: str,
delete_documents: bool,
**kwargs: Any,
) -> bool:
"""
Drop a Redis search index.
Args:
index_name (str): Name of the index to drop.
delete_documents (bool): Whether to drop the associated documents.
Returns:
bool: Whether or not the drop was successful.
"""
redis_url = get_from_dict_or_env(kwargs, "redis_url", "REDIS_URL")
try:
import redis
except ImportError:
raise ValueError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
)
try:
# We need to first remove redis_url from kwargs,
# otherwise passing it to Redis will result in an error.
if "redis_url" in kwargs:
kwargs.pop("redis_url")
client = redis.from_url(url=redis_url, **kwargs)
except ValueError as e:
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except ValueError as e:
raise ValueError(f"Your redis connected error: {e}")
# Check if index exists
try:
client.ft(index_name).dropindex(delete_documents)
logger.info("Drop index")
return True
except: # noqa: E722
# Index not exist
return False
[docs] @classmethod
def from_existing_index(
cls,
embedding: Embeddings,
index_name: str,
content_key: str = "content",
metadata_key: str = "metadata",
vector_key: str = "content_vector",
**kwargs: Any,
) -> Redis:
"""Connect to an existing Redis index."""
redis_url = get_from_dict_or_env(kwargs, "redis_url", "REDIS_URL")
try:
import redis
except ImportError:
raise ValueError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
)
try:
# We need to first remove redis_url from kwargs,
# otherwise passing it to Redis will result in an error.
if "redis_url" in kwargs:
kwargs.pop("redis_url")
client = redis.from_url(url=redis_url, **kwargs)
# check if redis has redisearch module installed
_check_redis_module_exist(client, REDIS_REQUIRED_MODULES)
# ensure that the index already exists
assert _check_index_exists(
client, index_name
), f"Index {index_name} does not exist"
except Exception as e:
raise ValueError(f"Redis failed to connect: {e}")
return cls(
redis_url,
index_name,
embedding.embed_query,
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return cls(
redis_url,
index_name,
embedding.embed_query,
content_key=content_key,
metadata_key=metadata_key,
vector_key=vector_key,
**kwargs,
)
[docs] def as_retriever(self, **kwargs: Any) -> RedisVectorStoreRetriever:
return RedisVectorStoreRetriever(vectorstore=self, **kwargs)
class RedisVectorStoreRetriever(VectorStoreRetriever, BaseModel):
vectorstore: Redis
search_type: str = "similarity"
k: int = 4
score_threshold: float = 0.4
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
@root_validator()
def validate_search_type(cls, values: Dict) -> Dict:
"""Validate search type."""
if "search_type" in values:
search_type = values["search_type"]
if search_type not in ("similarity", "similarity_limit"):
raise ValueError(f"search_type of {search_type} not allowed.")
return values
def get_relevant_documents(self, query: str) -> List[Document]:
if self.search_type == "similarity":
docs = self.vectorstore.similarity_search(query, k=self.k)
elif self.search_type == "similarity_limit":
docs = self.vectorstore.similarity_search_limit_score(
query, k=self.k, score_threshold=self.score_threshold
)
else:
raise ValueError(f"search_type of {self.search_type} not allowed.")
return docs
async def aget_relevant_documents(self, query: str) -> List[Document]:
raise NotImplementedError("RedisVectorStoreRetriever does not support async")
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raise NotImplementedError("RedisVectorStoreRetriever does not support async")
def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
"""Add documents to vectorstore."""
return self.vectorstore.add_documents(documents, **kwargs)
async def aadd_documents(
self, documents: List[Document], **kwargs: Any
) -> List[str]:
"""Add documents to vectorstore."""
return await self.vectorstore.aadd_documents(documents, **kwargs)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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|
Source code for langchain.vectorstores.supabase
from __future__ import annotations
from itertools import repeat
from typing import (
TYPE_CHECKING,
Any,
Iterable,
List,
Optional,
Tuple,
Type,
Union,
)
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance
if TYPE_CHECKING:
import supabase
[docs]class SupabaseVectorStore(VectorStore):
"""VectorStore for a Supabase postgres database. Assumes you have the `pgvector`
extension installed and a `match_documents` (or similar) function. For more details:
https://js.langchain.com/docs/modules/indexes/vector_stores/integrations/supabase
You can implement your own `match_documents` function in order to limit the search
space to a subset of documents based on your own authorization or business logic.
Note that the Supabase Python client does not yet support async operations.
If you'd like to use `max_marginal_relevance_search`, please review the instructions
below on modifying the `match_documents` function to return matched embeddings.
"""
_client: supabase.client.Client
# This is the embedding function. Don't confuse with the embedding vectors.
# We should perhaps rename the underlying Embedding base class to EmbeddingFunction
# or something
_embedding: Embeddings
table_name: str
query_name: str
def __init__(
self,
client: supabase.client.Client,
embedding: Embeddings,
table_name: str,
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embedding: Embeddings,
table_name: str,
query_name: Union[str, None] = None,
) -> None:
"""Initialize with supabase client."""
try:
import supabase # noqa: F401
except ImportError:
raise ValueError(
"Could not import supabase python package. "
"Please install it with `pip install supabase`."
)
self._client = client
self._embedding: Embeddings = embedding
self.table_name = table_name or "documents"
self.query_name = query_name or "match_documents"
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict[Any, Any]]] = None,
**kwargs: Any,
) -> List[str]:
docs = self._texts_to_documents(texts, metadatas)
vectors = self._embedding.embed_documents(list(texts))
return self.add_vectors(vectors, docs)
[docs] @classmethod
def from_texts(
cls: Type["SupabaseVectorStore"],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
client: Optional[supabase.client.Client] = None,
table_name: Optional[str] = "documents",
query_name: Union[str, None] = "match_documents",
**kwargs: Any,
) -> "SupabaseVectorStore":
"""Return VectorStore initialized from texts and embeddings."""
if not client:
raise ValueError("Supabase client is required.")
if not table_name:
raise ValueError("Supabase document table_name is required.")
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|
if not table_name:
raise ValueError("Supabase document table_name is required.")
embeddings = embedding.embed_documents(texts)
docs = cls._texts_to_documents(texts, metadatas)
_ids = cls._add_vectors(client, table_name, embeddings, docs)
return cls(
client=client,
embedding=embedding,
table_name=table_name,
query_name=query_name,
)
[docs] def add_vectors(
self, vectors: List[List[float]], documents: List[Document]
) -> List[str]:
return self._add_vectors(self._client, self.table_name, vectors, documents)
[docs] def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
vectors = self._embedding.embed_documents([query])
return self.similarity_search_by_vector(vectors[0], k)
[docs] def similarity_search_by_vector(
self, embedding: List[float], k: int = 4, **kwargs: Any
) -> List[Document]:
result = self.similarity_search_by_vector_with_relevance_scores(embedding, k)
documents = [doc for doc, _ in result]
return documents
[docs] def similarity_search_with_relevance_scores(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Tuple[Document, float]]:
vectors = self._embedding.embed_documents([query])
return self.similarity_search_by_vector_with_relevance_scores(vectors[0], k)
[docs] def similarity_search_by_vector_with_relevance_scores(
self, query: List[float], k: int
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|
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|
self, query: List[float], k: int
) -> List[Tuple[Document, float]]:
match_documents_params = dict(query_embedding=query, match_count=k)
res = self._client.rpc(self.query_name, match_documents_params).execute()
match_result = [
(
Document(
metadata=search.get("metadata", {}), # type: ignore
page_content=search.get("content", ""),
),
search.get("similarity", 0.0),
)
for search in res.data
if search.get("content")
]
return match_result
[docs] def similarity_search_by_vector_returning_embeddings(
self, query: List[float], k: int
) -> List[Tuple[Document, float, np.ndarray[np.float32, Any]]]:
match_documents_params = dict(query_embedding=query, match_count=k)
res = self._client.rpc(self.query_name, match_documents_params).execute()
match_result = [
(
Document(
metadata=search.get("metadata", {}), # type: ignore
page_content=search.get("content", ""),
),
search.get("similarity", 0.0),
# Supabase returns a vector type as its string represation (!).
# This is a hack to convert the string to numpy array.
np.fromstring(
search.get("embedding", "").strip("[]"), np.float32, sep=","
),
)
for search in res.data
if search.get("content")
]
return match_result
@staticmethod
def _texts_to_documents(
texts: Iterable[str],
metadatas: Optional[Iterable[dict[Any, Any]]] = None,
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|
metadatas: Optional[Iterable[dict[Any, Any]]] = None,
) -> List[Document]:
"""Return list of Documents from list of texts and metadatas."""
if metadatas is None:
metadatas = repeat({})
docs = [
Document(page_content=text, metadata=metadata)
for text, metadata in zip(texts, metadatas)
]
return docs
@staticmethod
def _add_vectors(
client: supabase.client.Client,
table_name: str,
vectors: List[List[float]],
documents: List[Document],
) -> List[str]:
"""Add vectors to Supabase table."""
rows: List[dict[str, Any]] = [
{
"content": documents[idx].page_content,
"embedding": embedding,
"metadata": documents[idx].metadata, # type: ignore
}
for idx, embedding in enumerate(vectors)
]
# According to the SupabaseVectorStore JS implementation, the best chunk size
# is 500
chunk_size = 500
id_list: List[str] = []
for i in range(0, len(rows), chunk_size):
chunk = rows[i : i + chunk_size]
result = client.from_(table_name).insert(chunk).execute() # type: ignore
if len(result.data) == 0:
raise Exception("Error inserting: No rows added")
# VectorStore.add_vectors returns ids as strings
ids = [str(i.get("id")) for i in result.data if i.get("id")]
id_list.extend(ids)
return id_list
[docs] def max_marginal_relevance_search_by_vector(
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|
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|
return id_list
[docs] def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
result = self.similarity_search_by_vector_returning_embeddings(
embedding, fetch_k
)
matched_documents = [doc_tuple[0] for doc_tuple in result]
matched_embeddings = [doc_tuple[2] for doc_tuple in result]
mmr_selected = maximal_marginal_relevance(
np.array([embedding], dtype=np.float32),
matched_embeddings,
k=k,
lambda_mult=lambda_mult,
)
filtered_documents = [matched_documents[i] for i in mmr_selected]
return filtered_documents
[docs] def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
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|
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|
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
`max_marginal_relevance_search` requires that `query_name` returns matched
embeddings alongside the match documents. The following function function
demonstrates how to do this:
```sql
CREATE FUNCTION match_documents_embeddings(query_embedding vector(1536),
match_count int)
RETURNS TABLE(
id bigint,
content text,
metadata jsonb,
embedding vector(1536),
similarity float)
LANGUAGE plpgsql
AS $$
# variable_conflict use_column
BEGIN
RETURN query
SELECT
id,
content,
metadata,
embedding,
1 -(docstore.embedding <=> query_embedding) AS similarity
FROM
docstore
ORDER BY
docstore.embedding <=> query_embedding
LIMIT match_count;
END;
$$;```
"""
embedding = self._embedding.embed_documents([query])
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|
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|
$$;```
"""
embedding = self._embedding.embed_documents([query])
docs = self.max_marginal_relevance_search_by_vector(
embedding[0], k, fetch_k, lambda_mult=lambda_mult
)
return docs
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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|
5827387a9d5b-0
|
Source code for langchain.vectorstores.qdrant
"""Wrapper around Qdrant vector database."""
from __future__ import annotations
import uuid
import warnings
from hashlib import md5
from operator import itemgetter
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Tuple,
Type,
Union,
)
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.vectorstores import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance
if TYPE_CHECKING:
from qdrant_client.http import models as rest
MetadataFilter = Dict[str, Union[str, int, bool, dict, list]]
[docs]class Qdrant(VectorStore):
"""Wrapper around Qdrant vector database.
To use you should have the ``qdrant-client`` package installed.
Example:
.. code-block:: python
from qdrant_client import QdrantClient
from langchain import Qdrant
client = QdrantClient()
collection_name = "MyCollection"
qdrant = Qdrant(client, collection_name, embedding_function)
"""
CONTENT_KEY = "page_content"
METADATA_KEY = "metadata"
def __init__(
self,
client: Any,
collection_name: str,
embeddings: Optional[Embeddings] = None,
content_payload_key: str = CONTENT_KEY,
metadata_payload_key: str = METADATA_KEY,
embedding_function: Optional[Callable] = None, # deprecated
):
"""Initialize with necessary components."""
try:
import qdrant_client
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"""Initialize with necessary components."""
try:
import qdrant_client
except ImportError:
raise ValueError(
"Could not import qdrant-client python package. "
"Please install it with `pip install qdrant-client`."
)
if not isinstance(client, qdrant_client.QdrantClient):
raise ValueError(
f"client should be an instance of qdrant_client.QdrantClient, "
f"got {type(client)}"
)
if embeddings is None and embedding_function is None:
raise ValueError(
"`embeddings` value can't be None. Pass `Embeddings` instance."
)
if embeddings is not None and embedding_function is not None:
raise ValueError(
"Both `embeddings` and `embedding_function` are passed. "
"Use `embeddings` only."
)
self.embeddings = embeddings
self._embeddings_function = embedding_function
self.client: qdrant_client.QdrantClient = client
self.collection_name = collection_name
self.content_payload_key = content_payload_key or self.CONTENT_KEY
self.metadata_payload_key = metadata_payload_key or self.METADATA_KEY
if embedding_function is not None:
warnings.warn(
"Using `embedding_function` is deprecated. "
"Pass `Embeddings` instance to `embeddings` instead."
)
if not isinstance(embeddings, Embeddings):
warnings.warn(
"`embeddings` should be an instance of `Embeddings`."
"Using `embeddings` as `embedding_function` which is deprecated"
)
self._embeddings_function = embeddings
self.embeddings = None
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)
self._embeddings_function = embeddings
self.embeddings = None
def _embed_query(self, query: str) -> List[float]:
"""Embed query text.
Used to provide backward compatibility with `embedding_function` argument.
Args:
query: Query text.
Returns:
List of floats representing the query embedding.
"""
if self.embeddings is not None:
embedding = self.embeddings.embed_query(query)
else:
if self._embeddings_function is not None:
embedding = self._embeddings_function(query)
else:
raise ValueError("Neither of embeddings or embedding_function is set")
return embedding.tolist() if hasattr(embedding, "tolist") else embedding
def _embed_texts(self, texts: Iterable[str]) -> List[List[float]]:
"""Embed search texts.
Used to provide backward compatibility with `embedding_function` argument.
Args:
texts: Iterable of texts to embed.
Returns:
List of floats representing the texts embedding.
"""
if self.embeddings is not None:
embeddings = self.embeddings.embed_documents(list(texts))
if hasattr(embeddings, "tolist"):
embeddings = embeddings.tolist()
elif self._embeddings_function is not None:
embeddings = []
for text in texts:
embedding = self._embeddings_function(text)
if hasattr(embeddings, "tolist"):
embedding = embedding.tolist()
embeddings.append(embedding)
else:
raise ValueError("Neither of embeddings or embedding_function is set")
return embeddings
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
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metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
Returns:
List of ids from adding the texts into the vectorstore.
"""
from qdrant_client.http import models as rest
texts = list(
texts
) # otherwise iterable might be exhausted after id calculation
ids = [md5(text.encode("utf-8")).hexdigest() for text in texts]
self.client.upsert(
collection_name=self.collection_name,
points=rest.Batch.construct(
ids=ids,
vectors=self._embed_texts(texts),
payloads=self._build_payloads(
texts,
metadatas,
self.content_payload_key,
self.metadata_payload_key,
),
),
)
return ids
[docs] def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[MetadataFilter] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
Returns:
List of Documents most similar to the query.
"""
results = self.similarity_search_with_score(query, k, filter)
return list(map(itemgetter(0), results))
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return list(map(itemgetter(0), results))
[docs] def similarity_search_with_score(
self, query: str, k: int = 4, filter: Optional[MetadataFilter] = 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.
filter: Filter by metadata. Defaults to None.
Returns:
List of Documents most similar to the query and score for each.
"""
results = self.client.search(
collection_name=self.collection_name,
query_vector=self._embed_query(query),
query_filter=self._qdrant_filter_from_dict(filter),
with_payload=True,
limit=k,
)
return [
(
self._document_from_scored_point(
result, self.content_payload_key, self.metadata_payload_key
),
result.score,
)
for result in results
]
[docs] def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
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Defaults to 20.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
embedding = self._embed_query(query)
results = self.client.search(
collection_name=self.collection_name,
query_vector=embedding,
with_payload=True,
with_vectors=True,
limit=fetch_k,
)
embeddings = [result.vector for result in results]
mmr_selected = maximal_marginal_relevance(
np.array(embedding), embeddings, k=k, lambda_mult=lambda_mult
)
return [
self._document_from_scored_point(
results[i], self.content_payload_key, self.metadata_payload_key
)
for i in mmr_selected
]
[docs] @classmethod
def from_texts(
cls: Type[Qdrant],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
location: Optional[str] = None,
url: Optional[str] = None,
port: Optional[int] = 6333,
grpc_port: int = 6334,
prefer_grpc: bool = False,
https: Optional[bool] = None,
api_key: Optional[str] = None,
prefix: Optional[str] = None,
timeout: Optional[float] = None,
host: Optional[str] = None,
path: Optional[str] = None,
collection_name: Optional[str] = None,
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path: Optional[str] = None,
collection_name: Optional[str] = None,
distance_func: str = "Cosine",
content_payload_key: str = CONTENT_KEY,
metadata_payload_key: str = METADATA_KEY,
**kwargs: Any,
) -> Qdrant:
"""Construct Qdrant wrapper from a list of texts.
Args:
texts: A list of texts to be indexed in Qdrant.
embedding: A subclass of `Embeddings`, responsible for text vectorization.
metadatas:
An optional list of metadata. If provided it has to be of the same
length as a list of texts.
location:
If `:memory:` - use in-memory Qdrant instance.
If `str` - use it as a `url` parameter.
If `None` - fallback to relying on `host` and `port` parameters.
url: either host or str of "Optional[scheme], host, Optional[port],
Optional[prefix]". Default: `None`
port: Port of the REST API interface. Default: 6333
grpc_port: Port of the gRPC interface. Default: 6334
prefer_grpc:
If true - use gPRC interface whenever possible in custom methods.
Default: False
https: If true - use HTTPS(SSL) protocol. Default: None
api_key: API key for authentication in Qdrant Cloud. Default: None
prefix:
If not None - add prefix to the REST URL path.
Example: service/v1 will result in
http://localhost:6333/service/v1/{qdrant-endpoint} for REST API.
Default: None
timeout:
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Default: None
timeout:
Timeout for REST and gRPC API requests.
Default: 5.0 seconds for REST and unlimited for gRPC
host:
Host name of Qdrant service. If url and host are None, set to
'localhost'. Default: None
path:
Path in which the vectors will be stored while using local mode.
Default: None
collection_name:
Name of the Qdrant collection to be used. If not provided,
it will be created randomly. Default: None
distance_func:
Distance function. One of: "Cosine" / "Euclid" / "Dot".
Default: "Cosine"
content_payload_key:
A payload key used to store the content of the document.
Default: "page_content"
metadata_payload_key:
A payload key used to store the metadata of the document.
Default: "metadata"
**kwargs:
Additional arguments passed directly into REST client initialization
This is a user friendly interface that:
1. Creates embeddings, one for each text
2. Initializes the Qdrant database as an in-memory docstore by default
(and overridable to a remote docstore)
3. Adds the text embeddings to the Qdrant database
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain import Qdrant
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
qdrant = Qdrant.from_texts(texts, embeddings, "localhost")
"""
try:
import qdrant_client
except ImportError:
raise ValueError(
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try:
import qdrant_client
except ImportError:
raise ValueError(
"Could not import qdrant-client python package. "
"Please install it with `pip install qdrant-client`."
)
from qdrant_client.http import models as rest
# Just do a single quick embedding to get vector size
partial_embeddings = embedding.embed_documents(texts[:1])
vector_size = len(partial_embeddings[0])
collection_name = collection_name or uuid.uuid4().hex
distance_func = distance_func.upper()
client = qdrant_client.QdrantClient(
location=location,
url=url,
port=port,
grpc_port=grpc_port,
prefer_grpc=prefer_grpc,
https=https,
api_key=api_key,
prefix=prefix,
timeout=timeout,
host=host,
path=path,
**kwargs,
)
client.recreate_collection(
collection_name=collection_name,
vectors_config=rest.VectorParams(
size=vector_size,
distance=rest.Distance[distance_func],
),
)
# Now generate the embeddings for all the texts
embeddings = embedding.embed_documents(texts)
client.upsert(
collection_name=collection_name,
points=rest.Batch.construct(
ids=[md5(text.encode("utf-8")).hexdigest() for text in texts],
vectors=embeddings,
payloads=cls._build_payloads(
texts, metadatas, content_payload_key, metadata_payload_key
),
),
)
return cls(
client=client,
collection_name=collection_name,
embeddings=embedding,
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client=client,
collection_name=collection_name,
embeddings=embedding,
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
)
@classmethod
def _build_payloads(
cls,
texts: Iterable[str],
metadatas: Optional[List[dict]],
content_payload_key: str,
metadata_payload_key: str,
) -> List[dict]:
payloads = []
for i, text in enumerate(texts):
if text is None:
raise ValueError(
"At least one of the texts is None. Please remove it before "
"calling .from_texts or .add_texts on Qdrant instance."
)
metadata = metadatas[i] if metadatas is not None else None
payloads.append(
{
content_payload_key: text,
metadata_payload_key: metadata,
}
)
return payloads
@classmethod
def _document_from_scored_point(
cls,
scored_point: Any,
content_payload_key: str,
metadata_payload_key: str,
) -> Document:
return Document(
page_content=scored_point.payload.get(content_payload_key),
metadata=scored_point.payload.get(metadata_payload_key) or {},
)
def _build_condition(self, key: str, value: Any) -> List[rest.FieldCondition]:
from qdrant_client.http import models as rest
out = []
if isinstance(value, dict):
for _key, value in value.items():
out.extend(self._build_condition(f"{key}.{_key}", value))
elif isinstance(value, list):
for _value in value:
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elif isinstance(value, list):
for _value in value:
if isinstance(_value, dict):
out.extend(self._build_condition(f"{key}[]", _value))
else:
out.extend(self._build_condition(f"{key}", _value))
else:
out.append(
rest.FieldCondition(
key=f"{self.metadata_payload_key}.{key}",
match=rest.MatchValue(value=value),
)
)
return out
def _qdrant_filter_from_dict(
self, filter: Optional[MetadataFilter]
) -> Optional[rest.Filter]:
from qdrant_client.http import models as rest
if not filter:
return None
return rest.Filter(
must=[
condition
for key, value in filter.items()
for condition in self._build_condition(key, value)
]
)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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Source code for langchain.vectorstores.milvus
"""Wrapper around the Milvus vector database."""
from __future__ import annotations
import logging
from typing import Any, Iterable, List, Optional, Tuple, Union
from uuid import uuid4
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance
logger = logging.getLogger(__name__)
DEFAULT_MILVUS_CONNECTION = {
"host": "localhost",
"port": "19530",
"user": "",
"password": "",
"secure": False,
}
[docs]class Milvus(VectorStore):
"""Wrapper around the Milvus vector database."""
def __init__(
self,
embedding_function: Embeddings,
collection_name: str = "LangChainCollection",
connection_args: Optional[dict[str, Any]] = None,
consistency_level: str = "Session",
index_params: Optional[dict] = None,
search_params: Optional[dict] = None,
drop_old: Optional[bool] = False,
):
"""Initialize wrapper around the milvus vector database.
In order to use this you need to have `pymilvus` installed and a
running Milvus/Zilliz Cloud instance.
See the following documentation for how to run a Milvus instance:
https://milvus.io/docs/install_standalone-docker.md
If looking for a hosted Milvus, take a looka this documentation:
https://zilliz.com/cloud
IF USING L2/IP metric IT IS HIGHLY SUGGESTED TO NORMALIZE YOUR DATA.
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The connection args used for this class comes in the form of a dict,
here are a few of the options:
address (str): The actual address of Milvus
instance. Example address: "localhost:19530"
uri (str): The uri of Milvus instance. Example uri:
"http://randomwebsite:19530",
"tcp:foobarsite:19530",
"https://ok.s3.south.com:19530".
host (str): The host of Milvus instance. Default at "localhost",
PyMilvus will fill in the default host if only port is provided.
port (str/int): The port of Milvus instance. Default at 19530, PyMilvus
will fill in the default port if only host is provided.
user (str): Use which user to connect to Milvus instance. If user and
password are provided, we will add related header in every RPC call.
password (str): Required when user is provided. The password
corresponding to the user.
secure (bool): Default is false. If set to true, tls will be enabled.
client_key_path (str): If use tls two-way authentication, need to
write the client.key path.
client_pem_path (str): If use tls two-way authentication, need to
write the client.pem path.
ca_pem_path (str): If use tls two-way authentication, need to write
the ca.pem path.
server_pem_path (str): If use tls one-way authentication, need to
write the server.pem path.
server_name (str): If use tls, need to write the common name.
Args:
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Args:
embedding_function (Embeddings): Function used to embed the text.
collection_name (str): Which Milvus collection to use. Defaults to
"LangChainCollection".
connection_args (Optional[dict[str, any]]): The arguments for connection to
Milvus/Zilliz instance. Defaults to DEFAULT_MILVUS_CONNECTION.
consistency_level (str): The consistency level to use for a collection.
Defaults to "Session".
index_params (Optional[dict]): Which index params to use. Defaults to
HNSW/AUTOINDEX depending on service.
search_params (Optional[dict]): Which search params to use. Defaults to
default of index.
drop_old (Optional[bool]): Whether to drop the current collection. Defaults
to False.
"""
try:
from pymilvus import Collection, utility
except ImportError:
raise ValueError(
"Could not import pymilvus python package. "
"Please install it with `pip install pymilvus`."
)
# Default search params when one is not provided.
self.default_search_params = {
"IVF_FLAT": {"metric_type": "L2", "params": {"nprobe": 10}},
"IVF_SQ8": {"metric_type": "L2", "params": {"nprobe": 10}},
"IVF_PQ": {"metric_type": "L2", "params": {"nprobe": 10}},
"HNSW": {"metric_type": "L2", "params": {"ef": 10}},
"RHNSW_FLAT": {"metric_type": "L2", "params": {"ef": 10}},
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"RHNSW_SQ": {"metric_type": "L2", "params": {"ef": 10}},
"RHNSW_PQ": {"metric_type": "L2", "params": {"ef": 10}},
"IVF_HNSW": {"metric_type": "L2", "params": {"nprobe": 10, "ef": 10}},
"ANNOY": {"metric_type": "L2", "params": {"search_k": 10}},
"AUTOINDEX": {"metric_type": "L2", "params": {}},
}
self.embedding_func = embedding_function
self.collection_name = collection_name
self.index_params = index_params
self.search_params = search_params
self.consistency_level = consistency_level
# In order for a collection to be compatible, pk needs to be auto'id and int
self._primary_field = "pk"
# In order for compatiblility, the text field will need to be called "text"
self._text_field = "text"
# In order for compatbility, the vector field needs to be called "vector"
self._vector_field = "vector"
self.fields: list[str] = []
# Create the connection to the server
if connection_args is None:
connection_args = DEFAULT_MILVUS_CONNECTION
self.alias = self._create_connection_alias(connection_args)
self.col: Optional[Collection] = None
# Grab the existing colection if it exists
if utility.has_collection(self.collection_name, using=self.alias):
self.col = Collection(
self.collection_name,
using=self.alias,
)
# If need to drop old, drop it
if drop_old and isinstance(self.col, Collection):
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if drop_old and isinstance(self.col, Collection):
self.col.drop()
self.col = None
# Initialize the vector store
self._init()
def _create_connection_alias(self, connection_args: dict) -> str:
"""Create the connection to the Milvus server."""
from pymilvus import MilvusException, connections
# Grab the connection arguments that are used for checking existing connection
host: str = connection_args.get("host", None)
port: Union[str, int] = connection_args.get("port", None)
address: str = connection_args.get("address", None)
uri: str = connection_args.get("uri", None)
user = connection_args.get("user", None)
# Order of use is host/port, uri, address
if host is not None and port is not None:
given_address = str(host) + ":" + str(port)
elif uri is not None:
given_address = uri.split("https://")[1]
elif address is not None:
given_address = address
else:
given_address = None
logger.debug("Missing standard address type for reuse atttempt")
# User defaults to empty string when getting connection info
if user is not None:
tmp_user = user
else:
tmp_user = ""
# If a valid address was given, then check if a connection exists
if given_address is not None:
for con in connections.list_connections():
addr = connections.get_connection_addr(con[0])
if (
con[1]
and ("address" in addr)
and (addr["address"] == given_address)
and ("user" in addr)
and (addr["user"] == tmp_user)
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and ("user" in addr)
and (addr["user"] == tmp_user)
):
logger.debug("Using previous connection: %s", con[0])
return con[0]
# Generate a new connection if one doesnt exist
alias = uuid4().hex
try:
connections.connect(alias=alias, **connection_args)
logger.debug("Created new connection using: %s", alias)
return alias
except MilvusException as e:
logger.error("Failed to create new connection using: %s", alias)
raise e
def _init(
self, embeddings: Optional[list] = None, metadatas: Optional[list[dict]] = None
) -> None:
if embeddings is not None:
self._create_collection(embeddings, metadatas)
self._extract_fields()
self._create_index()
self._create_search_params()
self._load()
def _create_collection(
self, embeddings: list, metadatas: Optional[list[dict]] = None
) -> None:
from pymilvus import (
Collection,
CollectionSchema,
DataType,
FieldSchema,
MilvusException,
)
from pymilvus.orm.types import infer_dtype_bydata
# Determine embedding dim
dim = len(embeddings[0])
fields = []
# Determine metadata schema
if metadatas:
# Create FieldSchema for each entry in metadata.
for key, value in metadatas[0].items():
# Infer the corresponding datatype of the metadata
dtype = infer_dtype_bydata(value)
# Datatype isnt compatible
if dtype == DataType.UNKNOWN or dtype == DataType.NONE:
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# Datatype isnt compatible
if dtype == DataType.UNKNOWN or dtype == DataType.NONE:
logger.error(
"Failure to create collection, unrecognized dtype for key: %s",
key,
)
raise ValueError(f"Unrecognized datatype for {key}.")
# Dataype is a string/varchar equivalent
elif dtype == DataType.VARCHAR:
fields.append(FieldSchema(key, DataType.VARCHAR, max_length=65_535))
else:
fields.append(FieldSchema(key, dtype))
# Create the text field
fields.append(
FieldSchema(self._text_field, DataType.VARCHAR, max_length=65_535)
)
# Create the primary key field
fields.append(
FieldSchema(
self._primary_field, DataType.INT64, is_primary=True, auto_id=True
)
)
# Create the vector field, supports binary or float vectors
fields.append(
FieldSchema(self._vector_field, infer_dtype_bydata(embeddings[0]), dim=dim)
)
# Create the schema for the collection
schema = CollectionSchema(fields)
# Create the collection
try:
self.col = Collection(
name=self.collection_name,
schema=schema,
consistency_level=self.consistency_level,
using=self.alias,
)
except MilvusException as e:
logger.error(
"Failed to create collection: %s error: %s", self.collection_name, e
)
raise e
def _extract_fields(self) -> None:
"""Grab the existing fields from the Collection"""
from pymilvus import Collection
if isinstance(self.col, Collection):
schema = self.col.schema
for x in schema.fields:
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schema = self.col.schema
for x in schema.fields:
self.fields.append(x.name)
# Since primary field is auto-id, no need to track it
self.fields.remove(self._primary_field)
def _get_index(self) -> Optional[dict[str, Any]]:
"""Return the vector index information if it exists"""
from pymilvus import Collection
if isinstance(self.col, Collection):
for x in self.col.indexes:
if x.field_name == self._vector_field:
return x.to_dict()
return None
def _create_index(self) -> None:
"""Create a index on the collection"""
from pymilvus import Collection, MilvusException
if isinstance(self.col, Collection) and self._get_index() is None:
try:
# If no index params, use a default HNSW based one
if self.index_params is None:
self.index_params = {
"metric_type": "L2",
"index_type": "HNSW",
"params": {"M": 8, "efConstruction": 64},
}
try:
self.col.create_index(
self._vector_field,
index_params=self.index_params,
using=self.alias,
)
# If default did not work, most likely on Zilliz Cloud
except MilvusException:
# Use AUTOINDEX based index
self.index_params = {
"metric_type": "L2",
"index_type": "AUTOINDEX",
"params": {},
}
self.col.create_index(
self._vector_field,
index_params=self.index_params,
using=self.alias,
)
logger.debug(
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using=self.alias,
)
logger.debug(
"Successfully created an index on collection: %s",
self.collection_name,
)
except MilvusException as e:
logger.error(
"Failed to create an index on collection: %s", self.collection_name
)
raise e
def _create_search_params(self) -> None:
"""Generate search params based on the current index type"""
from pymilvus import Collection
if isinstance(self.col, Collection) and self.search_params is None:
index = self._get_index()
if index is not None:
index_type: str = index["index_param"]["index_type"]
metric_type: str = index["index_param"]["metric_type"]
self.search_params = self.default_search_params[index_type]
self.search_params["metric_type"] = metric_type
def _load(self) -> None:
"""Load the collection if available."""
from pymilvus import Collection
if isinstance(self.col, Collection) and self._get_index() is not None:
self.col.load()
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
timeout: Optional[int] = None,
batch_size: int = 1000,
**kwargs: Any,
) -> List[str]:
"""Insert text data into Milvus.
Inserting data when the collection has not be made yet will result
in creating a new Collection. The data of the first entity decides
the schema of the new collection, the dim is extracted from the first
embedding and the columns are decided by the first metadata dict.
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embedding and the columns are decided by the first metadata dict.
Metada keys will need to be present for all inserted values. At
the moment there is no None equivalent in Milvus.
Args:
texts (Iterable[str]): The texts to embed, it is assumed
that they all fit in memory.
metadatas (Optional[List[dict]]): Metadata dicts attached to each of
the texts. Defaults to None.
timeout (Optional[int]): Timeout for each batch insert. Defaults
to None.
batch_size (int, optional): Batch size to use for insertion.
Defaults to 1000.
Raises:
MilvusException: Failure to add texts
Returns:
List[str]: The resulting keys for each inserted element.
"""
from pymilvus import Collection, MilvusException
texts = list(texts)
try:
embeddings = self.embedding_func.embed_documents(texts)
except NotImplementedError:
embeddings = [self.embedding_func.embed_query(x) for x in texts]
if len(embeddings) == 0:
logger.debug("Nothing to insert, skipping.")
return []
# If the collection hasnt been initialized yet, perform all steps to do so
if not isinstance(self.col, Collection):
self._init(embeddings, metadatas)
# Dict to hold all insert columns
insert_dict: dict[str, list] = {
self._text_field: texts,
self._vector_field: embeddings,
}
# Collect the metadata into the insert dict.
if metadatas is not None:
for d in metadatas:
for key, value in d.items():
if key in self.fields:
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for key, value in d.items():
if key in self.fields:
insert_dict.setdefault(key, []).append(value)
# Total insert count
vectors: list = insert_dict[self._vector_field]
total_count = len(vectors)
pks: list[str] = []
assert isinstance(self.col, Collection)
for i in range(0, total_count, batch_size):
# Grab end index
end = min(i + batch_size, total_count)
# Convert dict to list of lists batch for insertion
insert_list = [insert_dict[x][i:end] for x in self.fields]
# Insert into the collection.
try:
res: Collection
res = self.col.insert(insert_list, timeout=timeout, **kwargs)
pks.extend(res.primary_keys)
except MilvusException as e:
logger.error(
"Failed to insert batch starting at entity: %s/%s", i, total_count
)
raise e
return pks
[docs] def similarity_search(
self,
query: str,
k: int = 4,
param: Optional[dict] = None,
expr: Optional[str] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> List[Document]:
"""Perform a similarity search against the query string.
Args:
query (str): The text to search.
k (int, optional): How many results to return. Defaults to 4.
param (dict, optional): The search params for the index type.
Defaults to None.
expr (str, optional): Filtering expression. Defaults to None.
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Defaults to None.
expr (str, optional): Filtering expression. Defaults to None.
timeout (int, optional): How long to wait before timeout error.
Defaults to None.
kwargs: Collection.search() keyword arguments.
Returns:
List[Document]: Document results for search.
"""
if self.col is None:
logger.debug("No existing collection to search.")
return []
res = self.similarity_search_with_score(
query=query, k=k, param=param, expr=expr, timeout=timeout, **kwargs
)
return [doc for doc, _ in res]
[docs] def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
param: Optional[dict] = None,
expr: Optional[str] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> List[Document]:
"""Perform a similarity search against the query string.
Args:
embedding (List[float]): The embedding vector to search.
k (int, optional): How many results to return. Defaults to 4.
param (dict, optional): The search params for the index type.
Defaults to None.
expr (str, optional): Filtering expression. Defaults to None.
timeout (int, optional): How long to wait before timeout error.
Defaults to None.
kwargs: Collection.search() keyword arguments.
Returns:
List[Document]: Document results for search.
"""
if self.col is None:
logger.debug("No existing collection to search.")
return []
res = self.similarity_search_with_score_by_vector(
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return []
res = self.similarity_search_with_score_by_vector(
embedding=embedding, k=k, param=param, expr=expr, timeout=timeout, **kwargs
)
return [doc for doc, _ in res]
[docs] def similarity_search_with_score(
self,
query: str,
k: int = 4,
param: Optional[dict] = None,
expr: Optional[str] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Perform a search on a query string and return results with score.
For more information about the search parameters, take a look at the pymilvus
documentation found here:
https://milvus.io/api-reference/pymilvus/v2.2.6/Collection/search().md
Args:
query (str): The text being searched.
k (int, optional): The amount of results ot return. Defaults to 4.
param (dict): The search params for the specified index.
Defaults to None.
expr (str, optional): Filtering expression. Defaults to None.
timeout (int, optional): How long to wait before timeout error.
Defaults to None.
kwargs: Collection.search() keyword arguments.
Returns:
List[float], List[Tuple[Document, any, any]]:
"""
if self.col is None:
logger.debug("No existing collection to search.")
return []
# Embed the query text.
embedding = self.embedding_func.embed_query(query)
res = self.similarity_search_with_score_by_vector(
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res = self.similarity_search_with_score_by_vector(
embedding=embedding, k=k, param=param, expr=expr, timeout=timeout, **kwargs
)
return res
[docs] def similarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
param: Optional[dict] = None,
expr: Optional[str] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Perform a search on a query string and return results with score.
For more information about the search parameters, take a look at the pymilvus
documentation found here:
https://milvus.io/api-reference/pymilvus/v2.2.6/Collection/search().md
Args:
embedding (List[float]): The embedding vector being searched.
k (int, optional): The amount of results ot return. Defaults to 4.
param (dict): The search params for the specified index.
Defaults to None.
expr (str, optional): Filtering expression. Defaults to None.
timeout (int, optional): How long to wait before timeout error.
Defaults to None.
kwargs: Collection.search() keyword arguments.
Returns:
List[Tuple[Document, float]]: Result doc and score.
"""
if self.col is None:
logger.debug("No existing collection to search.")
return []
if param is None:
param = self.search_params
# Determine result metadata fields.
output_fields = self.fields[:]
output_fields.remove(self._vector_field)
# Perform the search.
res = self.col.search(
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# Perform the search.
res = self.col.search(
data=[embedding],
anns_field=self._vector_field,
param=param,
limit=k,
expr=expr,
output_fields=output_fields,
timeout=timeout,
**kwargs,
)
# Organize results.
ret = []
for result in res[0]:
meta = {x: result.entity.get(x) for x in output_fields}
doc = Document(page_content=meta.pop(self._text_field), metadata=meta)
pair = (doc, result.score)
ret.append(pair)
return ret
[docs] def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
param: Optional[dict] = None,
expr: Optional[str] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> List[Document]:
"""Perform a search and return results that are reordered by MMR.
Args:
query (str): The text being searched.
k (int, optional): How many results to give. Defaults to 4.
fetch_k (int, optional): Total results to select k from.
Defaults to 20.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5
param (dict, optional): The search params for the specified index.
Defaults to None.
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Defaults to None.
expr (str, optional): Filtering expression. Defaults to None.
timeout (int, optional): How long to wait before timeout error.
Defaults to None.
kwargs: Collection.search() keyword arguments.
Returns:
List[Document]: Document results for search.
"""
if self.col is None:
logger.debug("No existing collection to search.")
return []
embedding = self.embedding_func.embed_query(query)
return self.max_marginal_relevance_search_by_vector(
embedding=embedding,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
param=param,
expr=expr,
timeout=timeout,
**kwargs,
)
[docs] def max_marginal_relevance_search_by_vector(
self,
embedding: list[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
param: Optional[dict] = None,
expr: Optional[str] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> List[Document]:
"""Perform a search and return results that are reordered by MMR.
Args:
embedding (str): The embedding vector being searched.
k (int, optional): How many results to give. Defaults to 4.
fetch_k (int, optional): Total results to select k from.
Defaults to 20.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5
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to maximum diversity and 1 to minimum diversity.
Defaults to 0.5
param (dict, optional): The search params for the specified index.
Defaults to None.
expr (str, optional): Filtering expression. Defaults to None.
timeout (int, optional): How long to wait before timeout error.
Defaults to None.
kwargs: Collection.search() keyword arguments.
Returns:
List[Document]: Document results for search.
"""
if self.col is None:
logger.debug("No existing collection to search.")
return []
if param is None:
param = self.search_params
# Determine result metadata fields.
output_fields = self.fields[:]
output_fields.remove(self._vector_field)
# Perform the search.
res = self.col.search(
data=[embedding],
anns_field=self._vector_field,
param=param,
limit=fetch_k,
expr=expr,
output_fields=output_fields,
timeout=timeout,
**kwargs,
)
# Organize results.
ids = []
documents = []
scores = []
for result in res[0]:
meta = {x: result.entity.get(x) for x in output_fields}
doc = Document(page_content=meta.pop(self._text_field), metadata=meta)
documents.append(doc)
scores.append(result.score)
ids.append(result.id)
vectors = self.col.query(
expr=f"{self._primary_field} in {ids}",
output_fields=[self._primary_field, self._vector_field],
timeout=timeout,
)
# Reorganize the results from query to match search order.
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)
# Reorganize the results from query to match search order.
vectors = {x[self._primary_field]: x[self._vector_field] for x in vectors}
ordered_result_embeddings = [vectors[x] for x in ids]
# Get the new order of results.
new_ordering = maximal_marginal_relevance(
np.array(embedding), ordered_result_embeddings, k=k, lambda_mult=lambda_mult
)
# Reorder the values and return.
ret = []
for x in new_ordering:
# Function can return -1 index
if x == -1:
break
else:
ret.append(documents[x])
return ret
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
collection_name: str = "LangChainCollection",
connection_args: dict[str, Any] = DEFAULT_MILVUS_CONNECTION,
consistency_level: str = "Session",
index_params: Optional[dict] = None,
search_params: Optional[dict] = None,
drop_old: bool = False,
**kwargs: Any,
) -> Milvus:
"""Create a Milvus collection, indexes it with HNSW, and insert data.
Args:
texts (List[str]): Text data.
embedding (Embeddings): Embedding function.
metadatas (Optional[List[dict]]): Metadata for each text if it exists.
Defaults to None.
collection_name (str, optional): Collection name to use. Defaults to
"LangChainCollection".
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"LangChainCollection".
connection_args (dict[str, Any], optional): Connection args to use. Defaults
to DEFAULT_MILVUS_CONNECTION.
consistency_level (str, optional): Which consistency level to use. Defaults
to "Session".
index_params (Optional[dict], optional): Which index_params to use. Defaults
to None.
search_params (Optional[dict], optional): Which search params to use.
Defaults to None.
drop_old (Optional[bool], optional): Whether to drop the collection with
that name if it exists. Defaults to False.
Returns:
Milvus: Milvus Vector Store
"""
vector_db = cls(
embedding_function=embedding,
collection_name=collection_name,
connection_args=connection_args,
consistency_level=consistency_level,
index_params=index_params,
search_params=search_params,
drop_old=drop_old,
**kwargs,
)
vector_db.add_texts(texts=texts, metadatas=metadatas)
return vector_db
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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Source code for langchain.vectorstores.chroma
"""Wrapper around ChromaDB embeddings platform."""
from __future__ import annotations
import logging
import uuid
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Type
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.utils import xor_args
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance
if TYPE_CHECKING:
import chromadb
import chromadb.config
logger = logging.getLogger()
DEFAULT_K = 4 # Number of Documents to return.
def _results_to_docs(results: Any) -> List[Document]:
return [doc for doc, _ in _results_to_docs_and_scores(results)]
def _results_to_docs_and_scores(results: Any) -> List[Tuple[Document, float]]:
return [
# TODO: Chroma can do batch querying,
# we shouldn't hard code to the 1st result
(Document(page_content=result[0], metadata=result[1] or {}), result[2])
for result in zip(
results["documents"][0],
results["metadatas"][0],
results["distances"][0],
)
]
[docs]class Chroma(VectorStore):
"""Wrapper around ChromaDB embeddings platform.
To use, you should have the ``chromadb`` python package installed.
Example:
.. code-block:: python
from langchain.vectorstores import Chroma
from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = Chroma("langchain_store", embeddings.embed_query)
"""
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vectorstore = Chroma("langchain_store", embeddings.embed_query)
"""
_LANGCHAIN_DEFAULT_COLLECTION_NAME = "langchain"
def __init__(
self,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
embedding_function: Optional[Embeddings] = None,
persist_directory: Optional[str] = None,
client_settings: Optional[chromadb.config.Settings] = None,
collection_metadata: Optional[Dict] = None,
client: Optional[chromadb.Client] = None,
) -> None:
"""Initialize with Chroma client."""
try:
import chromadb
import chromadb.config
except ImportError:
raise ValueError(
"Could not import chromadb python package. "
"Please install it with `pip install chromadb`."
)
if client is not None:
self._client = client
else:
if client_settings:
self._client_settings = client_settings
else:
self._client_settings = chromadb.config.Settings()
if persist_directory is not None:
self._client_settings = chromadb.config.Settings(
chroma_db_impl="duckdb+parquet",
persist_directory=persist_directory,
)
self._client = chromadb.Client(self._client_settings)
self._embedding_function = embedding_function
self._persist_directory = persist_directory
self._collection = self._client.get_or_create_collection(
name=collection_name,
embedding_function=self._embedding_function.embed_documents
if self._embedding_function is not None
else None,
metadata=collection_metadata,
)
@xor_args(("query_texts", "query_embeddings"))
def __query_collection(
self,
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def __query_collection(
self,
query_texts: Optional[List[str]] = None,
query_embeddings: Optional[List[List[float]]] = None,
n_results: int = 4,
where: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Query the chroma collection."""
try:
import chromadb
except ImportError:
raise ValueError(
"Could not import chromadb python package. "
"Please install it with `pip install chromadb`."
)
for i in range(n_results, 0, -1):
try:
return self._collection.query(
query_texts=query_texts,
query_embeddings=query_embeddings,
n_results=i,
where=where,
**kwargs,
)
except chromadb.errors.NotEnoughElementsException:
logger.error(
f"Chroma collection {self._collection.name} "
f"contains fewer than {i} elements."
)
raise chromadb.errors.NotEnoughElementsException(
f"No documents found for Chroma collection {self._collection.name}"
)
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts (Iterable[str]): Texts to add to the vectorstore.
metadatas (Optional[List[dict]], optional): Optional list of metadatas.
ids (Optional[List[str]], optional): Optional list of IDs.
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ids (Optional[List[str]], optional): Optional list of IDs.
Returns:
List[str]: List of IDs of the added texts.
"""
# TODO: Handle the case where the user doesn't provide ids on the Collection
if ids is None:
ids = [str(uuid.uuid1()) for _ in texts]
embeddings = None
if self._embedding_function is not None:
embeddings = self._embedding_function.embed_documents(list(texts))
self._collection.add(
metadatas=metadatas, embeddings=embeddings, documents=texts, ids=ids
)
return ids
[docs] def similarity_search(
self,
query: str,
k: int = DEFAULT_K,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Run similarity search with Chroma.
Args:
query (str): Query text to search for.
k (int): Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List[Document]: List of documents most similar to the query text.
"""
docs_and_scores = self.similarity_search_with_score(query, k, filter=filter)
return [doc for doc, _ in docs_and_scores]
[docs] def similarity_search_by_vector(
self,
embedding: List[float],
k: int = DEFAULT_K,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
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"""Return docs most similar to embedding vector.
Args:
embedding (str): Embedding to look up documents similar to.
k (int): Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List of Documents most similar to the query vector.
"""
results = self.__query_collection(
query_embeddings=embedding, n_results=k, where=filter
)
return _results_to_docs(results)
[docs] def similarity_search_with_score(
self,
query: str,
k: int = DEFAULT_K,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Run similarity search with Chroma with distance.
Args:
query (str): Query text to search for.
k (int): Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List[Tuple[Document, float]]: List of documents most similar to the query
text with distance in float.
"""
if self._embedding_function is None:
results = self.__query_collection(
query_texts=[query], n_results=k, where=filter
)
else:
query_embedding = self._embedding_function.embed_query(query)
results = self.__query_collection(
query_embeddings=[query_embedding], n_results=k, where=filter
)
return _results_to_docs_and_scores(results)
[docs] def max_marginal_relevance_search_by_vector(
self,
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[docs] def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = DEFAULT_K,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List of Documents selected by maximal marginal relevance.
"""
results = self.__query_collection(
query_embeddings=embedding,
n_results=fetch_k,
where=filter,
include=["metadatas", "documents", "distances", "embeddings"],
)
mmr_selected = maximal_marginal_relevance(
np.array(embedding, dtype=np.float32),
results["embeddings"][0],
k=k,
lambda_mult=lambda_mult,
)
candidates = _results_to_docs(results)
selected_results = [r for i, r in enumerate(candidates) if i in mmr_selected]
return selected_results
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|
return selected_results
[docs] def max_marginal_relevance_search(
self,
query: str,
k: int = DEFAULT_K,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List of Documents selected by maximal marginal relevance.
"""
if self._embedding_function is None:
raise ValueError(
"For MMR search, you must specify an embedding function on" "creation."
)
embedding = self._embedding_function.embed_query(query)
docs = self.max_marginal_relevance_search_by_vector(
embedding, k, fetch_k, lambda_mul=lambda_mult, filter=filter
)
return docs
[docs] def delete_collection(self) -> None:
"""Delete the collection."""
self._client.delete_collection(self._collection.name)
[docs] def get(self, include: Optional[List[str]] = None) -> Dict[str, Any]:
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|
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|
"""Gets the collection.
Args:
include (Optional[List[str]]): List of fields to include from db.
Defaults to None.
"""
if include is not None:
return self._collection.get(include=include)
else:
return self._collection.get()
[docs] def persist(self) -> None:
"""Persist the collection.
This can be used to explicitly persist the data to disk.
It will also be called automatically when the object is destroyed.
"""
if self._persist_directory is None:
raise ValueError(
"You must specify a persist_directory on"
"creation to persist the collection."
)
self._client.persist()
[docs] def update_document(self, document_id: str, document: Document) -> None:
"""Update a document in the collection.
Args:
document_id (str): ID of the document to update.
document (Document): Document to update.
"""
text = document.page_content
metadata = document.metadata
self._collection.update_document(document_id, text, metadata)
[docs] @classmethod
def from_texts(
cls: Type[Chroma],
texts: List[str],
embedding: Optional[Embeddings] = None,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
persist_directory: Optional[str] = None,
client_settings: Optional[chromadb.config.Settings] = None,
client: Optional[chromadb.Client] = None,
**kwargs: Any,
) -> Chroma:
"""Create a Chroma vectorstore from a raw documents.
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|
) -> Chroma:
"""Create a Chroma vectorstore from a raw documents.
If a persist_directory is specified, the collection will be persisted there.
Otherwise, the data will be ephemeral in-memory.
Args:
texts (List[str]): List of texts to add to the collection.
collection_name (str): Name of the collection to create.
persist_directory (Optional[str]): Directory to persist the collection.
embedding (Optional[Embeddings]): Embedding function. Defaults to None.
metadatas (Optional[List[dict]]): List of metadatas. Defaults to None.
ids (Optional[List[str]]): List of document IDs. Defaults to None.
client_settings (Optional[chromadb.config.Settings]): Chroma client settings
Returns:
Chroma: Chroma vectorstore.
"""
chroma_collection = cls(
collection_name=collection_name,
embedding_function=embedding,
persist_directory=persist_directory,
client_settings=client_settings,
client=client,
)
chroma_collection.add_texts(texts=texts, metadatas=metadatas, ids=ids)
return chroma_collection
[docs] @classmethod
def from_documents(
cls: Type[Chroma],
documents: List[Document],
embedding: Optional[Embeddings] = None,
ids: Optional[List[str]] = None,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
persist_directory: Optional[str] = None,
client_settings: Optional[chromadb.config.Settings] = None,
client: Optional[chromadb.Client] = None, # Add this line
**kwargs: Any,
) -> Chroma:
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|
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|
**kwargs: Any,
) -> Chroma:
"""Create a Chroma vectorstore from a list of documents.
If a persist_directory is specified, the collection will be persisted there.
Otherwise, the data will be ephemeral in-memory.
Args:
collection_name (str): Name of the collection to create.
persist_directory (Optional[str]): Directory to persist the collection.
ids (Optional[List[str]]): List of document IDs. Defaults to None.
documents (List[Document]): List of documents to add to the vectorstore.
embedding (Optional[Embeddings]): Embedding function. Defaults to None.
client_settings (Optional[chromadb.config.Settings]): Chroma client settings
Returns:
Chroma: Chroma vectorstore.
"""
texts = [doc.page_content for doc in documents]
metadatas = [doc.metadata for doc in documents]
return cls.from_texts(
texts=texts,
embedding=embedding,
metadatas=metadatas,
ids=ids,
collection_name=collection_name,
persist_directory=persist_directory,
client_settings=client_settings,
client=client,
)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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|
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|
Source code for langchain.vectorstores.deeplake
"""Wrapper around Activeloop Deep Lake."""
from __future__ import annotations
import logging
import uuid
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance
logger = logging.getLogger(__name__)
distance_metric_map = {
"l2": lambda a, b: np.linalg.norm(a - b, axis=1, ord=2),
"l1": lambda a, b: np.linalg.norm(a - b, axis=1, ord=1),
"max": lambda a, b: np.linalg.norm(a - b, axis=1, ord=np.inf),
"cos": lambda a, b: np.dot(a, b.T)
/ (np.linalg.norm(a) * np.linalg.norm(b, axis=1)),
"dot": lambda a, b: np.dot(a, b.T),
}
def vector_search(
query_embedding: np.ndarray,
data_vectors: np.ndarray,
distance_metric: str = "L2",
k: Optional[int] = 4,
) -> Tuple[List, List]:
"""Naive search for nearest neighbors
args:
query_embedding: np.ndarray
data_vectors: np.ndarray
k (int): number of nearest neighbors
distance_metric: distance function 'L2' for Euclidean, 'L1' for Nuclear, 'Max'
l-infinity distnace, 'cos' for cosine similarity, 'dot' for dot product
returns:
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|
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|
returns:
nearest_indices: List, indices of nearest neighbors
"""
if data_vectors.shape[0] == 0:
return [], []
# Calculate the distance between the query_vector and all data_vectors
distances = distance_metric_map[distance_metric](query_embedding, data_vectors)
nearest_indices = np.argsort(distances)
nearest_indices = (
nearest_indices[::-1][:k] if distance_metric in ["cos"] else nearest_indices[:k]
)
return nearest_indices.tolist(), distances[nearest_indices].tolist()
def dp_filter(x: dict, filter: Dict[str, str]) -> bool:
"""Filter helper function for Deep Lake"""
metadata = x["metadata"].data()["value"]
return all(k in metadata and v == metadata[k] for k, v in filter.items())
[docs]class DeepLake(VectorStore):
"""Wrapper around Deep Lake, a data lake for deep learning applications.
We implement naive similarity search and filtering for fast prototyping,
but it can be extended with Tensor Query Language (TQL) for production use cases
over billion rows.
Why Deep Lake?
- Not only stores embeddings, but also the original data with version control.
- Serverless, doesn't require another service and can be used with major
cloud providers (S3, GCS, etc.)
- More than just a multi-modal vector store. You can use the dataset
to fine-tune your own LLM models.
To use, you should have the ``deeplake`` python package installed.
Example:
.. code-block:: python
from langchain.vectorstores import DeepLake
from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
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|
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|
embeddings = OpenAIEmbeddings()
vectorstore = DeepLake("langchain_store", embeddings.embed_query)
"""
_LANGCHAIN_DEFAULT_DEEPLAKE_PATH = "./deeplake/"
def __init__(
self,
dataset_path: str = _LANGCHAIN_DEFAULT_DEEPLAKE_PATH,
token: Optional[str] = None,
embedding_function: Optional[Embeddings] = None,
read_only: Optional[bool] = False,
ingestion_batch_size: int = 1024,
num_workers: int = 0,
verbose: bool = True,
**kwargs: Any,
) -> None:
"""Initialize with Deep Lake client."""
self.ingestion_batch_size = ingestion_batch_size
self.num_workers = num_workers
self.verbose = verbose
try:
import deeplake
from deeplake.constants import MB
except ImportError:
raise ValueError(
"Could not import deeplake python package. "
"Please install it with `pip install deeplake`."
)
self._deeplake = deeplake
self.dataset_path = dataset_path
creds_args = {"creds": kwargs["creds"]} if "creds" in kwargs else {}
if deeplake.exists(dataset_path, token=token, **creds_args) and not kwargs.get(
"overwrite", False
):
if "overwrite" in kwargs:
del kwargs["overwrite"]
self.ds = deeplake.load(
dataset_path,
token=token,
read_only=read_only,
verbose=self.verbose,
**kwargs,
)
logger.info(f"Loading deeplake {dataset_path} from storage.")
if self.verbose:
print(
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|
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|
if self.verbose:
print(
f"Deep Lake Dataset in {dataset_path} already exists, "
f"loading from the storage"
)
self.ds.summary()
else:
if "overwrite" in kwargs:
del kwargs["overwrite"]
self.ds = deeplake.empty(
dataset_path,
token=token,
overwrite=True,
verbose=self.verbose,
**kwargs,
)
with self.ds:
self.ds.create_tensor(
"text",
htype="text",
create_id_tensor=False,
create_sample_info_tensor=False,
create_shape_tensor=False,
chunk_compression="lz4",
)
self.ds.create_tensor(
"metadata",
htype="json",
create_id_tensor=False,
create_sample_info_tensor=False,
create_shape_tensor=False,
chunk_compression="lz4",
)
self.ds.create_tensor(
"embedding",
htype="generic",
dtype=np.float32,
create_id_tensor=False,
create_sample_info_tensor=False,
max_chunk_size=64 * MB,
create_shape_tensor=True,
)
self.ds.create_tensor(
"ids",
htype="text",
create_id_tensor=False,
create_sample_info_tensor=False,
create_shape_tensor=False,
chunk_compression="lz4",
)
self._embedding_function = embedding_function
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
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|
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts (Iterable[str]): Texts to add to the vectorstore.
metadatas (Optional[List[dict]], optional): Optional list of metadatas.
ids (Optional[List[str]], optional): Optional list of IDs.
Returns:
List[str]: List of IDs of the added texts.
"""
if ids is None:
ids = [str(uuid.uuid1()) for _ in texts]
text_list = list(texts)
if metadatas is None:
metadatas = [{}] * len(text_list)
elements = list(zip(text_list, metadatas, ids))
@self._deeplake.compute
def ingest(sample_in: list, sample_out: list) -> None:
text_list = [s[0] for s in sample_in]
embeds: Sequence[Optional[np.ndarray]] = []
if self._embedding_function is not None:
embeddings = self._embedding_function.embed_documents(text_list)
embeds = [np.array(e, dtype=np.float32) for e in embeddings]
else:
embeds = [None] * len(text_list)
for s, e in zip(sample_in, embeds):
sample_out.append(
{
"text": s[0],
"metadata": s[1],
"ids": s[2],
"embedding": e,
}
)
batch_size = min(self.ingestion_batch_size, len(elements))
if batch_size == 0:
return []
batched = [
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|
if batch_size == 0:
return []
batched = [
elements[i : i + batch_size] for i in range(0, len(elements), batch_size)
]
ingest().eval(
batched,
self.ds,
num_workers=min(self.num_workers, len(batched) // max(self.num_workers, 1)),
**kwargs,
)
self.ds.commit(allow_empty=True)
if self.verbose:
self.ds.summary()
return ids
def _search_helper(
self,
query: Any[str, None] = None,
embedding: Any[float, None] = None,
k: int = 4,
distance_metric: str = "L2",
use_maximal_marginal_relevance: Optional[bool] = False,
fetch_k: Optional[int] = 20,
filter: Optional[Any[Dict[str, str], Callable, str]] = None,
return_score: Optional[bool] = False,
**kwargs: Any,
) -> Any[List[Document], List[Tuple[Document, float]]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
embedding: Embedding function to use. Defaults to None.
k: Number of Documents to return. Defaults to 4.
distance_metric: `L2` for Euclidean, `L1` for Nuclear,
`max` L-infinity distance, `cos` for cosine similarity,
'dot' for dot product. Defaults to `L2`.
filter: Attribute filter by metadata example {'key': 'value'}. It can also
take [Deep Lake filter]
|
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|
take [Deep Lake filter]
(https://docs.deeplake.ai/en/latest/deeplake.core.dataset.html#deeplake.core.dataset.Dataset.filter)
Defaults to None.
maximal_marginal_relevance: Whether to use maximal marginal relevance.
Defaults to False.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
return_score: Whether to return the score. Defaults to False.
Returns:
List of Documents selected by the specified distance metric,
if return_score True, return a tuple of (Document, score)
"""
view = self.ds
# attribute based filtering
if filter is not None:
if isinstance(filter, dict):
filter = partial(dp_filter, filter=filter)
view = view.filter(filter)
if len(view) == 0:
return []
if self._embedding_function is None:
view = view.filter(lambda x: query in x["text"].data()["value"])
scores = [1.0] * len(view)
if use_maximal_marginal_relevance:
raise ValueError(
"For MMR search, you must specify an embedding function on"
"creation."
)
else:
emb = embedding or self._embedding_function.embed_query(
query
) # type: ignore
query_emb = np.array(emb, dtype=np.float32)
embeddings = view.embedding.numpy(fetch_chunks=True)
k_search = fetch_k if use_maximal_marginal_relevance else k
indices, scores = vector_search(
query_emb,
embeddings,
k=k_search,
distance_metric=distance_metric.lower(),
)
view = view[indices]
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|
distance_metric=distance_metric.lower(),
)
view = view[indices]
if use_maximal_marginal_relevance:
lambda_mult = kwargs.get("lambda_mult", 0.5)
indices = maximal_marginal_relevance(
query_emb,
embeddings[indices],
k=min(k, len(indices)),
lambda_mult=lambda_mult,
)
view = view[indices]
scores = [scores[i] for i in indices]
docs = [
Document(
page_content=el["text"].data()["value"],
metadata=el["metadata"].data()["value"],
)
for el in view
]
if return_score:
return [(doc, score) for doc, score in zip(docs, scores)]
return docs
[docs] def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: text to embed and run the query on.
k: Number of Documents to return.
Defaults to 4.
query: Text to look up documents similar to.
embedding: Embedding function to use.
Defaults to None.
k: Number of Documents to return.
Defaults to 4.
distance_metric: `L2` for Euclidean, `L1` for Nuclear, `max`
L-infinity distance, `cos` for cosine similarity, 'dot' for dot product
Defaults to `L2`.
filter: Attribute filter by metadata example {'key': 'value'}.
Defaults to None.
maximal_marginal_relevance: Whether to use maximal marginal relevance.
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|
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