id stringlengths 14 16 | text stringlengths 31 2.41k | source stringlengths 53 121 |
|---|---|---|
9b163cce2644-7 | )
return [doc for doc, _ in docs_and_scores]
[docs] def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[Dict[str, Any]] = None,
fetch_k: int = 20,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query.
Ar... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/faiss.html |
9b163cce2644-8 | k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch before filtering to
pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/faiss.html |
9b163cce2644-9 | selected_indices = [indices[0][i] for i in mmr_selected]
selected_scores = [scores[0][i] for i in mmr_selected]
docs_and_scores = []
for i, score in zip(selected_indices, selected_scores):
if i == -1:
# This happens when not enough docs are returned.
c... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/faiss.html |
9b163cce2644-10 | Returns:
List of Documents selected by maximal marginal relevance.
"""
docs_and_scores = self.max_marginal_relevance_search_with_score_by_vector(
embedding, k=k, fetch_k=fetch_k, lambda_mult=lambda_mult, filter=filter
)
return [doc for doc, _ in docs_and_scores]
[... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/faiss.html |
9b163cce2644-11 | [docs] def merge_from(self, target: FAISS) -> None:
"""Merge another FAISS object with the current one.
Add the target FAISS to the current one.
Args:
target: FAISS object you wish to merge into the current one
Returns:
None.
"""
if not isinstan... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/faiss.html |
9b163cce2644-12 | ) -> FAISS:
faiss = dependable_faiss_import()
index = faiss.IndexFlatL2(len(embeddings[0]))
vector = np.array(embeddings, dtype=np.float32)
if normalize_L2:
faiss.normalize_L2(vector)
index.add(vector)
documents = []
if ids is None:
ids = [... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/faiss.html |
9b163cce2644-13 | faiss = FAISS.from_texts(texts, embeddings)
"""
embeddings = embedding.embed_documents(texts)
return cls.__from(
texts,
embeddings,
embedding,
metadatas=metadatas,
ids=ids,
**kwargs,
)
[docs] @classmethod
def ... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/faiss.html |
9b163cce2644-14 | """Save FAISS index, docstore, and index_to_docstore_id to disk.
Args:
folder_path: folder path to save index, docstore,
and index_to_docstore_id to.
index_name: for saving with a specific index file name
"""
path = Path(folder_path)
path.mkdir(exi... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/faiss.html |
9b163cce2644-15 | )
# load docstore and index_to_docstore_id
with open(path / "{index_name}.pkl".format(index_name=index_name), "rb") as f:
docstore, index_to_docstore_id = pickle.load(f)
return cls(embeddings.embed_query, index, docstore, index_to_docstore_id)
def _similarity_search_with_relevanc... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/faiss.html |
d26caf541610-0 | Source code for langchain.vectorstores.clarifai
from __future__ import annotations
import logging
import os
import traceback
from typing import Any, Iterable, List, Optional, Tuple
import requests
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.vectorstor... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/clarifai.html |
d26caf541610-1 | """
try:
from clarifai.auth.helper import DEFAULT_BASE, ClarifaiAuthHelper
from clarifai.client import create_stub
except ImportError:
raise ValueError(
"Could not import clarifai python package. "
"Please install it with `pip install c... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/clarifai.html |
d26caf541610-2 | Args:
text (str): Text to post.
metadata (dict): Metadata to post.
Returns:
str: ID of the input.
"""
try:
from clarifai_grpc.grpc.api import resources_pb2, service_pb2
from clarifai_grpc.grpc.api.status import status_code_pb2
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/clarifai.html |
d26caf541610-3 | to a Clarifai application.
Application use base workflow that create and store embedding for each text.
Make sure you are using a base workflow that is compatible with text
(such as Language Understanding).
Args:
texts (Iterable[str]): Texts to add to the vectorstore.
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/clarifai.html |
d26caf541610-4 | 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 simmilar to the query text.
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/clarifai.html |
d26caf541610-5 | "Post searches failed, status: "
+ post_annotations_searches_response.status.description
)
# Retrieve hits
hits = post_annotations_searches_response.hits
docs_and_scores = []
# Iterate over hits and retrieve metadata and text
for hit in hits:
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/clarifai.html |
d26caf541610-6 | user_id: Optional[str] = None,
app_id: Optional[str] = None,
pat: Optional[str] = None,
number_of_docs: Optional[int] = None,
api_base: Optional[str] = None,
**kwargs: Any,
) -> Clarifai:
"""Create a Clarifai vectorstore from a list of texts.
Args:
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/clarifai.html |
d26caf541610-7 | api_base: Optional[str] = None,
**kwargs: Any,
) -> Clarifai:
"""Create a Clarifai vectorstore from a list of documents.
Args:
user_id (str): User ID.
app_id (str): App ID.
documents (List[Document]): List of documents to add.
pat (Optional[str... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/clarifai.html |
9c8692f05232-0 | 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.embedd... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/milvus.html |
9c8692f05232-1 | 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://randomw... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/milvus.html |
9c8692f05232-2 | 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 ... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/milvus.html |
9c8692f05232-3 | "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"... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/milvus.html |
9c8692f05232-4 | 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 impor... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/milvus.html |
9c8692f05232-5 | 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)
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/milvus.html |
9c8692f05232-6 | 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}.")
#... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/milvus.html |
9c8692f05232-7 | 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 pymil... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/milvus.html |
9c8692f05232-8 | 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 o... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/milvus.html |
9c8692f05232-9 | 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 memo... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/milvus.html |
9c8692f05232-10 | 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... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/milvus.html |
9c8692f05232-11 | 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.
"""
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/milvus.html |
9c8692f05232-12 | 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: O... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/milvus.html |
9c8692f05232-13 | 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] = ... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/milvus.html |
9c8692f05232-14 | # 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 resu... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/milvus.html |
9c8692f05232-15 | 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 resul... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/milvus.html |
9c8692f05232-16 | 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 lon... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/milvus.html |
9c8692f05232-17 | )
# 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.... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/milvus.html |
9c8692f05232-18 | "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], op... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/milvus.html |
adfb97a6e613-0 | Source code for langchain.vectorstores.sklearn
""" Wrapper around scikit-learn NearestNeighbors implementation.
The vector store can be persisted in json, bson or parquet format.
"""
import json
import math
import os
from abc import ABC, abstractmethod
from typing import Any, Dict, Iterable, List, Literal, Optional, Tu... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/sklearn.html |
adfb97a6e613-1 | with open(self.persist_path, "r") as fp:
return json.load(fp)
class BsonSerializer(BaseSerializer):
"""Serializes data in binary json using the bson python package."""
def __init__(self, persist_path: str) -> None:
super().__init__(persist_path)
self.bson = guard_import("bson")
@... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/sklearn.html |
adfb97a6e613-2 | raise exc
else:
os.remove(backup_path)
else:
self.pq.write_table(table, self.persist_path)
def load(self) -> Any:
table = self.pq.read_table(self.persist_path)
df = table.to_pandas()
return {col: series.tolist() for col, series in df.items()}
S... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/sklearn.html |
adfb97a6e613-3 | self._serializer = serializer_cls(persist_path=self._persist_path)
# data properties
self._embeddings: List[List[float]] = []
self._texts: List[str] = []
self._metadatas: List[dict] = []
self._ids: List[str] = []
# cache properties
self._embeddings_np: Any = np.as... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/sklearn.html |
adfb97a6e613-4 | **kwargs: Any,
) -> List[str]:
_texts = list(texts)
_ids = ids or [str(uuid4()) for _ in _texts]
self._texts.extend(_texts)
self._embeddings.extend(self._embedding_function.embed_documents(_texts))
self._metadatas.extend(metadatas or ([{}] * len(_texts)))
self._ids.ex... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/sklearn.html |
adfb97a6e613-5 | query_embedding = self._embedding_function.embed_query(query)
indices_dists = self._similarity_index_search_with_score(
query_embedding, k=k, **kwargs
)
return [
(
Document(
page_content=self._texts[idx],
metadata={"... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/sklearn.html |
adfb97a6e613-6 | 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... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/sklearn.html |
adfb97a6e613-7 | 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
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/sklearn.html |
67e1e46d7fe8-0 | Source code for langchain.vectorstores.tigris
from __future__ import annotations
import itertools
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple
from langchain.embeddings.base import Embeddings
from langchain.schema import Document
from langchain.vectorstores import VectorStore
if TYPE_CHECKING:... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/tigris.html |
67e1e46d7fe8-1 | metadatas: Optional list of metadatas associated with the texts.
ids: Optional list of ids for documents.
Ids will be autogenerated if not provided.
kwargs: vectorstore specific parameters
Returns:
List of ids from adding the texts into the vectorstore.
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/tigris.html |
67e1e46d7fe8-2 | vector=vector, k=k, filter_by=filter
)
docs: List[Tuple[Document, float]] = []
for r in result:
docs.append(
(
Document(
page_content=r.doc["text"], metadata=r.doc.get("metadata")
),
r... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/tigris.html |
67e1e46d7fe8-3 | "text": t,
"embeddings": e or [],
"metadata": m or {},
}
if _id:
doc["id"] = _id
docs.append(doc)
return docs | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/tigris.html |
b5d17a829d69-0 | Source code for langchain.vectorstores.qdrant
"""Wrapper around Qdrant vector database."""
from __future__ import annotations
import uuid
import warnings
from itertools import islice
from operator import itemgetter
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Opti... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
b5d17a829d69-1 | metadata_payload_key: str = METADATA_KEY,
embedding_function: Optional[Callable] = None, # deprecated
):
"""Initialize with necessary components."""
try:
import qdrant_client
except ImportError:
raise ValueError(
"Could not import qdrant-clien... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
b5d17a829d69-2 | "Using `embeddings` as `embedding_function` which is deprecated"
)
self._embeddings_function = embeddings
self.embeddings = None
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[Sequence[str]] =... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
b5d17a829d69-3 | ids=batch_ids,
vectors=self._embed_texts(batch_texts),
payloads=self._build_payloads(
batch_texts,
batch_metadatas,
self.content_payload_key,
self.metadata_payload_key,
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
b5d17a829d69-4 | - int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values pr... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
b5d17a829d69-5 | score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
b5d17a829d69-6 | **kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
searc... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
b5d17a829d69-7 | **kwargs,
)
return list(map(itemgetter(0), results))
[docs] def similarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
offset:... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
b5d17a829d69-8 | all of them
- 'all' - query all replicas, and return values present in all replicas
Returns:
List of documents most similar to the query text and cosine
distance in float for each.
Lower score represents more similarity.
"""
if filter is not No... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
b5d17a829d69-9 | 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 d... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
b5d17a829d69-10 | )
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.me... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
b5d17a829d69-11 | hnsw_config: Optional[common_types.HnswConfigDiff] = None,
optimizers_config: Optional[common_types.OptimizersConfigDiff] = None,
wal_config: Optional[common_types.WalConfigDiff] = None,
quantization_config: Optional[common_types.QuantizationConfig] = None,
init_from: Optional[common_typ... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
b5d17a829d69-12 | 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:
Timeout for REST and gRPC API requests.
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
b5d17a829d69-13 | Defines how many replicas should apply the operation for us to consider
it successful. Increasing this number will make the collection more
resilient to inconsistencies, but will also make it fail if not enough
replicas are available.
Does not have any per... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
b5d17a829d69-14 | 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 embe... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
b5d17a829d69-15 | metadatas_iterator = iter(metadatas or [])
ids_iterator = iter(ids or [uuid.uuid4().hex for _ in iter(texts)])
while batch_texts := list(islice(texts_iterator, batch_size)):
# Take the corresponding metadata and id for each text in a batch
batch_metadatas = list(islice(metadatas_... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
b5d17a829d69-16 | 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,
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
b5d17a829d69-17 | for condition in self._build_condition(key, value)
]
)
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... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
df5e2d8836cf-0 | 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 l... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/annoy.html |
df5e2d8836cf-1 | ):
"""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... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/annoy.html |
df5e2d8836cf-2 | 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 ... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/annoy.html |
df5e2d8836cf-3 | 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)
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/annoy.html |
df5e2d8836cf-4 | 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 =... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/annoy.html |
df5e2d8836cf-5 | 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(
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/annoy.html |
df5e2d8836cf-6 | 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... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/annoy.html |
df5e2d8836cf-7 | 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(
{inde... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/annoy.html |
df5e2d8836cf-8 | 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... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/annoy.html |
df5e2d8836cf-9 | 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, meta... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/annoy.html |
df5e2d8836cf-10 | 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_im... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/annoy.html |
785b30d08915-0 | Source code for langchain.vectorstores.mongodb_atlas
from __future__ import annotations
import logging
from typing import (
TYPE_CHECKING,
Any,
Dict,
Generator,
Iterable,
List,
Optional,
Tuple,
TypeVar,
Union,
)
from langchain.docstore.document import Document
from langchain.embe... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/mongodb_atlas.html |
785b30d08915-1 | """
Args:
collection: MongoDB collection to add the texts to.
embedding: Text embedding model to use.
text_key: MongoDB field that will contain the text for each
document.
embedding_key: MongoDB field that will contain the embedding for
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/mongodb_atlas.html |
785b30d08915-2 | """
batch_size = kwargs.get("batch_size", DEFAULT_INSERT_BATCH_SIZE)
_metadatas: Union[List, Generator] = metadatas or ({} for _ in texts)
texts_batch = []
metadatas_batch = []
result_ids = []
for i, (text, metadata) in enumerate(zip(texts, _metadatas)):
texts... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/mongodb_atlas.html |
785b30d08915-3 | """Return MongoDB documents most similar to query, along with scores.
Use the knnBeta Operator available in MongoDB Atlas Search
This feature is in early access and available only for evaluation purposes, to
validate functionality, and to gather feedback from a small closed group of
earl... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/mongodb_atlas.html |
785b30d08915-4 | docs.append((Document(page_content=text, metadata=res), score))
return docs
[docs] def similarity_search(
self,
query: str,
k: int = 4,
pre_filter: Optional[dict] = None,
post_filter_pipeline: Optional[List[Dict]] = None,
**kwargs: Any,
) -> List[Document]:... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/mongodb_atlas.html |
785b30d08915-5 | collection: Optional[Collection[MongoDBDocumentType]] = None,
**kwargs: Any,
) -> MongoDBAtlasVectorSearch:
"""Construct MongoDBAtlasVectorSearch wrapper from raw documents.
This is a user-friendly interface that:
1. Embeds documents.
2. Adds the documents to a provid... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/mongodb_atlas.html |
5533f41d5f69-0 | Source code for langchain.vectorstores.tair
"""Wrapper around Tair Vector."""
from __future__ import annotations
import json
import logging
import uuid
from typing import Any, Iterable, List, Optional, Type
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/tair.html |
5533f41d5f69-1 | index_type: str,
data_type: str,
**kwargs: Any,
) -> bool:
index = self.client.tvs_get_index(self.index_name)
if index is not None:
logger.info("Index already exists")
return False
self.client.tvs_create_index(
self.index_name,
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/tair.html |
5533f41d5f69-2 | """
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 simila... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/tair.html |
5533f41d5f69-3 | if "tair_url" in kwargs:
kwargs.pop("tair_url")
distance_type = tairvector.DistanceMetric.InnerProduct
if "distance_type" in kwargs:
distance_type = kwargs.pop("distance_typ")
index_type = tairvector.IndexType.HNSW
if "index_type" in kwargs:
index_type... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/tair.html |
5533f41d5f69-4 | cls,
documents: List[Document],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
index_name: str = "langchain",
content_key: str = "content",
metadata_key: str = "metadata",
**kwargs: Any,
) -> Tair:
texts = [d.page_content for d in docum... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/tair.html |
5533f41d5f69-5 | # index not exist
logger.info("Index does not exist")
return False
return True
[docs] @classmethod
def from_existing_index(
cls,
embedding: Embeddings,
index_name: str = "langchain",
content_key: str = "content",
metadata_key: str = "metadat... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/tair.html |
86da44d311d1-0 | 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 i... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/lancedb.html |
86da44d311d1-1 | 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... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/lancedb.html |
86da44d311d1-2 | """
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 doc... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/lancedb.html |
1f7b062637aa-0 | Source code for langchain.vectorstores.hologres
"""VectorStore wrapper around a Hologres database."""
from __future__ import annotations
import json
import logging
import uuid
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type
from langchain.docstore.document import Document
from langchain.embeddings.b... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/hologres.html |
1f7b062637aa-1 | '{"embedding":{"algorithm":"Graph",
"distance_method":"SquaredEuclidean",
"build_params":{"min_flush_proxima_row_count" : 1,
"min_compaction_proxima_row_count" : 1,
"max_total_size_to_merge_mb" : 2000}}}');"""
)
self.conn.commit()
def get_by_id(self, id: str) -> List[Tuple]:
statement = (
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/hologres.html |
1f7b062637aa-2 | params.append(key)
params.append(val)
filter_clause = "where " + " and ".join(conjuncts)
sql = (
f"select document, metadata::text, "
f"pm_approx_squared_euclidean_distance(array{json.dumps(embedding)}"
f"::float4[], embedding) as distance from"
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/hologres.html |
1f7b062637aa-3 | self.connection_string = connection_string
self.ndims = ndims
self.table_name = table_name
self.embedding_function = embedding_function
self.pre_delete_table = pre_delete_table
self.logger = logger or logging.getLogger(__name__)
self.__post_init__()
def __post_init__(... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/hologres.html |
1f7b062637aa-4 | embedding_function=embedding_function,
ndims=ndims,
table_name=table_name,
pre_delete_table=pre_delete_table,
)
store.add_embeddings(
texts=texts, embeddings=embeddings, metadatas=metadatas, ids=ids, **kwargs
)
return store
[docs] def ad... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/hologres.html |
1f7b062637aa-5 | List of ids from adding the texts into the vectorstore.
"""
if ids is None:
ids = [str(uuid.uuid1()) for _ in texts]
embeddings = self.embedding_function.embed_documents(list(texts))
if not metadatas:
metadatas = [{} for _ in texts]
self.add_embeddings(tex... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/hologres.html |
1f7b062637aa-6 | Returns:
List of Documents most similar to the query vector.
"""
docs_and_scores = self.similarity_search_with_score_by_vector(
embedding=embedding, k=k, filter=filter
)
return [doc for doc, _ in docs_and_scores]
[docs] def similarity_search_with_score(
... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/hologres.html |
1f7b062637aa-7 | ]
return docs
[docs] @classmethod
def from_texts(
cls: Type[Hologres],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
ndims: int = ADA_TOKEN_COUNT,
table_name: str = _LANGCHAIN_DEFAULT_TABLE_NAME,
ids: Optional[List... | https://api.python.langchain.com/en/stable/_modules/langchain/vectorstores/hologres.html |
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