id stringlengths 14 15 | text stringlengths 44 2.47k | source stringlengths 61 181 |
|---|---|---|
2c58abcc3b7c-2 | return getattr(self, item)
class Config:
env_file = ".env"
env_prefix = "clickhouse_"
env_file_encoding = "utf-8"
[docs]class Clickhouse(VectorStore):
"""`ClickHouse VectorSearch` vector store.
You need a `clickhouse-connect` python package, and a valid account
to connect to Clic... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
2c58abcc3b7c-3 | assert self.config
assert self.config.host and self.config.port
assert (
self.config.column_map
and self.config.database
and self.config.table
and self.config.metric
)
for k in ["id", "embedding", "document", "metadata", "uuid"]:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
2c58abcc3b7c-4 | """
self.dim = dim
self.BS = "\\"
self.must_escape = ("\\", "'")
self.embedding_function = embedding
self.dist_order = "ASC" # Only support ConsingDistance and L2Distance
# Create a connection to clickhouse
self.client = get_client(
host=self.config.h... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
2c58abcc3b7c-5 | self.client.command(_insert_query)
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
batch_size: int = 32,
ids: Optional[Iterable[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Insert more texts through the embedding... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
2c58abcc3b7c-6 | )
transac.append(v)
if len(transac) == batch_size:
if t:
t.join()
t = Thread(target=self._insert, args=[transac, keys])
t.start()
transac = []
if len(transac) > 0:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
2c58abcc3b7c-7 | Other keyword arguments will pass into
[clickhouse-connect](https://clickhouse.com/docs/en/integrations/python#clickhouse-connect-driver-api)
Returns:
ClickHouse Index
"""
ctx = cls(embedding, config, **kwargs)
ctx.add_texts(texts, ids=text_ids, batch_size=bat... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
2c58abcc3b7c-8 | if where_str:
where_str = f"PREWHERE {where_str}"
else:
where_str = ""
settings_strs = []
if self.config.index_query_params:
for k in self.config.index_query_params:
settings_strs.append(f"SETTING {k}={self.config.index_query_params[k]}")
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
2c58abcc3b7c-9 | self.embedding_function.embed_query(query), k, where_str, **kwargs
)
[docs] def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
where_str: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Perform a similarity search with C... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
2c58abcc3b7c-10 | ) -> List[Tuple[Document, float]]:
"""Perform a similarity search with ClickHouse
Args:
query (str): query string
k (int, optional): Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], optional): where condition string.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
1acb732ef70c-0 | Source code for langchain.vectorstores.cassandra
from __future__ import annotations
import typing
import uuid
from typing import (
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Tuple,
Type,
TypeVar,
Union,
)
import numpy as np
if typing.TYPE_CHECKING:
from cassandra.cluster ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/cassandra.html |
1acb732ef70c-1 | return filter_dict
def _get_embedding_dimension(self) -> int:
if self._embedding_dimension is None:
self._embedding_dimension = len(
self.embedding.embed_query("This is a sample sentence.")
)
return self._embedding_dimension
[docs] def __init__(
sel... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/cassandra.html |
1acb732ef70c-2 | so here the final score transformation is not reversing the interval:
"""
return self._dont_flip_the_cos_score
[docs] def delete_collection(self) -> None:
"""
Just an alias for `clear`
(to better align with other VectorStore implementations).
"""
self.clear()
[... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/cassandra.html |
1acb732ef70c-3 | ids (Optional[List[str]], optional): Optional list of IDs.
batch_size (int): Number of concurrent requests to send to the server.
ttl_seconds (Optional[int], optional): Optional time-to-live
for the added texts.
Returns:
List[str]: List of IDs of the added tex... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/cassandra.html |
1acb732ef70c-4 | ) -> List[Tuple[Document, float, str]]:
"""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.
Returns:
List of (Document, score, id), the most s... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/cassandra.html |
1acb732ef70c-5 | self,
embedding: List[float],
k: int = 4,
filter: Optional[Dict[str, str]] = None,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to embedding vector.
Args:
embedding (str): Embedding to look up documents similar to.
k (int): Number of ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/cassandra.html |
1acb732ef70c-6 | self,
query: str,
k: int = 4,
filter: Optional[Dict[str, str]] = None,
) -> List[Tuple[Document, float]]:
embedding_vector = self.embedding.embed_query(query)
return self.similarity_search_with_score_by_vector(
embedding_vector,
k,
filter=f... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/cassandra.html |
1acb732ef70c-7 | mmrChosenIndices = maximal_marginal_relevance(
np.array(embedding, dtype=np.float32),
[pfHit["embedding_vector"] for pfHit in prefetchHits],
k=k,
lambda_mult=lambda_mult,
)
mmrHits = [
pfHit
for pfIndex, pfHit in enumerate(prefetchH... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/cassandra.html |
1acb732ef70c-8 | embedding_vector,
k,
fetch_k,
lambda_mult=lambda_mult,
filter=filter,
)
[docs] @classmethod
def from_texts(
cls: Type[CVST],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
batch_size:... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/cassandra.html |
1acb732ef70c-9 | table_name: str = kwargs["table_name"]
return cls.from_texts(
texts=texts,
metadatas=metadatas,
embedding=embedding,
session=session,
keyspace=keyspace,
table_name=table_name,
) | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/cassandra.html |
b5cb05ebca5f-0 | Source code for langchain.vectorstores.zep
from __future__ import annotations
import logging
import warnings
from dataclasses import asdict, dataclass
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple
import numpy as np
from langchain.docstore.document import Document
from langchain.schema.em... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
b5cb05ebca5f-1 | Args:
api_url (str): The URL of the Zep API.
collection_name (str): The name of the collection in the Zep store.
api_key (Optional[str]): The API key for the Zep API.
config (Optional[CollectionConfig]): The configuration for the collection.
Required if the collection does no... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
b5cb05ebca5f-2 | @property
def embeddings(self) -> Optional[Embeddings]:
"""Access the query embedding object if available."""
return self._embedding
def _load_collection(self) -> DocumentCollection:
"""
Load the collection from the Zep backend.
"""
from zep_python import NotFound... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
b5cb05ebca5f-3 | embeddings = self._embedding.embed_documents(list(texts))
if self._collection and self._collection.embedding_dimensions != len(
embeddings[0]
):
raise ValueError(
"The embedding dimensions of the collection and the embedding"
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
b5cb05ebca5f-4 | "collection should be an instance of a Zep DocumentCollection"
)
documents = self._generate_documents_to_add(texts, metadatas, document_ids)
uuids = self._collection.add_documents(documents)
return uuids
[docs] async def aadd_texts(
self,
texts: Iterable[str],
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
b5cb05ebca5f-5 | "search_type to be 'similarity' or 'mmr'."
)
[docs] async def asearch(
self,
query: str,
search_type: str,
metadata: Optional[Dict[str, Any]] = None,
k: int = 3,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query using spec... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
b5cb05ebca5f-6 | **kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Run similarity search with distance."""
return self._similarity_search_with_relevance_scores(
query, k=k, metadata=metadata, **kwargs
)
def _similarity_search_with_relevance_scores(
self,
query: str,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
b5cb05ebca5f-7 | )
return [
(
Document(
page_content=doc.content,
metadata=doc.metadata,
),
doc.score or 0.0,
)
for doc in results
]
[docs] async def asimilarity_search_with_relevance_scores(
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
b5cb05ebca5f-8 | results = await self.asimilarity_search_with_relevance_scores(
query, k, metadata=metadata, **kwargs
)
return [doc for doc, _ in results]
[docs] def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
metadata: Optional[Dict[str, Any]] = ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
b5cb05ebca5f-9 | embedding=embedding, limit=k, metadata=metadata, **kwargs
)
return [
Document(
page_content=doc.content,
metadata=doc.metadata,
)
for doc in results
]
def _max_marginal_relevance_selection(
self,
query_vector... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
b5cb05ebca5f-10 | of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
metadata: Optional, metadata to filter the resulting set of retrieved docs
Returns:
List of Documents selected by maximal ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
b5cb05ebca5f-11 | embedding=query_vector, limit=k, metadata=metadata, **kwargs
)
else:
results, query_vector = await self._collection.asearch_return_query_vector(
query, limit=k, metadata=metadata, **kwargs
)
return self._max_marginal_relevance_selection(
qu... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
b5cb05ebca5f-12 | embedding=embedding, limit=k, metadata=metadata, **kwargs
)
return self._max_marginal_relevance_selection(
embedding, results, k=k, lambda_mult=lambda_mult
)
[docs] async def amax_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
b5cb05ebca5f-13 | texts (List[str]): The list of texts to add to the vectorstore.
embedding (Optional[Embeddings]): Optional embedding function to use to
embed the texts.
metadatas (Optional[List[Dict[str, Any]]]): Optional list of metadata
associated with the texts.
coll... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
337556dc637f-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,
)
import numpy as np
from langchain.docstore.document import Document
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
337556dc637f-1 | text_key: str = "text",
embedding_key: str = "embedding",
):
"""
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.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
337556dc637f-2 | return cls(collection, embedding, **kwargs)
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[Dict[str, Any]]] = None,
**kwargs: Any,
) -> List:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: I... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
337556dc637f-3 | {self._text_key: t, self._embedding_key: embedding, **m}
for t, m, embedding in zip(texts, metadatas, embeddings)
]
# insert the documents in MongoDB Atlas
insert_result = self._collection.insert_many(to_insert) # type: ignore
return insert_result.inserted_ids
def _simil... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
337556dc637f-4 | pre_filter: Optional[Dict] = None,
post_filter_pipeline: Optional[List[Dict]] = None,
) -> List[Tuple[Document, float]]:
"""Return MongoDB documents most similar to the given query and their scores.
Uses the knnBeta Operator available in MongoDB Atlas Search.
This feature is in early... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
337556dc637f-5 | Uses 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
early access users. It is not recommended for production deployments as we
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
337556dc637f-6 | Args:
query: Text to look up documents similar to.
k: (Optional) number of documents to return. Defaults to 4.
fetch_k: (Optional) number of documents to fetch before passing to MMR
algorithm. Defaults to 20.
lambda_mult: Number between 0 and 1 that determ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
337556dc637f-7 | **kwargs: Any,
) -> MongoDBAtlasVectorSearch:
"""Construct a `MongoDB Atlas Vector Search` vector store from raw documents.
This is a user-friendly interface that:
1. Embeds documents.
2. Adds the documents to a provided MongoDB Atlas Vector Search index
(Luce... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
9dba8091508e-0 | Source code for langchain.vectorstores.clarifai
from __future__ import annotations
import logging
import os
import traceback
from concurrent.futures import ThreadPoolExecutor
from typing import Any, Iterable, List, Optional, Tuple
import requests
from langchain.docstore.document import Document
from langchain.schema.em... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clarifai.html |
9dba8091508e-1 | ValueError: If user ID, app ID or personal access token is not provided.
"""
try:
from clarifai.auth.helper import DEFAULT_BASE, ClarifaiAuthHelper
from clarifai.client import create_stub
except ImportError:
raise ImportError(
"Could not import... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clarifai.html |
9dba8091508e-2 | ) -> List[str]:
"""Post text to Clarifai and return the ID of the input.
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, se... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clarifai.html |
9dba8091508e-3 | )
input_ids = []
for input in post_inputs_response.inputs:
input_ids.append(input.id)
return input_ids
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clarifai.html |
9dba8091508e-4 | result_ids = self._post_texts_as_inputs(batch_texts, batch_metadatas)
input_ids.extend(result_ids)
logger.debug(f"Input {result_ids} posted successfully.")
except Exception as error:
logger.warning(f"Post inputs failed: {error}")
traceback.prin... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clarifai.html |
9dba8091508e-5 | user_app_id=self._userDataObject,
searches=[
resources_pb2.Search(
query=resources_pb2.Query(
ranks=[
resources_pb2.Rank(
annotation=resources_pb2.Annotation(
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clarifai.html |
9dba8091508e-6 | off input: {hit.input.id}, text: {requested_text[:125]}"
)
return (Document(page_content=requested_text, metadata=metadata), hit.score)
# Iterate over hits and retrieve metadata and text
futures = [executor.submit(hit_to_document, hit) for hit in hits]
docs_and_scores = [... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clarifai.html |
9dba8091508e-7 | texts (List[str]): List of texts to add.
pat (Optional[str]): Personal access token. Defaults to None.
number_of_docs (Optional[int]): Number of documents to return
during vector search. Defaults to None.
api_base (Optional[str]): API base. Defaults to None.
m... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clarifai.html |
9dba8091508e-8 | during vector search. Defaults to None.
api_base (Optional[str]): API base. Defaults to None.
Returns:
Clarifai: Clarifai vectorstore.
"""
texts = [doc.page_content for doc in documents]
metadatas = [doc.metadata for doc in documents]
return cls.from_texts... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clarifai.html |
f799e335b8a8-0 | Source code for langchain.vectorstores.vald
"""Wrapper around Vald vector database."""
from __future__ import annotations
from typing import Any, Iterable, List, Optional, Tuple, Type
import numpy as np
from langchain.docstore.document import Document
from langchain.schema.embeddings import Embeddings
from langchain.sc... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vald.html |
f799e335b8a8-1 | metadatas: Optional[List[dict]] = None,
skip_strict_exist_check: bool = False,
**kwargs: Any,
) -> List[str]:
"""
Args:
skip_strict_exist_check: Deprecated. This is not used basically.
"""
try:
import grpc
from vald.v1.payload impor... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vald.html |
f799e335b8a8-2 | ) -> Optional[bool]:
"""
Args:
skip_strict_exist_check: Deprecated. This is not used basically.
"""
try:
import grpc
from vald.v1.payload import payload_pb2
from vald.v1.vald import remove_pb2_grpc
except ImportError:
ra... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vald.html |
f799e335b8a8-3 | docs.append(doc)
return docs
[docs] def similarity_search_with_score(
self,
query: str,
k: int = 4,
radius: float = -1.0,
epsilon: float = 0.01,
timeout: int = 3000000000,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
emb = self._embeddi... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vald.html |
f799e335b8a8-4 | from vald.v1.vald import search_pb2_grpc
except ImportError:
raise ValueError(
"Could not import vald-client-python python package. "
"Please install it with `pip install vald-client-python`."
)
channel = grpc.insecure_channel(self.target, options=... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vald.html |
f799e335b8a8-5 | timeout=timeout,
lambda_mult=lambda_mult,
)
return docs
[docs] def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
radius: float = -1.0,
epsilon: float =... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vald.html |
f799e335b8a8-6 | docs.append(doc)
mmr = maximal_marginal_relevance(
np.array(embedding),
embs,
lambda_mult=lambda_mult,
k=k,
)
channel.close()
return [docs[i] for i in mmr]
[docs] @classmethod
def from_texts(
cls: Type[Vald],
texts: L... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vald.html |
f799e335b8a8-7 | # ) -> List[str]:
# pass
#
# def _select_relevance_score_fn(self) -> Callable[[float], float]:
# pass
#
# def _similarity_search_with_relevance_scores(
# self,
# query: str,
# k: int = 4,
# **kwargs: Any,
# ) -> List[Tuple[Document, float]]:
# pass
#
# def... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vald.html |
2fa4c578ba06-0 | Source code for langchain.vectorstores.qdrant
from __future__ import annotations
import asyncio
import functools
import uuid
import warnings
from itertools import islice
from operator import itemgetter
from typing import (
TYPE_CHECKING,
Any,
AsyncGenerator,
Callable,
Dict,
Generator,
Iterab... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-1 | # by removing the first letter from the method name. For example,
# if the async method is called ``aaad_texts``, the synchronous method
# will be called ``aad_texts``.
sync_method = functools.partial(
getattr(self, method.__name__[1:]), *args, **kwargs
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-2 | "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 No... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-3 | def embeddings(self) -> Optional[Embeddings]:
return self._embeddings
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[Sequence[str]] = None,
batch_size: int = 64,
**kwargs: Any,
) -> List[str]:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-4 | 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. Ids have to be
uuid-like strings.
batch_size:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-5 | filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-6 | 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 N... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-7 | threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-8 | 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.
search_params: Additional search params
offset:
Offset of the first result to return.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-9 | **kwargs,
)
[docs] def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-10 | - 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to QdrantClient.search()
Returns:
List of Documents most similar to the query.
"""
results = self.similarity_search_with_score_by_ve... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-11 | Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-12 | **kwargs: Any,
) -> List[Tuple[Document, float]]:
"""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.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-13 | "filters directly: "
"https://qdrant.tech/documentation/concepts/filtering/",
DeprecationWarning,
)
qdrant_filter = self._qdrant_filter_from_dict(filter)
else:
qdrant_filter = filter
query_vector = embedding
if self.vector_name ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-14 | from qdrant_client import grpc # noqa
from qdrant_client.conversions.conversion import RestToGrpc
from qdrant_client.http import models as rest
if filter is not None and isinstance(filter, dict):
warnings.warn(
"Using dict as a `filter` is deprecated. Please use qdra... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-15 | offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding vector to look up do... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-16 | """
response = await self._asearch_with_score_by_vector(
embedding,
k=k,
filter=filter,
search_params=search_params,
offset=offset,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-17 | filter: Filter by metadata. Defaults to None.
search_params: Additional search params
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 hig... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-18 | lambda_mult: float = 0.5,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
"""Re... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-19 | all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to
QdrantClient.async_grpc_points.Search().
Returns:
List of Documents selected by maximal marginal rele... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-20 | of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
score_threshold:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-21 | self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
score_threshold: Optional[float] = None,
consistency: Optional[common... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-22 | - 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/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-23 | 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.
lambda_mult: Number between 0 and 1 t... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-24 | results = self.client.search(
collection_name=self.collection_name,
query_vector=query_vector,
query_filter=filter,
search_params=search_params,
limit=fetch_k,
with_payload=True,
with_vectors=True,
score_threshold=score_thre... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-25 | 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.
lambda_mult: Number between 0 and 1 t... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-26 | )
for i in mmr_selected
]
[docs] def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
"""Delete by vector ID or other criteria.
Args:
ids: List of ids to delete.
**kwargs: Other keyword arguments that subclasses might use.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-27 | vector_name: Optional[str] = VECTOR_NAME,
batch_size: int = 64,
shard_number: Optional[int] = None,
replication_factor: Optional[int] = None,
write_consistency_factor: Optional[int] = None,
on_disk_payload: Optional[bool] = None,
hnsw_config: Optional[common_types.HnswCon... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-28 | 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:... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-29 | Default: None
batch_size:
How many vectors upload per-request.
Default: 64
shard_number: Number of shards in collection. Default is 1, minimum is 1.
replication_factor:
Replication factor for collection. Default is 1, minimum is 1.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-30 | 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 langchai... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-31 | 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] = N... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-32 | 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.
ids:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-33 | '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
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-34 | It will be read from the disk every time it is requested.
This setting saves RAM by (slightly) increasing the response time.
Note: those payload values that are involved in filtering and are
indexed - remain in RAM.
hnsw_config: Params for HNSW index
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-35 | content_payload_key,
metadata_payload_key,
vector_name,
shard_number,
replication_factor,
write_consistency_factor,
on_disk_payload,
hnsw_config,
optimizers_config,
wal_config,
quantization_config,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-36 | 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/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-37 | if force_recreate:
raise ValueError
# Get the vector configuration of the existing collection and vector, if it
# was specified. If the old configuration does not match the current one,
# an exception is being thrown.
collection_info = client.get_collectio... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-38 | f"Existing Qdrant collection {collection_name} doesn't use named "
f"vectors. If you want to reuse it, please set `vector_name` to "
f"`None`. If you want to recreate the collection, set "
f"`force_recreate` parameter to `True`."
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-39 | on_disk=on_disk,
)
# If vector name was provided, we're going to use the named vectors feature
# with just a single vector.
if vector_name is not None:
vectors_config = { # type: ignore[assignment]
vector_name: vectors_config,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-40 | prefix: Optional[str] = None,
timeout: Optional[float] = None,
host: Optional[str] = None,
path: Optional[str] = None,
collection_name: Optional[str] = None,
distance_func: str = "Cosine",
content_payload_key: str = CONTENT_KEY,
metadata_payload_key: str = METADAT... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
2fa4c578ba06-41 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
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