id stringlengths 14 16 | text stringlengths 4 1.28k | source stringlengths 54 121 |
|---|---|---|
d13c8ecaa8b9-1 | def __init__(
self,
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,
) -> None:
"""Initialize with Clarifai client.
Args:
user_id (... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clarifai.html |
d13c8ecaa8b9-2 | Raises:
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 ValueError(
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clarifai.html |
d13c8ecaa8b9-3 | raise ValueError(
"Could not find CLARIFAI_USER_ID, CLARIFAI_APP_ID or\
CLARIFAI_PAT in your environment. "
"Please set those env variables with a valid user ID, \
app ID and personal access token \
from https://clarifai.com/settings/securi... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clarifai.html |
d13c8ecaa8b9-4 | """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, service_pb2
fr... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clarifai.html |
d13c8ecaa8b9-5 | user_app_id=self._userDataObject,
inputs=[
resources_pb2.Input(
data=resources_pb2.Data(
text=resources_pb2.Text(raw=text),
metadata=input_metadata,
)
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clarifai.html |
d13c8ecaa8b9-6 | ) -> List[str]:
"""Add texts to the Clarifai vectorstore. This will push the text
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 Underst... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clarifai.html |
d13c8ecaa8b9-7 | metadatas
), "Number of texts and metadatas should be the same."
input_ids = []
for idx, text in enumerate(texts):
try:
metadata = metadatas[idx] if metadatas else {}
input_id = self._post_text_input(text, metadata)
input_ids.append... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clarifai.html |
d13c8ecaa8b9-8 | """Run similarity search with score using Clarifai.
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[Documen... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clarifai.html |
d13c8ecaa8b9-9 | # Get number of docs to return
if self._number_of_docs is not None:
k = self._number_of_docs
post_annotations_searches_response = self._stub.PostAnnotationsSearches(
service_pb2.PostAnnotationsSearchesRequest(
user_app_id=self._userDataObject,
sear... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clarifai.html |
d13c8ecaa8b9-10 | raise Exception(
"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
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clarifai.html |
d13c8ecaa8b9-11 | )
return docs_and_scores
[docs] def similarity_search(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Document]:
"""Run similarity search using Clarifai.
Args:
query: Text to look up documents similar to.
k: Number of Documen... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clarifai.html |
d13c8ecaa8b9-12 | 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/latest/_modules/langchain/vectorstores/clarifai.html |
d13c8ecaa8b9-13 | Defaults to None.
Returns:
Clarifai: Clarifai vectorstore.
"""
clarifai_vector_db = cls(
user_id=user_id,
app_id=app_id,
pat=pat,
number_of_docs=number_of_docs,
api_base=api_base,
)
clarifai_vector_db.add_tex... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clarifai.html |
d13c8ecaa8b9-14 | **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]): Personal access token. Defaults to N... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clarifai.html |
d13c8ecaa8b9-15 | texts=texts,
pat=pat,
number_of_docs=number_of_docs,
api_base=api_base,
metadatas=metadatas,
) | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clarifai.html |
f3a79db35451-0 | 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 langc... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
f3a79db35451-1 | 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["doc... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
f3a79db35451-2 | embeddings = OpenAIEmbeddings()
vectorstore = Chroma("langchain_store", embeddings)
"""
_LANGCHAIN_DEFAULT_COLLECTION_NAME = "langchain"
def __init__(
self,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
embedding_function: Optional[Embeddings] = None,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
f3a79db35451-3 | )
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:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
f3a79db35451-4 | 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."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
f3a79db35451-5 | 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 vector... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
f3a79db35451-6 | embeddings = self._embedding_function.embed_documents(list(texts))
self._collection.upsert(
metadatas=metadatas, embeddings=embeddings, documents=texts, ids=ids
)
return ids
[docs] def similarity_search(
self,
query: str,
k: int = DEFAULT_K,
filter:... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
f3a79db35451-7 | """
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,
**kwar... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
f3a79db35451-8 | 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,
**kwa... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
f3a79db35451-9 | Lower score represents more similarity.
"""
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)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
f3a79db35451-10 | 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 optim... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
f3a79db35451-11 | 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,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
f3a79db35451-12 | 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 opt... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
f3a79db35451-13 | 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 sp... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
f3a79db35451-14 | where: Optional[Where] = None,
limit: Optional[int] = None,
offset: Optional[int] = None,
where_document: Optional[WhereDocument] = None,
include: Optional[List[str]] = None,
) -> Dict[str, Any]:
"""Gets the collection.
Args:
ids: The ids of the embeddings... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
f3a79db35451-15 | include: A list of what to include in the results.
Can contain `"embeddings"`, `"metadatas"`, `"documents"`.
Ids are always included.
Defaults to `["metadatas", "documents"]`. Optional.
"""
kwargs = {
"ids": ids,
"whe... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
f3a79db35451-16 | 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:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
f3a79db35451-17 | )
[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_di... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
f3a79db35451-18 | 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.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
f3a79db35451-19 | 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: Opt... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
f3a79db35451-20 | 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.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
f3a79db35451-21 | client=client,
)
[docs] def delete(self, ids: List[str]) -> None:
"""Delete by vector IDs.
Args:
ids: List of ids to delete.
"""
self._collection.delete(ids=ids) | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
bd06d29fdf84-0 | 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/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-1 | MetadataFilter = Union[DictFilter, common_types.Filter]
[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 langc... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-2 | embedding_function: Optional[Callable] = None, # deprecated
):
"""Initialize with necessary components."""
try:
import qdrant_client
except ImportError:
raise ValueError(
"Could not import qdrant-client python package. "
"Please instal... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-3 | 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_... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-4 | )
self._embeddings_function = embeddings
self.embeddings = None
[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 |
bd06d29fdf84-5 | Default: 64
Returns:
List of ids from adding the texts into the vectorstore.
"""
from qdrant_client.http import models as rest
added_ids = []
texts_iterator = iter(texts)
metadatas_iterator = iter(metadatas or [])
ids_iterator = iter(ids or [uuid.uuid4... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-6 | 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/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-7 | 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.
May be used to paginate results.
Note: large offset val... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-8 | queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all repl... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-9 | k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Documen... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-10 | 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/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-11 | distance in float for each.
Lower score represents more similarity.
"""
return self.similarity_search_with_score_by_vector(
self._embed_query(query),
k,
filter=filter,
search_params=search_params,
offset=offset,
score_th... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-12 | ) -> 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.
search_params: Additiona... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-13 | queried before returning the result.
Values:
- 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
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-14 | [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: int = 0,
score_threshold: Optional[float] = None,
co... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-15 | Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be return... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-16 | 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/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-17 | search_params=search_params,
limit=k,
offset=offset,
with_payload=True,
with_vectors=False, # Langchain does not expect vectors to be returned
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
return... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-18 | 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/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-19 | 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 |
bd06d29fdf84-20 | 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
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-21 | 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,
distance_f... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-22 | 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_types.InitFrom] = None,
**kwargs: Any,
) -> Qdrant:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-23 | uuid-like strings.
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],... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-24 | 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/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-25 | 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"
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-26 | 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 performance impact.
Have effect only in distributed mode.
on_di... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-27 | **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)... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-28 | "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_na... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-29 | collection_name=collection_name,
vectors_config=rest.VectorParams(
size=vector_size,
distance=rest.Distance[distance_func],
),
shard_number=shard_number,
replication_factor=replication_factor,
write_consistency_factor=write_cons... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-30 | # Take the corresponding metadata and id for each text in a batch
batch_metadatas = list(islice(metadatas_iterator, batch_size)) or None
batch_ids = list(islice(ids_iterator, batch_size))
# Generate the embeddings for all the texts in a batch
batch_embeddings = embedding.... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-31 | )
@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:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-32 | 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_con... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-33 | 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 ou... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-34 | 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 ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
bd06d29fdf84-35 | 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:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
c8f38cddf6f5-0 | Source code for langchain.vectorstores.azuresearch
"""Wrapper around Azure Cognitive Search."""
from __future__ import annotations
import base64
import json
import logging
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Tuple,
Type,
)
im... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
c8f38cddf6f5-1 | FIELDS_ID = get_from_env(
key="AZURESEARCH_FIELDS_ID", env_key="AZURESEARCH_FIELDS_ID", default="id"
)
FIELDS_CONTENT = get_from_env(
key="AZURESEARCH_FIELDS_CONTENT",
env_key="AZURESEARCH_FIELDS_CONTENT",
default="content",
)
FIELDS_CONTENT_VECTOR = get_from_env(
key="AZURESEARCH_FIELDS_CONTENT_VEC... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
c8f38cddf6f5-2 | ) -> SearchClient:
from azure.core.credentials import AzureKeyCredential
from azure.core.exceptions import ResourceNotFoundError
from azure.identity import DefaultAzureCredential
from azure.search.documents import SearchClient
from azure.search.documents.indexes import SearchIndexClient
from azu... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
c8f38cddf6f5-3 | index_client.get_index(name=index_name)
except ResourceNotFoundError:
# Fields configuration
fields = [
SimpleField(
name=FIELDS_ID,
type=SearchFieldDataType.String,
key=True,
filterable=True,
),
Sear... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
c8f38cddf6f5-4 | ]
# Vector search configuration
vector_search = VectorSearch(
algorithm_configurations=[
VectorSearchAlgorithmConfiguration(
name="default",
kind="hnsw",
hnsw_parameters={
"m": 4,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
c8f38cddf6f5-5 | ]
)
)
# Create the search index with the semantic settings and vector search
index = SearchIndex(
name=index_name,
fields=fields,
vector_search=vector_search,
semantic_settings=semantic_settings,
)
index_client.create_in... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
c8f38cddf6f5-6 | """Initialize with necessary components."""
# Initialize base class
self.embedding_function = embedding_function
self.client = _get_search_client(
azure_search_endpoint,
azure_search_key,
index_name,
embedding_function,
semantic_configu... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
c8f38cddf6f5-7 | # Use provided key otherwise use default key
key = keys[i] if keys else str(uuid.uuid4())
# Encoding key for Azure Search valid characters
key = base64.urlsafe_b64encode(bytes(key, "utf-8")).decode("ascii")
metadata = metadatas[i] if metadatas else {}
# Add da... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
c8f38cddf6f5-8 | # Check if all documents were successfully uploaded
if not all([r.succeeded for r in response]):
raise Exception(response)
# Reset data
data = []
# Considering case where data is an exact multiple of batch-size entries
if len(data) == 0... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
c8f38cddf6f5-9 | if search_type == "similarity":
docs = self.vector_search(query, k=k, **kwargs)
elif search_type == "hybrid":
docs = self.hybrid_search(query, k=k, **kwargs)
elif search_type == "semantic_hybrid":
docs = self.semantic_hybrid_search(query, k=k, **kwargs)
else:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
c8f38cddf6f5-10 | Returns:
List[Document]: A list of documents that are most similar to the query text.
"""
docs_and_scores = self.vector_search_with_score(
query, k=k, filters=kwargs.get("filters", None)
)
return [doc for doc, _ in docs_and_scores]
[docs] def vector_search_with... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
c8f38cddf6f5-11 | results = self.client.search(
search_text="",
vector=Vector(
value=np.array(
self.embedding_function(query), dtype=np.float32
).tolist(),
k=k,
fields=FIELDS_CONTENT_VECTOR,
),
select=[f"{F... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
c8f38cddf6f5-12 | Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
Returns:
List[Document]: A list of documents that are most similar to the query text.
"""
docs_and_scores = self.hybrid_search_with... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
c8f38cddf6f5-13 | k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query and score for each
"""
from azure.search.documents.models import Vector
results = self.client.search(
search_text=query,
vector=Vector(
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
c8f38cddf6f5-14 | ]
return docs
[docs] def semantic_hybrid_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""
Returns the most similar indexed documents to the query text.
Args:
query (str): The query text for which to find similar documents.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
c8f38cddf6f5-15 | self, query: str, k: int = 4, filters: Optional[str] = None
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query with an hybrid query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
c8f38cddf6f5-16 | filter=filters,
query_type="semantic",
query_language=self.semantic_query_language,
semantic_configuration_name=self.semantic_configuration_name,
query_caption="extractive",
query_answer="extractive",
top=k,
)
# Get Semantic Answers... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
c8f38cddf6f5-17 | "highlights": result.get("@search.captions", [{}])[
0
].highlights,
}
if result.get("@search.captions")
else {},
"answers": semantic_answers... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
c8f38cddf6f5-18 | ) -> AzureSearch:
# Creating a new Azure Search instance
azure_search = cls(
azure_search_endpoint,
azure_search_key,
index_name,
embedding.embed_query,
)
azure_search.add_texts(texts, metadatas, **kwargs)
return azure_search
class ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
c8f38cddf6f5-19 | search_type = values["search_type"]
if search_type not in ("similarity", "hybrid", "semantic_hybrid"):
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 == "simi... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
c8f38cddf6f5-20 | raise NotImplementedError(
"AzureSearchVectorStoreRetriever does not support async"
) | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
825682b7a014-0 | Source code for langchain.vectorstores.cassandra
"""Wrapper around Cassandra vector-store capabilities, based on cassIO."""
from __future__ import annotations
import hashlib
import typing
from typing import Any, Iterable, List, Optional, Tuple, Type, TypeVar
import numpy as np
if typing.TYPE_CHECKING:
from cassandr... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/cassandra.html |
825682b7a014-1 | [docs]class Cassandra(VectorStore):
"""Wrapper around Cassandra embeddings platform.
There is no notion of a default table name, since each embedding
function implies its own vector dimension, which is part of the schema.
Example:
.. code-block:: python
from langchain.vectorstore... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/cassandra.html |
825682b7a014-2 | def __init__(
self,
embedding: Embeddings,
session: Session,
keyspace: str,
table_name: str,
ttl_seconds: int | None = CASSANDRA_VECTORSTORE_DEFAULT_TTL_SECONDS,
) -> None:
try:
from cassio.vector import VectorTable
except (ImportError, Mod... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/cassandra.html |
825682b7a014-3 | keyspace=keyspace,
table=table_name,
embedding_dimension=self._getEmbeddingDimension(),
auto_id=False, # the `add_texts` contract admits user-provided ids
)
[docs] def delete_collection(self) -> None:
"""
Just an alias for `clear`
(to better align ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/cassandra.html |
825682b7a014-4 | **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]], opti... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/cassandra.html |
825682b7a014-5 | if metadatas is None:
metadatas = [{} for _ in _texts]
#
ttl_seconds = kwargs.get("ttl_seconds", self.ttl_seconds)
#
embedding_vectors = self.embedding.embed_documents(_texts)
for text, embedding_vector, text_id, metadata in zip(
_texts, embedding_vectors,... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/cassandra.html |
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