id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 59 127 |
|---|---|---|
ffbc7c5e91ef-1 | embedding: Embeddings,
table_name: str,
query_name: Union[str, None] = None,
) -> None:
"""Initialize with supabase client."""
try:
import supabase # noqa: F401
except ImportError:
raise ValueError(
"Could not import supabase python pa... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/supabase.html |
ffbc7c5e91ef-2 | raise ValueError("Supabase document table_name is required.")
embeddings = embedding.embed_documents(texts)
docs = cls._texts_to_documents(texts, metadatas)
_ids = cls._add_vectors(client, table_name, embeddings, docs)
return cls(
client=client,
embedding=embeddin... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/supabase.html |
ffbc7c5e91ef-3 | ) -> List[Tuple[Document, float]]:
match_documents_params = dict(query_embedding=query, match_count=k)
res = self._client.rpc(self.query_name, match_documents_params).execute()
match_result = [
(
Document(
metadata=search.get("metadata", {}), # ty... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/supabase.html |
ffbc7c5e91ef-4 | ) -> List[Document]:
"""Return list of Documents from list of texts and metadatas."""
if metadatas is None:
metadatas = repeat({})
docs = [
Document(page_content=text, metadata=metadata)
for text, metadata in zip(texts, metadatas)
]
return docs... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/supabase.html |
ffbc7c5e91ef-5 | self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/supabase.html |
ffbc7c5e91ef-6 | **kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/supabase.html |
ffbc7c5e91ef-7 | )
return docs
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/supabase.html |
dc67dafeea5b-0 | Source code for langchain.vectorstores.pinecone
"""Wrapper around Pinecone vector database."""
from __future__ import annotations
import logging
import uuid
from typing import Any, Callable, Iterable, List, Optional, Tuple
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.bas... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/pinecone.html |
dc67dafeea5b-1 | f"client should be an instance of pinecone.index.Index, "
f"got {type(index)}"
)
self._index = index
self._embedding_function = embedding_function
self._text_key = text_key
self._namespace = namespace
[docs] def add_texts(
self,
texts: Itera... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/pinecone.html |
dc67dafeea5b-2 | self,
query: str,
k: int = 4,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
) -> List[Tuple[Document, float]]:
"""Return pinecone documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/pinecone.html |
dc67dafeea5b-3 | """Return pinecone documents most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Dictionary of argument(s) to filter on metadata
namespace: Namespace to search in. Default will search i... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/pinecone.html |
dc67dafeea5b-4 | """
if namespace is None:
namespace = self._namespace
results = self._index.query(
[embedding],
top_k=fetch_k,
include_values=True,
include_metadata=True,
namespace=namespace,
filter=filter,
)
mmr_selecte... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/pinecone.html |
dc67dafeea5b-5 | Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
embedding = self._embedding_function(query)
return self.max_marginal_relevance_search_by_vector(
embedding, k, fetch_k, lambda_mult, filter, namespace
)
[docs] @clas... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/pinecone.html |
dc67dafeea5b-6 | "Please install it with `pip install pinecone-client`."
)
indexes = pinecone.list_indexes() # checks if provided index exists
if index_name in indexes:
index = pinecone.Index(index_name)
elif len(indexes) == 0:
raise ValueError(
"No active ind... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/pinecone.html |
dc67dafeea5b-7 | return cls(index, embedding.embed_query, text_key, namespace)
[docs] @classmethod
def from_existing_index(
cls,
index_name: str,
embedding: Embeddings,
text_key: str = "text",
namespace: Optional[str] = None,
) -> Pinecone:
"""Load pinecone vectorstore from ind... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/pinecone.html |
96ac11b0e0b1-0 | Source code for langchain.vectorstores.weaviate
"""Wrapper around weaviate vector database."""
from __future__ import annotations
import datetime
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Type
from uuid import uuid4
import numpy as np
from langchain.docstore.document import Document
from ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/weaviate.html |
96ac11b0e0b1-1 | if weaviate_api_key is not None
else None
)
client = weaviate.Client(weaviate_url, auth_client_secret=auth)
return client
def _default_score_normalizer(val: float) -> float:
return 1 - 1 / (1 + np.exp(val))
def _json_serializable(value: Any) -> Any:
if isinstance(value, datetime.datetime):
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/weaviate.html |
96ac11b0e0b1-2 | )
if not isinstance(client, weaviate.Client):
raise ValueError(
f"client should be an instance of weaviate.Client, got {type(client)}"
)
self._client = client
self._index_name = index_name
self._embedding = embedding
self._text_key = text_k... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/weaviate.html |
96ac11b0e0b1-3 | class_name=self._index_name,
uuid=_id,
vector=vector,
)
ids.append(_id)
return ids
[docs] def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query.
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/weaviate.html |
96ac11b0e0b1-4 | if kwargs.get("where_filter"):
query_obj = query_obj.with_where(kwargs.get("where_filter"))
if kwargs.get("additional"):
query_obj = query_obj.with_additional(kwargs.get("additional"))
result = query_obj.with_near_text(content).with_limit(k).do()
if "errors" in result:
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/weaviate.html |
96ac11b0e0b1-5 | k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/weaviate.html |
96ac11b0e0b1-6 | among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/weaviate.html |
96ac11b0e0b1-7 | """
Return list of documents most similar to the query
text and cosine distance in float for each.
Lower score represents more similarity.
"""
if self._embedding is None:
raise ValueError(
"_embedding cannot be None for similarity_search_with_score"
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/weaviate.html |
96ac11b0e0b1-8 | **kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and relevance scores, normalized on a scale from 0 to 1.
0 is dissimilar, 1 is most similar.
"""
if self._relevance_score_fn is None:
raise ValueError(
"relevance_score_fn must be provided to... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/weaviate.html |
96ac11b0e0b1-9 | )
"""
client = _create_weaviate_client(**kwargs)
from weaviate.util import get_valid_uuid
index_name = kwargs.get("index_name", f"LangChain_{uuid4().hex}")
embeddings = embedding.embed_documents(texts) if embedding else None
text_key = "text"
schema = _default_sch... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/weaviate.html |
96ac11b0e0b1-10 | batch.add_data_object(**params)
batch.flush()
relevance_score_fn = kwargs.get("relevance_score_fn")
by_text: bool = kwargs.get("by_text", False)
return cls(
client,
index_name,
text_key,
embedding=embedding,
attributes=attri... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/weaviate.html |
bbbc79145bb2-0 | Source code for langchain.vectorstores.zilliz
from __future__ import annotations
import logging
from typing import Any, List, Optional
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.milvus import Milvus
logger = logging.getLogger(__name__)
[docs]class Zilliz(Milvus):
def _create_index(... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/zilliz.html |
bbbc79145bb2-1 | "Failed to create an index on collection: %s", self.collection_name
)
raise e
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
collection_name: str = "LangChainCollecti... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/zilliz.html |
bbbc79145bb2-2 | """
vector_db = cls(
embedding_function=embedding,
collection_name=collection_name,
connection_args=connection_args,
consistency_level=consistency_level,
index_params=index_params,
search_params=search_params,
drop_old=drop_old,... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/zilliz.html |
88eb7242e864-0 | Source code for langchain.vectorstores.singlestoredb
"""Wrapper around SingleStore DB."""
from __future__ import annotations
import json
from typing import (
Any,
ClassVar,
Collection,
Iterable,
List,
Optional,
Tuple,
Type,
)
from sqlalchemy.pool import QueuePool
from langchain.docstore.... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/singlestoredb.html |
88eb7242e864-1 | timeout: float = 30,
**kwargs: Any,
):
"""Initialize with necessary components.
Args:
embedding (Embeddings): A text embedding model.
table_name (str, optional): Specifies the name of the table in use.
Defaults to "embeddings".
content_fiel... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/singlestoredb.html |
88eb7242e864-2 | local_infile (bool, optional): Allows local file uploads.
charset (str, optional): Specifies the character set for string values.
ssl_key (str, optional): Specifies the path of the file containing the SSL
key.
ssl_cert (str, optional): Specifies the path of the file c... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/singlestoredb.html |
88eb7242e864-3 | .. code-block:: python
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import SingleStoreDB
vectorstore = SingleStoreDB(
OpenAIEmbeddings(),
host="127.0.0.1",
port=3306,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/singlestoredb.html |
88eb7242e864-4 | {} BLOB, {} JSON);""".format(
self.table_name,
self.content_field,
self.vector_field,
self.metadata_field,
),
)
finally:
cur.close()
finally:
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/singlestoredb.html |
88eb7242e864-5 | finally:
cur.close()
finally:
conn.close()
return []
[docs] def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Returns the most similar indexed documents to the query text.
Uses cosine similarity.
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/singlestoredb.html |
88eb7242e864-6 | self.vector_field,
self.table_name,
),
(
"[{}]".format(",".join(map(str, embedding))),
k,
),
)
for row in cur.fetchall():
doc = Document... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/singlestoredb.html |
88eb7242e864-7 | )
"""
instance = cls(
embedding,
table_name=table_name,
content_field=content_field,
metadata_field=metadata_field,
vector_field=vector_field,
pool_size=pool_size,
max_overflow=max_overflow,
timeout=timeout,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/singlestoredb.html |
dabc47655c46-0 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/annoy.html |
dabc47655c46-1 | ):
"""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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/annoy.html |
dabc47655c46-2 | 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 ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/annoy.html |
dabc47655c46-3 | 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)
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/annoy.html |
dabc47655c46-4 | 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 =... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/annoy.html |
dabc47655c46-5 | 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(
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/annoy.html |
dabc47655c46-6 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/annoy.html |
dabc47655c46-7 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/annoy.html |
dabc47655c46-8 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/annoy.html |
dabc47655c46-9 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/annoy.html |
dabc47655c46-10 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/annoy.html |
1f15a931ccb2-0 | Source code for langchain.vectorstores.elastic_vector_search
"""Wrapper around Elasticsearch vector database."""
from __future__ import annotations
import uuid
from abc import ABC
from typing import (
TYPE_CHECKING,
Any,
Dict,
Iterable,
List,
Mapping,
Optional,
Tuple,
Union,
)
from l... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/elastic_vector_search.html |
1f15a931ccb2-1 | # defined as an abstract base class itself, allowing the creation of subclasses with
# their own specific implementations. If you plan to subclass ElasticVectorSearch,
# you can inherit from it and define your own implementation of the necessary methods
# and attributes.
[docs]class ElasticVectorSearch(VectorStore, ABC... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/elastic_vector_search.html |
1f15a931ccb2-2 | 4. Click "Reset password"
5. Follow the prompts to reset the password
The format for Elastic Cloud URLs is
https://username:password@cluster_id.region_id.gcp.cloud.es.io:9243.
Example:
.. code-block:: python
from langchain import ElasticVectorSearch
from langchain.embeddi... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/elastic_vector_search.html |
1f15a931ccb2-3 | self.index_name = index_name
_ssl_verify = ssl_verify or {}
try:
self.client = elasticsearch.Elasticsearch(elasticsearch_url, **_ssl_verify)
except ValueError as e:
raise ValueError(
f"Your elasticsearch client string is mis-formatted. Got error: {e} "
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/elastic_vector_search.html |
1f15a931ccb2-4 | for i, text in enumerate(texts):
metadata = metadatas[i] if metadatas else {}
_id = str(uuid.uuid4())
request = {
"_op_type": "index",
"_index": self.index_name,
"vector": embeddings[i],
"text": text,
"me... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/elastic_vector_search.html |
1f15a931ccb2-5 | """
embedding = self.embedding.embed_query(query)
script_query = _default_script_query(embedding, filter)
response = self.client_search(
self.client, self.index_name, script_query, size=k
)
hits = [hit for hit in response["hits"]["hits"]]
docs_and_scores = [
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/elastic_vector_search.html |
1f15a931ccb2-6 | )
"""
elasticsearch_url = elasticsearch_url or get_from_env(
"elasticsearch_url", "ELASTICSEARCH_URL"
)
index_name = index_name or uuid.uuid4().hex
vectorsearch = cls(elasticsearch_url, index_name, embedding, **kwargs)
vectorsearch.add_texts(
texts... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/elastic_vector_search.html |
1f15a931ccb2-7 | """
def __init__(
self,
index_name: str,
embedding: Embeddings,
es_connection: Optional["Elasticsearch"] = None,
es_cloud_id: Optional[str] = None,
es_user: Optional[str] = None,
es_password: Optional[str] = None,
vector_query_field: Optional[str] = "v... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/elastic_vector_search.html |
1f15a931ccb2-8 | if es_cloud_id and es_user and es_password:
self.client = elasticsearch.Elasticsearch(
cloud_id=es_cloud_id, basic_auth=(es_user, es_password)
)
else:
raise ValueError(
"""Either provide a pre-existing Elasticsearch conn... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/elastic_vector_search.html |
1f15a931ccb2-9 | knn["query_vector_builder"] = {
"text_embedding": {
"model_id": model_id, # use 'model_id' argument
"model_text": query, # use 'query' argument
}
}
else:
raise ValueError(
"Either `query_vector` or ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/elastic_vector_search.html |
1f15a931ccb2-10 | `query` is provided.
size: The number of search hits to return. Defaults to 10.
source: Whether to include the source of each hit in the results.
fields: The fields to include in the source of each hit. If None, all
fields are included.
vector_query_field:... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/elastic_vector_search.html |
1f15a931ccb2-11 | ] = None,
) -> Dict[Any, Any]:
"""Performs a hybrid k-nearest neighbor (k-NN) and text-based search on the
Elasticsearch index.
The search can be conducted using either a raw query vector or a model ID.
The method first generates
the body of the k-NN search query and the ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/elastic_vector_search.html |
1f15a931ccb2-12 | both are provided.
"""
knn_query_body = self._default_knn_query(
query_vector=query_vector, query=query, model_id=model_id, k=k
)
# Modify the knn_query_body to add a "boost" parameter
knn_query_body["boost"] = knn_boost
# Generate the body of the standard Ela... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/elastic_vector_search.html |
46209c3a05aa-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
46209c3a05aa-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
46209c3a05aa-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]] =... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
46209c3a05aa-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,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
46209c3a05aa-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
46209c3a05aa-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
46209c3a05aa-6 | with_vectors=False, # Langchain does not expect vectors to be returned
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
return [
(
self._document_from_scored_point(
result, self.content_payload_key,... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
46209c3a05aa-7 | 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 that determines the degree
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
46209c3a05aa-8 | 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_func: str = "Cosine",
content_payload_key: str = CONTENT_KEY,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
46209c3a05aa-9 | location:
If `:memory:` - use in-memory Qdrant instance.
If `str` - use it as a `url` parameter.
If `None` - fallback to relying on `host` and `port` parameters.
url: either host or str of "Optional[scheme], host, Optional[port],
Optional[prefi... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
46209c3a05aa-10 | 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"
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
46209c3a05aa-11 | **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)... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
46209c3a05aa-12 | )
client.recreate_collection(
collection_name=collection_name,
vectors_config=rest.VectorParams(
size=vector_size,
distance=rest.Distance[distance_func],
),
shard_number=shard_number,
replication_factor=replication_facto... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
46209c3a05aa-13 | embeddings=embedding,
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
)
@classmethod
def _build_payloads(
cls,
texts: Iterable[str],
metadatas: Optional[List[dict]],
content_payload_key: str,
metadata_pay... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
46209c3a05aa-14 | for _value in value:
if isinstance(_value, dict):
out.extend(self._build_condition(f"{key}[]", _value))
else:
out.extend(self._build_condition(f"{key}", _value))
else:
out.append(
rest.FieldCondition(
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
46209c3a05aa-15 | Args:
texts: Iterable of texts to embed.
Returns:
List of floats representing the texts embedding.
"""
if self.embeddings is not None:
embeddings = self.embeddings.embed_documents(list(texts))
if hasattr(embeddings, "tolist"):
embed... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/qdrant.html |
20f80b0c4075-0 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/hologres.html |
20f80b0c4075-1 | '{"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 = (
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/hologres.html |
20f80b0c4075-2 | 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"
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/hologres.html |
20f80b0c4075-3 | ) -> None:
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__()
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/hologres.html |
20f80b0c4075-4 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/hologres.html |
20f80b0c4075-5 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/hologres.html |
20f80b0c4075-6 | 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(
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/hologres.html |
20f80b0c4075-7 | ]
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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/hologres.html |
20f80b0c4075-8 | Return VectorStore initialized from documents and embeddings.
Postgres connection string is required
"Either pass it as a parameter
or set the HOLOGRES_CONNECTION_STRING environment variable.
Example:
.. code-block:: python
from langchain import Hologres
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/hologres.html |
20f80b0c4075-9 | embedding_function=embedding,
pre_delete_table=pre_delete_table,
)
return store
[docs] @classmethod
def get_connection_string(cls, kwargs: Dict[str, Any]) -> str:
connection_string: str = get_from_dict_or_env(
data=kwargs,
key="connection_string",
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/hologres.html |
20f80b0c4075-10 | ndims=ndims,
table_name=table_name,
**kwargs,
)
[docs] @classmethod
def connection_string_from_db_params(
cls,
host: str,
port: int,
database: str,
user: str,
password: str,
) -> str:
"""Return connection string from data... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/hologres.html |
889538c38d3a-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/sklearn.html |
889538c38d3a-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")
@... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/sklearn.html |
889538c38d3a-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/sklearn.html |
889538c38d3a-3 | # 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.asarray([])
if self._persist_path is not None and os.path.isfile(self... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/sklearn.html |
889538c38d3a-4 | ) -> 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.extend(_ids)
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/sklearn.html |
889538c38d3a-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={"... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/sklearn.html |
889538c38d3a-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/sklearn.html |
889538c38d3a-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
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/sklearn.html |
efa99fd80b9c-0 | Source code for langchain.vectorstores.myscale
"""Wrapper around MyScale vector database."""
from __future__ import annotations
import json
import logging
from hashlib import sha1
from threading import Thread
from typing import Any, Dict, Iterable, List, Optional, Tuple
from pydantic import BaseSettings
from langchain.... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/myscale.html |
efa99fd80b9c-1 | .. code-block:: python
{
'id': 'text_id',
'vector': 'text_embedding',
'text': 'text_plain',
'metadata': 'metadata_dictionary_in_json',
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/myscale.html |
efa99fd80b9c-2 | config: Optional[MyScaleSettings] = None,
**kwargs: Any,
) -> None:
"""MyScale Wrapper to LangChain
embedding_function (Embeddings):
config (MyScaleSettings): Configuration to MyScale Client
Other keyword arguments will pass into
[clickhouse-connect](https://docs.... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/myscale.html |
efa99fd80b9c-3 | CREATE TABLE IF NOT EXISTS {self.config.database}.{self.config.table}(
{self.config.column_map['id']} String,
{self.config.column_map['text']} String,
{self.config.column_map['vector']} Array(Float32),
{self.config.column_map['metadata']} JSON,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/myscale.html |
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