id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
|---|---|---|
9cdde1982c83-1 | ]
embeddings = OpenAIEmbeddings()
supabase_client = create_client("my_supabase_url", "my_supabase_key")
vector_store = SupabaseVectorStore.from_documents(
docs,
embeddings,
client=supabase_client,
table_name="documents",
query_name="mat... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
9cdde1982c83-2 | self.query_name = query_name or "match_documents"
self.chunk_size = chunk_size or 500
# According to the SupabaseVectorStore JS implementation, the best chunk size
# is 500. Though for large datasets it can be too large so it is configurable.
@property
def embeddings(self) -> Embeddings:... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
9cdde1982c83-3 | embeddings = embedding.embed_documents(texts)
ids = [str(uuid.uuid4()) for _ in texts]
docs = cls._texts_to_documents(texts, metadatas)
cls._add_vectors(client, table_name, embeddings, docs, ids, chunk_size)
return cls(
client=client,
embedding=embedding,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
9cdde1982c83-4 | self,
query: str,
k: int = 4,
filter: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
vector = self._embedding.embed_query(query)
return self.similarity_search_by_vector_with_relevance_scores(
vector, k=k, filter=filter
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
9cdde1982c83-5 | if search.get("content")
]
return match_result
[docs] def similarity_search_by_vector_returning_embeddings(
self,
query: List[float],
k: int,
filter: Optional[Dict[str, Any]] = None,
postgrest_filter: Optional[str] = None,
) -> List[Tuple[Document, float, n... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
9cdde1982c83-6 | if metadatas is None:
metadatas = repeat({})
docs = [
Document(page_content=text, metadata=metadata)
for text, metadata in zip(texts, metadatas)
]
return docs
@staticmethod
def _add_vectors(
client: supabase.client.Client,
table_name: s... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
9cdde1982c83-7 | lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up d... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
9cdde1982c83-8 | Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
9cdde1982c83-9 | """Delete by vector IDs.
Args:
ids: List of ids to delete.
"""
if ids is None:
raise ValueError("No ids provided to delete.")
rows: List[Dict[str, Any]] = [
{
"id": id,
}
for id in ids
]
# TODO: C... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
950c0400f22a-0 | Source code for langchain.vectorstores.sqlitevss
from __future__ import annotations
import json
import logging
import warnings
from typing import (
TYPE_CHECKING,
Any,
Iterable,
List,
Optional,
Tuple,
Type,
)
from langchain.docstore.document import Document
from langchain.schema.embeddings i... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/sqlitevss.html |
950c0400f22a-1 | self._embedding = embedding
self.create_table_if_not_exists()
[docs] def create_table_if_not_exists(self) -> None:
self._connection.execute(
f"""
CREATE TABLE IF NOT EXISTS {self._table}
(
rowid INTEGER PRIMARY KEY AUTOINCREMENT,
text TE... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/sqlitevss.html |
950c0400f22a-2 | max_id = 0
embeds = self._embedding.embed_documents(list(texts))
if not metadatas:
metadatas = [{} for _ in texts]
data_input = [
(text, json.dumps(metadata), json.dumps(embed))
for text, metadata, embed in zip(texts, metadatas, embeds)
]
self.... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/sqlitevss.html |
950c0400f22a-3 | documents.append((doc, row["distance"]))
return documents
[docs] def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query."""
embedding = self._embedding.embed_query(query)
documents = self.similarity_sear... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/sqlitevss.html |
950c0400f22a-4 | connection = cls.create_connection(db_file)
vss = cls(
table=table, connection=connection, db_file=db_file, embedding=embedding
)
vss.add_texts(texts=texts, metadatas=metadatas)
return vss
[docs] @staticmethod
def create_connection(db_file: str) -> sqlite3.Connection:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/sqlitevss.html |
2afc1092a31b-0 | Source code for langchain.vectorstores.awadb
from __future__ import annotations
import logging
import uuid
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Set, Tuple, Type
import numpy as np
from langchain.docstore.document import Document
from langchain.schema.embeddings import Embeddings
from l... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
2afc1092a31b-1 | "Please install it with `pip install awadb`."
)
if client is not None:
self.awadb_client = client
else:
if log_and_data_dir is not None:
self.awadb_client = awadb.Client(log_and_data_dir)
else:
self.awadb_client = awadb.Clie... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
2afc1092a31b-2 | """
if self.awadb_client is None:
raise ValueError("AwaDB client is None!!!")
embeddings = None
if self.using_table_name in self.table2embeddings:
embeddings = self.table2embeddings[self.using_table_name].embed_documents(
list(texts)
)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
2afc1092a31b-3 | E.g. `{"max_price" : 15.66, "min_price": 4.20}`
`price` is the metadata field, means range filter(4.20<'price'<15.66).
E.g. `{"maxe_price" : 15.66, "mine_price": 4.20}`
`price` is the metadata field, means range filter(4.20<='price'<=15.66).
kwargs: Any possible extend pa... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
2afc1092a31b-4 | Args:
query: Text query.
k: The k most similar documents to the text query.
text_in_page_content: Filter by the text in page_content of Document.
meta_filter: Filter by metadata. Defaults to None.
kwargs: Any possible extend parameters in the future.
R... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
2afc1092a31b-5 | [docs] def similarity_search_by_vector(
self,
embedding: Optional[List[float]] = None,
k: int = DEFAULT_TOPN,
text_in_page_content: Optional[str] = None,
meta_filter: Optional[dict] = None,
not_include_fields_in_metadata: Optional[Set[str]] = None,
**kwargs: An... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
2afc1092a31b-6 | if item_key in not_include_fields_in_metadata:
continue
meta_data[item_key] = item_detail[item_key]
results.append(Document(page_content=content, metadata=meta_data))
return results
[docs] def max_marginal_relevance_search(
self,
query: str,... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
2afc1092a31b-7 | else:
from awadb import AwaEmbedding
embedding = AwaEmbedding().Embedding(query)
if embedding.__len__() == 0:
return []
results = self.max_marginal_relevance_search_by_vector(
embedding,
k,
fetch_k,
lambda_mult=lambda_mu... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
2afc1092a31b-8 | """
if self.awadb_client is None:
raise ValueError("AwaDB client is None!!!")
results: List[Document] = []
if embedding is None:
return results
not_include_fields: set = {"_id", "score"}
retrieved_docs = self.similarity_search_by_vector(
embedd... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
2afc1092a31b-9 | limit: The number of documents to return. Defaults to 5. Optional.
Returns:
Documents which satisfy the input conditions.
"""
if self.awadb_client is None:
raise ValueError("AwaDB client is None!!!")
docs_detail = self.awadb_client.Get(
ids=ids,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
2afc1092a31b-10 | return ret
ret = self.awadb_client.Delete(ids)
return ret
[docs] def update(
self,
ids: List[str],
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Update the documents which have the specified ids.
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
2afc1092a31b-11 | return ret
[docs] def list_tables(
self,
**kwargs: Any,
) -> List[str]:
"""List all the tables created by the client."""
if self.awadb_client is None:
return []
return self.awadb_client.ListAllTables()
[docs] def get_current_table(
self,
**kw... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
2afc1092a31b-12 | log_and_data_dir=log_and_data_dir,
client=client,
)
awadb_client.add_texts(texts=texts, metadatas=metadatas)
return awadb_client
[docs] @classmethod
def from_documents(
cls: Type[AwaDB],
documents: List[Document],
embedding: Optional[Embeddings] = None,... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
8f68c7541f78-0 | Source code for langchain.vectorstores.pgembedding
from __future__ import annotations
import logging
import uuid
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type
import sqlalchemy
from sqlalchemy import func
from sqlalchemy.dialects.postgresql import JSON, UUID
from sqlalchemy.orm import Session, rel... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
8f68c7541f78-1 | ) -> Tuple["CollectionStore", bool]:
"""
Get or create a collection.
Returns [Collection, bool] where the bool is True if the collection was created.
"""
created = False
collection = cls.get_by_name(session, name)
if collection:
return collection, crea... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
8f68c7541f78-2 | `langchain.embeddings.base.Embeddings` interface.
- `collection_name` is the name of the collection to use. (default: langchain)
- NOTE: This is not the name of the table, but the name of the collection.
The tables will be created when initializing the store (if not exists)
So, make ... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
8f68c7541f78-3 | engine = sqlalchemy.create_engine(self.connection_string)
conn = engine.connect()
return conn
[docs] def create_hnsw_extension(self) -> None:
try:
with Session(self._conn) as session:
statement = sqlalchemy.text("CREATE EXTENSION IF NOT EXISTS embedding")
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
8f68c7541f78-4 | )
# Execute the queries
try:
with Session(self._conn) as session:
# Create the HNSW index
session.execute(create_index_query)
session.commit()
print("HNSW extension and index created successfully.")
except Exception as e:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
8f68c7541f78-5 | pre_delete_collection=pre_delete_collection,
)
store.add_embeddings(
texts=texts, embeddings=embeddings, metadatas=metadatas, ids=ids, **kwargs
)
return store
[docs] def add_embeddings(
self,
texts: List[str],
embeddings: List[List[float]],
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
8f68c7541f78-6 | embedding_store = EmbeddingStore(
embedding=embedding,
document=text,
cmetadata=metadata,
custom_id=id,
)
collection.embeddings.append(embedding_store)
session.add(embedding_store)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
8f68c7541f78-7 | if filter is not None:
filter_clauses = []
for key, value in filter.items():
IN = "in"
if isinstance(value, dict) and IN in map(str.lower, value):
value_case_insensitive = {
k.lower(): v for k, v ... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
8f68c7541f78-8 | metadata=result.EmbeddingStore.cmetadata,
),
result.distance if self.embedding_function is not None else None,
)
for result in results
]
return docs
[docs] def similarity_search_by_vector(
self,
embedding: List[float],
k:... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
8f68c7541f78-9 | ids: Optional[List[str]] = None,
pre_delete_collection: bool = False,
**kwargs: Any,
) -> PGEmbedding:
texts = [t[0] for t in text_embeddings]
embeddings = [t[1] for t in text_embeddings]
return cls._initialize_from_embeddings(
texts,
embeddings,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
8f68c7541f78-10 | def from_documents(
cls: Type[PGEmbedding],
documents: List[Document],
embedding: Embeddings,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
ids: Optional[List[str]] = None,
pre_delete_collection: bool = False,
**kwargs: Any,
) -> PGEmbedding:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
b806188a751d-0 | Source code for langchain.vectorstores.timescalevector
"""VectorStore wrapper around a Postgres-TimescaleVector database."""
from __future__ import annotations
import enum
import logging
import uuid
from datetime import timedelta
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/timescalevector.html |
b806188a751d-1 | from langchain.embeddings.openai import OpenAIEmbeddings
SERVICE_URL = "postgres://tsdbadmin:<password>@<id>.tsdb.cloud.timescale.com:<port>/tsdb?sslmode=require"
COLLECTION_NAME = "state_of_the_union_test"
embeddings = OpenAIEmbeddings()
vectorestore = TimescaleVector.fr... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/timescalevector.html |
b806188a751d-2 | self._time_partition_interval = time_partition_interval
self.sync_client = client.Sync(
self.service_url,
self.collection_name,
self.num_dimensions,
self._distance_strategy.value.lower(),
time_partition_interval=self._time_partition_interval,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/timescalevector.html |
b806188a751d-3 | metadatas = [{} for _ in texts]
if service_url is None:
service_url = cls.get_service_url(kwargs)
store = cls(
service_url=service_url,
num_dimensions=num_dimensions,
collection_name=collection_name,
embedding=embedding,
distance_st... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/timescalevector.html |
b806188a751d-4 | **kwargs,
)
await store.aadd_embeddings(
texts=texts, embeddings=embeddings, metadatas=metadatas, ids=ids, **kwargs
)
return store
[docs] def add_embeddings(
self,
texts: Iterable[str],
embeddings: List[List[float]],
metadatas: Optional[List... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/timescalevector.html |
b806188a751d-5 | kwargs: vectorstore specific parameters
"""
if ids is None:
ids = [str(uuid.uuid1()) for _ in texts]
if not metadatas:
metadatas = [{} for _ in texts]
records = list(zip(ids, metadatas, texts, embeddings))
await self.async_client.upsert(records)
re... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/timescalevector.html |
b806188a751d-6 | kwargs: vectorstore specific parameters
Returns:
List of ids from adding the texts into the vectorstore.
"""
embeddings = self.embedding.embed_documents(list(texts))
return await self.aadd_embeddings(
texts=texts, embeddings=embeddings, metadatas=metadatas, ids=id... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/timescalevector.html |
b806188a751d-7 | filter: Optional[Union[dict, list]] = None,
predicates: Optional[Predicates] = None,
**kwargs: Any,
) -> List[Document]:
"""Run similarity search with TimescaleVector with distance.
Args:
query (str): Query text to search for.
k (int): Number of results to ret... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/timescalevector.html |
b806188a751d-8 | predicates=predicates,
**kwargs,
)
return docs
[docs] async def asimilarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[Union[dict, list]] = None,
predicates: Optional[Predicates] = None,
**kwargs: Any,
) -> List[Tu... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/timescalevector.html |
b806188a751d-9 | "Please install it with `pip install timescale-vector`."
)
return client.UUIDTimeRange(**constructor_args)
[docs] def similarity_search_with_score_by_vector(
self,
embedding: Optional[List[float]],
k: int = 4,
filter: Optional[Union[dict, list]] = None,
pre... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/timescalevector.html |
b806188a751d-10 | raise ImportError(
"Could not import timescale_vector python package. "
"Please install it with `pip install timescale-vector`."
)
results = await self.async_client.search(
embedding,
limit=k,
filter=filter,
predicates=p... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/timescalevector.html |
b806188a751d-11 | embedding: Optional[List[float]],
k: int = 4,
filter: Optional[Union[dict, list]] = None,
predicates: Optional[Predicates] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up do... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/timescalevector.html |
b806188a751d-12 | embeddings,
embedding,
metadatas=metadatas,
ids=ids,
collection_name=collection_name,
distance_strategy=distance_strategy,
pre_delete_collection=pre_delete_collection,
**kwargs,
)
[docs] @classmethod
async def afrom_texts... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/timescalevector.html |
b806188a751d-13 | ids: Optional[List[str]] = None,
pre_delete_collection: bool = False,
**kwargs: Any,
) -> TimescaleVector:
"""Construct TimescaleVector wrapper from raw documents and pre-
generated embeddings.
Return VectorStore initialized from documents and embeddings.
Postgres con... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/timescalevector.html |
b806188a751d-14 | **kwargs: Any,
) -> TimescaleVector:
"""Construct TimescaleVector wrapper from raw documents and pre-
generated embeddings.
Return VectorStore initialized from documents and embeddings.
Postgres connection string is required
"Either pass it as a parameter
or set the T... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/timescalevector.html |
b806188a751d-15 | """
service_url = cls.get_service_url(kwargs)
store = cls(
service_url=service_url,
collection_name=collection_name,
embedding=embedding,
distance_strategy=distance_strategy,
pre_delete_collection=pre_delete_collection,
)
return... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/timescalevector.html |
b806188a751d-16 | # Default strategy is to rely on distance strategy provided
# in vectorstore constructor
if self._distance_strategy == DistanceStrategy.COSINE:
return self._cosine_relevance_score_fn
elif self._distance_strategy == DistanceStrategy.EUCLIDEAN_DISTANCE:
return self._euclide... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/timescalevector.html |
b806188a751d-17 | False otherwise, None if not implemented.
"""
self.sync_client.delete_by_metadata(filter)
return True
class IndexType(str, enum.Enum):
"""Enumerator for the supported Index types"""
TIMESCALE_VECTOR = "tsv"
PGVECTOR_IVFFLAT = "ivfflat"
PGVECTOR_HNSW = "hnsw"
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/timescalevector.html |
4cb2cc145e35-0 | 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
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
4cb2cc145e35-1 | 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.
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
4cb2cc145e35-2 | )
db_name, collection_name = namespace.split(".")
collection = client[db_name][collection_name]
return cls(collection, embedding, **kwargs)
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[Dict[str, Any]]] = None,
**kwargs: Any,
) ... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
4cb2cc145e35-3 | embeddings = self._embedding.embed_documents(texts)
to_insert = [
{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_ma... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
4cb2cc145e35-4 | query: str,
*,
k: int = 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 Mon... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
4cb2cc145e35-5 | """Return MongoDB documents most similar to the given query.
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 acces... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
4cb2cc145e35-6 | 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... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
4cb2cc145e35-7 | **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... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
32546b1c8542-0 | Source code for langchain.vectorstores.astradb
from __future__ import annotations
import uuid
import warnings
from concurrent.futures import ThreadPoolExecutor
from typing import (
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Set,
Tuple,
Type,
TypeVar,
)
import numpy as np
from... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/astradb.html |
32546b1c8542-1 | visited_keys.add(item_key)
new_lst.append(item)
return new_lst
[docs]class AstraDB(VectorStore):
"""Wrapper around DataStax Astra DB for vector-store workloads.
To use it, you need a recent installation of the `astrapy` library
and an Astra DB cloud database.
For quickstart and details, ... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/astradb.html |
32546b1c8542-2 | namespace: Optional[str] = None,
metric: Optional[str] = None,
batch_size: Optional[int] = None,
bulk_insert_batch_concurrency: Optional[int] = None,
bulk_insert_overwrite_concurrency: Optional[int] = None,
bulk_delete_concurrency: Optional[int] = None,
) -> None:
try... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/astradb.html |
32546b1c8542-3 | available in Astra DB. If left out, it will use Astra DB API's
defaults (i.e. "cosine" - but, for performance reasons,
"dot_product" is suggested if embeddings are normalized to one).
Advanced arguments (coming with sensible defaults):
batch_size (Optional[int]): Size... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/astradb.html |
32546b1c8542-4 | "'token' and 'api_endpoint'."
)
self.embedding = embedding
self.collection_name = collection_name
self.token = token
self.api_endpoint = api_endpoint
self.namespace = namespace
# Concurrency settings
self.batch_size: int = batch_size or DEFAULT_BAT... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/astradb.html |
32546b1c8542-5 | are set other than actual deletion on the backend.
"""
_ = self.astra_db.delete_collection(
collection_name=self.collection_name,
)
return None
def _provision_collection(self) -> None:
"""
Run the API invocation to create the collection on the backend.
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/astradb.html |
32546b1c8542-6 | """
deletion_response = self.collection.delete(document_id)
return ((deletion_response or {}).get("status") or {}).get(
"deletedCount", 0
) == 1
[docs] def delete(
self,
ids: Optional[List[str]] = None,
concurrency: Optional[int] = None,
**kwargs: A... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/astradb.html |
32546b1c8542-7 | return None
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
*,
batch_size: Optional[int] = None,
batch_concurrency: Optional[int] = None,
overwrite_concurrency: Optional[int] = N... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/astradb.html |
32546b1c8542-8 | docs.datastax.com/en/astra-serverless/docs/develop/dev-with-json.html
Returns:
List[str]: List of ids of the added texts.
"""
if kwargs:
warnings.warn(
"Method 'add_texts' of AstraDB vector store invoked with "
f"unsupported arguments ({', ... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/astradb.html |
32546b1c8542-9 | f"API Exception while running bulk insertion: {str(im_result)}"
)
batch_inserted = im_result["status"]["insertedIds"]
# estimation of the preexisting documents that failed
missed_inserted_ids = {
document["_id"] for document in document_batch
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/astradb.html |
32546b1c8542-10 | _b_max_workers = batch_concurrency or self.bulk_insert_batch_concurrency
with ThreadPoolExecutor(max_workers=_b_max_workers) as tpe:
all_ids_nested = tpe.map(
_handle_batch,
batch_iterate(
batch_size or self.batch_size,
uniqued_... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/astradb.html |
32546b1c8542-11 | hit["_id"],
)
for hit in hits
]
[docs] def similarity_search_with_score_id(
self,
query: str,
k: int = 4,
filter: Optional[Dict[str, str]] = None,
) -> List[Tuple[Document, float, str]]:
embedding_vector = self.embedding.embed_query(query)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/astradb.html |
32546b1c8542-12 | return self.similarity_search_by_vector(
embedding_vector,
k,
filter=filter,
)
[docs] def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Do... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/astradb.html |
32546b1c8542-13 | fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Returns:
Lis... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/astradb.html |
32546b1c8542-14 | **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 (str): Text to look up documents similar to.
k (i... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/astradb.html |
32546b1c8542-15 | *Additional arguments*: you can pass any argument that you would
to 'add_texts' and/or to the 'AstraDB' class constructor
(see these methods for details). These arguments will be
routed to the respective methods as they are.
Returns:
an `AstraDb` vecto... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/astradb.html |
32546b1c8542-16 | bulk_insert_batch_concurrency=kwargs.get("bulk_insert_batch_concurrency"),
bulk_insert_overwrite_concurrency=kwargs.get(
"bulk_insert_overwrite_concurrency"
),
bulk_delete_concurrency=kwargs.get("bulk_delete_concurrency"),
)
astra_db_store.add_texts(
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/astradb.html |
1544452feae7-0 | Source code for langchain.vectorstores.myscale
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 langchain.docstore.document import Document
from langchain.pydantic_v1 import BaseSettings... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
1544452feae7-1 | column_map (Dict) : Column type map to project column name onto langchain
semantics. Must have keys: `text`, `id`, `vector`,
must be same size to number of columns. For example:
.. code-block:: python
{
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
1544452feae7-2 | constraints and even sub-queries.
For more information, please visit
[myscale official site](https://docs.myscale.com/en/overview/)
"""
[docs] def __init__(
self,
embedding: Embeddings,
config: Optional[MyScaleSettings] = None,
**kwargs: Any,
) -> None:
"""... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
1544452feae7-3 | logger.warning(
"Lower case metric types will be deprecated "
"the future. Please use one of ('IP', 'Cosine', 'L2')"
)
# initialize the schema
dim = len(embedding.embed_query("try this out"))
index_params = (
", " + ",".join([f"'{k}={v}'" f... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
1544452feae7-4 | password=self.config.password,
**kwargs,
)
self.client.command("SET allow_experimental_object_type=1")
self.client.command(schema_)
@property
def embeddings(self) -> Embeddings:
return self._embeddings
[docs] def escape_str(self, value: str) -> str:
return ... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
1544452feae7-5 | ids: Optional list of ids to associate with the texts.
batch_size: Batch size of insertion
metadata: Optional column data to be inserted
Returns:
List of ids from adding the texts into the vectorstore.
"""
# Embed and create the documents
ids = ids or ... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
1544452feae7-6 | return [i for i in ids]
except Exception as e:
logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
return []
[docs] @classmethod
def from_texts(
cls,
texts: Iterable[str],
embedding: Embeddings,
metadatas: Optional[List[Dict[Any... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
1544452feae7-7 | """Text representation for myscale, prints backends, username and schemas.
Easy to use with `str(Myscale())`
Returns:
repr: string to show connection info and data schema
"""
_repr = f"\033[92m\033[1m{self.config.database}.{self.config.table} @ "
_repr += f"{self.... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
1544452feae7-8 | AS dist {self.dist_order}
LIMIT {topk}
"""
return q_str
[docs] def similarity_search(
self, query: str, k: int = 4, where_str: Optional[str] = None, **kwargs: Any
) -> List[Document]:
"""Perform a similarity search with MyScale
Args:
query (str)... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
1544452feae7-9 | of SQL injection. When dealing with metadatas, remember to
use `{self.metadata_column}.attribute` instead of `attribute`
alone. The default name for it is `metadata`.
Returns:
List[Document]: List of (Document, similarity)
"""
q_str = self._build_q... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
1544452feae7-10 | and cosine distance in float for each.
Lower score represents more similarity.
"""
q_str = self._build_qstr(self._embeddings.embed_query(query), k, where_str)
try:
return [
(
Document(
page_content=r[self.config.... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
1544452feae7-11 | conds = []
if ids:
conds.extend([f"{self.config.column_map['id']} = '{id}'" for id in ids])
if where_str:
conds.append(where_str)
assert len(conds) > 0
where_str_final = " AND ".join(conds)
qstr = (
f"DELETE FROM {self.config.database}.{self.co... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
1544452feae7-12 | ) -> str:
q_emb_str = ",".join(map(str, q_emb))
if where_str:
where_str = f"PREWHERE {where_str}"
else:
where_str = ""
q_str = f"""
SELECT {self.config.column_map['text']}, dist,
{','.join(self.must_have_cols)}
FROM {self.c... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
1544452feae7-13 | Document(
page_content=r[self.config.column_map["text"]],
metadata={k: r[k] for k in self.must_have_cols},
)
for r in self.client.query(q_str).named_results()
]
except Exception as e:
logger.error(f"\033[91m\033[1m{t... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
1544452feae7-14 | ),
r["dist"],
)
for r in self.client.query(q_str).named_results()
]
except Exception as e:
logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
return []
@property
def metadata_column(self) -> str... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
9e5865f764a7-0 | Source code for langchain.vectorstores.meilisearch
from __future__ import annotations
import uuid
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Type
from langchain.docstore.document import Document
from langchain.schema.embeddings import Embeddings
from langchain.schema.vectorstore impor... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/meilisearch.html |
9e5865f764a7-1 | To use this, you need to have `meilisearch` python package installed,
and a running Meilisearch instance.
To learn more about Meilisearch Python, refer to the in-depth
Meilisearch Python documentation: https://meilisearch.github.io/meilisearch-python/.
See the following documentation for how to run a Me... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/meilisearch.html |
9e5865f764a7-2 | self._index_name = index_name
self._embedding = embedding
self._text_key = text_key
self._metadata_key = metadata_key
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: An... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/meilisearch.html |
9e5865f764a7-3 | return ids
[docs] def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return meilisearch documents most similar to the query.
Args:
query (str): Query text for whic... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/meilisearch.html |
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