# TODO: Remove below import when minimum supported Python version is 3.10
from __future__ import annotations
from typing import Any, Callable, Iterable, Optional, Sequence
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore
from .async_vectorstore import AsyncPGVectorStore
from .engine import PGEngine
from .indexes import (
DEFAULT_DISTANCE_STRATEGY,
BaseIndex,
DistanceStrategy,
QueryOptions,
)
[docs]
class PGVectorStore(VectorStore):
"""Postgres Vector Store class"""
__create_key = object()
[docs]
def __init__(self, key: object, engine: PGEngine, vs: AsyncPGVectorStore):
"""PGVectorStore constructor.
Args:
key (object): Prevent direct constructor usage.
engine (PGEngine): Connection pool engine for managing connections to Postgres database.
vs (AsyncPGVectorStore): The async only VectorStore implementation
Raises:
Exception: If called directly by user.
"""
if key != PGVectorStore.__create_key:
raise Exception(
"Only create class through 'create' or 'create_sync' methods!"
)
self._engine = engine
self.__vs = vs
[docs]
@classmethod
async def create(
cls: type[PGVectorStore],
engine: PGEngine,
embedding_service: Embeddings,
table_name: str,
schema_name: str = "public",
content_column: str = "content",
embedding_column: str = "embedding",
metadata_columns: Optional[list[str]] = None,
ignore_metadata_columns: Optional[list[str]] = None,
id_column: str = "langchain_id",
metadata_json_column: Optional[str] = "langchain_metadata",
distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
index_query_options: Optional[QueryOptions] = None,
) -> PGVectorStore:
"""Create an PGVectorStore instance.
Args:
engine (PGEngine): Connection pool engine for managing connections to postgres database.
embedding_service (Embeddings): Text embedding model to use.
table_name (str): Name of an existing table.
schema_name (str, optional): Name of the database schema. Defaults to "public".
content_column (str): Column that represent a Document's page_content. Defaults to "content".
embedding_column (str): Column for embedding vectors. The embedding is generated from the document value. Defaults to "embedding".
metadata_columns (list[str]): Column(s) that represent a document's metadata.
ignore_metadata_columns (list[str]): Column(s) to ignore in pre-existing tables for a document's metadata. Can not be used with metadata_columns. Defaults to None.
id_column (str): Column that represents the Document's id. Defaults to "langchain_id".
metadata_json_column (str): Column to store metadata as JSON. Defaults to "langchain_metadata".
distance_strategy (DistanceStrategy): Distance strategy to use for vector similarity search. Defaults to COSINE_DISTANCE.
k (int): Number of Documents to return from search. Defaults to 4.
fetch_k (int): Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float): 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. Defaults to 0.5.
index_query_options (QueryOptions): Index query option.
Returns:
PGVectorStore
"""
coro = AsyncPGVectorStore.create(
engine,
embedding_service,
table_name,
schema_name=schema_name,
content_column=content_column,
embedding_column=embedding_column,
metadata_columns=metadata_columns,
ignore_metadata_columns=ignore_metadata_columns,
metadata_json_column=metadata_json_column,
id_column=id_column,
distance_strategy=distance_strategy,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
index_query_options=index_query_options,
)
vs = await engine._run_as_async(coro)
return cls(cls.__create_key, engine, vs)
[docs]
@classmethod
def create_sync(
cls,
engine: PGEngine,
embedding_service: Embeddings,
table_name: str,
schema_name: str = "public",
content_column: str = "content",
embedding_column: str = "embedding",
metadata_columns: Optional[list[str]] = None,
ignore_metadata_columns: Optional[list[str]] = None,
id_column: str = "langchain_id",
metadata_json_column: str = "langchain_metadata",
distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
index_query_options: Optional[QueryOptions] = None,
) -> PGVectorStore:
"""Create an PGVectorStore instance.
Args:
key (object): Prevent direct constructor usage.
engine (PGEngine): Connection pool engine for managing connections to postgres database.
embedding_service (Embeddings): Text embedding model to use.
table_name (str): Name of an existing table.
schema_name (str, optional): Name of the database schema. Defaults to "public".
content_column (str, optional): Column that represent a Document's page_content. Defaults to "content".
embedding_column (str, optional): Column for embedding vectors. The embedding is generated from the document value. Defaults to "embedding".
metadata_columns (list[str], optional): Column(s) that represent a document's metadata. Defaults to None.
ignore_metadata_columns (Optional[list[str]]): Column(s) to ignore in pre-existing tables for a document's metadata. Can not be used with metadata_columns. Defaults to None.
id_column (str, optional): Column that represents the Document's id. Defaults to "langchain_id".
metadata_json_column (str, optional): Column to store metadata as JSON. Defaults to "langchain_metadata".
distance_strategy (DistanceStrategy, optional): Distance strategy to use for vector similarity search. Defaults to COSINE_DISTANCE.
k (int, optional): Number of Documents to return from search. Defaults to 4.
fetch_k (int, optional): Number of Documents to fetch to pass to MMR algorithm. Defaults to 20.
lambda_mult (float, optional): 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. Defaults to 0.5.
index_query_options (Optional[QueryOptions], optional): Index query option. Defaults to None.
Returns:
PGVectorStore
"""
coro = AsyncPGVectorStore.create(
engine,
embedding_service,
table_name,
schema_name=schema_name,
content_column=content_column,
embedding_column=embedding_column,
metadata_columns=metadata_columns,
ignore_metadata_columns=ignore_metadata_columns,
metadata_json_column=metadata_json_column,
id_column=id_column,
distance_strategy=distance_strategy,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
index_query_options=index_query_options,
)
vs = engine._run_as_sync(coro)
return cls(cls.__create_key, engine, vs)
@property
def embeddings(self) -> Embeddings:
return self.__vs.embedding_service
[docs]
async def aadd_embeddings(
self,
texts: Iterable[str],
embeddings: list[list[float]],
metadatas: Optional[list[dict]] = None,
ids: Optional[list[str]] = None,
**kwargs: Any,
) -> list[str]:
"""Add data along with embeddings to the table."""
return await self._engine._run_as_async(
self.__vs.aadd_embeddings(texts, embeddings, metadatas, ids, **kwargs)
)
[docs]
async def aadd_texts(
self,
texts: Iterable[str],
metadatas: Optional[list[dict]] = None,
ids: Optional[list] = None,
**kwargs: Any,
) -> list[str]:
"""Embed texts and add to the table.
Raises:
:class:`InvalidTextRepresentationError <asyncpg.exceptions.InvalidTextRepresentationError>`: if the `ids` data type does not match that of the `id_column`.
"""
return await self._engine._run_as_async(
self.__vs.aadd_texts(texts, metadatas, ids, **kwargs)
)
[docs]
async def aadd_documents(
self,
documents: list[Document],
ids: Optional[list] = None,
**kwargs: Any,
) -> list[str]:
"""Embed documents and add to the table.
Raises:
:class:`InvalidTextRepresentationError <asyncpg.exceptions.InvalidTextRepresentationError>`: if the `ids` data type does not match that of the `id_column`.
"""
return await self._engine._run_as_async(
self.__vs.aadd_documents(documents, ids, **kwargs)
)
[docs]
def add_embeddings(
self,
texts: Iterable[str],
embeddings: list[list[float]],
metadatas: Optional[list[dict]] = None,
ids: Optional[list[str]] = None,
**kwargs: Any,
) -> list[str]:
"""Add data along with embeddings to the table."""
return self._engine._run_as_sync(
self.__vs.aadd_embeddings(texts, embeddings, metadatas, ids, **kwargs)
)
[docs]
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[list[dict]] = None,
ids: Optional[list] = None,
**kwargs: Any,
) -> list[str]:
"""Embed texts and add to the table.
Raises:
:class:`InvalidTextRepresentationError <asyncpg.exceptions.InvalidTextRepresentationError>`: if the `ids` data type does not match that of the `id_column`.
"""
return self._engine._run_as_sync(
self.__vs.aadd_texts(texts, metadatas, ids, **kwargs)
)
[docs]
def add_documents(
self,
documents: list[Document],
ids: Optional[list] = None,
**kwargs: Any,
) -> list[str]:
"""Embed documents and add to the table.
Raises:
:class:`InvalidTextRepresentationError <asyncpg.exceptions.InvalidTextRepresentationError>`: if the `ids` data type does not match that of the `id_column`.
"""
return self._engine._run_as_sync(
self.__vs.aadd_documents(documents, ids, **kwargs)
)
[docs]
async def adelete(
self,
ids: Optional[list] = None,
**kwargs: Any,
) -> Optional[bool]:
"""Delete records from the table.
Raises:
:class:`InvalidTextRepresentationError <asyncpg.exceptions.InvalidTextRepresentationError>`: if the `ids` data type does not match that of the `id_column`.
"""
return await self._engine._run_as_async(self.__vs.adelete(ids, **kwargs))
[docs]
def delete(
self,
ids: Optional[list] = None,
**kwargs: Any,
) -> Optional[bool]:
"""Delete records from the table.
Raises:
:class:`InvalidTextRepresentationError <asyncpg.exceptions.InvalidTextRepresentationError>`: if the `ids` data type does not match that of the `id_column`.
"""
return self._engine._run_as_sync(self.__vs.adelete(ids, **kwargs))
[docs]
@classmethod
async def afrom_texts( # type: ignore[override]
cls: type[PGVectorStore],
texts: list[str],
embedding: Embeddings,
engine: PGEngine,
table_name: str,
schema_name: str = "public",
metadatas: Optional[list[dict]] = None,
ids: Optional[list] = None,
content_column: str = "content",
embedding_column: str = "embedding",
metadata_columns: Optional[list[str]] = None,
ignore_metadata_columns: Optional[list[str]] = None,
id_column: str = "langchain_id",
metadata_json_column: str = "langchain_metadata",
distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
index_query_options: Optional[QueryOptions] = None,
**kwargs: Any,
) -> PGVectorStore:
"""Create an PGVectorStore instance from texts.
Args:
texts (list[str]): Texts to add to the vector store.
embedding (Embeddings): Text embedding model to use.
engine (PGEngine): Connection pool engine for managing connections to postgres database.
table_name (str): Name of an existing table.
schema_name (str, optional): Name of the database schema. Defaults to "public".
metadatas (Optional[list[dict]], optional): List of metadatas to add to table records. Defaults to None.
ids: (Optional[list]): List of IDs to add to table records. Defaults to None.
content_column (str, optional): Column that represent a Document's page_content. Defaults to "content".
embedding_column (str, optional): Column for embedding vectors. The embedding is generated from the document value. Defaults to "embedding".
metadata_columns (list[str], optional): Column(s) that represent a document's metadata. Defaults to an empty list.
ignore_metadata_columns (Optional[list[str]], optional): Column(s) to ignore in pre-existing tables for a document's metadata. Can not be used with metadata_columns. Defaults to None.
id_column (str, optional): Column that represents the Document's id. Defaults to "langchain_id".
metadata_json_column (str, optional): Column to store metadata as JSON. Defaults to "langchain_metadata".
distance_strategy (DistanceStrategy): Distance strategy to use for vector similarity search. Defaults to COSINE_DISTANCE.
k (int): Number of Documents to return from search. Defaults to 4.
fetch_k (int): Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float): 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. Defaults to 0.5.
index_query_options (QueryOptions): Index query option.
Raises:
:class:`InvalidTextRepresentationError <asyncpg.exceptions.InvalidTextRepresentationError>`: if the `ids` data type does not match that of the `id_column`.
Returns:
PGVectorStore
"""
vs = await cls.create(
engine,
embedding,
table_name,
schema_name=schema_name,
content_column=content_column,
embedding_column=embedding_column,
metadata_columns=metadata_columns,
ignore_metadata_columns=ignore_metadata_columns,
metadata_json_column=metadata_json_column,
id_column=id_column,
distance_strategy=distance_strategy,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
index_query_options=index_query_options,
)
await vs.aadd_texts(texts, metadatas=metadatas, ids=ids)
return vs
[docs]
@classmethod
async def afrom_documents( # type: ignore[override]
cls: type[PGVectorStore],
documents: list[Document],
embedding: Embeddings,
engine: PGEngine,
table_name: str,
schema_name: str = "public",
ids: Optional[list] = None,
content_column: str = "content",
embedding_column: str = "embedding",
metadata_columns: Optional[list[str]] = None,
ignore_metadata_columns: Optional[list[str]] = None,
id_column: str = "langchain_id",
metadata_json_column: str = "langchain_metadata",
distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
index_query_options: Optional[QueryOptions] = None,
**kwargs: Any,
) -> PGVectorStore:
"""Create an PGVectorStore instance from documents.
Args:
documents (list[Document]): Documents to add to the vector store.
embedding (Embeddings): Text embedding model to use.
engine (PGEngine): Connection pool engine for managing connections to postgres database.
table_name (str): Name of an existing table.
schema_name (str, optional): Name of the database schema. Defaults to "public".
ids: (Optional[list]): List of IDs to add to table records. Defaults to None.
content_column (str, optional): Column that represent a Document's page_content. Defaults to "content".
embedding_column (str, optional): Column for embedding vectors. The embedding is generated from the document value. Defaults to "embedding".
metadata_columns (list[str], optional): Column(s) that represent a document's metadata. Defaults to an empty list.
ignore_metadata_columns (Optional[list[str]], optional): Column(s) to ignore in pre-existing tables for a document's metadata. Can not be used with metadata_columns. Defaults to None.
id_column (str, optional): Column that represents the Document's id. Defaults to "langchain_id".
metadata_json_column (str, optional): Column to store metadata as JSON. Defaults to "langchain_metadata".
distance_strategy (DistanceStrategy): Distance strategy to use for vector similarity search. Defaults to COSINE_DISTANCE.
k (int): Number of Documents to return from search. Defaults to 4.
fetch_k (int): Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float): 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. Defaults to 0.5.
index_query_options (QueryOptions): Index query option.
Raises:
:class:`InvalidTextRepresentationError <asyncpg.exceptions.InvalidTextRepresentationError>`: if the `ids` data type does not match that of the `id_column`.
Returns:
PGVectorStore
"""
vs = await cls.create(
engine,
embedding,
table_name,
schema_name=schema_name,
content_column=content_column,
embedding_column=embedding_column,
metadata_columns=metadata_columns,
ignore_metadata_columns=ignore_metadata_columns,
metadata_json_column=metadata_json_column,
id_column=id_column,
distance_strategy=distance_strategy,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
index_query_options=index_query_options,
)
await vs.aadd_documents(documents, ids=ids)
return vs
[docs]
@classmethod
def from_texts( # type: ignore[override]
cls: type[PGVectorStore],
texts: list[str],
embedding: Embeddings,
engine: PGEngine,
table_name: str,
schema_name: str = "public",
metadatas: Optional[list[dict]] = None,
ids: Optional[list] = None,
content_column: str = "content",
embedding_column: str = "embedding",
metadata_columns: Optional[list[str]] = None,
ignore_metadata_columns: Optional[list[str]] = None,
id_column: str = "langchain_id",
metadata_json_column: str = "langchain_metadata",
distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
index_query_options: Optional[QueryOptions] = None,
**kwargs: Any,
) -> PGVectorStore:
"""Create an PGVectorStore instance from texts.
Args:
texts (list[str]): Texts to add to the vector store.
embedding (Embeddings): Text embedding model to use.
engine (PGEngine): Connection pool engine for managing connections to postgres database.
table_name (str): Name of an existing table.
schema_name (str, optional): Name of the database schema. Defaults to "public".
metadatas (Optional[list[dict]], optional): List of metadatas to add to table records. Defaults to None.
ids: (Optional[list]): List of IDs to add to table records. Defaults to None.
content_column (str, optional): Column that represent a Document's page_content. Defaults to "content".
embedding_column (str, optional): Column for embedding vectors. The embedding is generated from the document value. Defaults to "embedding".
metadata_columns (list[str], optional): Column(s) that represent a document's metadata. Defaults to empty list.
ignore_metadata_columns (Optional[list[str]], optional): Column(s) to ignore in pre-existing tables for a document's metadata. Can not be used with metadata_columns. Defaults to None.
id_column (str, optional): Column that represents the Document's id. Defaults to "langchain_id".
metadata_json_column (str, optional): Column to store metadata as JSON. Defaults to "langchain_metadata".
distance_strategy (DistanceStrategy): Distance strategy to use for vector similarity search. Defaults to COSINE_DISTANCE.
k (int): Number of Documents to return from search. Defaults to 4.
fetch_k (int): Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float): 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. Defaults to 0.5.
index_query_options (QueryOptions): Index query option.
Raises:
:class:`InvalidTextRepresentationError <asyncpg.exceptions.InvalidTextRepresentationError>`: if the `ids` data type does not match that of the `id_column`.
Returns:
PGVectorStore
"""
vs = cls.create_sync(
engine,
embedding,
table_name,
schema_name=schema_name,
content_column=content_column,
embedding_column=embedding_column,
metadata_columns=metadata_columns,
ignore_metadata_columns=ignore_metadata_columns,
metadata_json_column=metadata_json_column,
id_column=id_column,
distance_strategy=distance_strategy,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
index_query_options=index_query_options,
**kwargs,
)
vs.add_texts(texts, metadatas=metadatas, ids=ids)
return vs
[docs]
@classmethod
def from_documents( # type: ignore[override]
cls: type[PGVectorStore],
documents: list[Document],
embedding: Embeddings,
engine: PGEngine,
table_name: str,
schema_name: str = "public",
ids: Optional[list] = None,
content_column: str = "content",
embedding_column: str = "embedding",
metadata_columns: Optional[list[str]] = None,
ignore_metadata_columns: Optional[list[str]] = None,
id_column: str = "langchain_id",
metadata_json_column: str = "langchain_metadata",
distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
index_query_options: Optional[QueryOptions] = None,
**kwargs: Any,
) -> PGVectorStore:
"""Create an PGVectorStore instance from documents.
Args:
documents (list[Document]): Documents to add to the vector store.
embedding (Embeddings): Text embedding model to use.
engine (PGEngine): Connection pool engine for managing connections to postgres database.
table_name (str): Name of an existing table.
schema_name (str, optional): Name of the database schema. Defaults to "public".
ids: (Optional[list]): List of IDs to add to table records. Defaults to None.
content_column (str, optional): Column that represent a Document's page_content. Defaults to "content".
embedding_column (str, optional): Column for embedding vectors. The embedding is generated from the document value. Defaults to "embedding".
metadata_columns (list[str], optional): Column(s) that represent a document's metadata. Defaults to an empty list.
ignore_metadata_columns (Optional[list[str]], optional): Column(s) to ignore in pre-existing tables for a document's metadata. Can not be used with metadata_columns. Defaults to None.
id_column (str, optional): Column that represents the Document's id. Defaults to "langchain_id".
metadata_json_column (str, optional): Column to store metadata as JSON. Defaults to "langchain_metadata".
distance_strategy (DistanceStrategy): Distance strategy to use for vector similarity search. Defaults to COSINE_DISTANCE.
k (int): Number of Documents to return from search. Defaults to 4.
fetch_k (int): Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float): 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. Defaults to 0.5.
index_query_options (QueryOptions): Index query option.
Raises:
:class:`InvalidTextRepresentationError <asyncpg.exceptions.InvalidTextRepresentationError>`: if the `ids` data type does not match that of the `id_column`.
Returns:
PGVectorStore
"""
vs = cls.create_sync(
engine,
embedding,
table_name,
schema_name=schema_name,
content_column=content_column,
embedding_column=embedding_column,
metadata_columns=metadata_columns,
ignore_metadata_columns=ignore_metadata_columns,
metadata_json_column=metadata_json_column,
id_column=id_column,
distance_strategy=distance_strategy,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
index_query_options=index_query_options,
**kwargs,
)
vs.add_documents(documents, ids=ids)
return vs
[docs]
def similarity_search(
self,
query: str,
k: Optional[int] = None,
filter: Optional[dict] = None,
**kwargs: Any,
) -> list[Document]:
"""Return docs selected by similarity search on query."""
return self._engine._run_as_sync(
self.__vs.asimilarity_search(query, k, filter, **kwargs)
)
[docs]
async def asimilarity_search(
self,
query: str,
k: Optional[int] = None,
filter: Optional[dict] = None,
**kwargs: Any,
) -> list[Document]:
"""Return docs selected by similarity search on query."""
return await self._engine._run_as_async(
self.__vs.asimilarity_search(query, k, filter, **kwargs)
)
# Required for (a)similarity_search_with_relevance_scores
def _select_relevance_score_fn(self) -> Callable[[float], float]:
"""Select a relevance function based on distance strategy."""
# Calculate distance strategy provided in vectorstore constructor
if self.__vs.distance_strategy == DistanceStrategy.COSINE_DISTANCE:
return self._cosine_relevance_score_fn
if self.__vs.distance_strategy == DistanceStrategy.INNER_PRODUCT:
return self._max_inner_product_relevance_score_fn
elif self.__vs.distance_strategy == DistanceStrategy.EUCLIDEAN:
return self._euclidean_relevance_score_fn
[docs]
async def asimilarity_search_with_score(
self,
query: str,
k: Optional[int] = None,
filter: Optional[dict] = None,
**kwargs: Any,
) -> list[tuple[Document, float]]:
"""Return docs and distance scores selected by similarity search on query."""
return await self._engine._run_as_async(
self.__vs.asimilarity_search_with_score(query, k, filter, **kwargs)
)
[docs]
async def asimilarity_search_by_vector(
self,
embedding: list[float],
k: Optional[int] = None,
filter: Optional[dict] = None,
**kwargs: Any,
) -> list[Document]:
"""Return docs selected by vector similarity search."""
return await self._engine._run_as_async(
self.__vs.asimilarity_search_by_vector(embedding, k, filter, **kwargs)
)
[docs]
async def asimilarity_search_with_score_by_vector(
self,
embedding: list[float],
k: Optional[int] = None,
filter: Optional[dict] = None,
**kwargs: Any,
) -> list[tuple[Document, float]]:
"""Return docs and distance scores selected by vector similarity search."""
return await self._engine._run_as_async(
self.__vs.asimilarity_search_with_score_by_vector(
embedding, k, filter, **kwargs
)
)
[docs]
async def amax_marginal_relevance_search(
self,
query: str,
k: Optional[int] = None,
fetch_k: Optional[int] = None,
lambda_mult: Optional[float] = None,
filter: Optional[dict] = None,
**kwargs: Any,
) -> list[Document]:
"""Return docs selected using the maximal marginal relevance."""
return await self._engine._run_as_async(
self.__vs.amax_marginal_relevance_search(
query, k, fetch_k, lambda_mult, filter, **kwargs
)
)
[docs]
async def amax_marginal_relevance_search_by_vector(
self,
embedding: list[float],
k: Optional[int] = None,
fetch_k: Optional[int] = None,
lambda_mult: Optional[float] = None,
filter: Optional[dict] = None,
**kwargs: Any,
) -> list[Document]:
"""Return docs selected using the maximal marginal relevance."""
return await self._engine._run_as_async(
self.__vs.amax_marginal_relevance_search_by_vector(
embedding, k, fetch_k, lambda_mult, filter, **kwargs
)
)
[docs]
async def amax_marginal_relevance_search_with_score_by_vector(
self,
embedding: list[float],
k: Optional[int] = None,
fetch_k: Optional[int] = None,
lambda_mult: Optional[float] = None,
filter: Optional[dict] = None,
**kwargs: Any,
) -> list[tuple[Document, float]]:
"""Return docs and distance scores selected using the maximal marginal relevance."""
return await self._engine._run_as_async(
self.__vs.amax_marginal_relevance_search_with_score_by_vector(
embedding, k, fetch_k, lambda_mult, filter, **kwargs
)
)
[docs]
def similarity_search_with_score(
self,
query: str,
k: Optional[int] = None,
filter: Optional[dict] = None,
**kwargs: Any,
) -> list[tuple[Document, float]]:
"""Return docs and distance scores selected by similarity search on query."""
return self._engine._run_as_sync(
self.__vs.asimilarity_search_with_score(query, k, filter, **kwargs)
)
[docs]
def similarity_search_by_vector(
self,
embedding: list[float],
k: Optional[int] = None,
filter: Optional[dict] = None,
**kwargs: Any,
) -> list[Document]:
"""Return docs selected by vector similarity search."""
return self._engine._run_as_sync(
self.__vs.asimilarity_search_by_vector(embedding, k, filter, **kwargs)
)
[docs]
def similarity_search_with_score_by_vector(
self,
embedding: list[float],
k: Optional[int] = None,
filter: Optional[dict] = None,
**kwargs: Any,
) -> list[tuple[Document, float]]:
"""Return docs and distance scores selected by similarity search on vector."""
return self._engine._run_as_sync(
self.__vs.asimilarity_search_with_score_by_vector(
embedding, k, filter, **kwargs
)
)
[docs]
def max_marginal_relevance_search(
self,
query: str,
k: Optional[int] = None,
fetch_k: Optional[int] = None,
lambda_mult: Optional[float] = None,
filter: Optional[dict] = None,
**kwargs: Any,
) -> list[Document]:
"""Return docs selected using the maximal marginal relevance."""
return self._engine._run_as_sync(
self.__vs.amax_marginal_relevance_search(
query, k, fetch_k, lambda_mult, filter, **kwargs
)
)
[docs]
def max_marginal_relevance_search_by_vector(
self,
embedding: list[float],
k: Optional[int] = None,
fetch_k: Optional[int] = None,
lambda_mult: Optional[float] = None,
filter: Optional[dict] = None,
**kwargs: Any,
) -> list[Document]:
"""Return docs selected using the maximal marginal relevance."""
return self._engine._run_as_sync(
self.__vs.amax_marginal_relevance_search_by_vector(
embedding, k, fetch_k, lambda_mult, filter, **kwargs
)
)
[docs]
def max_marginal_relevance_search_with_score_by_vector(
self,
embedding: list[float],
k: Optional[int] = None,
fetch_k: Optional[int] = None,
lambda_mult: Optional[float] = None,
filter: Optional[dict] = None,
**kwargs: Any,
) -> list[tuple[Document, float]]:
"""Return docs and distance scores selected using the maximal marginal relevance."""
return self._engine._run_as_sync(
self.__vs.amax_marginal_relevance_search_with_score_by_vector(
embedding, k, fetch_k, lambda_mult, filter, **kwargs
)
)
[docs]
async def aapply_vector_index(
self,
index: BaseIndex,
name: Optional[str] = None,
concurrently: bool = False,
) -> None:
"""Create an index on the vector store table."""
return await self._engine._run_as_async(
self.__vs.aapply_vector_index(index, name, concurrently=concurrently)
)
[docs]
def apply_vector_index(
self,
index: BaseIndex,
name: Optional[str] = None,
concurrently: bool = False,
) -> None:
"""Create an index on the vector store table."""
return self._engine._run_as_sync(
self.__vs.aapply_vector_index(index, name, concurrently=concurrently)
)
[docs]
async def areindex(self, index_name: Optional[str] = None) -> None:
"""Re-index the vector store table."""
return await self._engine._run_as_async(self.__vs.areindex(index_name))
[docs]
def reindex(self, index_name: Optional[str] = None) -> None:
"""Re-index the vector store table."""
return self._engine._run_as_sync(self.__vs.areindex(index_name))
[docs]
async def adrop_vector_index(
self,
index_name: Optional[str] = None,
) -> None:
"""Drop the vector index."""
return await self._engine._run_as_async(
self.__vs.adrop_vector_index(index_name)
)
[docs]
def drop_vector_index(
self,
index_name: Optional[str] = None,
) -> None:
"""Drop the vector index."""
return self._engine._run_as_sync(self.__vs.adrop_vector_index(index_name))
[docs]
async def ais_valid_index(
self,
index_name: Optional[str] = None,
) -> bool:
"""Check if index exists in the table."""
return await self._engine._run_as_async(self.__vs.is_valid_index(index_name))
[docs]
def is_valid_index(
self,
index_name: Optional[str] = None,
) -> bool:
"""Check if index exists in the table."""
return self._engine._run_as_sync(self.__vs.is_valid_index(index_name))
[docs]
async def aget_by_ids(self, ids: Sequence[str]) -> list[Document]:
"""Get documents by ids."""
return await self._engine._run_as_async(self.__vs.aget_by_ids(ids=ids))
[docs]
def get_by_ids(self, ids: Sequence[str]) -> list[Document]:
"""Get documents by ids."""
return self._engine._run_as_sync(self.__vs.aget_by_ids(ids=ids))
[docs]
def get_table_name(self) -> str:
return self.__vs.table_name
