from __future__ import annotations import contextlib import enum import logging import uuid from typing import ( Any, Callable, Dict, Generator, Iterable, List, Optional, Tuple, Type, ) import numpy as np import sqlalchemy from sqlalchemy import delete from sqlalchemy.dialects.postgresql import JSON, UUID from sqlalchemy.orm import Session, relationship try: from sqlalchemy.orm import declarative_base except ImportError: from sqlalchemy.ext.declarative import declarative_base from langchain_core.documents import Document from langchain_core.embeddings import Embeddings from langchain_core.runnables.config import run_in_executor from langchain_core.utils import get_from_dict_or_env from langchain_core.vectorstores import VectorStore from langchain_community.vectorstores.utils import maximal_marginal_relevance class DistanceStrategy(str, enum.Enum): """Enumerator of the Distance strategies.""" EUCLIDEAN = "l2" COSINE = "cosine" MAX_INNER_PRODUCT = "inner" DEFAULT_DISTANCE_STRATEGY = DistanceStrategy.COSINE Base = declarative_base() # type: Any _LANGCHAIN_DEFAULT_COLLECTION_NAME = "langchain" class BaseModel(Base): """Base model for the SQL stores.""" __abstract__ = True uuid = sqlalchemy.Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4) _classes: Any = None def _get_embedding_collection_store(vector_dimension: Optional[int] = None) -> Any: global _classes if _classes is not None: return _classes from pgvector.sqlalchemy import Vector class CollectionStore(BaseModel): """Collection store.""" __tablename__ = "langchain_pg_collection" name = sqlalchemy.Column(sqlalchemy.String) cmetadata = sqlalchemy.Column(JSON) embeddings = relationship( "EmbeddingStore", back_populates="collection", passive_deletes=True, ) @classmethod def get_by_name( cls, session: Session, name: str ) -> Optional["CollectionStore"]: return session.query(cls).filter(cls.name == name).first() # type: ignore @classmethod def get_or_create( cls, session: Session, name: str, cmetadata: Optional[dict] = None, ) -> Tuple["CollectionStore", bool]: """ Get or create a collection. Returns [Collection, bool] where the bool is True if the collection was created. """ # noqa: E501 created = False collection = cls.get_by_name(session, name) if collection: return collection, created collection = cls(name=name, cmetadata=cmetadata) session.add(collection) session.commit() created = True return collection, created class EmbeddingStore(BaseModel): """Embedding store.""" __tablename__ = "langchain_pg_embedding" collection_id = sqlalchemy.Column( UUID(as_uuid=True), sqlalchemy.ForeignKey( f"{CollectionStore.__tablename__}.uuid", ondelete="CASCADE", ), ) collection = relationship(CollectionStore, back_populates="embeddings") embedding: Vector = sqlalchemy.Column(Vector(vector_dimension)) document = sqlalchemy.Column(sqlalchemy.String, nullable=True) cmetadata = sqlalchemy.Column(JSON, nullable=True) # custom_id : any user defined id custom_id = sqlalchemy.Column(sqlalchemy.String, nullable=True) _classes = (EmbeddingStore, CollectionStore) return _classes def _results_to_docs(docs_and_scores: Any) -> List[Document]: """Return docs from docs and scores.""" return [doc for doc, _ in docs_and_scores] class PGVector(VectorStore): """`Postgres`/`PGVector` vector store. To use, you should have the ``pgvector`` python package installed. Args: connection_string: Postgres connection string. embedding_function: Any embedding function implementing `langchain.embeddings.base.Embeddings` interface. embedding_length: The length of the embedding vector. (default: None) NOTE: This is not mandatory. Defining it will prevent vectors of any other size to be added to the embeddings table but, without it, the embeddings can't be indexed. collection_name: 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 sure the user has the right permissions to create tables. distance_strategy: The distance strategy to use. (default: COSINE) pre_delete_collection: If True, will delete the collection if it exists. (default: False). Useful for testing. engine_args: SQLAlchemy's create engine arguments. Example: .. code-block:: python from langchain_community.vectorstores import PGVector from langchain_community.embeddings.openai import OpenAIEmbeddings CONNECTION_STRING = "postgresql+psycopg2://hwc@localhost:5432/test3" COLLECTION_NAME = "state_of_the_union_test" embeddings = OpenAIEmbeddings() vectorestore = PGVector.from_documents( embedding=embeddings, documents=docs, collection_name=COLLECTION_NAME, connection_string=CONNECTION_STRING, ) """ def __init__( self, connection_string: str, embedding_function: Embeddings, embedding_length: Optional[int] = None, collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME, collection_metadata: Optional[dict] = None, distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY, pre_delete_collection: bool = False, logger: Optional[logging.Logger] = None, relevance_score_fn: Optional[Callable[[float], float]] = None, *, connection: Optional[sqlalchemy.engine.Connection] = None, engine_args: Optional[dict[str, Any]] = None, ) -> None: self.connection_string = connection_string self.embedding_function = embedding_function self._embedding_length = embedding_length self.collection_name = collection_name self.collection_metadata = collection_metadata self._distance_strategy = distance_strategy self.pre_delete_collection = pre_delete_collection self.logger = logger or logging.getLogger(__name__) self.override_relevance_score_fn = relevance_score_fn self.engine_args = engine_args or {} self._bind = connection if connection else self._create_engine() self.__post_init__() def __post_init__( self, ) -> None: """Initialize the store.""" self.create_vector_extension() EmbeddingStore, CollectionStore = _get_embedding_collection_store( self._embedding_length ) self.CollectionStore = CollectionStore self.EmbeddingStore = EmbeddingStore self.create_tables_if_not_exists() self.create_collection() def __del__(self) -> None: if isinstance(self._bind, sqlalchemy.engine.Connection): self._bind.close() @property def embeddings(self) -> Embeddings: return self.embedding_function def _create_engine(self) -> sqlalchemy.engine.Engine: return sqlalchemy.create_engine(url=self.connection_string, **self.engine_args) def create_vector_extension(self) -> None: try: with Session(self._bind) as session: # type: ignore[arg-type] # The advisor lock fixes issue arising from concurrent # creation of the vector extension. # https://github.com/langchain-ai/langchain/issues/12933 # For more information see: # https://www.postgresql.org/docs/16/explicit-locking.html#ADVISORY-LOCKS statement = sqlalchemy.text( "BEGIN;" "SELECT pg_advisory_xact_lock(1573678846307946496);" "CREATE EXTENSION IF NOT EXISTS vector;" "COMMIT;" ) session.execute(statement) session.commit() except Exception as e: raise Exception(f"Failed to create vector extension: {e}") from e def create_tables_if_not_exists(self) -> None: with Session(self._bind) as session, session.begin(): # type: ignore[arg-type] Base.metadata.create_all(session.get_bind()) def drop_tables(self) -> None: with Session(self._bind) as session, session.begin(): # type: ignore[arg-type] Base.metadata.drop_all(session.get_bind()) def create_collection(self) -> None: if self.pre_delete_collection: self.delete_collection() with Session(self._bind) as session: # type: ignore[arg-type] self.CollectionStore.get_or_create( session, self.collection_name, cmetadata=self.collection_metadata ) def delete_collection(self) -> None: self.logger.debug("Trying to delete collection") with Session(self._bind) as session: # type: ignore[arg-type] collection = self.get_collection(session) if not collection: self.logger.warning("Collection not found") return session.delete(collection) session.commit() @contextlib.contextmanager def _make_session(self) -> Generator[Session, None, None]: """Create a context manager for the session, bind to _conn string.""" yield Session(self._bind) # type: ignore[arg-type] def delete( self, ids: Optional[List[str]] = None, collection_only: bool = False, **kwargs: Any, ) -> None: """Delete vectors by ids or uuids. Args: ids: List of ids to delete. collection_only: Only delete ids in the collection. """ with Session(self._bind) as session: # type: ignore[arg-type] if ids is not None: self.logger.debug( "Trying to delete vectors by ids (represented by the model " "using the custom ids field)" ) stmt = delete(self.EmbeddingStore) if collection_only: collection = self.get_collection(session) if not collection: self.logger.warning("Collection not found") return stmt = stmt.where( self.EmbeddingStore.collection_id == collection.uuid ) stmt = stmt.where(self.EmbeddingStore.custom_id.in_(ids)) session.execute(stmt) session.commit() def get_collection(self, session: Session) -> Any: return self.CollectionStore.get_by_name(session, self.collection_name) @classmethod def __from( cls, texts: List[str], embeddings: List[List[float]], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME, distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY, connection_string: Optional[str] = None, pre_delete_collection: bool = False, **kwargs: Any, ) -> PGVector: if ids is None: ids = [str(uuid.uuid1()) for _ in texts] if not metadatas: metadatas = [{} for _ in texts] if connection_string is None: connection_string = cls.get_connection_string(kwargs) store = cls( connection_string=connection_string, collection_name=collection_name, embedding_function=embedding, distance_strategy=distance_strategy, pre_delete_collection=pre_delete_collection, **kwargs, ) store.add_embeddings( texts=texts, embeddings=embeddings, metadatas=metadatas, ids=ids, **kwargs ) return store 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 embeddings to the vectorstore. Args: texts: Iterable of strings to add to the vectorstore. embeddings: List of list of embedding vectors. metadatas: List of metadatas associated with the texts. kwargs: vectorstore specific parameters """ if ids is None: ids = [str(uuid.uuid1()) for _ in texts] if not metadatas: metadatas = [{} for _ in texts] with Session(self._bind) as session: # type: ignore[arg-type] collection = self.get_collection(session) if not collection: raise ValueError("Collection not found") documents = [] for text, metadata, embedding, id in zip(texts, metadatas, embeddings, ids): embedding_store = self.EmbeddingStore( embedding=embedding, document=text, cmetadata=metadata, custom_id=id, collection_id=collection.uuid, ) documents.append(embedding_store) session.bulk_save_objects(documents) session.commit() return ids def add_texts( self, texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any, ) -> List[str]: """Run more texts through the embeddings and add to the vectorstore. Args: texts: Iterable of strings to add to the vectorstore. metadatas: Optional list of metadatas associated with the texts. kwargs: vectorstore specific parameters Returns: List of ids from adding the texts into the vectorstore. """ embeddings = self.embedding_function.embed_documents(list(texts)) return self.add_embeddings( texts=texts, embeddings=embeddings, metadatas=metadatas, ids=ids, **kwargs ) def similarity_search( self, query: str, k: int = 4, filter: Optional[dict] = None, **kwargs: Any, ) -> List[Document]: """Run similarity search with PGVector with distance. Args: query (str): Query text to search for. k (int): Number of results to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List of Documents most similar to the query. """ embedding = self.embedding_function.embed_query(text=query) return self.similarity_search_by_vector( embedding=embedding, k=k, filter=filter, ) def similarity_search_with_score( self, query: str, k: int = 4, filter: Optional[dict] = None, ) -> List[Tuple[Document, float]]: """Return docs most similar to query. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List of Documents most similar to the query and score for each. """ embedding = self.embedding_function.embed_query(query) docs = self.similarity_search_with_score_by_vector( embedding=embedding, k=k, filter=filter ) return docs @property def distance_strategy(self) -> Any: if self._distance_strategy == DistanceStrategy.EUCLIDEAN: return self.EmbeddingStore.embedding.l2_distance elif self._distance_strategy == DistanceStrategy.COSINE: return self.EmbeddingStore.embedding.cosine_distance elif self._distance_strategy == DistanceStrategy.MAX_INNER_PRODUCT: return self.EmbeddingStore.embedding.max_inner_product else: raise ValueError( f"Got unexpected value for distance: {self._distance_strategy}. " f"Should be one of {', '.join([ds.value for ds in DistanceStrategy])}." ) def similarity_search_with_score_by_vector( self, embedding: List[float], k: int = 4, filter: Optional[dict] = None, ) -> List[Tuple[Document, float]]: results = self.__query_collection(embedding=embedding, k=k, filter=filter) return self._results_to_docs_and_scores(results) def _results_to_docs_and_scores(self, results: Any) -> List[Tuple[Document, float]]: """Return docs and scores from results.""" docs = [ ( Document( page_content=result.EmbeddingStore.document, metadata=result.EmbeddingStore.cmetadata, ), result.distance if self.embedding_function is not None else None, ) for result in results ] return docs def _create_filter_clause(self, key, value): # type: ignore[no-untyped-def] IN, NIN, BETWEEN, GT, LT, NE = "in", "nin", "between", "gt", "lt", "ne" EQ, LIKE, CONTAINS, OR, AND = "eq", "like", "contains", "or", "and" value_case_insensitive = {k.lower(): v for k, v in value.items()} if IN in map(str.lower, value): filter_by_metadata = self.EmbeddingStore.cmetadata[key].astext.in_( value_case_insensitive[IN] ) elif NIN in map(str.lower, value): filter_by_metadata = self.EmbeddingStore.cmetadata[key].astext.not_in( value_case_insensitive[NIN] ) elif BETWEEN in map(str.lower, value): filter_by_metadata = self.EmbeddingStore.cmetadata[key].astext.between( str(value_case_insensitive[BETWEEN][0]), str(value_case_insensitive[BETWEEN][1]), ) elif GT in map(str.lower, value): filter_by_metadata = self.EmbeddingStore.cmetadata[key].astext > str( value_case_insensitive[GT] ) elif LT in map(str.lower, value): filter_by_metadata = self.EmbeddingStore.cmetadata[key].astext < str( value_case_insensitive[LT] ) elif NE in map(str.lower, value): filter_by_metadata = self.EmbeddingStore.cmetadata[key].astext != str( value_case_insensitive[NE] ) elif EQ in map(str.lower, value): filter_by_metadata = self.EmbeddingStore.cmetadata[key].astext == str( value_case_insensitive[EQ] ) elif LIKE in map(str.lower, value): filter_by_metadata = self.EmbeddingStore.cmetadata[key].astext.like( value_case_insensitive[LIKE] ) elif CONTAINS in map(str.lower, value): filter_by_metadata = self.EmbeddingStore.cmetadata[key].astext.contains( value_case_insensitive[CONTAINS] ) elif OR in map(str.lower, value): or_clauses = [ self._create_filter_clause(key, sub_value) for sub_value in value_case_insensitive[OR] ] filter_by_metadata = sqlalchemy.or_(*or_clauses) elif AND in map(str.lower, value): and_clauses = [ self._create_filter_clause(key, sub_value) for sub_value in value_case_insensitive[AND] ] filter_by_metadata = sqlalchemy.and_(*and_clauses) else: filter_by_metadata = None return filter_by_metadata def __query_collection( self, embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, ) -> List[Any]: """Query the collection.""" with Session(self._bind) as session: # type: ignore[arg-type] collection = self.get_collection(session) if not collection: raise ValueError("Collection not found") filter_by = self.EmbeddingStore.collection_id == collection.uuid if filter is not None: filter_clauses = [] for key, value in filter.items(): if isinstance(value, dict): filter_by_metadata = self._create_filter_clause(key, value) if filter_by_metadata is not None: filter_clauses.append(filter_by_metadata) else: filter_by_metadata = self.EmbeddingStore.cmetadata[ key ].astext == str(value) filter_clauses.append(filter_by_metadata) filter_by = sqlalchemy.and_(filter_by, *filter_clauses) _type = self.EmbeddingStore results: List[Any] = ( session.query( self.EmbeddingStore, self.distance_strategy(embedding).label("distance"), # type: ignore ) .filter(filter_by) .order_by(sqlalchemy.asc("distance")) .join( self.CollectionStore, self.EmbeddingStore.collection_id == self.CollectionStore.uuid, ) .limit(k) .all() ) return results def similarity_search_by_vector( self, embedding: List[float], k: int = 4, filter: Optional[dict] = None, **kwargs: Any, ) -> List[Document]: """Return docs most similar to embedding vector. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. 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 _results_to_docs(docs_and_scores) @classmethod def from_texts( cls: Type[PGVector], texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME, distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY, ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any, ) -> PGVector: """ Return VectorStore initialized from texts and embeddings. Postgres connection string is required "Either pass it as a parameter or set the PGVECTOR_CONNECTION_STRING environment variable. """ embeddings = embedding.embed_documents(list(texts)) return cls.__from( texts, embeddings, embedding, metadatas=metadatas, ids=ids, collection_name=collection_name, distance_strategy=distance_strategy, pre_delete_collection=pre_delete_collection, **kwargs, ) @classmethod def from_embeddings( cls, text_embeddings: List[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME, distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY, ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any, ) -> PGVector: """Construct PGVector 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 PGVECTOR_CONNECTION_STRING environment variable. Example: .. code-block:: python from langchain_community.vectorstores import PGVector from langchain_community.embeddings import OpenAIEmbeddings embeddings = OpenAIEmbeddings() text_embeddings = embeddings.embed_documents(texts) text_embedding_pairs = list(zip(texts, text_embeddings)) faiss = PGVector.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, metadatas=metadatas, ids=ids, collection_name=collection_name, distance_strategy=distance_strategy, pre_delete_collection=pre_delete_collection, **kwargs, ) @classmethod def from_existing_index( cls: Type[PGVector], embedding: Embeddings, collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME, distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY, pre_delete_collection: bool = False, **kwargs: Any, ) -> PGVector: """ Get instance of an existing PGVector store.This method will return the instance of the store without inserting any new embeddings """ connection_string = cls.get_connection_string(kwargs) store = cls( connection_string=connection_string, collection_name=collection_name, embedding_function=embedding, distance_strategy=distance_strategy, pre_delete_collection=pre_delete_collection, ) return store @classmethod def get_connection_string(cls, kwargs: Dict[str, Any]) -> str: connection_string: str = get_from_dict_or_env( data=kwargs, key="connection_string", env_key="PGVECTOR_CONNECTION_STRING", ) if not connection_string: raise ValueError( "Postgres connection string is required" "Either pass it as a parameter" "or set the PGVECTOR_CONNECTION_STRING environment variable." ) return connection_string @classmethod def from_documents( cls: Type[PGVector], documents: List[Document], embedding: Embeddings, collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME, distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY, ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any, ) -> PGVector: """ Return VectorStore initialized from documents and embeddings. Postgres connection string is required "Either pass it as a parameter or set the PGVECTOR_CONNECTION_STRING environment variable. """ texts = [d.page_content for d in documents] metadatas = [d.metadata for d in documents] connection_string = cls.get_connection_string(kwargs) kwargs["connection_string"] = connection_string return cls.from_texts( texts=texts, pre_delete_collection=pre_delete_collection, embedding=embedding, distance_strategy=distance_strategy, metadatas=metadatas, ids=ids, collection_name=collection_name, **kwargs, ) @classmethod def connection_string_from_db_params( cls, driver: str, host: str, port: int, database: str, user: str, password: str, ) -> str: """Return connection string from database parameters.""" return f"postgresql+{driver}://{user}:{password}@{host}:{port}/{database}" def _select_relevance_score_fn(self) -> Callable[[float], float]: """ The 'correct' relevance function may differ depending on a few things, including: - the distance / similarity metric used by the VectorStore - the scale of your embeddings (OpenAI's are unit normed. Many others are not!) - embedding dimensionality - etc. """ if self.override_relevance_score_fn is not None: return self.override_relevance_score_fn # 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: return self._euclidean_relevance_score_fn elif self._distance_strategy == DistanceStrategy.MAX_INNER_PRODUCT: return self._max_inner_product_relevance_score_fn else: raise ValueError( "No supported normalization function" f" for distance_strategy of {self._distance_strategy}." "Consider providing relevance_score_fn to PGVector constructor." ) def max_marginal_relevance_search_with_score_by_vector( self, embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, str]] = None, **kwargs: Any, ) -> List[Tuple[Document, float]]: """Return docs selected using the maximal marginal relevance with score to embedding vector. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: embedding: Embedding to look up documents similar to. k (int): Number of Documents to return. Defaults to 4. fetch_k (int): Number of Documents to fetch to pass to MMR algorithm. Defaults to 20. 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. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Tuple[Document, float]]: List of Documents selected by maximal marginal relevance to the query and score for each. """ results = self.__query_collection(embedding=embedding, k=fetch_k, filter=filter) embedding_list = [result.EmbeddingStore.embedding for result in results] mmr_selected = maximal_marginal_relevance( np.array(embedding, dtype=np.float32), embedding_list, k=k, lambda_mult=lambda_mult, ) candidates = self._results_to_docs_and_scores(results) return [r for i, r in enumerate(candidates) if i in mmr_selected] def max_marginal_relevance_search( self, query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, str]] = None, **kwargs: Any, ) -> List[Document]: """Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: query (str): Text to look up documents similar to. k (int): Number of Documents to return. Defaults to 4. fetch_k (int): Number of Documents to fetch to pass to MMR algorithm. Defaults to 20. 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. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents selected by maximal marginal relevance. """ embedding = self.embedding_function.embed_query(query) return self.max_marginal_relevance_search_by_vector( embedding, k=k, fetch_k=fetch_k, lambda_mult=lambda_mult, filter=filter, **kwargs, ) def max_marginal_relevance_search_with_score( self, query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[dict] = None, **kwargs: Any, ) -> List[Tuple[Document, float]]: """Return docs selected using the maximal marginal relevance with score. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: query (str): Text to look up documents similar to. k (int): Number of Documents to return. Defaults to 4. fetch_k (int): Number of Documents to fetch to pass to MMR algorithm. Defaults to 20. 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. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Tuple[Document, float]]: List of Documents selected by maximal marginal relevance to the query and score for each. """ embedding = self.embedding_function.embed_query(query) docs = self.max_marginal_relevance_search_with_score_by_vector( embedding=embedding, k=k, fetch_k=fetch_k, lambda_mult=lambda_mult, filter=filter, **kwargs, ) return docs def max_marginal_relevance_search_by_vector( self, embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, str]] = None, **kwargs: Any, ) -> List[Document]: """Return docs selected using the maximal marginal relevance to embedding vector. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: embedding (str): Text to look up documents similar to. k (int): Number of Documents to return. Defaults to 4. fetch_k (int): Number of Documents to fetch to pass to MMR algorithm. Defaults to 20. 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. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents selected by maximal marginal relevance. """ docs_and_scores = self.max_marginal_relevance_search_with_score_by_vector( embedding, k=k, fetch_k=fetch_k, lambda_mult=lambda_mult, filter=filter, **kwargs, ) return _results_to_docs(docs_and_scores) async def amax_marginal_relevance_search_by_vector( self, embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, str]] = None, **kwargs: Any, ) -> List[Document]: """Return docs selected using the maximal marginal relevance.""" # This is a temporary workaround to make the similarity search # asynchronous. The proper solution is to make the similarity search # asynchronous in the vector store implementations. return await run_in_executor( None, self.max_marginal_relevance_search_by_vector, embedding, k=k, fetch_k=fetch_k, lambda_mult=lambda_mult, filter=filter, **kwargs, )