Source code for langchain_community.vectorstores.hologres

from __future__ import annotations

import logging
import uuid
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type

from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.utils import get_from_dict_or_env
from langchain_core.vectorstores import VectorStore

ADA_TOKEN_COUNT = 1536
_LANGCHAIN_DEFAULT_TABLE_NAME = "langchain_pg_embedding"


[docs]class Hologres(VectorStore): """`Hologres API` vector store. - `connection_string` is a hologres connection string. - `embedding_function` any embedding function implementing `langchain.embeddings.base.Embeddings` interface. - `ndims` is the number of dimensions of the embedding output. - `table_name` is the name of the table to store embeddings and data. (default: langchain_pg_embedding) - NOTE: The table will be created when initializing the store (if not exists) So, make sure the user has the right permissions to create tables. - `pre_delete_table` if True, will delete the table if it exists. (default: False) - Useful for testing. """
[docs] def __init__( self, connection_string: str, embedding_function: Embeddings, ndims: int = ADA_TOKEN_COUNT, table_name: str = _LANGCHAIN_DEFAULT_TABLE_NAME, pre_delete_table: bool = False, logger: Optional[logging.Logger] = None, ) -> None: self.connection_string = connection_string self.ndims = ndims self.table_name = table_name self.embedding_function = embedding_function self.pre_delete_table = pre_delete_table self.logger = logger or logging.getLogger(__name__) self.__post_init__()
def __post_init__( self, ) -> None: """ Initialize the store. """ from hologres_vector import HologresVector self.storage = HologresVector( self.connection_string, ndims=self.ndims, table_name=self.table_name, table_schema={"document": "text"}, pre_delete_table=self.pre_delete_table, ) @property def embeddings(self) -> Embeddings: return self.embedding_function @classmethod def __from( cls, texts: List[str], embeddings: List[List[float]], embedding_function: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, ndims: int = ADA_TOKEN_COUNT, table_name: str = _LANGCHAIN_DEFAULT_TABLE_NAME, pre_delete_table: bool = False, **kwargs: Any, ) -> Hologres: if ids is None: ids = [str(uuid.uuid4()) for _ in texts] if not metadatas: metadatas = [{} for _ in texts] connection_string = cls.get_connection_string(kwargs) store = cls( connection_string=connection_string, embedding_function=embedding_function, ndims=ndims, table_name=table_name, pre_delete_table=pre_delete_table, ) store.add_embeddings( texts=texts, embeddings=embeddings, metadatas=metadatas, ids=ids, **kwargs ) return store
[docs] def add_embeddings( self, texts: Iterable[str], embeddings: List[List[float]], metadatas: List[dict], ids: List[str], **kwargs: Any, ) -> None: """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 """ try: schema_datas = [{"document": t} for t in texts] self.storage.upsert_vectors(embeddings, ids, metadatas, schema_datas) except Exception as e: self.logger.exception(e)
[docs] 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. """ if ids is None: ids = [str(uuid.uuid4()) for _ in texts] embeddings = self.embedding_function.embed_documents(list(texts)) if not metadatas: metadatas = [{} for _ in texts] self.add_embeddings(texts, embeddings, metadatas, ids, **kwargs) return ids
[docs] 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 [doc for doc, _ in docs_and_scores]
[docs] 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
[docs] def similarity_search_with_score_by_vector( self, embedding: List[float], k: int = 4, filter: Optional[dict] = None, ) -> List[Tuple[Document, float]]: results: List[dict[str, Any]] = self.storage.search( embedding, k=k, select_columns=["document"], metadata_filters=filter ) docs = [ ( Document( page_content=result["document"], metadata=result["metadata"], ), result["distance"], ) for result in results ] return docs
[docs] @classmethod def from_texts( cls: Type[Hologres], texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ndims: int = ADA_TOKEN_COUNT, table_name: str = _LANGCHAIN_DEFAULT_TABLE_NAME, ids: Optional[List[str]] = None, pre_delete_table: bool = False, **kwargs: Any, ) -> Hologres: """ Return VectorStore initialized from texts and embeddings. Hologres connection string is required "Either pass it as a parameter or set the HOLOGRES_CONNECTION_STRING environment variable. Create the connection string by calling HologresVector.connection_string_from_db_params """ embeddings = embedding.embed_documents(list(texts)) return cls.__from( texts, embeddings, embedding, metadatas=metadatas, ids=ids, ndims=ndims, table_name=table_name, pre_delete_table=pre_delete_table, **kwargs, )
[docs] @classmethod def from_embeddings( cls, text_embeddings: List[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ndims: int = ADA_TOKEN_COUNT, table_name: str = _LANGCHAIN_DEFAULT_TABLE_NAME, ids: Optional[List[str]] = None, pre_delete_table: bool = False, **kwargs: Any, ) -> Hologres: """Construct Hologres wrapper from raw documents and pre- generated embeddings. Return VectorStore initialized from documents and embeddings. Hologres connection string is required "Either pass it as a parameter or set the HOLOGRES_CONNECTION_STRING environment variable. Create the connection string by calling HologresVector.connection_string_from_db_params Example: .. code-block:: python from langchain_community.vectorstores import Hologres from langchain_community.embeddings import OpenAIEmbeddings embeddings = OpenAIEmbeddings() text_embeddings = embeddings.embed_documents(texts) text_embedding_pairs = list(zip(texts, text_embeddings)) faiss = Hologres.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, ndims=ndims, table_name=table_name, pre_delete_table=pre_delete_table, **kwargs, )
[docs] @classmethod def from_existing_index( cls: Type[Hologres], embedding: Embeddings, ndims: int = ADA_TOKEN_COUNT, table_name: str = _LANGCHAIN_DEFAULT_TABLE_NAME, pre_delete_table: bool = False, **kwargs: Any, ) -> Hologres: """ Get instance of an existing Hologres 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, ndims=ndims, table_name=table_name, embedding_function=embedding, pre_delete_table=pre_delete_table, ) return store
[docs] @classmethod def get_connection_string(cls, kwargs: Dict[str, Any]) -> str: connection_string: str = get_from_dict_or_env( data=kwargs, key="connection_string", env_key="HOLOGRES_CONNECTION_STRING", ) if not connection_string: raise ValueError( "Hologres connection string is required" "Either pass it as a parameter" "or set the HOLOGRES_CONNECTION_STRING environment variable." "Create the connection string by calling" "HologresVector.connection_string_from_db_params" ) return connection_string
[docs] @classmethod def from_documents( cls: Type[Hologres], documents: List[Document], embedding: Embeddings, ndims: int = ADA_TOKEN_COUNT, table_name: str = _LANGCHAIN_DEFAULT_TABLE_NAME, ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any, ) -> Hologres: """ Return VectorStore initialized from documents and embeddings. Hologres connection string is required "Either pass it as a parameter or set the HOLOGRES_CONNECTION_STRING environment variable. Create the connection string by calling HologresVector.connection_string_from_db_params """ 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, metadatas=metadatas, ids=ids, ndims=ndims, table_name=table_name, **kwargs, )
[docs] @classmethod def connection_string_from_db_params( cls, host: str, port: int, database: str, user: str, password: str, ) -> str: """Return connection string from database parameters.""" return ( f"dbname={database} user={user} password={password} host={host} port={port}" )