Source code for langchain_community.vectorstores.cassandra

from __future__ import annotations

import asyncio
import typing
import uuid
from typing import (
    Any,
    Awaitable,
    Callable,
    Dict,
    Iterable,
    List,
    Optional,
    Tuple,
    Type,
    TypeVar,
    Union,
)

import numpy as np

if typing.TYPE_CHECKING:
    from cassandra.cluster import Session

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

from langchain_community.utilities.cassandra import SetupMode
from langchain_community.vectorstores.utils import maximal_marginal_relevance

CVST = TypeVar("CVST", bound="Cassandra")


[docs]class Cassandra(VectorStore): _embedding_dimension: Union[int, None] def _get_embedding_dimension(self) -> int: if self._embedding_dimension is None: self._embedding_dimension = len( self.embedding.embed_query("This is a sample sentence.") ) return self._embedding_dimension async def _aget_embedding_dimension(self) -> int: if self._embedding_dimension is None: self._embedding_dimension = len( await self.embedding.aembed_query("This is a sample sentence.") ) return self._embedding_dimension
[docs] def __init__( self, embedding: Embeddings, session: Optional[Session] = None, keyspace: Optional[str] = None, table_name: str = "", ttl_seconds: Optional[int] = None, *, body_index_options: Optional[List[Tuple[str, Any]]] = None, setup_mode: SetupMode = SetupMode.SYNC, ) -> None: """Apache Cassandra(R) for vector-store workloads. To use it, you need a recent installation of the `cassio` library and a Cassandra cluster / Astra DB instance supporting vector capabilities. Visit the cassio.org website for extensive quickstarts and code examples. Example: .. code-block:: python from langchain_community.vectorstores import Cassandra from langchain_openai import OpenAIEmbeddings embeddings = OpenAIEmbeddings() session = ... # create your Cassandra session object keyspace = 'my_keyspace' # the keyspace should exist already table_name = 'my_vector_store' vectorstore = Cassandra(embeddings, session, keyspace, table_name) Args: embedding: Embedding function to use. session: Cassandra driver session. If not provided, it is resolved from cassio. keyspace: Cassandra key space. If not provided, it is resolved from cassio. table_name: Cassandra table (required). ttl_seconds: Optional time-to-live for the added texts. body_index_options: Optional options used to create the body index. Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER] setup_mode: mode used to create the Cassandra table (SYNC, ASYNC or OFF). """ try: from cassio.table import MetadataVectorCassandraTable except (ImportError, ModuleNotFoundError): raise ImportError( "Could not import cassio python package. " "Please install it with `pip install cassio`." ) if not table_name: raise ValueError("Missing required parameter 'table_name'.") self.embedding = embedding self.session = session self.keyspace = keyspace self.table_name = table_name self.ttl_seconds = ttl_seconds # self._embedding_dimension = None # kwargs: Dict[str, Any] = {} if body_index_options is not None: kwargs["body_index_options"] = body_index_options if setup_mode == SetupMode.ASYNC: kwargs["async_setup"] = True embedding_dimension: Union[int, Awaitable[int], None] = None if setup_mode == SetupMode.ASYNC: embedding_dimension = self._aget_embedding_dimension() elif setup_mode == SetupMode.SYNC: embedding_dimension = self._get_embedding_dimension() self.table = MetadataVectorCassandraTable( session=session, keyspace=keyspace, table=table_name, vector_dimension=embedding_dimension, metadata_indexing="all", primary_key_type="TEXT", skip_provisioning=setup_mode == SetupMode.OFF, **kwargs, )
@property def embeddings(self) -> Embeddings: return self.embedding def _select_relevance_score_fn(self) -> Callable[[float], float]: """ The underlying VectorTable already returns a "score proper", i.e. one in [0, 1] where higher means more *similar*, so here the final score transformation is not reversing the interval: """ return lambda score: score
[docs] def delete_collection(self) -> None: """ Just an alias for `clear` (to better align with other VectorStore implementations). """ self.clear()
[docs] async def adelete_collection(self) -> None: """ Just an alias for `aclear` (to better align with other VectorStore implementations). """ await self.aclear()
[docs] def clear(self) -> None: """Empty the table.""" self.table.clear()
[docs] async def aclear(self) -> None: """Empty the table.""" await self.table.aclear()
[docs] def delete_by_document_id(self, document_id: str) -> None: """Delete by document ID. Args: document_id: the document ID to delete. """ return self.table.delete(row_id=document_id)
[docs] async def adelete_by_document_id(self, document_id: str) -> None: """Delete by document ID. Args: document_id: the document ID to delete. """ return await self.table.adelete(row_id=document_id)
[docs] def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]: """Delete by vector IDs. Args: ids: List of ids to delete. Returns: Optional[bool]: True if deletion is successful, False otherwise, None if not implemented. """ if ids is None: raise ValueError("No ids provided to delete.") for document_id in ids: self.delete_by_document_id(document_id) return True
[docs] async def adelete( self, ids: Optional[List[str]] = None, **kwargs: Any ) -> Optional[bool]: """Delete by vector IDs. Args: ids: List of ids to delete. Returns: Optional[bool]: True if deletion is successful, False otherwise, None if not implemented. """ if ids is None: raise ValueError("No ids provided to delete.") for document_id in ids: await self.adelete_by_document_id(document_id) return True
[docs] def add_texts( self, texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, batch_size: int = 16, ttl_seconds: Optional[int] = None, **kwargs: Any, ) -> List[str]: """Run more texts through the embeddings and add to the vectorstore. Args: texts: Texts to add to the vectorstore. metadatas: Optional list of metadatas. ids: Optional list of IDs. batch_size: Number of concurrent requests to send to the server. ttl_seconds: Optional time-to-live for the added texts. Returns: List[str]: List of IDs of the added texts. """ _texts = list(texts) ids = ids or [uuid.uuid4().hex for _ in _texts] metadatas = metadatas or [{}] * len(_texts) ttl_seconds = ttl_seconds or self.ttl_seconds embedding_vectors = self.embedding.embed_documents(_texts) for i in range(0, len(_texts), batch_size): batch_texts = _texts[i : i + batch_size] batch_embedding_vectors = embedding_vectors[i : i + batch_size] batch_ids = ids[i : i + batch_size] batch_metadatas = metadatas[i : i + batch_size] futures = [ self.table.put_async( row_id=text_id, body_blob=text, vector=embedding_vector, metadata=metadata or {}, ttl_seconds=ttl_seconds, ) for text, embedding_vector, text_id, metadata in zip( batch_texts, batch_embedding_vectors, batch_ids, batch_metadatas ) ] for future in futures: future.result() return ids
[docs] async def aadd_texts( self, texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, concurrency: int = 16, ttl_seconds: Optional[int] = None, **kwargs: Any, ) -> List[str]: """Run more texts through the embeddings and add to the vectorstore. Args: texts: Texts to add to the vectorstore. metadatas: Optional list of metadatas. ids: Optional list of IDs. concurrency: Number of concurrent queries to the database. Defaults to 16. ttl_seconds: Optional time-to-live for the added texts. Returns: List[str]: List of IDs of the added texts. """ _texts = list(texts) ids = ids or [uuid.uuid4().hex for _ in _texts] _metadatas: List[dict] = metadatas or [{}] * len(_texts) ttl_seconds = ttl_seconds or self.ttl_seconds embedding_vectors = await self.embedding.aembed_documents(_texts) sem = asyncio.Semaphore(concurrency) async def send_concurrently( row_id: str, text: str, embedding_vector: List[float], metadata: dict ) -> None: async with sem: await self.table.aput( row_id=row_id, body_blob=text, vector=embedding_vector, metadata=metadata or {}, ttl_seconds=ttl_seconds, ) for i in range(0, len(_texts)): tasks = [ asyncio.create_task( send_concurrently( ids[i], _texts[i], embedding_vectors[i], _metadatas[i] ) ) ] await asyncio.gather(*tasks) return ids
@staticmethod def _search_to_documents( hits: Iterable[Dict[str, Any]], ) -> List[Tuple[Document, float, str]]: # We stick to 'cos' distance as it can be normalized on a 0-1 axis # (1=most relevant), as required by this class' contract. return [ ( Document( page_content=hit["body_blob"], metadata=hit["metadata"], ), 0.5 + 0.5 * hit["distance"], hit["row_id"], ) for hit in hits ] # id-returning search facilities
[docs] def similarity_search_with_score_id_by_vector( self, embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None, ) -> List[Tuple[Document, float, str]]: """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: Filter on the metadata to apply. body_search: Document textual search terms to apply. Only supported by Astra DB at the moment. Returns: List of (Document, score, id), the most similar to the query vector. """ kwargs: Dict[str, Any] = {} if filter is not None: kwargs["metadata"] = filter if body_search is not None: kwargs["body_search"] = body_search hits = self.table.metric_ann_search( vector=embedding, n=k, metric="cos", **kwargs, ) return self._search_to_documents(hits)
[docs] async def asimilarity_search_with_score_id_by_vector( self, embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None, ) -> List[Tuple[Document, float, str]]: """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: Filter on the metadata to apply. body_search: Document textual search terms to apply. Only supported by Astra DB at the moment. Returns: List of (Document, score, id), the most similar to the query vector. """ kwargs: Dict[str, Any] = {} if filter is not None: kwargs["metadata"] = filter if body_search is not None: kwargs["body_search"] = body_search hits = await self.table.ametric_ann_search( vector=embedding, n=k, metric="cos", **kwargs, ) return self._search_to_documents(hits)
[docs] def similarity_search_with_score_id( self, query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None, ) -> List[Tuple[Document, float, str]]: """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: Filter on the metadata to apply. body_search: Document textual search terms to apply. Only supported by Astra DB at the moment. Returns: List of (Document, score, id), the most similar to the query vector. """ embedding_vector = self.embedding.embed_query(query) return self.similarity_search_with_score_id_by_vector( embedding=embedding_vector, k=k, filter=filter, body_search=body_search, )
[docs] async def asimilarity_search_with_score_id( self, query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None, ) -> List[Tuple[Document, float, str]]: """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: Filter on the metadata to apply. body_search: Document textual search terms to apply. Only supported by Astra DB at the moment. Returns: List of (Document, score, id), the most similar to the query vector. """ embedding_vector = await self.embedding.aembed_query(query) return await self.asimilarity_search_with_score_id_by_vector( embedding=embedding_vector, k=k, filter=filter, body_search=body_search, )
# id-unaware search facilities
[docs] def similarity_search_with_score_by_vector( self, embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None, ) -> List[Tuple[Document, float]]: """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: Filter on the metadata to apply. body_search: Document textual search terms to apply. Only supported by Astra DB at the moment. Returns: List of (Document, score), the most similar to the query vector. """ return [ (doc, score) for (doc, score, docId) in self.similarity_search_with_score_id_by_vector( embedding=embedding, k=k, filter=filter, body_search=body_search, ) ]
[docs] async def asimilarity_search_with_score_by_vector( self, embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = None, ) -> List[Tuple[Document, float]]: """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: Filter on the metadata to apply. body_search: Document textual search terms to apply. Only supported by Astra DB at the moment. Returns: List of (Document, score), the most similar to the query vector. """ return [ (doc, score) for ( doc, score, _, ) in await self.asimilarity_search_with_score_id_by_vector( embedding=embedding, k=k, filter=filter, body_search=body_search, ) ]
[docs] def similarity_search_by_vector( self, embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = 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: Filter on the metadata to apply. body_search: Document textual search terms to apply. Only supported by Astra DB at the moment. Returns: List of Document, the most similar to the query vector. """ return [ doc for doc, _ in self.similarity_search_with_score_by_vector( embedding, k, filter=filter, body_search=body_search, ) ]
[docs] async def asimilarity_search_by_vector( self, embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = 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: Filter on the metadata to apply. body_search: Document textual search terms to apply. Only supported by Astra DB at the moment. Returns: List of Document, the most similar to the query vector. """ return [ doc for doc, _ in await self.asimilarity_search_with_score_by_vector( embedding, k, filter=filter, body_search=body_search, ) ]
[docs] def similarity_search_with_score( self, query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = 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: Filter on the metadata to apply. body_search: Document textual search terms to apply. Only supported by Astra DB at the moment. Returns: List of (Document, score), the most similar to the query vector. """ embedding_vector = self.embedding.embed_query(query) return self.similarity_search_with_score_by_vector( embedding_vector, k, filter=filter, body_search=body_search, )
[docs] async def asimilarity_search_with_score( self, query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, body_search: Optional[Union[str, List[str]]] = 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: Filter on the metadata to apply. body_search: Document textual search terms to apply. Only supported by Astra DB at the moment. Returns: List of (Document, score), the most similar to the query vector. """ embedding_vector = await self.embedding.aembed_query(query) return await self.asimilarity_search_with_score_by_vector( embedding_vector, k, filter=filter, body_search=body_search, )
@staticmethod def _mmr_search_to_documents( prefetch_hits: List[Dict[str, Any]], embedding: List[float], k: int, lambda_mult: float, ) -> List[Document]: # let the mmr utility pick the *indices* in the above array mmr_chosen_indices = maximal_marginal_relevance( np.array(embedding, dtype=np.float32), [pf_hit["vector"] for pf_hit in prefetch_hits], k=k, lambda_mult=lambda_mult, ) mmr_hits = [ pf_hit for pf_index, pf_hit in enumerate(prefetch_hits) if pf_index in mmr_chosen_indices ] return [ Document( page_content=hit["body_blob"], metadata=hit["metadata"], ) for hit in mmr_hits ]
[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, body_search: Optional[Union[str, List[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: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch to pass to MMR algorithm. Defaults to 20. 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. Defaults to 0.5. filter: Filter on the metadata to apply. body_search: Document textual search terms to apply. Only supported by Astra DB at the moment. Returns: List of Documents selected by maximal marginal relevance. """ _kwargs: Dict[str, Any] = {} if filter is not None: _kwargs["metadata"] = filter if body_search is not None: _kwargs["body_search"] = body_search prefetch_hits = list( self.table.metric_ann_search( vector=embedding, n=fetch_k, metric="cos", **_kwargs, ) ) return self._mmr_search_to_documents(prefetch_hits, embedding, k, lambda_mult)
[docs] 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, body_search: Optional[Union[str, List[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: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch to pass to MMR algorithm. Defaults to 20. 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. Defaults to 0.5. filter: Filter on the metadata to apply. body_search: Document textual search terms to apply. Only supported by Astra DB at the moment. Returns: List of Documents selected by maximal marginal relevance. """ _kwargs: Dict[str, Any] = {} if filter is not None: _kwargs["metadata"] = filter if body_search is not None: _kwargs["body_search"] = body_search prefetch_hits = list( await self.table.ametric_ann_search( vector=embedding, n=fetch_k, metric="cos", **_kwargs, ) ) return self._mmr_search_to_documents(prefetch_hits, embedding, k, lambda_mult)
[docs] @classmethod def from_texts( cls: Type[CVST], texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, *, session: Optional[Session] = None, keyspace: Optional[str] = None, table_name: str = "", ids: Optional[List[str]] = None, batch_size: int = 16, ttl_seconds: Optional[int] = None, body_index_options: Optional[List[Tuple[str, Any]]] = None, **kwargs: Any, ) -> CVST: """Create a Cassandra vectorstore from raw texts. Args: texts: Texts to add to the vectorstore. embedding: Embedding function to use. metadatas: Optional list of metadatas associated with the texts. session: Cassandra driver session. If not provided, it is resolved from cassio. keyspace: Cassandra key space. If not provided, it is resolved from cassio. table_name: Cassandra table (required). ids: Optional list of IDs associated with the texts. batch_size: Number of concurrent requests to send to the server. Defaults to 16. ttl_seconds: Optional time-to-live for the added texts. body_index_options: Optional options used to create the body index. Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER] Returns: a Cassandra vectorstore. """ store = cls( embedding=embedding, session=session, keyspace=keyspace, table_name=table_name, ttl_seconds=ttl_seconds, body_index_options=body_index_options, ) store.add_texts( texts=texts, metadatas=metadatas, ids=ids, batch_size=batch_size ) return store
[docs] @classmethod async def afrom_texts( cls: Type[CVST], texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, *, session: Optional[Session] = None, keyspace: Optional[str] = None, table_name: str = "", ids: Optional[List[str]] = None, concurrency: int = 16, ttl_seconds: Optional[int] = None, body_index_options: Optional[List[Tuple[str, Any]]] = None, **kwargs: Any, ) -> CVST: """Create a Cassandra vectorstore from raw texts. Args: texts: Texts to add to the vectorstore. embedding: Embedding function to use. metadatas: Optional list of metadatas associated with the texts. session: Cassandra driver session. If not provided, it is resolved from cassio. keyspace: Cassandra key space. If not provided, it is resolved from cassio. table_name: Cassandra table (required). ids: Optional list of IDs associated with the texts. concurrency: Number of concurrent queries to send to the database. Defaults to 16. ttl_seconds: Optional time-to-live for the added texts. body_index_options: Optional options used to create the body index. Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER] Returns: a Cassandra vectorstore. """ store = cls( embedding=embedding, session=session, keyspace=keyspace, table_name=table_name, ttl_seconds=ttl_seconds, setup_mode=SetupMode.ASYNC, body_index_options=body_index_options, ) await store.aadd_texts( texts=texts, metadatas=metadatas, ids=ids, concurrency=concurrency ) return store
[docs] @classmethod def from_documents( cls: Type[CVST], documents: List[Document], embedding: Embeddings, *, session: Optional[Session] = None, keyspace: Optional[str] = None, table_name: str = "", ids: Optional[List[str]] = None, batch_size: int = 16, ttl_seconds: Optional[int] = None, body_index_options: Optional[List[Tuple[str, Any]]] = None, **kwargs: Any, ) -> CVST: """Create a Cassandra vectorstore from a document list. Args: documents: Documents to add to the vectorstore. embedding: Embedding function to use. session: Cassandra driver session. If not provided, it is resolved from cassio. keyspace: Cassandra key space. If not provided, it is resolved from cassio. table_name: Cassandra table (required). ids: Optional list of IDs associated with the documents. batch_size: Number of concurrent requests to send to the server. Defaults to 16. ttl_seconds: Optional time-to-live for the added documents. body_index_options: Optional options used to create the body index. Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER] Returns: a Cassandra vectorstore. """ texts = [doc.page_content for doc in documents] metadatas = [doc.metadata for doc in documents] return cls.from_texts( texts=texts, embedding=embedding, metadatas=metadatas, session=session, keyspace=keyspace, table_name=table_name, ids=ids, batch_size=batch_size, ttl_seconds=ttl_seconds, body_index_options=body_index_options, **kwargs, )
[docs] @classmethod async def afrom_documents( cls: Type[CVST], documents: List[Document], embedding: Embeddings, *, session: Optional[Session] = None, keyspace: Optional[str] = None, table_name: str = "", ids: Optional[List[str]] = None, concurrency: int = 16, ttl_seconds: Optional[int] = None, body_index_options: Optional[List[Tuple[str, Any]]] = None, **kwargs: Any, ) -> CVST: """Create a Cassandra vectorstore from a document list. Args: documents: Documents to add to the vectorstore. embedding: Embedding function to use. session: Cassandra driver session. If not provided, it is resolved from cassio. keyspace: Cassandra key space. If not provided, it is resolved from cassio. table_name: Cassandra table (required). ids: Optional list of IDs associated with the documents. concurrency: Number of concurrent queries to send to the database. Defaults to 16. ttl_seconds: Optional time-to-live for the added documents. body_index_options: Optional options used to create the body index. Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER] Returns: a Cassandra vectorstore. """ texts = [doc.page_content for doc in documents] metadatas = [doc.metadata for doc in documents] return await cls.afrom_texts( texts=texts, embedding=embedding, metadatas=metadatas, session=session, keyspace=keyspace, table_name=table_name, ids=ids, concurrency=concurrency, ttl_seconds=ttl_seconds, body_index_options=body_index_options, **kwargs, )