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community[minor]: Add hybrid search to Cassandra VectorStore (#20286)
Only supported by Astra DB at the moment. **Twitter handle:** cbornet_
This commit is contained in:
Christophe Bornet
GitHub
parent
d2d01370bc
commit
8f0b5687a3
@ -57,17 +57,12 @@ class Cassandra(VectorStore):
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keyspace: Cassandra key space.
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table_name: Cassandra table.
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ttl_seconds: Optional time-to-live for the added texts.
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body_index_options: Optional options used to create the body index.
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Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
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"""
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_embedding_dimension: Union[int, None]
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@staticmethod
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def _filter_to_metadata(filter_dict: Optional[Dict[str, str]]) -> Dict[str, Any]:
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if filter_dict is None:
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return {}
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else:
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return filter_dict
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def _get_embedding_dimension(self) -> int:
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if self._embedding_dimension is None:
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self._embedding_dimension = len(
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@ -82,6 +77,8 @@ class Cassandra(VectorStore):
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keyspace: str,
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table_name: str,
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ttl_seconds: Optional[int] = None,
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*,
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body_index_options: Optional[List[Tuple[str, Any]]] = None,
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) -> None:
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try:
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from cassio.table import MetadataVectorCassandraTable
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@ -99,6 +96,10 @@ class Cassandra(VectorStore):
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#
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self._embedding_dimension = None
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#
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kwargs = {}
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if body_index_options is not None:
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kwargs["body_index_options"] = body_index_options
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self.table = MetadataVectorCassandraTable(
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session=session,
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keyspace=keyspace,
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@ -106,6 +107,7 @@ class Cassandra(VectorStore):
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vector_dimension=self._get_embedding_dimension(),
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metadata_indexing="all",
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primary_key_type="TEXT",
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**kwargs,
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)
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@property
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@ -212,22 +214,30 @@ class Cassandra(VectorStore):
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embedding: List[float],
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k: int = 4,
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filter: Optional[Dict[str, str]] = None,
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body_search: Optional[Union[str, List[str]]] = None,
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) -> List[Tuple[Document, float, str]]:
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"""Return docs most similar to embedding vector.
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Args:
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embedding (str): Embedding to look up documents similar to.
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k (int): Number of Documents to return. Defaults to 4.
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embedding: Embedding to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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filter: Filter on the metadata to apply.
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body_search: Document textual search terms to apply.
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Only supported by Astra DB at the moment.
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Returns:
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List of (Document, score, id), the most similar to the query vector.
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"""
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search_metadata = self._filter_to_metadata(filter)
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kwargs: Dict[str, Any] = {}
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if filter is not None:
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kwargs["metadata"] = filter
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if body_search is not None:
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kwargs["body_search"] = body_search
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#
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hits = self.table.metric_ann_search(
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vector=embedding,
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n=k,
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metric="cos",
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metadata=search_metadata,
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**kwargs,
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)
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# We stick to 'cos' distance as it can be normalized on a 0-1 axis
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# (1=most relevant), as required by this class' contract.
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@ -248,12 +258,14 @@ class Cassandra(VectorStore):
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query: str,
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k: int = 4,
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filter: Optional[Dict[str, str]] = None,
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body_search: Optional[Union[str, List[str]]] = None,
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) -> List[Tuple[Document, float, str]]:
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embedding_vector = self.embedding.embed_query(query)
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return self.similarity_search_with_score_id_by_vector(
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embedding=embedding_vector,
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k=k,
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filter=filter,
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body_search=body_search,
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)
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# id-unaware search facilities
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@ -262,12 +274,16 @@ class Cassandra(VectorStore):
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embedding: List[float],
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k: int = 4,
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filter: Optional[Dict[str, str]] = None,
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body_search: Optional[Union[str, List[str]]] = None,
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) -> List[Tuple[Document, float]]:
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"""Return docs most similar to embedding vector.
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Args:
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embedding (str): Embedding to look up documents similar to.
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k (int): Number of Documents to return. Defaults to 4.
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embedding: Embedding to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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filter: Filter on the metadata to apply.
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body_search: Document textual search terms to apply.
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Only supported by Astra DB at the moment.
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Returns:
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List of (Document, score), the most similar to the query vector.
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"""
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@ -277,6 +293,7 @@ class Cassandra(VectorStore):
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embedding=embedding,
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k=k,
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filter=filter,
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body_search=body_search,
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)
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]
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@ -285,6 +302,7 @@ class Cassandra(VectorStore):
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query: str,
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k: int = 4,
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filter: Optional[Dict[str, str]] = None,
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body_search: Optional[Union[str, List[str]]] = None,
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**kwargs: Any,
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) -> List[Document]:
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embedding_vector = self.embedding.embed_query(query)
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@ -292,6 +310,7 @@ class Cassandra(VectorStore):
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embedding_vector,
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k,
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filter=filter,
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body_search=body_search,
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)
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def similarity_search_by_vector(
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@ -299,6 +318,7 @@ class Cassandra(VectorStore):
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embedding: List[float],
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k: int = 4,
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filter: Optional[Dict[str, str]] = None,
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body_search: Optional[Union[str, List[str]]] = None,
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**kwargs: Any,
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) -> List[Document]:
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return [
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@ -307,6 +327,7 @@ class Cassandra(VectorStore):
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embedding,
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k,
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filter=filter,
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body_search=body_search,
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)
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]
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@ -315,12 +336,14 @@ class Cassandra(VectorStore):
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query: str,
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k: int = 4,
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filter: Optional[Dict[str, str]] = None,
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body_search: Optional[Union[str, List[str]]] = None,
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) -> List[Tuple[Document, float]]:
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embedding_vector = self.embedding.embed_query(query)
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return self.similarity_search_with_score_by_vector(
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embedding_vector,
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k,
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filter=filter,
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body_search=body_search,
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)
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def max_marginal_relevance_search_by_vector(
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@ -330,6 +353,7 @@ class Cassandra(VectorStore):
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fetch_k: int = 20,
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lambda_mult: float = 0.5,
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filter: Optional[Dict[str, str]] = None,
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body_search: Optional[Union[str, List[str]]] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance.
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@ -337,22 +361,31 @@ class Cassandra(VectorStore):
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among selected documents.
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Args:
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embedding: Embedding to look up documents similar to.
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k: Number of Documents to return.
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k: Number of Documents to return. Defaults to 4.
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fetch_k: Number of Documents to fetch to pass to MMR algorithm.
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Defaults to 20.
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lambda_mult: Number between 0 and 1 that determines the degree
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of diversity among the results with 0 corresponding to maximum
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diversity and 1 to minimum diversity.
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Defaults to 0.5.
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filter: Filter on the metadata to apply.
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body_search: Document textual search terms to apply.
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Only supported by Astra DB at the moment.
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Returns:
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List of Documents selected by maximal marginal relevance.
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"""
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search_metadata = self._filter_to_metadata(filter)
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_kwargs: Dict[str, Any] = {}
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if filter is not None:
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_kwargs["metadata"] = filter
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if body_search is not None:
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_kwargs["body_search"] = body_search
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prefetch_hits = list(
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self.table.metric_ann_search(
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vector=embedding,
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n=fetch_k,
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metric="cos",
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metadata=search_metadata,
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**_kwargs,
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)
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)
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# let the mmr utility pick the *indices* in the above array
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@ -382,6 +415,7 @@ class Cassandra(VectorStore):
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fetch_k: int = 20,
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lambda_mult: float = 0.5,
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filter: Optional[Dict[str, str]] = None,
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body_search: Optional[Union[str, List[str]]] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance.
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@ -389,12 +423,16 @@ class Cassandra(VectorStore):
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among selected documents.
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Args:
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query: Text to look up documents similar to.
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k: Number of Documents to return.
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k: Number of Documents to return. Defaults to 4.
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fetch_k: Number of Documents to fetch to pass to MMR algorithm.
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Defaults to 20.
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lambda_mult: Number between 0 and 1 that determines the degree
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of diversity among the results with 0 corresponding to maximum
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diversity and 1 to minimum diversity.
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Defaults to 0.5.
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filter: Filter on the metadata to apply.
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body_search: Document textual search terms to apply.
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Only supported by Astra DB at the moment.
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Returns:
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List of Documents selected by maximal marginal relevance.
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"""
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@ -405,6 +443,7 @@ class Cassandra(VectorStore):
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fetch_k,
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lambda_mult=lambda_mult,
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filter=filter,
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body_search=body_search,
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)
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@classmethod
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@ -420,6 +459,7 @@ class Cassandra(VectorStore):
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ids: Optional[List[str]] = None,
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batch_size: int = 16,
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ttl_seconds: Optional[int] = None,
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body_index_options: Optional[List[Tuple[str, Any]]] = None,
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**kwargs: Any,
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) -> CVST:
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"""Create a Cassandra vectorstore from raw texts.
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@ -435,6 +475,8 @@ class Cassandra(VectorStore):
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batch_size: Number of concurrent requests to send to the server.
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Defaults to 16.
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ttl_seconds: Optional time-to-live for the added texts.
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body_index_options: Optional options used to create the body index.
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Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
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Returns:
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a Cassandra vectorstore.
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@ -451,6 +493,7 @@ class Cassandra(VectorStore):
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keyspace=keyspace,
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table_name=table_name,
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ttl_seconds=ttl_seconds,
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body_index_options=body_index_options,
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)
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store.add_texts(
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texts=texts, metadatas=metadatas, ids=ids, batch_size=batch_size
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@ -469,6 +512,7 @@ class Cassandra(VectorStore):
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ids: Optional[List[str]] = None,
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batch_size: int = 16,
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ttl_seconds: Optional[int] = None,
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body_index_options: Optional[List[Tuple[str, Any]]] = None,
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**kwargs: Any,
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) -> CVST:
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"""Create a Cassandra vectorstore from a document list.
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@ -483,6 +527,8 @@ class Cassandra(VectorStore):
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batch_size: Number of concurrent requests to send to the server.
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Defaults to 16.
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ttl_seconds: Optional time-to-live for the added documents.
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body_index_options: Optional options used to create the body index.
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Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
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Returns:
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a Cassandra vectorstore.
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@ -499,5 +545,6 @@ class Cassandra(VectorStore):
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ids=ids,
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batch_size=batch_size,
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ttl_seconds=ttl_seconds,
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body_index_options=body_index_options,
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**kwargs,
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)
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