Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs and relevance scores in the range [0, 1], asynchronously.
0 is dissimilar, 1 is most similar.
Parameters
query – input text
k – Number of Documents to return. Defaults to 4.
**kwargs – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance asynchronously.
delete(ids: Optional[List[str]] = None, **kwargs: Any) → None[source]¶
Delete by vector IDs.
Parameters
ids – List of ids to delete.
delete_collection() → None[source]¶
Delete the collection.
encode_image(uri: str) → str[source]¶
Get base64 string from image URI.
classmethod from_documents(documents: List[Document], embedding: Optional[Embeddings] = None, ids: Optional[List[str]] = None, collection_name: str = 'langchain', persist_directory: Optional[str] = None, client_settings: Optional[chromadb.config.Settings] = None, client: Optional[chromadb.Client] = None, collection_metadata: Optional[Dict] = None, **kwargs: Any) → Chroma[source]¶
Create a Chroma vectorstore from a list of documents.
If a persist_directory is specified, the collection will be persisted there.
Otherwise, the data will be ephemeral in-memory.
Parameters