Chroma

Get base64 string from image URI.

Run more images through the embeddings and add to the vectorstore.

Run more texts through the embeddings and add to the vectorstore.

Run similarity search with Chroma.

Return docs most similar to embedding vector. Args: embedding (List[float]): Embedding to look up documents similar to. k (int): 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.

Return docs most similar to embedding vector and similarity score.

Run similarity search with Chroma with distance.

Search for similar images based on the given image URI.

Search for similar images based on the given image URI.

Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Delete the collection.

Gets the collection.

Update a document in the collection.

Update a document in the collection.

Create a Chroma vectorstore from a raw documents.

If a persist_directory is specified, the collection will be persisted there. Otherwise, the data will be ephemeral in-memory.

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.

Delete by vector IDs.