Embed text(s) into vectors.

Args: texts: One or more strings to embed. dim: Matryoshka truncation dimension; one of 512, 256, 128, 64, 32. normalize: L2-normalize the output (recommended for cosine similarity).

Returns a dict with the embeddings (list of float lists) and dimension.

Cosine similarity matrix between two sets of texts.

Args: texts_a: First set of strings (rows of the result matrix). texts_b: Second set of strings (columns of the result matrix). dim: Matryoshka truncation dimension; one of 512, 256, 128, 64, 32.

Returns a dict with a 2D similarity matrix of shape (len(a), len(b)).

Rank documents by relevance to a query (stateless semantic search).

Args: query: The search query. documents: Candidate documents to rank. top_k: Return only the top K results (default: all). dim: Matryoshka truncation dimension; one of 512, 256, 128, 64, 32.

Returns a dict with results sorted by score (descending).

Add documents to the persistent in-memory index for later querying.

Args: documents: Documents to embed and store. ids: Optional ids (auto-generated if omitted). metadatas: Optional metadata dict per document.

Returns the number added and their ids.

Query the in-memory index for the most similar stored documents.

Args: query: The search query. top_k: Number of results to return.

Return the number of documents currently in the index.

Remove all documents from the in-memory index.