maximal_marginal_relevance() — langchain Function Reference

Architecture documentation for the maximal_marginal_relevance() function in _utils.py from the langchain codebase.

Function python LangChainCore ApiManagement calls 1

Entity Profile

LangChainCore→ ApiManagement→ maximal_marginal_relevance() — langchain Function Reference

Dependency Diagram

graph TD
  10f8fdda_fd19_0b90_833b_65e011409aca["maximal_marginal_relevance()"]
  48e8e174_6e33_503a_69b3_d3e96ea57b6b["_utils.py"]
  10f8fdda_fd19_0b90_833b_65e011409aca -->|defined in| 48e8e174_6e33_503a_69b3_d3e96ea57b6b
  7aa6829d_f62b_eb02_3655_b23659d1fd00["cosine_similarity()"]
  10f8fdda_fd19_0b90_833b_65e011409aca -->|calls| 7aa6829d_f62b_eb02_3655_b23659d1fd00
  style 10f8fdda_fd19_0b90_833b_65e011409aca fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/partners/qdrant/langchain_qdrant/_utils.py lines 8–39

def maximal_marginal_relevance(
    query_embedding: np.ndarray,
    embedding_list: list,
    lambda_mult: float = 0.5,
    k: int = 4,
) -> list[int]:
    """Calculate maximal marginal relevance."""
    if min(k, len(embedding_list)) <= 0:
        return []
    if query_embedding.ndim == 1:
        query_embedding = np.expand_dims(query_embedding, axis=0)
    similarity_to_query = cosine_similarity(query_embedding, embedding_list)[0]
    most_similar = int(np.argmax(similarity_to_query))
    idxs = [most_similar]
    selected = np.array([embedding_list[most_similar]])
    while len(idxs) < min(k, len(embedding_list)):
        best_score = -np.inf
        idx_to_add = -1
        similarity_to_selected = cosine_similarity(embedding_list, selected)
        for i, query_score in enumerate(similarity_to_query):
            if i in idxs:
                continue
            redundant_score = max(similarity_to_selected[i])
            equation_score = (
                lambda_mult * query_score - (1 - lambda_mult) * redundant_score
            )
            if equation_score > best_score:
                best_score = equation_score
                idx_to_add = i
        idxs.append(idx_to_add)
        selected = np.append(selected, [embedding_list[idx_to_add]], axis=0)
    return idxs