fastrag.retrieval.reranking_retriever#

RerankingRetriever: narrowing any retrieval strategy with a reranker.

Classes

RerankingRetriever(*, reranker[, retriever, ...])

Over-fetch with a retriever, then narrow on relevance.
class fastrag.retrieval.reranking_retriever.RerankingRetriever(*, reranker, retriever=None, vector_store=None, embedder=None, candidate_k=None)[source]#

Bases: Retriever

Over-fetch with a retriever, then narrow on relevance.

Composition, not configuration: a plain VectorRetriever over-fetches k candidates on cosine similarity, and reranker then narrows them to top_k on relevance — a cross-encoder reading query and document together, which similarity search alone cannot do. The returned score/rank are the reranker’s own, not the vector search’s — see rerank().

async with MilvusVectorStore(url=MILVUS_URI) as vector_store:
    retriever = RerankingRetriever(
        vector_store=vector_store,
        embedder=Model2VecEmbedder(model=EMBEDDING_MODEL),
        reranker=CohereReranker(
            model="rerank-v3.5", base_url=RERANK_BASE_URL, api_key=api_key
        ),
    )
    documents = await retriever.retrieve("What is AgrospAI?", k=200, top_k=5)
Parameters:

  • vector_store (VectorStore | None) – The collection to over-fetch from. Opened by the caller; this retriever neither opens nor closes it.

  • embedder (Embedder | None) – Embeds the query text — see VectorRetriever.

  • retriever (Retriever | None) – Any SDK retriever, including a hybrid retriever. Mutually exclusive with the legacy vector_store/embedder pair.

  • candidate_k (int | None) – Optional over-fetch count used even when the caller requests only the final k results.

  • reranker (Reranker) – Scores the over-fetched documents against the query.

async retrieve(query, *, k=5, top_k=None)[source]#

Fetch candidates through the wrapped retriever, then rerank to top_k.

Parameters:

  • query (str) – Natural-language query text.

  • k (int) – How many documents the vector store over-fetches.

  • top_k (int | None) – How many documents the reranker keeps, best first. Defaults to k when omitted.

Return type:

list[Document]