Retrieval Pipelines

Algorithms that take a query and return relevant documents.

Available Pipelines

Pipeline	Algorithm	Modality
BM25	Sparse (term frequency)	Text
Hybrid	RRF / Convex Combination	Text
Vector Search	Dense (vector similarity)	Text
HyDE	Dense (hypothetical document embeddings)	Text

Base Class

All retrieval pipelines extend BaseRetrievalPipeline:

from autorag_research.pipelines.retrieval import BaseRetrievalPipeline


class MyRetrievalPipeline(BaseRetrievalPipeline):
    def _get_retrieval_func(self):
        def retrieve(queries: list[str], top_k: int) -> list[list[dict]]:
            # Return list of results per query
            # Each result: {"doc_id": ..., "score": ...}
            pass

        return retrieve

    def _get_pipeline_config(self):
        return {"type": "my_pipeline"}

Methods

Method	Description
retrieve(query, top_k)	Single query retrieval
run(top_k, batch_size)	Batch retrieval for all queries