Create Retrieval Plugin

Workflow

1. Scaffold

autorag-research plugin create my_search --type=retrieval

Read the generated pipeline.py, pyproject.toml, YAML config, and test file to understand the structure.

2. Implement

For the shared pipeline implementation and testing rules, read:

• ai_instructions/pipeline_implementer.md
• ai_instructions/pipeline_test_writer.md
• ai_instructions/pipeline_architecture_mapper.md

Implement the two abstract methods in the pipeline class:

• _retrieve_by_id(query_id, top_k) — retrieve using query ID (query exists in DB with stored embedding)
• _retrieve_by_text(query_text, top_k) — retrieve using raw text (may need on-the-fly embedding)

Both must return list[dict[str, Any]] with doc_id (chunk ID) and score keys.

DO NOT add your own asyncio.gather, asyncio.Semaphore, or any concurrency control. The base pipeline's run() already handles parallel execution of all queries via run_with_concurrency_limit() (semaphore + gather), controlled by the max_concurrency config parameter. Your method is called once per single query — just implement the retrieval logic for that one query.

Custom parameters: Add fields to your config class and pass them via get_pipeline_kwargs() → accept them in the pipeline constructor. See bm25.py for a real example.

3. Write tests and install

cd my_search_plugin
pip install -e .   # or: uv pip install -e .
cd .. && autorag-research plugin sync

Verify: ls configs/pipelines/retrieval/my_search.yaml

Key Files

Examples

Study these existing implementations for patterns:

• autorag_research/pipelines/retrieval/bm25.py — BM25 retrieval (simple)
• autorag_research/pipelines/retrieval/vector_search.py — Vector similarity search
• autorag_research/pipelines/retrieval/hybrid.py — Hybrid (BM25 + vector)
• autorag_research/pipelines/retrieval/hyde.py — HyDE (Hypothetical Document Embeddings)
• YAML configs: configs/pipelines/retrieval/bm25.yaml, configs/pipelines/retrieval/vector_search.yaml