---

name: rag-document-processing description: "Guides LazyLLM RAG, document ingestion, readers, transforms, retrievers, rerankers, stores, BM25, and parser-service workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

LazyLLM RAG and Document Processing

Use this sub-skill for LazyLLM tasks involving Document, DocNode, document readers, transforms, node groups, retrievers, rerankers, BM25, stores/indexes, document service metadata, parser service, Milvus/OpenSearch, or RAG examples.

Start here when

• The task mentions lazyllm.tools.rag, Document, Retriever, Reranker, DocNode, node groups, chunking, embedding, reranking, or BM25.
• An import error says to run lazyllm install rag.
• The user wants a local RAG app, parser-service architecture, URL/file retriever, map store, Milvus/OpenSearch index, or RAG with online/local model modules.
• You need to separate local text retrieval from optional embedding/vector database/model backends.

Files to read

• rag-workflows.md for local RAG recipes, object relationships, and backend escalation.
• data-and-service-formats.md for document/node/service request shapes and parser-service boundaries.
• troubleshooting.md for missing dependencies, parser service, vector DB, and retrieval issues.
• scripts/rag_bm25_smoke.py for a no-network BM25 retrieval check.

Safe RAG workflow

1. Install/check the RAG extra when importing RAG objects:

   lazyllm install rag
   python ../../scripts/check_lazyllm_env.py --require-rag
   

2. Start local and text-only. Use DocNode and BM25 or local readers before adding embeddings, rerankers, vector DBs, or model modules.
3. Decide ownership of model nodes. Model modules used for embeddings/reranking/chat belong to model-deployment for backend checks.
4. Decide service boundaries. Parser service, document service, Milvus/OpenSearch/Redis, object storage, and external URLs require service credentials or local fixtures.
5. Run local smoke.

   python scripts/rag_bm25_smoke.py
   

Key public surfaces

• Document(*args, **kw) is the main RAG container and index manager.
• Retriever(doc, group_name, similarity=None, similarity_cut_off=-inf, index='default', topk=6, embed_keys=None, target=None, output_format=None, join=False, weight=None, priority=None, **kwargs) pulls contexts from a document/index.
• Reranker(name='ModuleReranker', *args, **kwargs) reranks retrieved contexts and may depend on a model/backend.
• DocNode represents text chunks with metadata and relationships.
• BM25 supports local English/Chinese retrieval with no embedding server.

Backend posture

• Required for local guidance: base LazyLLM plus rag extra.
• Safe CPU candidates: BM25, document/node transforms, reader utilities, retriever logic, local doc-service manager with SQLite/mocked parser client.
• Optional external services: Milvus, OpenSearch/Elasticsearch, Redis/RedisVL, parser service workers, object storage, remote URLs.
• Optional model/GPU: embedding models, rerankers, local LLM answer generation, multimodal OCR/audio ingestion.

Handoff checklist

When you complete a RAG task, report:

• document source type and data format,
• node/chunking/grouping plan,
• retrieval method and index/store backend,
• model/embedding/reranker backend status,
• local smoke command or reason it is optional/skipped,
• clear separation between local artifacts and external services.