RAG Examples

Two library-mode walk-throughs of agentscope.rag — no FastAPI service, no manager, no message bus. Each script wires the building blocks (parser, chunker, embedding model, vector store, KnowledgeBase handle) by hand so the data flow is visible end-to-end.

Script	What it shows
`index_and_search.py`	The minimal pipeline: parse → chunk → embed → insert, then `KnowledgeBase.search`. Start here.
`integrate_with_agent.py`	Attaches the same `KnowledgeBase` to an `Agent` via `RAGMiddleware`, in both `static` (auto-inject) and `agentic` (tool-driven) modes.

Both examples use an in-memory Qdrant store (location=":memory:") and the DashScope text-embedding-v4 model, so no external services are required.

Install

bash

# From PyPI
uv pip install "agentscope[rag]"

# Or from source (repo root)
uv pip install -e ".[rag]"

integrate_with_agent.py additionally uses DashScopeChatModel, which is already in the base agentscope dependencies.

Run

bash

export DASHSCOPE_API_KEY=sk-...

python examples/rag/index_and_search.py
python examples/rag/integrate_with_agent.py

Service mode

The two scripts above are library-mode — you drive the pipeline yourself in a single process. For the full service-mode experience (FastAPI endpoints for knowledge base CRUD, document upload, indexing workers, and search), see examples/agent_service for the backend and examples/web_ui for the chat-style UI.