Agentic RAG handles multi-document synthesis by giving the LLM agent control over a retrieval loop — it queries Milvus, reads the results, identifies what’s missing, queries again with refined criteria, and synthesizes a final answer only when it judges the retrieved context to be sufficient.
The key difference from standard RAG is that synthesis happens after multiple retrieval iterations rather than after a single pass. The agent maintains an internal working memory of what it has retrieved so far and uses this to determine whether it has enough coverage to answer the question. If the question spans 5 documents and the first Milvus query only surfaces 2, the agent issues follow-up queries targeting the missing aspects.
Implementing this in Milvus requires thinking about retrieval diversity. Use ef and nprobe parameters to control the breadth of the search — looser settings on later iterations help the agent surface content it hasn’t seen yet. Also consider storing document-level metadata in Milvus’s scalar fields so the agent can filter by source, date, or author and prevent retrieval from over-indexing on a single high-similarity document.
For production deployments, set a maximum iteration count (typically 3-5) to bound latency, and log the agent’s retrieval decisions to understand where the synthesis logic breaks down in edge cases.
Related Resources
- Agentic RAG with Milvus and LangGraph — multi-step retrieval
- RAG with Milvus and LlamaIndex — orchestration framework
- OpenAI Agents with Milvus — agent integration