Agentic RAG requires vector databases that support semantic search, metadata filtering, and high-throughput query iteration.
Critical features:
1. Low-latency retrieval: Agents iterate through queries rapidly; each retrieval must be <100ms to keep response times practical.
2. Metadata filtering: Agents need to constrain searches by dates, sources, document types, or custom fields (e.g., “retrieve only Q4 2025 supplier invoices”).
3. Sparse + dense hybrid search: Production agentic workflows combine vector similarity with keyword search. Dense vectors catch semantic meaning; sparse vectors ensure exact-match precision. This is the production baseline for 2026.
4. Batch retrieval: Agents often retrieve multiple query results in parallel; the database must handle batch operations efficiently.
5. Schema flexibility: Agents work with heterogeneous data—documents, code snippets, structured records, embeddings. The database must store multiple data types in a single schema.
6. Multi-collection support: Different agents in a workflow may query different vector stores. Milvus handles this natively.
Milvus provides all of these out of the box, enabling agents to query dynamically without worrying about the underlying retrieval infrastructure.
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