Hybrid search combines vector similarity with traditional filters or keyword matching. It allows an agent to search semantically while respecting structured constraints—such as document type, creation date, or source. In Milvus, each vector record can include metadata fields that are filterable during query execution. When a LangGraph node issues a search, it can specify both the embedding vector and SQL-like conditions.
For example, a retrieval node could request the top-10 most relevant vectors where category="policy" and year>2023. Milvus’s hybrid engine evaluates metadata filters first, then runs vector similarity on the reduced candidate set. This approach yields faster, more precise results than pure vector search.
In LangGraph workflows, hybrid search helps agents retrieve domain-specific knowledge without manual post-filtering. Nodes can define retrieval templates with parameterized filters, ensuring every query respects organizational rules or freshness thresholds. The combination of LangGraph orchestration and Milvus’s hybrid capability gives developers fine-grained control over context injection and compliance.