Semantic search enables agents to retrieve information based on meaning rather than exact text. In LangGraph, where agents interpret user intent and generate reasoning chains, this is crucial. A keyword-only lookup would miss paraphrased or conceptually similar data; embeddings close that gap by encoding semantics into high-dimensional vectors.
When agents use Milvus for retrieval, they can locate contextually related passages even if phrasing differs. For instance, a query about “renewable energy storage” will find documents discussing “battery capacity for solar systems.” This capability lets LangGraph agents handle natural language inputs robustly, producing grounded answers instead of hallucinations.
Semantic retrieval also improves generalization. As agents learn new terms or encounter unseen phrasing, they can still map meaning to nearby vectors. Developers can fine-tune embedding models and adjust distance metrics in Milvus to balance precision and recall. The outcome is a graph of agents that understands intent, retrieves relevant data, and maintains consistency across tasks and domains.