Knowledge Retrieval Capabilities: How Agents Find Information
Agents can't know everything. The ability to retrieve relevant information from vast knowledge bases is crucial. This guide explores how agents find the right information at the right time through semantic search, ranking, and retrieval optimization.
Retrieval Architecture
RAG Pipeline
Semantic Search
Beyond Keywords
Find documents by meaning, not exact word matches:
- • Document about "refund policy" (high similarity)
- • Document about "returns process" (high similarity)
- • Document about "cancellations" (medium similarity)
Ranking Algorithms
Not all retrieved documents are equally relevant. Ranking factors:
Relevance Signals
- • Semantic similarity score
- • Keyword overlap
- • Recency (newer = better)
- • Document authority/quality
Context Signals
- • User's past queries
- • User's role/permissions
- • Current task context
- • Geographic location
Hybrid Search
Combine Multiple Search Methods
Retrieval Optimization
- Chunk Size: 200-500 tokens optimal for most use cases
- Top-K: Retrieve 5-10 documents, more creates noise
- Reranking: Use cross-encoder for final ranking
Conclusion
Knowledge retrieval capabilities enable agents to access vast information while staying within context limits. Master semantic search, hybrid approaches, and ranking optimization to build agents that always find the right information.
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