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Knowledge bases provide agents with access to domain-specific information, documents, and organizational knowledge through semantic search.

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Available Knowledge Base Types

Milvus

Vector Database ✅Production-ready vector database with semantic search capabilities.
Additional vector database integrations will be supported in future releases.

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Milvus Knowledge Base

Milvus is a production-ready vector database that enables semantic search over your documents using dense vector embeddings.

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Configuration

agents:
  - name: "support_agent"
    agent_type: "llm_agent"
    
    knowledge_bases:
      - name: "company_docs"
        knowledge_base_type: "milvus"
        knowledge_base_id: "kb_abc123"
        retrieval_config:
          top_k: 10
          search_ef: 64
          metric_type: "COSINE"

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Required Fields

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name
string
required
Unique identifier for this knowledge base instance
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knowledge_base_type
string
required
Must be set to "milvus"
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knowledge_base_id
string
required
Knowledge base ID for isolation and multi-tenancy

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Retrieval Configuration

The retrieval_config object controls how documents are retrieved:
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retrieval_config.top_k
integer
default:"10"
Number of most relevant results to returnRange: 1-1000
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retrieval_config.search_ef
integer
default:"64"
HNSW search parameter controlling accuracy vs speed trade-offRange: 1-512
Higher values = more accurate but slower
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retrieval_config.metric_type
string
default:"COSINE"
Distance metric for similarity searchOptions:
  • COSINE - Cosine similarity (recommended)
  • L2 - Euclidean distance
  • IP - Inner product
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retrieval_config.offset
integer
default:"0"
Number of results to skip (for pagination)
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retrieval_config.score_threshold
float
Minimum similarity score thresholdRange: 0.0-1.0
Only return results above this score
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retrieval_config.enable_rerank
boolean
default:"false"
Enable reranking of search results for improved relevance
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retrieval_config.rerank_model
string
Reranking model to use (required if enable_rerank is true)

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Usage Examples

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Basic Knowledge Base Agent

agents:
  - name: "support_agent"
    agent_type: "llm_agent"
    
    knowledge_bases:
      - name: "support_kb"
        knowledge_base_type: "milvus"
        knowledge_base_id: "kb_support_001"
        retrieval_config:
          top_k: 5
          metric_type: "COSINE"
    
    system_prompt: |
      You are a customer support agent with access to our support documentation.
      
      Always search the knowledge base first for answers to customer questions.
      Provide accurate information based on our documentation.

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Advanced Configuration with Reranking

agents:
  - name: "research_agent"
    agent_type: "react_agent"
    
    tools:
      - "tavily_search"
    
    knowledge_bases:
      - name: "research_kb"
        knowledge_base_type: "milvus"
        knowledge_base_id: "kb_research_001"
        retrieval_config:
          top_k: 20
          search_ef: 128
          metric_type: "COSINE"
          score_threshold: 0.7
          enable_rerank: true
          rerank_model: "cross-encoder"
    
    system_prompt: |
      You are a research assistant with access to internal research papers.
      
      Search the knowledge base for relevant research before using web search.
      Prioritize high-quality, relevant results.

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Multiple Knowledge Bases

agents:
  - name: "comprehensive_agent"
    agent_type: "llm_agent"
    
    knowledge_bases:
      - name: "technical_docs"
        knowledge_base_type: "milvus"
        knowledge_base_id: "kb_tech_001"
        retrieval_config:
          top_k: 10
      
      - name: "company_policies"
        knowledge_base_type: "milvus"
        knowledge_base_id: "kb_policy_001"
        retrieval_config:
          top_k: 5
    
    system_prompt: |
      You have access to multiple knowledge bases:
      - Technical documentation
      - Company policies
      
      Search the appropriate knowledge base based on the question type.

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Best Practices

Top K Selection: Start with top_k: 5-10 for most use cases. Increase if you need more context.
Search Accuracy: Use search_ef: 64 for balanced performance. Increase to 128+ for higher accuracy needs.
Metric Type: Use COSINE for most semantic search applications as it’s normalized and works well with embeddings.
Score Threshold: Set a score_threshold (e.g., 0.7) to filter out low-relevance results.
Higher search_ef values improve accuracy but increase query latency. Balance based on your performance requirements.

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Next Steps

Agent Capabilities

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Agent Types

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