Knowledge Base

Enables Retrieval-Augmented Generation (RAG) by connecting document collections to AI agents through semantic search, vector embeddings, and intelligent chunk retrieval for contextual information enhancement.

Configuration

Connections

RAG Architecture

The Knowledge Base implements a sophisticated retrieval system that enhances agent responses with relevant contextual information:

Vector Embedding Process

1. Document Processing - Documents are chunked into manageable segments
2. Embedding Generation - Text chunks are converted to high-dimensional vectors
3. Index Storage - Vectors are stored with metadata for efficient retrieval
4. Query Processing - User queries are embedded using the same model
5. Similarity Matching - Cosine similarity identifies relevant chunks
6. Context Integration - Retrieved chunks are formatted for agent consumption

Input Parameters

Retrieval Optimization

Score Threshold Guidelines

Fine-tune retrieval quality by adjusting the similarity threshold:

Chunk Limit Optimization

Balance context richness with token efficiency:

Configuration Examples

Integration Workflow

Agent-Knowledge Base Connection

Context Integration Process

1. Query Analysis - User input is analyzed and embedded using vector models
2. Vector Search - Similarity search identifies relevant document chunks
3. Score Filtering - Chunks below threshold are excluded based on relevance
4. Context Assembly - Retrieved chunks are formatted and concatenated
5. Template Injection - Context is injected into {{ctx.knowledge_base}} variable
6. Agent Processing - Agent uses context for informed responses
7. Tool Enhancement - Agent can use additional tools to process, validate, or enhance the retrieved knowledge

Advanced RAG Implementation Patterns

Semantic Search Optimization Strategies

Enhance retrieval quality through advanced semantic search techniques:

Multi-Knowledge Base Orchestration

Coordinate multiple knowledge bases for comprehensive context coverage:

Adaptive Retrieval Configuration

Dynamic threshold and limit adjustment based on query characteristics:

Query Complexity-Based Adaptation:

{
  "kbId": "adaptive-knowledge-base",
  "limit": "{{ctx.query_length > 50 ? 8 : 5}}",
  "scoreThreshold": "{{ctx.query_complexity >= 0.8 ? 0.9 : 0.75}}"
}

Domain-Specific Optimization:

{
  "kbId": "{{ctx.domain === 'legal' ? 'legal-kb' : 'general-kb'}}",
  "limit": "{{ctx.domain === 'legal' ? 4 : 6}}",
  "scoreThreshold": "{{ctx.domain === 'legal' ? 0.95 : 0.75}}"
}

RAG-Tool Integration Patterns

Combine knowledge retrieval with tool execution for enhanced capabilities:

Performance Optimization

Content Optimization

Retrieval Tuning

• Embedding Quality: Use domain-specific embedding models when available
• Chunk Overlap: Implement overlapping chunks for continuity
• Metadata Filtering: Use document metadata for pre-filtering
• Index Optimization: Regular reindexing for updated content

Monitoring Metrics

Track these performance indicators:

• Retrieval Rate: Percentage of queries returning results
• Relevance Score Distribution: Average similarity scores
• Context Usage: How often retrieved context influences responses
• Response Quality: User satisfaction with KB-enhanced responses

Troubleshooting

Common Issues

No Results Retrieved

• Lower the scoreThreshold to increase coverage
• Verify knowledge base contains relevant content
• Check query embedding compatibility

Irrelevant Results

• Increase scoreThreshold for better precision
• Review document chunking strategy
• Improve document structure and keyword usage

Token Limit Exceeded

• Reduce limit to fewer chunks
• Optimize chunk sizes in knowledge base
• Implement context compression techniques

Quality Assessment

Evaluating Retrieval Quality:

1. Manual review of retrieved chunks for sample queries
2. A/B testing with different threshold values
3. User feedback on response relevance
4. Automated similarity scoring validation

Best Practices

Related Concepts

Within AI & Knowledge Ecosystem

• AI Agent - Core agent architecture that consumes knowledge base context via template variables
• AI Sub-Agent - Multi-agent orchestration with specialized knowledge distribution
• AI & Knowledge Overview - Ecosystem architecture and RAG integration strategies

Supporting Tools

• File Storage Nodes - Document processing and content management
• HTTP Request Nodes - Real-time data enhancement for retrieved context
• JavaScript Nodes - Custom logic for knowledge processing and validation

Next Steps:

• Core Integration: AI Agent for implementing knowledge-enhanced agents
• Multi-Agent RAG: AI Sub-Agent for distributed knowledge architectures
• Ecosystem Planning: AI & Knowledge Overview for comprehensive workflow design