context-engineering-intro/use-cases/agent-factory-with-subagents/agents/rag_agent/planning/INITIAL.md

Agent Requirements: Semantic Search Agent

Executive Summary

A simple yet powerful semantic search agent that leverages PGVector to provide intelligent document retrieval and summarized insights. The agent automatically chooses between semantic and hybrid search while maintaining a clean CLI interface for user interactions.

Agent Classification

• Type: Tool-Enabled Agent with structured output capabilities
• Complexity: Medium
• Priority Features:
  1. Semantic search with embeddings
  2. Intelligent search type selection
  3. Search result summarization

Functional Requirements

Core Functionality

1. Semantic Search Operation

   • Execute semantic similarity search using PGVector embeddings
   • Automatically generate query embeddings using OpenAI text-embedding-3-small (1536 dimensions)
   • Return top-k relevant document chunks with similarity scores
   • Acceptance Criteria: Successfully retrieve and rank documents by semantic similarity

2. Hybrid Search with Auto-Selection

   • Automatically determine when to use semantic vs hybrid search based on query characteristics
   • Allow manual override when user explicitly specifies search type
   • Combine vector similarity with full-text search for enhanced results
   • Acceptance Criteria: Intelligently route queries to optimal search method

3. Search Result Summarization

   • Analyze retrieved chunks and generate concise insights
   • Synthesize information from multiple sources into coherent summaries
   • Maintain source attribution for transparency
   • Acceptance Criteria: Provide meaningful summaries with proper source references

Input/Output Specifications

• Input Types:
  • Natural language queries via CLI
  • Optional search type specification ("semantic", "hybrid", or auto-detect)
  • Optional result limit (default: 10)
• Output Format: String responses with structured summaries and source citations
• Validation Requirements: Query length validation, result limit bounds (1-50)

Technical Requirements

Model Configuration

• Primary Model: openai:gpt-4o-mini (cost-effective for summarization tasks)
• Embedding Model: text-embedding-3-small (1536 dimensions, matches database schema)
• Context Window Needs: ~8K tokens for processing multiple search results

External Integrations

1. PostgreSQL with PGVector:

   • Purpose: Semantic similarity search and hybrid search operations
   • Authentication: DATABASE_URL environment variable
   • Functions used: match_chunks() and hybrid_search()
   • Connection: asyncpg with connection pooling

2. OpenAI Embeddings API:

   • Purpose: Generate query embeddings for semantic search
   • Authentication: OPENAI_API_KEY environment variable
   • Model: text-embedding-3-small

Tool Requirements

1. semantic_search:

   • Purpose: Execute pure semantic similarity search using embeddings
   • Parameters: query (str), limit (int, default=10)
   • Error Handling: Database connection retry, empty result handling

2. hybrid_search:

   • Purpose: Execute combined semantic + keyword search
   • Parameters: query (str), limit (int, default=10), text_weight (float, default=0.3)
   • Error Handling: Fallback to semantic search if text search fails

3. auto_search:

   • Purpose: Automatically select search type based on query analysis
   • Parameters: query (str), limit (int, default=10)
   • Error Handling: Default to semantic search on classification failure

Dependencies and Environment

API Keys and Credentials

• DATABASE_URL: PostgreSQL connection string with PGVector extension
• OPENAI_API_KEY: OpenAI API key for embeddings and LLM
• LLM_MODEL: Model name (default: gpt-4o-mini)

Python Packages

• pydantic-ai (core framework)
• asyncpg (PostgreSQL async driver)
• python-dotenv (environment variable management)
• rich (CLI formatting)
• openai (embeddings API)
• numpy (embedding vector operations)

System Requirements

• Python version: 3.11+
• PostgreSQL with PGVector extension
• Memory requirements: ~256MB for embeddings cache
• Network requirements: Internet access for OpenAI API

Success Criteria

1. Search Accuracy: Retrieve semantically relevant results with >0.7 similarity threshold
2. Response Time: Complete search and summarization within 3-5 seconds
3. Auto-Selection Accuracy: Correctly choose search type in >80% of cases
4. Summary Quality: Generate coherent summaries that capture key insights from search results

Security and Compliance

• Data Privacy: Database queries and results handled securely, no data logging
• API Key Management: Environment variables only, no hardcoded credentials
• Input Sanitization: Query length limits, SQL injection prevention via parameterized queries
• Audit Logging: Search queries and result counts logged for performance monitoring

Testing Requirements

• Unit Tests: Individual tool functions, search type classification logic
• Integration Tests: End-to-end database connectivity and search operations
• Performance Tests: Search response times under different query types and database sizes
• Security Tests: Input validation, SQL injection prevention, API key security

Constraints and Limitations

• Database Schema: Must work with existing documents/chunks tables and PGVector functions
• Embedding Dimensions: Fixed at 1536 to match existing database schema
• Search Result Limit: Maximum 50 results to prevent performance issues
• Query Length: Maximum 1000 characters to prevent embedding API limits

Future Enhancements (Optional)

• Search result caching for frequently asked questions
• Advanced query preprocessing (entity extraction, query expansion)
• Multi-language search support
• Search analytics and result ranking improvements
• Integration with document ingestion pipeline

Assumptions Made

1. Database Setup: PGVector extension is properly installed and configured
2. Existing Data: Documents and chunks tables are populated with embedded content
3. Search Patterns: Users will primarily perform knowledge-based queries
4. Performance: Database has appropriate indexes for efficient vector operations
5. API Access: Stable internet connection for OpenAI API calls
6. CLI Usage: Primary interface will be command-line with rich formatting

Approval Checklist

• All core requirements defined (semantic search, auto-selection, summarization)
• External dependencies identified (PostgreSQL/PGVector, OpenAI)
• Security considerations addressed (env vars, input validation)
• Testing strategy outlined (unit, integration, performance)
• Success criteria measurable (accuracy, response time, auto-selection)

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Generated: 2025-08-22 Status: Ready for Component Development