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

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