Search Implementation Status & Next Steps

📋 Current Status Overview

The FastAPI admin application has a sophisticated search infrastructure implemented but not actively used by the search interface. This document outlines the current state, the gap between implementation and usage, and the roadmap to fully activate the hybrid search capabilities.

🎯 Goal of Hybrid Search

The hybrid search system is designed to provide comprehensive content discovery for a marketing firm’s conversation and document management system by combining:

1. Keyword Search (FTS5 + BM25)

• Purpose: Precise keyword matching and exact term search
• Technology: SQLite’s FTS5 extension with BM25 ranking algorithm
• Use Case: Finding conversations/documents containing specific terms, phrases, or exact matches
• Example: Search for “social media strategy” finds exact mentions

2. Semantic Search (FAISS + OpenAI Embeddings)

• Purpose: Understanding meaning and context beyond exact keywords
• Technology: FAISS vector similarity search with OpenAI embeddings (1536 dimensions)
• Use Case: Finding relevant content even when different words are used
• Example: Search for “marketing approach” finds content about “brand strategy” or “promotional tactics”

3. Hybrid Scoring

• Purpose: Combine both approaches for optimal results
• Formula: hybrid_score = (0.35 × BM25_score) + (0.65 × cosine_similarity_score)
• Benefit: Gets both precise keyword matches AND semantically relevant content

🏗️ Current Implementation Status

✅ What’s Implemented (But Not Used)

1. Core Services

• HybridSearchService - Complete implementation with FAISS + FTS5 integration
• EmbeddingService - OpenAI API integration for generating embeddings
• ChunkingService - Content chunking for granular search capabilities

2. Database Models

• Chunk - Conversation content chunks for searchable pieces
• ChunkEmbedding - Vector embeddings stored as BLOB data
• DocumentChunk - Document content chunks
• DocumentChunkEmbedding - Document vector embeddings

3. Dependencies

• faiss-cpu>=1.12.0 - Vector similarity search library
• numpy>=2.3.2 - Numerical operations for embeddings
• openai>=1.106.1 - Embedding generation API

4. Architecture Design

• SQLite as single source of truth with both FTS5 and FAISS
• In-memory FAISS index rebuildable from SQLite data
• Hybrid scoring system with configurable weights

❌ What’s Actually Used (Basic Implementation)

1. Frontend Search Interface

// Current implementation - basic text input
<input type="text" 
       x-model="searchQuery" 
       @input.debounce.300ms="searchConversations()"
       placeholder="Search conversations...">

2. Backend Search Endpoint

# Current implementation - basic SQL LIKE queries
if q:
    search_conditions = [
        Conversation.title.contains(q),      # Basic SQL LIKE
        ConversationFolder.name.contains(q), # Basic SQL LIKE  
        Client.name.contains(q),             # Basic SQL LIKE
        Project.name.contains(q)             # Basic SQL LIKE
    ]
    conditions.append(or_(*search_conditions))

3. Search Scope

• Only searches: Conversation titles, folder names, client names, project names
• Does NOT search: Conversation content, message text, document content
• No semantic understanding: Cannot find related concepts or synonyms

🔍 The Gap Analysis

Infrastructure vs. Usage Gap

Missing Components

1. FTS5 Virtual Tables: chunks_fts and document_chunks_fts are referenced but not created
2. Content Processing: No automatic chunking of conversations/documents
3. Embedding Generation: No automatic embedding creation for searchable content
4. API Integration: Search endpoints don’t use HybridSearchService
5. Frontend Integration: UI doesn’t expose advanced search capabilities

🚀 Next Steps Roadmap

Phase 1: Database Setup (Priority: High)

1.1 Create FTS5 Virtual Tables

-- Create FTS5 virtual tables for full-text search
CREATE VIRTUAL TABLE chunks_fts USING fts5(
    content,
    chunk_type,
    content='chunks',
    content_rowid='rowid'
);

CREATE VIRTUAL TABLE document_chunks_fts USING fts5(
    content,
    content='document_chunks', 
    content_rowid='rowid'
);

1.2 Database Migration

• Create Alembic migration for FTS5 tables
• Add triggers to keep FTS5 tables in sync with main tables
• Test FTS5 functionality with sample data

Phase 2: Content Processing (Priority: High)

2.1 Implement Content Chunking

• Conversation Chunking: Break conversations into searchable chunks
• Document Chunking: Process uploaded documents into chunks
• Automatic Processing: Chunk content when conversations/documents are created/updated

2.2 Embedding Generation

• Automatic Embeddings: Generate embeddings for all chunks
• Batch Processing: Process existing content to create embeddings
• Update Triggers: Generate embeddings when content changes

Phase 3: API Integration (Priority: Medium)

3.1 Update Search Endpoints

# Replace basic SQL search with hybrid search
from services.hybrid_search_service import HybridSearchService
from services.embedding_service import EmbeddingService

async def search_conversations_hybrid(q: str):
    embedding_service = EmbeddingService()
    hybrid_service = HybridSearchService(embedding_service)
    
    # Use hybrid search instead of basic SQL
    results = await hybrid_service.hybrid_search(
        query=q,
        limit=20,
        search_type="conversation"
    )
    return results

3.2 Add New Search Endpoints

• /api/search/hybrid - Full hybrid search with both keyword and semantic
• /api/search/keyword - Keyword-only search using FTS5
• /api/search/semantic - Semantic-only search using FAISS
• /api/search/documents - Document-specific search

Phase 4: Frontend Enhancement (Priority: Medium)

4.1 Advanced Search Interface

• Search Type Selection: Choose between keyword, semantic, or hybrid
• Search Scope: Search conversations, documents, or both
• Result Ranking: Show relevance scores and search type used
• Search Suggestions: Auto-complete based on existing content

4.2 Search Results Enhancement

• Relevance Scoring: Display hybrid scores and search method
• Content Previews: Show matching content snippets
• Context Highlighting: Highlight matching terms in results
• Search Analytics: Track search patterns and popular queries

Phase 5: Performance & Optimization (Priority: Low)

5.1 FAISS Index Management

• Index Persistence: Save FAISS index to disk for faster startup
• Incremental Updates: Update index when content changes
• Memory Management: Optimize memory usage for large datasets

5.2 Search Performance

• Caching: Cache frequent search results
• Async Processing: Background embedding generation
• Search Analytics: Monitor search performance and usage

📊 Expected Benefits

For Users

• Better Search Results: Find content even with different wording
• Comprehensive Coverage: Search both exact terms and related concepts
• Relevance Ranking: Most relevant results appear first
• Content Discovery: Find related conversations and documents

For the Marketing Firm

• Knowledge Management: Better organization and retrieval of client work
• Content Reuse: Find existing content for new projects
• Client Insights: Discover patterns across client conversations
• Efficiency: Faster content discovery and project management

🔧 Technical Considerations

Performance

• FAISS Index Size: Monitor memory usage as content grows
• Embedding Costs: Track OpenAI API usage and costs
• Search Latency: Optimize for sub-second search responses

Scalability

• Database Growth: Plan for increasing content volume
• Index Rebuilding: Handle FAISS index updates efficiently
• Concurrent Searches: Support multiple simultaneous searches

Maintenance

• Content Sync: Keep FTS5 tables synchronized with main tables
• Embedding Updates: Handle content changes and embedding regeneration
• Error Handling: Robust error handling for API failures

📝 Implementation Notes

Current Dependencies

# Already in pyproject.toml
"faiss-cpu>=1.12.0",
"numpy>=2.3.2", 
"openai>=1.106.1",
"aiosqlite>=0.21.0"

Environment Variables Needed

OPENROUTER_API_KEY=your_key_here  # For embeddings

Database Schema

• Existing: chunks, chunk_embeddings, document_chunks, document_chunk_embeddings
• Needed: FTS5 virtual tables and sync triggers

🎯 Success Metrics

Phase 1 Success

• FTS5 virtual tables created and functional
• Basic keyword search working with BM25 scoring
• Database migrations applied successfully

Phase 2 Success

• Content chunking processing existing conversations
• Embeddings generated for all searchable content
• FAISS index built and searchable

Phase 3 Success

• Hybrid search API endpoints functional
• Search results include both keyword and semantic matches
• Relevance scoring working correctly

Phase 4 Success

• Frontend search interface updated
• Users can choose search types
• Search results show relevance scores and context

Overall Success

• Search finds content that basic SQL queries miss
• Users report improved content discovery
• Search performance meets requirements (< 1 second response time)
• System handles expected content volume efficiently

Last Updated: January 2025
Status: Infrastructure Complete, Integration Needed
Next Action: Create FTS5 virtual tables and implement content chunking