RAG Implementation Strategy & Roadmap

📋 Current Implementation Status

🔍 Quick Assessment:

  • Implementation: Working RAG system with JSON storage (internal/rag/)

  • Knowledge Base: 351KB knowledge.json with 351 documents from 3 PDFs

  • Source Data: 16MB of PDF documents in kb/ directory

  • Integration: Fully operational with Slack MCP client and custom prompts

  • Performance: Already experiencing scalability challenges at current size

📊 Real Usage Data:

$ ls -la knowledge.json kb/
-rw-r--r--  1 user  staff  360938 Jun 29 13:22 knowledge.json  # 351KB
drwxr-xr-x  5 user  staff     160 Jun 29 13:22 kb/
$ ./slack-mcp-client --rag-search "market demand" | wc -l
# Returns 5 documents with good relevance

🚨 Immediate Challenges:

  • JSON file already 351KB with just 3 PDFs - demonstrating scalability issues

  • Memory usage growing linearly (all 351 documents loaded on startup)

  • Search performance degrading as knowledge base grows

  • No deduplication (risk of ingesting same PDFs multiple times)

✅ What's Working Well:

  • Successful LLM integration (custom prompts working)

  • Proper MCP tool registration and discovery

  • PDF processing pipeline functional

  • Multi-word search with basic scoring

  • Production deployment already validated

🎯 Strategic Roadmap Overview

Current State: Ultra-Simplified RAG ✅ COMPLETED

  • Architecture: JSON storage with MCP tool integration

  • Performance: Good for <1K documents, 351 documents currently

  • Implementation: ~250 lines of Go code

  • Status: Completed ✅

  • Details: See Ultra-Simplified Implementation

Next Phase: SQLite Migration 🎯 RECOMMENDED

  • Performance: 100x faster search, supports 50K+ documents

  • Memory: Independent of document count (<50MB regardless of size)

  • Compatibility: Backward compatible API, auto-migration from JSON

  • Scope: Moderate effort, significant performance improvement

  • Details: See SQLite Migration Plan

Future Phases: Advanced Features 🔮 OPTIONAL

  • Semantic Search: Vector embeddings for similarity matching

  • Enterprise Features: Access control, audit logging, monitoring

  • Multi-format Support: DOCX, HTML, markdown, etc.

  • Scope: Additional phases as requirements evolve

📈 Performance Evolution Path

Phase
Document Capacity
Memory Usage
Search Speed
Implementation Effort

Current (JSON)

~1,000 docs

100MB+ (linear)

O(n) scan

✅ Complete

SQLite FTS5

50,000+ docs

<50MB (indexed)

O(log n)

Moderate

Vector Search

100,000+ docs

<100MB

O(log n) semantic

Advanced

Enterprise

Unlimited

Configurable

<100ms

Complex

🎯 Immediate Priorities

✅ Completed: Configuration Refactoring

RAG Package Modernization - Essential for maintainability and configuration consistency:

  • Status: ✅ COMPLETED - RAG now uses structured configuration directly

  • Solution: Refactored RAG package to use config.RAGConfig and config.LLMConfig

  • Benefit: Eliminated complexity, aligned with unified config architecture

  • Result: Clean integration with structured configuration, all options exposed

  • Details: See RAG Refactoring Plan

Quick Wins (After Refactoring)

  1. Text file support - Expand beyond PDF-only

  2. Duplicate detection - Content hashing to prevent re-ingestion

  3. Better search scoring - Word proximity and relevance ranking

  4. Metadata filtering - Search by file type, date, etc.

Performance Fix (Future Priority)

SQLite Migration - The critical upgrade needed for real scalability:

  • Problem: Current 351KB JSON shows scalability cliff approaching

  • Solution: SQLite FTS5 provides enterprise-grade search performance

  • Benefit: 100x faster search, unlimited document capacity

  • Risk: Low - backward compatible with automatic migration

🛠 Implementation Strategy

Philosophy: Evolutionary Architecture

  • Start Simple: Current JSON approach proved the concept ✅

  • Upgrade When Needed: SQLite when hitting performance limits (now)

  • Maintain Compatibility: API stays consistent across all phases

  • Zero Downtime: Migrations preserve existing functionality

Risk Mitigation

  • Incremental Changes: Each phase is fully functional

  • Automatic Migration: One-command upgrade from JSON to SQLite

  • Rollback Support: Keep JSON as fallback option

  • API Stability: Interface remains consistent

Success Metrics

  • Phase 1 (Current): ✅ Functional RAG integration

  • Phase 2 (SQLite): Support 50K+ docs with <100ms search

  • Phase 3 (Advanced): Semantic search with >80% relevance

  • Phase 4 (Enterprise): Production monitoring and security

📚 Documentation Structure

Document
Purpose
Audience

rag.md

High-level strategy and roadmap

Decision makers, architects

rag-sqlite.md

Detailed SQLite migration plan

Developers, implementers

rag-quick-start.md

User guide for current system

End users, operators

🚀 Next Steps

✅ Phase 1: Configuration Refactoring - COMPLETED

  1. ✅ Structured Config Implemented: RAG package now uses config.RAGConfig directly

  2. ✅ Integration Simplified: Clean integration through Slack client raw client map

  3. ✅ All Options Exposed: Complete configuration coverage including vectorStoreId, maxResults, similarityMetric

  4. ✅ Validated: All existing functionality working with new architecture

Phase 2: Feature Enhancement (Current Priority)

  1. Implement Quick Wins: Text file support, duplicate detection, better scoring

  2. Monitor Performance: Track JSON file size and search latency

  3. Plan SQLite Migration: Review detailed implementation in rag-sqlite.md

  4. Schedule Migration: Plan SQLite upgrade when performance becomes critical

✅ The current system works well and is now fully aligned with the unified configuration system. RAG refactoring is complete - ready for feature enhancements and performance improvements.

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