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QUICK_START_RAG.md

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Quick Start: RAG for Large Codebases

TL;DR

Problem: Current AIWB can't handle codebases > 50K lines of code Solution: Add RAG (Retrieval-Augmented Generation) with vector embeddings

5-Minute Implementation Guide

Step 1: Install Dependencies

pip3 install sentence-transformers chromadb

Step 2: Add to AIWB (lib/rag.sh)

Copy the implementation from CONTEXT_LIMITATION_SOLUTION_ANALYSIS.md section 4.

Step 3: Index Your Codebase

cd /path/to/large/project
aiwb rag-index
# Takes 5-10 minutes for 100K LOC, one-time operation

Step 4: Query

aiwb rq "How does authentication work?"
# Returns answer in < 1 second
# Cost: $0.0004 per query

How It Works

User Query
    │
    ▼
Vector Search (local, free, <100ms)
    │
    ▼
Find Top 5 Relevant Code Chunks (2K tokens)
    │
    ▼
Send to Gemini 2.5 Flash ($0.0004)
    │
    ▼
Answer

Performance

Codebase Size Setup Time Query Time Cost/Query
10K LOC 1 min 0.5 sec $0.0004
100K LOC 10 min 1 sec $0.0004
1M LOC 2 hours 1 sec $0.0004

Key insight: Query cost and time are constant regardless of codebase size!

Cost Comparison

Without RAG (current):

100K LOC = 125K tokens
Single query to Claude 3.5 Sonnet
Cost: $0.375 (input) + $0.03 (output) = $0.41
Result: FAILS (exceeds context window)

With RAG:

100K LOC indexed once (free, local embeddings)
Each query retrieves ~5 chunks (2.5K tokens)
Cost: $0.00025 (input) + $0.00015 (output) = $0.0004
Result: ✓ Works perfectly

Savings: ~1,000× cheaper per query

When to Use RAG

✓ Use RAG for:

  • Large codebases (> 10K LOC)
  • Frequent queries on same codebase
  • Interactive exploration ("How does X work?")
  • Code search + understanding

✗ Don't use RAG for:

  • Small scripts (< 1K LOC) - just send the whole thing
  • One-off analysis - not worth indexing
  • Real-time code generation - use current modes

Implementation Checklist for AIWB v2.1

  • Add lib/rag.sh with functions above
  • Update aiwb main dispatcher with new commands
  • Add setup_rag() to initial setup wizard
  • Document in README.md
  • Add to /help command output
  • Create unit tests

Estimated implementation time: 1 week Estimated value: Enables AIWB to handle enterprise-scale projects

Advanced: Hybrid Modes

# Combine RAG with existing modes

# Mode 1: RAG-enhanced /make
aiwb make --with-rag
# Automatically includes relevant context from entire codebase

# Mode 2: RAG-enhanced /debug
aiwb debug --with-rag error.log
# Finds similar code and known issues

# Mode 3: RAG + Parallel
aiwb parallel-analyze ./src --use-rag
# Processes in parallel, stores in vector DB

FAQ

Q: Do I need to re-index after every code change? A: Only for major changes. Minor edits don't affect most queries.

Q: Can I use this without internet? A: Embeddings are local. Only the final LLM call needs internet.

Q: How much disk space does it use? A: ~300 bytes per code chunk. 100K LOC ≈ 10K chunks ≈ 3MB.

Q: What if my query needs the FULL codebase? A: Fall back to hierarchical summarization or Map-Reduce (see main doc).

Q: Can I use different embedding models? A: Yes! Try:

  • all-MiniLM-L6-v2: 384-dim, 80MB, fast (default)
  • all-mpnet-base-v2: 768-dim, 420MB, more accurate
  • text-embedding-ada-002: OpenAI API (costs money)

Example: Full Workflow

# Clone a large open-source project
git clone https://github.com/torvalds/linux.git
cd linux

# Initialize AIWB workspace
aiwb init

# Enable RAG (one-time)
aiwb rag-setup

# Index the kernel (~2M LOC, will take ~4 hours)
aiwb rag-index

# Now you can query instantly:
aiwb rq "How does the process scheduler work?"
aiwb rq "Find all uses of spinlocks"
aiwb rq "Explain memory management in kernel/mm/"
aiwb rq "What are common security vulnerabilities?"

# All queries: < 1 second, < $0.001 each
# Total cost for 1000 queries: < $1

Next Steps

  1. Read full analysis: CONTEXT_LIMITATION_SOLUTION_ANALYSIS.md
  2. Review architecture diagrams: CONTEXT_FLOW_DIAGRAM.md
  3. Implement RAG in your AIWB fork
  4. Test on your largest codebase
  5. Share results!

Bottom line: RAG makes AIWB scale from toy projects to enterprise systems. It's not optional for v2.1+.