# Semantic Indexing Summary
## Current Status
### ✅ Completed (9 repositories)
- **c_phoenix**: 302 embeddings (30 files)
- **c_redis**: 5,810 embeddings (766 files)
- **cpp_grpc**: 3,278 embeddings (partial - timed out)
- **go_gin**: 416 embeddings
- **code-embeddings**: 495 embeddings (django)
- **typescript collections**: ~19k embeddings (from earlier runs)
### 📋 Ready to Index (13 repositories)
Small to medium repositories that can be indexed quickly:
- c/curl
- cpp/json
- dart/flutter_examples
- go/terraform
- java/kafka
- javascript/express
- kotlin/kotlin
- python/flask
- python/requests
- rust/rust
- rust/tokio
- scala/akka
- swift/alamofire
### ⏭️ Skipped - Large Repositories (7)
These require dedicated indexing sessions due to size:
- **typescript/TypeScript**: ~50,000 files
- **javascript/react**: ~6,000 files
- **python/django**: ~5,000 files
- **csharp/aspnetcore**: ~5,000 files
- **ruby/rails**: ~5,000 files
- **php/laravel**: ~4,000 files
- **java/spring-boot**: ~3,000 files
## SQL Indexing Status
- **17 out of 29 repositories** have SQL indexes
- **Total documents**: ~220,000
- SQL indexing appears complete with no artificial limits
## Key Findings
1. **Semantic indexing is NOT complete** - only 9/29 repositories have embeddings
2. **SQL indexing has no limits** - uses `recursive=True` without file restrictions
3. **Semantic indexing challenges**:
- Time intensive (Redis took 6 minutes for 766 files)
- API costs (Voyage AI charges per token)
- Large repositories cause timeouts
## Scripts Updated
### `index_all_repos_semantic_simple.py`
- ✅ Updated to process entire files (no 2000-line limit)
- ✅ Chunks large files appropriately
- ✅ Processes test_repos directory directly
- ✅ Handles both SQL-indexed and filesystem sources
### New Scripts
- `index_missing_repos_semantic.py` - Identifies missing repositories
- `index_all_repos_semantic_full.py` - Full indexing without limits
## Recommendations
1. **Complete small/medium repos first** - Run the updated script for the 13 ready repositories
2. **Handle large repos separately** - Create dedicated scripts with:
- Resume capability
- Progress tracking
- Batch processing
3. **Consider partial indexing** for very large repos:
- Index only key directories
- Sample files strategically
- Focus on most-used code
## Next Steps
To complete semantic indexing:
```bash
# Index the 13 small/medium repositories
export $(grep -v '^#' .env | xargs)
python scripts/index_all_repos_semantic_simple.py
# For large repositories, use dedicated sessions:
python scripts/index_large_repo.py --repo django --batch-size 100
```
