# OpenTelemetry Storage Setup - Summary

## Completed Tasks

### 1. Configuration Updates

#### File: `/Users/les/Projects/mahavishnu/settings/mahavishnu.yaml`

Added complete `otel_storage` configuration section:

```yaml
otel_storage:
  enabled: false  # Set to true after PostgreSQL setup
  connection_string: "postgresql://postgres:password@localhost:5432/otel_traces"
  embedding_model: "all-MiniLM-L6-v2"
  embedding_dimension: 384
  cache_size: 1000
  similarity_threshold: 0.85
  batch_size: 100
  batch_interval_seconds: 5
  max_retries: 3
  circuit_breaker_threshold: 5
```

#### File: `/Users/les/Projects/mahavishnu/mahavishnu/core/config.py`

Added OTel storage configuration fields to `MahavishnuSettings`:

- `otel_storage_enabled` - Enable/disable OTel storage
- `otel_storage_connection_string` - PostgreSQL connection string
- `otel_storage_embedding_model` - Sentence transformer model
- `otel_storage_embedding_dimension` - Vector dimension
- `otel_storage_cache_size` - Embedding cache size
- `otel_storage_similarity_threshold` - Minimum similarity for search
- `otel_storage_batch_size` - Batch write size
- `otel_storage_batch_interval_seconds` - Batch flush interval
- `otel_storage_max_retries` - Retry attempts
- `otel_storage_circuit_breaker_threshold` - Circuit breaker threshold

Added validator for connection string format.

### 2. Dependency Updates

#### File: `/Users/les/Projects/mahavishnu/pyproject.toml`

Added three new dependencies to the main dependencies list:

```toml
# OpenTelemetry trace storage with semantic search
"asyncpg>=0.29.0",  # Async PostgreSQL driver for OTel trace storage
"pgvector>=0.2.5",  # Vector similarity search for semantic trace search
"sentence-transformers>=2.2.2",  # Sentence transformer embeddings
```

### 3. Documentation

#### File: `/Users/les/Projects/mahavishnu/docs/ONEIRIC_OTEL_STORAGE.md`

Comprehensive 500+ line documentation covering:

- **Architecture Overview**: How semantic trace search works
- **Prerequisites**: PostgreSQL, pgvector, Python dependencies
- **Database Schema**: Complete SQL schema with indexes
- **Configuration**: All configuration options and their meanings
- **Usage Examples**:
  - Basic adapter initialization
  - Storing traces
  - Semantic search
  - Retrieving traces by ID
  - Batch operations
  - Health checks
- **OpenTelemetry SDK Integration**: Automatic and manual trace export
- **Migration Guide**: From Jaeger, Tempo, and other backends
- **Performance Tuning**: PostgreSQL settings, batch size tuning, cache configuration
- **Testing and Validation**: Unit tests, integration tests, load testing
- **Troubleshooting**: Common issues and solutions
- **Best Practices**: Security, monitoring, error handling

### 4. Database Setup Script

#### File: `/Users/les/Projects/mahavishnu/scripts/setup_otel_storage.sh`

Executable bash script that:

- Checks PostgreSQL connectivity
- Verifies pgvector extension installation
- Creates database and user
- Sets up database schema with:
  - Traces table with vector column
  - Performance indexes (including IVFFlat vector index)
  - Updated_at trigger
  - Proper permissions
- Verifies setup
- Tests database connection
- Shows configuration summary

Usage:
```bash
# With defaults
./scripts/setup_otel_storage.sh

# With custom configuration
OTEL_DB_NAME=my_traces \
OTEL_DB_USER=my_user \
OTEL_DB_PASSWORD=secure_password \
./scripts/setup_otel_storage.sh
```

### 5. Example Usage Code

#### File: `/Users/les/Projects/mahavishnu/examples/otel_storage_example.py`

Comprehensive Python examples demonstrating:

1. **Basic Usage** - Adapter initialization
2. **Health Check** - Database connectivity verification
3. **Store Traces** - Single trace storage
4. **Semantic Search** - Natural language queries
5. **Retrieve Trace** - Get trace by ID
6. **Batch Operations** - Bulk trace storage
7. **Search with Filters** - Filtered semantic search
8. **Statistics** - Database statistics

Usage:
```bash
# Run examples (requires OTel storage to be enabled and configured)
python examples/otel_storage_example.py
```

### 6. Implementation Plan

#### File: `/Users/les/Projects/mahavishnu/OTEL_STORAGE_SETUP_PLAN.md`

Detailed implementation plan including:

- Current state analysis
- Implementation tasks (1-5)
- Prerequisites
- Success criteria
- File changes summary
- Implementation order

## Configuration Structure

All configurations follow Oneiric patterns with layered loading:

1. **Default values** (in Pydantic models)
2. **Committed YAML** (`settings/mahavishnu.yaml`)
3. **Local YAML** (`settings/local.yaml`, gitignored)
4. **Environment variables** (`MAHAVISHNU_OTEL_STORAGE__*`)

Example environment variable overrides:

```bash
export MAHAVISHNU_OTEL_STORAGE__ENABLED=true
export MAHAVISHNU_OTEL_STORAGE__CONNECTION_STRING="postgresql://user:pass@host:5432/db"
export MAHAVISHNU_OTEL_STORAGE__EMBEDDING_MODEL="all-MiniLM-L6-v2"
export MAHAVISHNU_OTEL_STORAGE__EMBEDDING_DIMENSION=384
export MAHAVISHNU_OTEL_STORAGE__SIMILARITY_THRESHOLD=0.85
```

## Next Steps

### Immediate (Required)

1. **Install PostgreSQL and pgvector**:
   ```bash
   # macOS
   brew install postgresql@16
   git clone https://github.com/pgvector/pgvector.git
   cd pgvector && make && sudo make install

   # Or use Docker
   docker run -d --name postgres-otel -p 5432:5432 pgvector/pgvector:pg16
   ```

2. **Run database setup script**:
   ```bash
   cd /Users/les/Projects/mahavishnu
   ./scripts/setup_otel_storage.sh
   ```

3. **Update dependencies**:
   ```bash
   cd /Users/les/Projects/mahavishnu
   uv sync
   ```

4. **Enable OTel storage**:
   Edit `settings/mahavishnu.yaml`:
   ```yaml
   otel_storage:
     enabled: true
     connection_string: "postgresql://otel_user:password@localhost:5432/otel_traces"
   ```

### Short-term (Recommended)

5. **Test the setup**:
   ```bash
   python examples/otel_storage_example.py
   ```

6. **Configure OpenTelemetry SDK** to export traces to OTelStorageAdapter

7. **Set up monitoring** for database metrics

### Long-term (Optional)

8. **Optimize performance**:
   - Tune PostgreSQL settings
   - Adjust batch sizes based on load
   - Optimize IVFFlat index parameters

9. **Set up backups**:
   - Regular PostgreSQL dumps
   - Point-in-time recovery

10. **Implement data retention policies**:
    - Archive old traces
    - Delete traces older than retention period

## File Locations

All files are at `/Users/les/Projects/mahavishnu/`:

- `settings/mahavishnu.yaml` - Configuration
- `mahavishnu/core/config.py` - Pydantic settings model
- `pyproject.toml` - Dependencies
- `docs/ONEIRIC_OTEL_STORAGE.md` - Documentation
- `scripts/setup_otel_storage.sh` - Database setup script
- `examples/otel_storage_example.py` - Usage examples
- `OTEL_STORAGE_SETUP_PLAN.md` - Implementation plan
- `OTEL_STORAGE_SETUP_SUMMARY.md` - This file

## Key Features Implemented

- **Configuration**: Complete YAML and environment variable support
- **Dependencies**: All required packages added
- **Documentation**: Comprehensive 500+ line guide
- **Database Schema**: Optimized schema with vector indexes
- **Setup Script**: Automated database setup
- **Examples**: 8 different usage scenarios
- **Best Practices**: Security, monitoring, tuning guidelines

## Validation Checklist

Before enabling OTel storage in production:

- [ ] PostgreSQL 14+ installed and running
- [ ] pgvector extension installed and enabled
- [ ] Database schema created with indexes
- [ ] Connection string verified
- [ ] Embedding model downloaded (automatic on first use)
- [ ] Health check passes
- [ ] Can store traces successfully
- [ ] Semantic search returns relevant results
- [ ] Batch operations work correctly
- [ ] Error handling and retries tested
- [ ] Monitoring configured
- [ ] Backup strategy implemented

## Support and Troubleshooting

See `docs/ONEIRIC_OTEL_STORAGE.md` for:
- Complete troubleshooting guide
- Common error messages and solutions
- Performance optimization tips
- Best practices

## Architecture Benefits

1. **Semantic Search**: Find traces by meaning, not exact matches
2. **Scalability**: PostgreSQL + pgvector scales to millions of traces
3. **Performance**: Vector indexes for fast similarity search
4. **Reliability**: Battle-tested PostgreSQL with proven track record
5. **Flexibility**: Multiple embedding models supported
6. **Integration**: Seamless Oneiric and Mahavishnu integration
7. **Resilience**: Circuit breakers, retries, connection pooling
8. **Privacy**: Self-hosted, no external dependencies

## Conclusion

All configuration files have been updated with Oneiric-compliant settings, comprehensive documentation has been created, database setup script is ready, and example code demonstrates all major features.

The setup is ready for deployment once PostgreSQL + pgvector are installed and configured.
