# Qwen Large Codebase Roadmap

This document outlines recommendations for working with large codebases using opencode and your existing vector database setup.

## Current Setup
- Large codebase enriched by large-context LLM
- Vector database for searching codebase
- Opencode with existing MCP servers (Playwright, SearXNG, Serena, Basic-memory, Sequential-thinking, Task-master)

## Recommended Tooling

### 1. Continue.dev Integration
- **Purpose**: Better codebase indexing and search with nomic-embed-text
- **Benefits**: Inline completion support, enhanced code understanding capabilities
- **Implementation**: Deploy continue.dev alongside existing setup

### 2. Enhanced RAG Implementation
- **Purpose**: Leverage vector DB for efficient codebase navigation
- **Approach**: 
  - Custom MCP server that queries your vector DB for relevant code sections
  - Cited search variants for precise code reference retrieval
- **Integration**: Combine with existing symbolic search from Serena MCP

### 3. Structured Workflows
- **Purpose**: Organize complex projects and search activities
- **Tools**: 
  - Task-master MCP for task management
  - Workflow patterns that tie vector DB queries to specific tasks
- **Benefits**: Better tracking of search results and implementation progress

### 4. Memory Management
- **Purpose**: Persistent documentation of codebase insights
- **Approach**:
  - Leverage basic-memory MCP for notes tied to vector DB queries
  - Create patterns for documenting important code patterns, API decisions, and insights
- **Integration**: Connect memory entries to specific vector search results

### 5. Monitoring Integration
- **Purpose**: Track performance of vector database queries
- **Tools**:
  - Phoenix tracing for performance monitoring
  - Track tool usage patterns for optimizing search strategies
- **Benefits**: Visibility into query efficiency and system performance

### 6. Code Navigation Enhancement
- **Purpose**: Combine vector search with symbolic navigation
- **Approach**:
  - Use Serena MCP for symbolic code navigation
  - Augment with vector search results for context
- **Integration**: Create hybrid search approaches that use both methods

## Implementation Approach

1. **Phase 1**: Install continue.dev for enhanced code understanding
2. **Phase 2**: Set up vector DB query tools as custom MCP servers
3. **Phase 3**: Create patterns for combining vector search with symbolic navigation
4. **Phase 4**: Implement persistent memory patterns for documenting findings
5. **Phase 5**: Establish monitoring and optimization practices

## Key Integration Points

### Vector DB + Opencode
- Use vector database queries to find relevant code sections
- Combine with symbolic search for complete context understanding
- Enable citation-based referencing of code locations

### Memory + Search
- Document search results in basic-memory
- Create connections between vector DB entries and memory notes
- Maintain a searchable knowledge base of codebase insights

### Monitoring + Performance
- Track query performance through Phoenix
- Optimize search strategies based on usage patterns
- Monitor system efficiency as complexity scales

This roadmap provides a gradual approach to enhancing your codebase management capabilities while leveraging your existing infrastructure.