noisedestroyers
a29793032d
Snapshot of where opencode + Qwen3-Coder + MCPs + Kimi-Linear + voice + Phoenix tracing land today, plus in-flight (oc-tree, kimi-linear context ramp) and next (ComfyUI) items with pointers to per-project NEXT_STEPS.md guides.
77 lines
3.2 KiB
Markdown
77 lines
3.2 KiB
Markdown
# Qwen Large Codebase Roadmap
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This document outlines recommendations for working with large codebases using opencode and your existing vector database setup.
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## Current Setup
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- Large codebase enriched by large-context LLM
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- Vector database for searching codebase
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- Opencode with existing MCP servers (Playwright, SearXNG, Serena, Basic-memory, Sequential-thinking, Task-master)
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## Recommended Tooling
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### 1. Continue.dev Integration
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- **Purpose**: Better codebase indexing and search with nomic-embed-text
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- **Benefits**: Inline completion support, enhanced code understanding capabilities
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- **Implementation**: Deploy continue.dev alongside existing setup
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### 2. Enhanced RAG Implementation
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- **Purpose**: Leverage vector DB for efficient codebase navigation
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- **Approach**:
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- Custom MCP server that queries your vector DB for relevant code sections
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- Cited search variants for precise code reference retrieval
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- **Integration**: Combine with existing symbolic search from Serena MCP
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### 3. Structured Workflows
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- **Purpose**: Organize complex projects and search activities
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- **Tools**:
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- Task-master MCP for task management
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- Workflow patterns that tie vector DB queries to specific tasks
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- **Benefits**: Better tracking of search results and implementation progress
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### 4. Memory Management
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- **Purpose**: Persistent documentation of codebase insights
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- **Approach**:
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- Leverage basic-memory MCP for notes tied to vector DB queries
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- Create patterns for documenting important code patterns, API decisions, and insights
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- **Integration**: Connect memory entries to specific vector search results
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### 5. Monitoring Integration
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- **Purpose**: Track performance of vector database queries
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- **Tools**:
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- Phoenix tracing for performance monitoring
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- Track tool usage patterns for optimizing search strategies
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- **Benefits**: Visibility into query efficiency and system performance
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### 6. Code Navigation Enhancement
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- **Purpose**: Combine vector search with symbolic navigation
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- **Approach**:
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- Use Serena MCP for symbolic code navigation
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- Augment with vector search results for context
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- **Integration**: Create hybrid search approaches that use both methods
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## Implementation Approach
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1. **Phase 1**: Install continue.dev for enhanced code understanding
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2. **Phase 2**: Set up vector DB query tools as custom MCP servers
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3. **Phase 3**: Create patterns for combining vector search with symbolic navigation
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4. **Phase 4**: Implement persistent memory patterns for documenting findings
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5. **Phase 5**: Establish monitoring and optimization practices
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## Key Integration Points
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### Vector DB + Opencode
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- Use vector database queries to find relevant code sections
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- Combine with symbolic search for complete context understanding
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- Enable citation-based referencing of code locations
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### Memory + Search
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- Document search results in basic-memory
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- Create connections between vector DB entries and memory notes
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- Maintain a searchable knowledge base of codebase insights
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### Monitoring + Performance
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- Track query performance through Phoenix
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- Optimize search strategies based on usage patterns
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- Monitor system efficiency as complexity scales
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This roadmap provides a gradual approach to enhancing your codebase management capabilities while leveraging your existing infrastructure. |