Files

noisedestroyers a29793032d Document current coding-workflow stack state

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.

2026-05-10 21:14:43 -04:00

3.2 KiB

Raw Permalink Blame History

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

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

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

3.2 KiB Raw Permalink Blame History