AI编码Agent代码知识图谱预索引方案设计器

You are an expert in code intelligence and knowledge graph systems. I need you to design a **Pre-indexed Code Knowledge Graph** system that allows AI coding agents (Claude Code, Codex, Cursor) to understand a codebase BEFORE starting a conversation, drastically reducing token usage and tool calls. ## Project Context - Language(s): [e.g., TypeScript + Python] - Repo size: [e.g., 500 files / 100K LOC] - Framework(s): [e.g., Next.js + FastAPI] - Current pain: [e.g., Agent reads 50+ files per task, burning 200K tokens] ## Please Design: ### 1. Graph Schema Define node types and relationships: - Files, Functions, Classes, Interfaces, Modules - Import/Export edges, Call edges, Inheritance edges - Semantic clusters (feature domains, layers) ### 2. Indexing Pipeline - AST parsing strategy per language - Symbol resolution and cross-file reference tracking - Incremental update on git diff (only re-index changed files) - Embedding generation for semantic search - Storage format (JSON-LD, SQLite, Neo4j, or custom) ### 3. Query Interface for Agents - Natural language → graph traversal translation - "Find all callers of function X" in O(1) - "What files are affected if I change interface Y?" - "Show me the data flow from API endpoint to database" - Context window budget-aware result truncation ### 4. Agent Integration - How to inject graph context into system prompt - Token budget allocation: graph summary vs raw code - Lazy loading strategy (summary first, details on demand) - Cache invalidation on file changes ### 5. Metrics & Evaluation - Token savings percentage vs naive file reading - Tool call reduction ratio - Answer accuracy impact (does less context hurt quality?) - Index build time and storage overhead ### 6. Implementation Roadmap Provide a 3-phase plan: - Phase 1: MVP with static analysis only - Phase 2: Add semantic embeddings - Phase 3: Real-time incremental updates Output as a technical design document with architecture diagrams (Mermaid), example queries, and concrete token savings estimates.