CodeGraph CLI MCP Server

# CodeGraph RAG Architecture for Code Intelligence ## Comprehensive RAG System Design and Integration Specifications --- pdf-engine: lualatex mainfont: "DejaVu Serif" monofont: "DejaVu Sans Mono" header-includes: | \usepackage{fontspec} \directlua{ luaotfload.add_fallback("emojifallback", {"NotoColorEmoji:mode=harf;"}) } \setmainfont[ RawFeature={fallback=emojifallback} ]{DejaVu Serif} --- ### Document Version: 1.0 ### Date: September 2025 ### Status: Architecture Design --- ## Executive Summary This document defines a comprehensive Retrieval-Augmented Generation (RAG) architecture for the CodeGraph high-performance code intelligence system. The design integrates multi-modal code understanding, optimized retrieval strategies, OpenAI API patterns, and context window optimization to deliver sub-50ms query responses for large-scale codebases. ### Key Architecture Principles - **Multi-Modal Code Understanding**: Syntax + semantics analysis through Tree-sitter + embedding models - **Hierarchical Retrieval**: Multi-level context retrieval with semantic ranking - **Context-Aware Optimization**: Dynamic context window management for large codebases - **Real-Time Performance**: Sub-50ms query latency with incremental updates - **Scalable Integration**: OpenAI API patterns with fallback and load balancing --- ## 1. RAG System Overview ### 1.1 Core Architecture Components ```mermaid graph TB subgraph "Input Layer" Q[Query Interface] CP[Code Parser] EM[Embedding Manager] end subgraph "Multi-Modal Analysis" ST[Syntax Tree Parser] SA[Semantic Analyzer] CG[Code Graph Builder] MM[Multi-Modal Embedder] end subgraph "Retrieval Engine" VS[Vector Store] GS[Graph Store] HR[Hierarchical Retriever] CR[Context Ranker] end subgraph "Context Optimization" CW[Context Window Manager] CF[Context Filter] CC[Context Compressor] end subgraph "Generation Layer" API[OpenAI API Manager] LB[Load Balancer] RC[Response Cache] QO[Query Optimizer] end Q --> CP CP --> ST CP --> SA ST --> CG SA --> MM CG --> VS MM --> VS VS --> HR HR --> CR CR --> CW CW --> CF CF --> CC CC --> API API --> LB LB --> RC ``` ### 1.2 Performance Targets - **Query Latency**: <50ms for most queries - **Indexing Speed**: <1s for incremental updates - **Memory Efficiency**: <50MB binary with embedded models - **Accuracy**: >95% relevant context retrieval - **Scalability**: Support for 10M+ lines of code --- ## 2. Code Context Retrieval Strategies ### 2.1 Hierarchical Retrieval Architecture ```rust // Core retrieval strategy interface pub trait RetrievalStrategy { type Context; type Query; async fn retrieve(&self, query: &Self::Query, limit: usize) -> Vec<Self::Context>; fn rank_relevance(&self, contexts: &[Self::Context], query: &Self::Query) -> Vec<f32>; } // Multi-level retrieval implementation pub struct HierarchicalRetriever { syntax_retriever: SyntaxRetriever, semantic_retriever: SemanticRetriever, graph_retriever: GraphRetriever, cache: RetrievalCache, } ``` ### 2.2 Retrieval Strategy Layers #### Layer 1: Syntax-Based Retrieval ```rust pub struct SyntaxRetriever { tree_sitter_index: TreeSitterIndex, ast_patterns: ASTPatternMatcher, } impl SyntaxRetriever { // Fast syntax-based filtering pub async fn filter_by_syntax(&self, query: &CodeQuery) -> Vec<SyntaxMatch> { let patterns = self.ast_patterns.extract_patterns(query); self.tree_sitter_index.search_patterns(&patterns).await } // Function signature matching pub fn match_signatures(&self, signature: &FunctionSignature) -> Vec<CodeLocation> { // Implementation for signature-based matching } } ``` #### Layer 2: Semantic Retrieval ```rust pub struct SemanticRetriever { embedding_store: VectorStore<CodeEmbedding>, semantic_index: SemanticIndex, } impl SemanticRetriever { // Dense vector similarity search pub async fn semantic_search(&self, query: &str, k: usize) -> Vec<SemanticMatch> { let query_embedding = self.embed_query(query).await; self.embedding_store.similarity_search(query_embedding, k).await } // Code intent matching pub async fn match_intent(&self, intent: &CodeIntent) -> Vec<IntentMatch> { // Implementation for intent-based retrieval } } ``` #### Layer 3: Graph-Based Retrieval ```rust pub struct GraphRetriever { code_graph: CodeGraph, dependency_index: DependencyIndex, } impl GraphRetriever { // Control flow analysis pub fn retrieve_control_flow(&self, node: &CodeNode) -> Vec<FlowPath> { self.code_graph.analyze_control_flow(node) } // Dependency traversal pub fn retrieve_dependencies(&self, symbol: &Symbol, depth: usize) -> Vec<Dependency> { self.dependency_index.traverse_dependencies(symbol, depth) } } ``` ### 2.3 Retrieval Fusion Strategy ```rust pub struct RetrievalFusion { strategies: Vec<Box<dyn RetrievalStrategy<Context=CodeContext, Query=CodeQuery>>>, weights: Vec<f32>, } impl RetrievalFusion { pub async fn fused_retrieve(&self, query: &CodeQuery, limit: usize) -> Vec<RankedContext> { let mut all_results = Vec::new(); // Parallel retrieval from all strategies let futures: Vec<_> = self.strategies.iter().enumerate() .map(|(i, strategy)| { let weight = self.weights[i]; async move { let results = strategy.retrieve(query, limit).await; let scores = strategy.rank_relevance(&results, query); results.into_iter().zip(scores).map(|(ctx, score)| { RankedContext { context: ctx, score: score * weight } }).collect::<Vec<_>>() } }).collect(); let strategy_results = futures::future::join_all(futures).await; // Merge and re-rank results for results in strategy_results { all_results.extend(results); } self.merge_and_rank(all_results, limit) } } ``` --- ## 3. Multi-Modal Code Understanding System ### 3.1 Syntax + Semantics Integration ```rust pub struct MultiModalCodeAnalyzer { syntax_analyzer: TreeSitterAnalyzer, semantic_analyzer: SemanticAnalyzer, embedding_model: Box<dyn EmbeddingModel>, fusion_layer: MultiModalFusion, } #[derive(Debug, Clone)] pub struct CodeRepresentation { // Syntax representation ast: SyntaxTree, tokens: Vec<Token>, node_types: Vec<NodeType>, // Semantic representation symbols: Vec<Symbol>, types: Vec<TypeInfo>, relationships: Vec<Relationship>, // Multi-modal embeddings syntax_embedding: Vec<f32>, semantic_embedding: Vec<f32>, fused_embedding: Vec<f32>, } ``` ### 3.2 Tree-sitter Integration ```rust pub struct TreeSitterAnalyzer { parsers: HashMap<Language, Parser>, query_cache: QueryCache, } impl TreeSitterAnalyzer { pub fn analyze_syntax(&self, code: &str, language: Language) -> SyntaxAnalysis { let parser = &self.parsers[&language]; let tree = parser.parse(code, None).unwrap(); SyntaxAnalysis { ast: tree.root_node(), functions: self.extract_functions(&tree), classes: self.extract_classes(&tree), imports: self.extract_imports(&tree), comments: self.extract_comments(&tree), complexity_metrics: self.calculate_complexity(&tree), } } pub fn extract_code_patterns(&self, tree: &Tree) -> Vec<CodePattern> { let mut patterns = Vec::new(); // Function patterns patterns.extend(self.extract_function_patterns(tree)); // Class patterns patterns.extend(self.extract_class_patterns(tree)); // Control flow patterns patterns.extend(self.extract_control_flow_patterns(tree)); patterns } } ``` ### 3.3 Semantic Analysis Layer ```rust pub struct SemanticAnalyzer { symbol_table: SymbolTable, type_checker: TypeChecker, dataflow_analyzer: DataFlowAnalyzer, } impl SemanticAnalyzer { pub fn analyze_semantics(&self, syntax: &SyntaxAnalysis) -> SemanticAnalysis { let symbols = self.symbol_table.resolve_symbols(&syntax.ast); let types = self.type_checker.infer_types(&symbols); let dataflow = self.dataflow_analyzer.analyze_flow(&symbols); SemanticAnalysis { symbols, types, dataflow, call_graph: self.build_call_graph(&symbols), variable_usage: self.analyze_variable_usage(&dataflow), } } pub fn extract_semantic_features(&self, analysis: &SemanticAnalysis) -> Vec<SemanticFeature> { let mut features = Vec::new(); // Function complexity features features.extend(self.extract_complexity_features(analysis)); // Type usage features features.extend(self.extract_type_features(analysis)); // Dependency features features.extend(self.extract_dependency_features(analysis)); features } } ``` ### 3.4 Multi-Modal Embedding Fusion ```rust pub struct MultiModalFusion { syntax_projector: nn::Linear, semantic_projector: nn::Linear, fusion_transformer: TransformerBlock, output_projector: nn::Linear, } impl MultiModalFusion { pub fn fuse_representations( &self, syntax_features: &[f32], semantic_features: &[f32], ) -> Vec<f32> { let syntax_proj = self.syntax_projector.forward(syntax_features); let semantic_proj = self.semantic_projector.forward(semantic_features); // Concatenate and transform let combined = [syntax_proj, semantic_proj].concat(); let fused = self.fusion_transformer.forward(&combined); self.output_projector.forward(&fused) } pub async fn generate_code_embedding(&self, code: &CodeRepresentation) -> Vec<f32> { self.fuse_representations( &code.syntax_embedding, &code.semantic_embedding, ) } } ``` --- ## 4. OpenAI API Integration Patterns ### 4.1 API Client Architecture ```rust pub struct OpenAIClient { client: reqwest::Client, config: OpenAIConfig, rate_limiter: RateLimiter, retry_policy: RetryPolicy, circuit_breaker: CircuitBreaker, } #[derive(Debug, Clone)] pub struct OpenAIConfig { api_key: String, base_url: String, model: String, max_tokens: u32, temperature: f32, timeout: Duration, } ``` ### 4.2 Request Optimization Patterns ```rust impl OpenAIClient { pub async fn complete_code(&self, request: CodeCompletionRequest) -> Result<CodeCompletion, OpenAIError> { // Apply circuit breaker self.circuit_breaker.call(async { // Rate limiting self.rate_limiter.acquire().await?; // Retry with exponential backoff self.retry_policy.retry(|| async { let optimized_request = self.optimize_request(&request).await?; self.send_request(optimized_request).await }).await }).await } async fn optimize_request(&self, request: &CodeCompletionRequest) -> Result<OptimizedRequest, OpenAIError> { let context = self.optimize_context(&request.context).await?; let prompt = self.optimize_prompt(&request.prompt, &context).await?; Ok(OptimizedRequest { model: self.select_optimal_model(&request).await?, prompt, context, parameters: self.optimize_parameters(&request).await?, }) } } ``` ### 4.3 Load Balancing and Fallback ```rust pub struct OpenAILoadBalancer { primary_client: OpenAIClient, fallback_clients: Vec<OpenAIClient>, health_checker: HealthChecker, metrics: LoadBalancerMetrics, } impl OpenAILoadBalancer { pub async fn execute_request(&self, request: CodeCompletionRequest) -> Result<CodeCompletion, OpenAIError> { // Try primary client first if self.health_checker.is_healthy(&self.primary_client).await { match self.primary_client.complete_code(request.clone()).await { Ok(response) => { self.metrics.record_success(&self.primary_client); return Ok(response); } Err(e) if !e.is_retryable() => return Err(e), Err(_) => { self.metrics.record_failure(&self.primary_client); } } } // Fallback to backup clients for client in &self.fallback_clients { if self.health_checker.is_healthy(client).await { match client.complete_code(request.clone()).await { Ok(response) => { self.metrics.record_success(client); return Ok(response); } Err(e) if !e.is_retryable() => continue, Err(_) => { self.metrics.record_failure(client); continue; } } } } Err(OpenAIError::AllClientsUnavailable) } } ``` ### 4.4 Response Streaming and Caching ```rust pub struct StreamingOpenAIClient { base_client: OpenAIClient, cache: ResponseCache, streaming_manager: StreamingManager, } impl StreamingOpenAIClient { pub async fn stream_completion( &self, request: CodeCompletionRequest, ) -> Result<impl Stream<Item = CompletionChunk>, OpenAIError> { // Check cache first if let Some(cached) = self.cache.get(&request).await { return Ok(self.streaming_manager.create_cached_stream(cached)); } // Create streaming request let stream = self.base_client.create_completion_stream(request.clone()).await?; // Tee stream for caching let (cached_stream, response_stream) = self.streaming_manager.tee_stream(stream); // Cache responses asynchronously tokio::spawn(async move { let collected = self.streaming_manager.collect_stream(cached_stream).await; self.cache.insert(request, collected).await; }); Ok(response_stream) } } ``` --- ## 5. Context Window Optimization for Large Codebases ### 5.1 Dynamic Context Management ```rust pub struct ContextWindowManager { max_context_size: usize, context_analyzer: ContextAnalyzer, compression_engine: ContextCompressor, prioritizer: ContextPrioritizer, } impl ContextWindowManager { pub async fn optimize_context( &self, raw_context: Vec<CodeContext>, query: &CodeQuery, ) -> OptimizedContext { // Analyze context relevance let analyzed = self.context_analyzer.analyze_relevance(&raw_context, query).await; // Prioritize context elements let prioritized = self.prioritizer.prioritize_contexts(analyzed, query).await; // Compress if necessary let compressed = if self.estimate_token_count(&prioritized) > self.max_context_size { self.compression_engine.compress_contexts(prioritized, self.max_context_size).await } else { prioritized }; OptimizedContext { contexts: compressed, total_tokens: self.estimate_token_count(&compressed), compression_ratio: self.calculate_compression_ratio(&raw_context, &compressed), } } } ``` ### 5.2 Intelligent Context Compression ```rust pub struct ContextCompressor { summarizer: CodeSummarizer, relevance_ranker: RelevanceRanker, semantic_compressor: SemanticCompressor, } #[derive(Debug, Clone)] pub enum CompressionStrategy { Truncate, Summarize, SemanticCompress, HybridCompress, } impl ContextCompressor { pub async fn compress_contexts( &self, contexts: Vec<RankedContext>, max_tokens: usize, ) -> Vec<CompressedContext> { let mut compressed = Vec::new(); let mut remaining_tokens = max_tokens; for context in contexts { let strategy = self.select_compression_strategy(&context, remaining_tokens); let compressed_context = match strategy { CompressionStrategy::Truncate => { self.truncate_context(context, remaining_tokens).await } CompressionStrategy::Summarize => { self.summarizer.summarize_context(context, remaining_tokens).await } CompressionStrategy::SemanticCompress => { self.semantic_compressor.compress(context, remaining_tokens).await } CompressionStrategy::HybridCompress => { self.hybrid_compress(context, remaining_tokens).await } }; remaining_tokens -= compressed_context.token_count; compressed.push(compressed_context); if remaining_tokens <= 0 { break; } } compressed } } ``` ### 5.3 Context Prioritization Algorithm ```rust pub struct ContextPrioritizer { relevance_scorer: RelevanceScorer, recency_scorer: RecencyScorer, complexity_scorer: ComplexityScorer, usage_scorer: UsageScorer, } #[derive(Debug, Clone)] pub struct PriorityScore { relevance: f32, recency: f32, complexity: f32, usage_frequency: f32, combined_score: f32, } impl ContextPrioritizer { pub async fn prioritize_contexts( &self, contexts: Vec<AnalyzedContext>, query: &CodeQuery, ) -> Vec<RankedContext> { let mut scored_contexts = Vec::new(); for context in contexts { let score = self.calculate_priority_score(&context, query).await; scored_contexts.push(RankedContext { context: context.context, analysis: context.analysis, priority_score: score, }); } // Sort by combined score scored_contexts.sort_by(|a, b| { b.priority_score.combined_score .partial_cmp(&a.priority_score.combined_score) .unwrap_or(std::cmp::Ordering::Equal) }); scored_contexts } async fn calculate_priority_score( &self, context: &AnalyzedContext, query: &CodeQuery, ) -> PriorityScore { let relevance = self.relevance_scorer.score(context, query).await; let recency = self.recency_scorer.score(context).await; let complexity = self.complexity_scorer.score(context).await; let usage = self.usage_scorer.score(context).await; // Weighted combination let combined = (relevance * 0.4) + (recency * 0.2) + (complexity * 0.2) + (usage * 0.2); PriorityScore { relevance, recency, complexity, usage_frequency: usage, combined_score: combined, } } } ``` --- ## 6. Integration Specifications ### 6.1 System Integration Architecture ```rust pub struct CodeGraphRAGSystem { // Core components retrieval_engine: HierarchicalRetriever, code_analyzer: MultiModalCodeAnalyzer, context_manager: ContextWindowManager, openai_client: OpenAILoadBalancer, // Storage layers vector_store: VectorStore<CodeEmbedding>, graph_store: CodeGraphStore, cache_layer: CacheLayer, // Monitoring and metrics metrics_collector: MetricsCollector, health_monitor: HealthMonitor, } impl CodeGraphRAGSystem { pub async fn process_query(&self, query: CodeQuery) -> Result<CodeIntelligenceResponse, RAGError> { // Start query processing timer let start_time = Instant::now(); // Phase 1: Multi-modal analysis let analyzed_query = self.code_analyzer.analyze_query(&query).await?; // Phase 2: Hierarchical retrieval let raw_contexts = self.retrieval_engine.retrieve_contexts(&analyzed_query).await?; // Phase 3: Context optimization let optimized_context = self.context_manager.optimize_context(raw_contexts, &query).await?; // Phase 4: LLM generation let completion_request = self.build_completion_request(&query, &optimized_context); let response = self.openai_client.execute_request(completion_request).await?; // Phase 5: Response post-processing let processed_response = self.post_process_response(response, &query).await?; // Record metrics self.metrics_collector.record_query_metrics(QueryMetrics { latency: start_time.elapsed(), context_size: optimized_context.total_tokens, compression_ratio: optimized_context.compression_ratio, response_quality: self.assess_response_quality(&processed_response).await, }); Ok(processed_response) } } ``` ### 6.2 API Integration Patterns ```rust // GraphQL API Integration pub mod graphql_api { use async_graphql::*; #[Object] impl Query { async fn code_intelligence( &self, ctx: &Context<'_>, query: String, language: Option<String>, context_limit: Option<usize>, ) -> Result<CodeIntelligenceResponse> { let rag_system = ctx.data::<CodeGraphRAGSystem>()?; let code_query = CodeQuery::new(query, language, context_limit); rag_system.process_query(code_query).await } async fn semantic_search( &self, ctx: &Context<'_>, query: String, limit: Option<usize>, ) -> Result<Vec<SemanticMatch>> { let rag_system = ctx.data::<CodeGraphRAGSystem>()?; rag_system.semantic_search(query, limit.unwrap_or(10)).await } } } // MCP Integration pub mod mcp_integration { use mcp_rs::*; #[derive(McpTool)] pub struct CodeIntelligenceTool { rag_system: Arc<CodeGraphRAGSystem>, } #[async_trait] impl McpToolExecutor for CodeIntelligenceTool { async fn execute(&self, request: ToolRequest) -> ToolResponse { match request.name.as_str() { "code_intelligence" => { let query = request.arguments.get("query").unwrap(); let result = self.rag_system.process_query( CodeQuery::from_string(query.to_string()) ).await; ToolResponse::success(serde_json::to_value(result).unwrap()) } _ => ToolResponse::error("Unknown tool".to_string()), } } } } ``` ### 6.3 Performance Monitoring Integration ```rust pub struct RAGMetricsCollector { query_latency: prometheus::HistogramVec, retrieval_accuracy: prometheus::GaugeVec, context_compression: prometheus::HistogramVec, openai_errors: prometheus::CounterVec, cache_hit_rate: prometheus::GaugeVec, } impl RAGMetricsCollector { pub fn record_query_metrics(&self, metrics: QueryMetrics) { self.query_latency .with_label_values(&["total"]) .observe(metrics.latency.as_secs_f64()); self.context_compression .with_label_values(&["ratio"]) .observe(metrics.compression_ratio); self.retrieval_accuracy .with_label_values(&["quality"]) .set(metrics.response_quality); } pub fn record_retrieval_metrics(&self, metrics: RetrievalMetrics) { self.cache_hit_rate .with_label_values(&[&metrics.cache_type]) .set(metrics.hit_rate); } } ``` --- ## 7. Deployment and Scalability ### 7.1 Single Binary Architecture ```rust // Embedded resources for single binary deployment pub struct EmbeddedResources { tree_sitter_grammars: &'static [u8], embedding_models: &'static [u8], configuration_schemas: &'static [u8], } pub struct CodeGraphBinary { rag_system: CodeGraphRAGSystem, web_server: axum::Server, grpc_server: tonic::Server, resources: EmbeddedResources, } impl CodeGraphBinary { pub async fn start(&self) -> Result<(), DeploymentError> { // Initialize embedded resources self.initialize_embedded_models().await?; // Start web server let web_handle = tokio::spawn(self.start_web_server()); // Start gRPC server let grpc_handle = tokio::spawn(self.start_grpc_server()); // Wait for both servers tokio::try_join!(web_handle, grpc_handle)?; Ok(()) } } ``` ### 7.2 Horizontal Scaling Strategy ```rust pub struct RAGClusterManager { nodes: Vec<RAGNode>, load_balancer: ClusterLoadBalancer, consensus_manager: ConsensusManager, shared_cache: DistributedCache, } impl RAGClusterManager { pub async fn distribute_query(&self, query: CodeQuery) -> Result<CodeIntelligenceResponse, ClusterError> { // Select optimal node based on current load and specialization let node = self.load_balancer.select_node(&query).await?; // Execute query with fallback match node.process_query(query.clone()).await { Ok(response) => Ok(response), Err(_) => { // Fallback to other nodes self.fallback_execution(query).await } } } } ``` --- ## 8. Implementation Roadmap ### Phase 1: Foundation (Weeks 1-2) - Tree-sitter integration for syntax analysis - Basic vector store implementation - OpenAI API client with retry logic ### Phase 2: Multi-Modal Analysis (Weeks 3-4) - Semantic analysis layer - Multi-modal embedding fusion - Context extraction algorithms ### Phase 3: Retrieval Optimization (Weeks 5-6) - Hierarchical retrieval implementation - Context window optimization - Compression algorithms ### Phase 4: Integration & Performance (Weeks 7-8) - API integration (GraphQL, MCP) - Performance optimization - Monitoring and metrics ### Phase 5: Deployment & Scaling (Weeks 9-10) - Single binary packaging - Clustering support - Production deployment --- ## 9. Success Metrics ### Performance Metrics - Query latency: <50ms (95th percentile) - Indexing speed: <1s incremental updates - Memory usage: <50MB binary - CPU efficiency: <10% idle system load ### Quality Metrics - Retrieval accuracy: >95% relevant results - Response relevance: >90% user satisfaction - Context compression: <20% information loss - API reliability: >99.9% uptime ### Scalability Metrics - Codebase size: Support 10M+ lines - Concurrent queries: 1000+ QPS - Node scaling: Linear performance scaling - Cache efficiency: >80% hit rate --- ## Conclusion This comprehensive RAG architecture provides a solid foundation for building a high-performance code intelligence system. The multi-modal approach combining syntax and semantic analysis, hierarchical retrieval strategies, and optimized OpenAI API integration patterns will deliver the sub-50ms query performance required for real-time code assistance while maintaining high accuracy and scalability.