Obsidian Elite RAG MCP Server

# Multi-Layer RAG System Implementation ## System Architecture ### Overview The elite RAG system implements 5 distinct retrieval layers that work together to provide contextually rich, accurate, and relevant responses for different types of queries and use cases. ## Layer 1: Semantic Context Retrieval ### Purpose Find conceptually similar content using vector embeddings and semantic similarity. ### Implementation ```python # Core semantic search with multiple embedding models class SemanticRetriever: def __init__(self): self.models = { 'general': 'text-embedding-3-large', 'technical': 'text-embedding-ada-002', 'domain_specific': 'custom-trained-embeddings' } def retrieve(self, query: str, context_type: str = 'general') -> List[Document]: model = self.models[context_type] # Hybrid semantic + keyword search semantic_results = self.vector_search(query, model) keyword_results = self.bm25_search(query) return self.rerank(semantic_results + keyword_results, query) ``` ### Retrieval Strategies - **Hybrid Search**: Combine semantic similarity with keyword matching - **Domain-Aware**: Use specialized embeddings for different knowledge domains - **Context-Weighted**: Prioritize recent and frequently accessed content ## Layer 2: Graph Traversal Retrieval ### Purpose Expand context by following links and relationships in the knowledge graph. ### Implementation ```python class GraphRetriever: def __init__(self): self.graph = self.build_knowledge_graph() def retrieve(self, seed_documents: List[Document], depth: int = 2) -> List[Document]: expanded_context = [] for doc in seed_documents: # Follow direct links linked_docs = self.get_linked_documents(doc) # Find related concepts through co-occurrence related_docs = self.find_related_concepts(doc) # Expand based on tag similarity tag_similar = self.find_tag_similar(doc) expanded_context.extend(linked_docs + related_docs + tag_similar) return self.rank_by_relevance(expanded_context, seed_documents) ``` ### Traversal Strategies - **Direct Links**: Follow explicit `[[link]]` connections - **Backlink Expansion**: Include documents that link to the source - **Tag Clustering**: Group by shared tags and contexts - **Temporal Proximity**: Include recently accessed related content ## Layer 3: Temporal Context Retrieval ### Purpose Ensure context is temporally relevant and evolution-aware. ### Implementation ```python class TemporalRetriever: def retrieve(self, query: str, base_documents: List[Document]) -> List[Document]: # Get historical context historical = self.get_historical_context(query, base_documents) # Get recent developments recent = self.get_recent_developments(query) # Get evolution timeline timeline = self.build_evolution_timeline(query) return self.synthesize_temporal_context(historical, recent, timeline) ``` ### Temporal Strategies - **Recency Bias**: Prioritize recent content for fast-moving domains - **Historical Context**: Include evolution and background for deep understanding - **Temporal Patterns**: Identify recurring themes and cycles - **Future Projections**: Include forward-looking content and plans ## Layer 4: Domain Specialization ### Purpose Apply domain-specific retrieval strategies and context weighting. ### Implementation ```python class DomainSpecializedRetriever: def __init__(self): self.domain_configs = { 'technical': { 'priority_tags': ['#code', '#implementation', '#architecture'], 'context_weight': 0.8, 'recency_bias': 0.6 }, 'research': { 'priority_tags': ['#paper', '#methodology', '#results'], 'context_weight': 0.9, 'recency_bias': 0.4 }, 'workflow': { 'priority_tags': ['#process', '#automation', '#tool'], 'context_weight': 0.7, 'recency_bias': 0.3 } } def retrieve(self, query: str, domain: str, base_docs: List[Document]) -> List[Document]: config = self.domain_configs[domain] return self.apply_domain_filtering(base_docs, config) ``` ### Domain Strategies - **Technical**: Prioritize implementation details and code examples - **Research**: Emphasize methodology, evidence, and citations - **Workflow**: Focus on processes, tools, and automation - **Learning**: Include explanations, examples, and practice materials ## Layer 5: Meta-Knowledge Retrieval ### Purpose Retrieve knowledge about knowledge - patterns, methodologies, and learning insights. ### Implementation ```python class MetaKnowledgeRetriever: def retrieve(self, query: str, context_summary: str) -> List[Document]: # Find patterns and methodologies patterns = self.find_patterns(query) # Get learning insights insights = self.get_learning_insights(query) # Find similar problems/solutions analogies = self.find_analogical_cases(query) return self.synthesize_meta_context(patterns, insights, analogies) ``` ### Meta-Knowledge Types - **Learning Patterns**: How similar concepts were learned - **Methodology Templates**: Proven approaches and frameworks - **Problem-Solution Patterns**: Recurring issue resolutions - **Insight Synthesis**: Higher-level abstractions and connections ## Query Classification and Routing ### Intelligent Query Routing ```python class QueryRouter: def route_query(self, query: str, context: Dict) -> List[str]: query_type = self.classify_query(query, context) routing_map = { 'factual_lookup': ['semantic', 'domain'], 'conceptual_understanding': ['semantic', 'graph', 'meta'], 'problem_solving': ['semantic', 'graph', 'temporal', 'domain', 'meta'], 'creative_synthesis': ['graph', 'temporal', 'meta'], 'procedural_guidance': ['semantic', 'domain', 'workflow'] } return routing_map.get(query_type, ['semantic', 'graph']) ``` ### Query Types - **Factual Lookup**: Simple information retrieval - **Conceptual Understanding**: Deep explanation of concepts - **Problem Solving**: Complex issue resolution - **Creative Synthesis**: Novel idea generation - **Procedural Guidance**: Step-by-step instructions ## Context Synthesis and Ranking ### Multi-Source Context Fusion ```python class ContextSynthesizer: def synthesize_context(self, layer_results: Dict[str, List[Document]], query: str) -> str: # Weight by relevance and recency weighted_docs = self.weight_documents(layer_results) # Remove redundancy deduplicated = self.deduplicate(weighted_docs) # Organize by theme organized = self.organize_by_theme(deduplicated) # Generate coherent context return self.generate_coherent_context(organized, query) ``` ### Ranking Algorithm 1. **Semantic Relevance**: Query-document similarity 2. **Graph Centrality**: Importance in knowledge network 3. **Temporal Freshness**: Recency and relevance 4. **Domain Authority**: Expertise level and source quality 5. **Context Coverage**: Diversity and completeness ## Performance Optimization ### Caching Strategy - **Query Cache**: Store results for similar queries - **Document Cache**: Pre-compute embeddings and links - **Graph Cache**: Cache frequently traversed paths - **Context Cache**: Store synthesized contexts ### Index Optimization - **Vector Index**: Optimized for semantic search - **Link Index**: Fast graph traversal - **Tag Index**: Multi-dimensional tag queries - **Temporal Index**: Time-based access patterns This multi-layer RAG system ensures comprehensive, contextually relevant, and highly accurate knowledge retrieval by combining semantic understanding, graph relationships, temporal awareness, domain expertise, and meta-knowledge synthesis.