Add Knowledge Base Hybrid RAG MCP Server

servers/knowledge-base-hybrid-rag/readme.md

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+# Knowledge Base Hybrid RAG MCP Server
+
+A powerful MCP server providing **37 tools** for Hybrid RAG (Retrieval-Augmented Generation) with PostgreSQL/pgvector.
+
+## Features
+
+- **Hybrid RAG Search**: Semantic + Keyword search with configurable weights
+- **LLM Reranking**: +5-10% accuracy improvement
+- **In-Memory Caching**: 2-3x speed improvement
+- **Knowledge Graph**: Entity/relation-based knowledge management
+- **Auto Entity Extraction**: LLM-based NER with automatic KG save
+- **Conversational Memory**: Session-based memory for conversational RAG
+- **Multiple Chunking Strategies**: fixed, paragraph, semantic
+- **Evaluation Metrics**: Precision, Recall, F1, MRR
+
+## Documentation
+
+Full documentation: https://github.com/hwandam77/Knowledge-Base-Hybrid-RAG
+
+## Requirements
+
+- PostgreSQL with pgvector extension
+- Ollama with embedding model (qwen3-embedding:0.6b)
+- Ollama with LLM model (gemma3:12b or similar)
+
+## License
+
+MIT License

servers/knowledge-base-hybrid-rag/server.yaml

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+name: knowledge-base-hybrid-rag
+image: mcp/knowledge-base-hybrid-rag
+type: server
+meta:
+  category: database
+  tags:
+    - database
+    - rag
+    - knowledge-graph
+    - vector-search
+    - postgresql
+    - hybrid-search
+    - semantic-search
+    - ai
+about:
+  title: Knowledge Base Hybrid RAG
+  description: A powerful MCP server providing 37 tools for Hybrid RAG (Retrieval-Augmented Generation) with PostgreSQL/pgvector. Features include semantic + keyword search, LLM-based reranking (+5-10% accuracy), in-memory caching (2-3x speed), Knowledge Graph with entity extraction, conversational memory, multiple chunking strategies, and search quality evaluation metrics (Precision, Recall, F1, MRR).
+  icon: https://www.google.com/s2/favicons?domain=github.com&sz=64
+source:
+  project: https://github.com/hwandam77/Knowledge-Base-Hybrid-RAG
+  branch: main
+config:
+  description: Configure PostgreSQL database and Ollama LLM connection for Knowledge Base RAG
+  secrets:
+    - name: knowledge-base-hybrid-rag.db_password
+      env: DB_PASSWORD
+      example: your_secure_password
+  env:
+    - name: DB_HOST
+      example: "10.5.5.10"
+      value: "{{knowledge-base-hybrid-rag.db_host}}"
+    - name: DB_PORT
+      example: "5432"
+      value: "{{knowledge-base-hybrid-rag.db_port}}"
+    - name: DB_NAME
+      example: "knowledge_base_db"
+      value: "{{knowledge-base-hybrid-rag.db_name}}"
+    - name: DB_USER
+      example: "postgres"
+      value: "{{knowledge-base-hybrid-rag.db_user}}"
+    - name: LOCAL_OLLAMA_URL
+      example: "http://host.docker.internal:11434"
+      value: "{{knowledge-base-hybrid-rag.ollama_url}}"
+    - name: EMBEDDING_MODEL
+      example: "qwen3-embedding:0.6b"
+      value: "{{knowledge-base-hybrid-rag.embedding_model}}"
+    - name: LLM_MODEL
+      example: "gemma3:12b"
+      value: "{{knowledge-base-hybrid-rag.llm_model}}"
+    - name: KB_PROJECT
+      example: "default"
+      value: "{{knowledge-base-hybrid-rag.kb_project}}"
+  parameters:
+    type: object
+    properties:
+      db_host:
+        type: string
+        description: PostgreSQL host address
+      db_port:
+        type: string
+        description: PostgreSQL port
+      db_name:
+        type: string
+        description: Database name
+      db_user:
+        type: string
+        description: Database username
+      ollama_url:
+        type: string
+        description: Ollama API endpoint URL
+      embedding_model:
+        type: string
+        description: Ollama embedding model name
+      llm_model:
+        type: string
+        description: Ollama LLM model name
+      kb_project:
+        type: string
+        description: Project isolation key
+    required:
+      - db_host

servers/knowledge-base-hybrid-rag/tools.json

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+[
+  {
+    "name": "kb_health_check",
+    "description": "Check system health status (DB, Ollama connection)"
+  },
+  {
+    "name": "kb_search",
+    "description": "Basic vector search for documents"
+  },
+  {
+    "name": "kb_search_hybrid",
+    "description": "Hybrid search combining semantic + keyword search"
+  },
+  {
+    "name": "kb_search_hybrid_v2",
+    "description": "Enhanced hybrid search with adjustable weights"
+  },
+  {
+    "name": "kb_search_semantic",
+    "description": "Pure semantic vector search"
+  },
+  {
+    "name": "kb_document_search_within",
+    "description": "Search within a specific document"
+  },
+  {
+    "name": "kb_ask",
+    "description": "RAG-based Q&A with LLM"
+  },
+  {
+    "name": "kb_summarize",
+    "description": "Auto-summarize documents using LLM"
+  },
+  {
+    "name": "kb_auto_tag",
+    "description": "Auto-extract tags from documents using LLM"
+  },
+  {
+    "name": "kb_image_analyze",
+    "description": "Analyze images using Vision model"
+  },
+  {
+    "name": "kb_embedding_generate",
+    "description": "Generate embedding vectors for text"
+  },
+  {
+    "name": "kb_document_create",
+    "description": "Create a new document"
+  },
+  {
+    "name": "kb_document_get",
+    "description": "Get full document content"
+  },
+  {
+    "name": "kb_document_get_meta",
+    "description": "Get document metadata only"
+  },
+  {
+    "name": "kb_document_get_chunk",
+    "description": "Get document by chunk"
+  },
+  {
+    "name": "kb_document_list",
+    "description": "List all documents"
+  },
+  {
+    "name": "kb_document_update",
+    "description": "Update document content"
+  },
+  {
+    "name": "kb_document_delete",
+    "description": "Delete a document"
+  },
+  {
+    "name": "kg_create_entities",
+    "description": "Create entities in Knowledge Graph"
+  },
+  {
+    "name": "kg_create_relations",
+    "description": "Create relations between entities"
+  },
+  {
+    "name": "kg_add_observations",
+    "description": "Add observations to entities"
+  },
+  {
+    "name": "kg_add_tags",
+    "description": "Add tags to entities"
+  },
+  {
+    "name": "kg_search_knowledge",
+    "description": "Search Knowledge Graph (fuzzy/exact)"
+  },
+  {
+    "name": "kg_read_graph",
+    "description": "Read entire Knowledge Graph"
+  },
+  {
+    "name": "unified_search",
+    "description": "Search both documents and Knowledge Graph"
+  },
+  {
+    "name": "kb_search_with_rerank",
+    "description": "Hybrid search with LLM-based reranking for improved accuracy (+5-10%)"
+  },
+  {
+    "name": "kb_rerank",
+    "description": "Rerank existing search results using LLM relevance scoring"
+  },
+  {
+    "name": "kb_cache_stats",
+    "description": "Get cache statistics (hit rate, size, memory usage)"
+  },
+  {
+    "name": "kb_cache_clear",
+    "description": "Clear cache (search, embedding, or all)"
+  },
+  {
+    "name": "kb_extract_entities",
+    "description": "Extract entities from document using LLM-based NER"
+  },
+  {
+    "name": "kb_extract_and_save",
+    "description": "Extract entities and automatically save to Knowledge Graph"
+  },
+  {
+    "name": "kb_memory_save",
+    "description": "Save content to session memory for conversational RAG"
+  },
+  {
+    "name": "kb_memory_recall",
+    "description": "Recall relevant memories from session using semantic search"
+  },
+  {
+    "name": "kb_memory_clear",
+    "description": "Clear all memories for a session"
+  },
+  {
+    "name": "kb_document_create_chunked",
+    "description": "Create document with advanced chunking strategy (fixed/paragraph/semantic)"
+  },
+  {
+    "name": "kb_evaluate_search",
+    "description": "Evaluate search quality with precision, recall, F1, MRR metrics"
+  },
+  {
+    "name": "kb_benchmark",
+    "description": "Run performance benchmark on search operations"
+  }
+]