L'électron rare
d2fed6087a
- Introduced `mcp-server.js` to expose KXKM personas as MCP tools, supporting chat, persona listing, web search, and status checks. - Implemented `mcp-server-smoke.js` for testing the MCP server functionality, ensuring compatibility with both new and legacy message formats. - Created `setup-voice-clone.sh` for managing voice cloning environment setup, including bootstrapping, sample generation, and smoke testing. - Added `state.json` to track project status and task outputs for various batches. - Generated summary files for deep cycle and overall project status, capturing performance and security findings.
150 lines
4.8 KiB
YAML
150 lines
4.8 KiB
YAML
# =============================================================================
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# LightRAG Configuration Example for kxkm_clown
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# =============================================================================
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# Ce fichier sert de reference pour configurer LightRAG sur kxkm-ai.
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# Copier vers .env ou utiliser comme base pour docker-compose.yml
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# =============================================================================
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# ---------------------------------------------------------------------------
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# LLM Configuration (via Ollama)
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# ---------------------------------------------------------------------------
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llm:
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provider: ollama
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model: qwen3:8b
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base_url: http://localhost:11434
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# Contexte max du modele (qwen3:8b supporte 32K)
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max_context_size: 32768
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# Nombre max d'appels LLM asynchrones simultanes
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max_async: 4
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# Parametres additionnels pour la generation
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kwargs:
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temperature: 0.0
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num_predict: 2048
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# ---------------------------------------------------------------------------
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# Embedding Configuration (via Ollama)
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# ---------------------------------------------------------------------------
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embedding:
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provider: ollama
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model: nomic-embed-text
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base_url: http://localhost:11434
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# Dimension des embeddings nomic-embed-text
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embedding_dim: 768
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max_token_size: 8192
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# Nombre de textes par batch d'embedding
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batch_num: 32
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max_async: 16
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# ---------------------------------------------------------------------------
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# Storage Configuration (PostgreSQL all-in-one)
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# ---------------------------------------------------------------------------
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storage:
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# KV Storage : metadonnees documents et chunks
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kv_storage: PGKVStorage
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# Vector Storage : embeddings (requiert extension pgvector)
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vector_storage: PGVectorStorage
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# Graph Storage : graphe de connaissances (requiert extension age)
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graph_storage: PGGraphStorage
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# Document Status Storage
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doc_status_storage: PGDocStatusStorage
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postgres:
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host: localhost
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port: 5432
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database: lightrag
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user: kxkm
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password: "${LIGHTRAG_PG_PASSWORD}" # A definir dans .env
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# Creer la database si elle n'existe pas
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# CREATE DATABASE lightrag;
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# CREATE EXTENSION IF NOT EXISTS vector; -- pgvector
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# CREATE EXTENSION IF NOT EXISTS age; -- Apache AGE pour graph storage
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# ---------------------------------------------------------------------------
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# Working Directory
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# ---------------------------------------------------------------------------
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working_dir: data/lightrag
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# ---------------------------------------------------------------------------
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# RAG Parameters
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# ---------------------------------------------------------------------------
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rag:
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# Taille des chunks en tokens
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chunk_token_size: 1200
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# Overlap entre chunks
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chunk_overlap_token_size: 100
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# Nombre de boucles d'extraction d'entites (1 = standard, 2+ = plus precis mais plus lent)
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entity_extract_max_gleaning: 1
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# Langue pour l'extraction d'entites et la generation
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language: French
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# Types d'entites a extraire (adaptes au projet artistique)
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entity_types:
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- personne
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- artiste
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- oeuvre
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- concept
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- lieu
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- evenement
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- technique
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- instrument
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- mouvement_artistique
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# Cache des reponses LLM
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enable_llm_cache: true
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# ---------------------------------------------------------------------------
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# Query Defaults
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# ---------------------------------------------------------------------------
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query:
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# Mode par defaut pour les requetes
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default_mode: mix
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# Nombre de resultats top-k
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top_k: 30
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# Seuil de similarite cosine
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cosine_better_than_threshold: 0.2
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# Format de reponse
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response_type: "Multiple Paragraphs"
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# Activer le reranking (necessite un reranker configure)
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enable_rerank: false
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# ---------------------------------------------------------------------------
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# Server Configuration (lightrag-server)
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# ---------------------------------------------------------------------------
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server:
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host: 0.0.0.0
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port: 9621
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# Authentification (optionnel)
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# api_key: "${LIGHTRAG_API_KEY}"
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# ---------------------------------------------------------------------------
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# Docker Compose equivalent (.env)
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# ---------------------------------------------------------------------------
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# Pour deployer via Docker Compose, creer un fichier .env avec :
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#
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# # LLM
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# LLM_PROVIDER=ollama
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# LLM_MODEL=qwen3:8b
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# OLLAMA_HOST=http://host.docker.internal:11434
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#
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# # Embedding
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# EMBEDDING_PROVIDER=ollama
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# EMBEDDING_MODEL=nomic-embed-text
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# EMBEDDING_DIM=768
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#
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# # Storage (PostgreSQL)
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# KV_STORAGE=PGKVStorage
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# VECTOR_STORAGE=PGVectorStorage
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# GRAPH_STORAGE=PGGraphStorage
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# DOC_STATUS_STORAGE=PGDocStatusStorage
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# POSTGRES_HOST=host.docker.internal
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# POSTGRES_PORT=5432
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# POSTGRES_DATABASE=lightrag
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# POSTGRES_USER=kxkm
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# POSTGRES_PASSWORD=changeme
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#
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# # RAG
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# LANGUAGE=French
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# CHUNK_TOKEN_SIZE=1200
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# CHUNK_OVERLAP_TOKEN_SIZE=100
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#
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# # Server
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# PORT=9621
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