94 lines
2.0 KiB
Plaintext
94 lines
2.0 KiB
Plaintext
MAX_HISTORY_MESSAGES=10
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MAX_TOOL_ITERATIONS=10
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REQUEST_TIMEOUT=30
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# LLM Settings
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LLM_TEMPERATURE=0.2
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# Persistence
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DATA_STORAGE_DIR=data
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# Network configuration
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HOST=0.0.0.0
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PORT=3080
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# Build informations (Synced with pyproject.toml via bootstrap)
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ALFRED_VERSION=
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IMAGE_NAME=
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LIBRECHAT_VERSION=
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PYTHON_VERSION=
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PYTHON_VERSION_SHORT=
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RAG_VERSION=
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RUNNER=
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SERVICE_NAME=
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# --- SECURITY KEYS (CRITICAL) ---
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# These are used for session tokens and encrypting sensitive data in MongoDB.
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# If you lose these, you lose access to encrypted stored credentials.
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JWT_SECRET=
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JWT_REFRESH_SECRET=
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CREDS_KEY=
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CREDS_IV=
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# --- DATABASES (AUTO-SECURED) ---
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# Alfred uses MongoDB for application state and PostgreSQL for Vector RAG.
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# Passwords will be generated as 24-character secure tokens if left blank.
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# MongoDB (Application Data)
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MONGO_URI=
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MONGO_HOST=mongodb
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MONGO_PORT=27017
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MONGO_USER=alfred
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MONGO_PASSWORD=
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MONGO_DB_NAME=LibreChat
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# PostgreSQL (Vector Database / RAG)
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POSTGRES_URI=
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POSTGRES_HOST=vectordb
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POSTGRES_PORT=5432
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POSTGRES_USER=alfred
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POSTGRES_PASSWORD=
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POSTGRES_DB_NAME=alfred
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# --- EXTERNAL SERVICES ---
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# Media Metadata (Required)
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# Get your key at https://www.themoviedb.org/
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TMDB_API_KEY=
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TMDB_BASE_URL=https://api.themoviedb.org/3
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# qBittorrent integration
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QBITTORRENT_URL=http://qbittorrent:16140
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QBITTORRENT_USERNAME=admin
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QBITTORRENT_PASSWORD=
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QBITTORRENT_PORT=16140
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# Meilisearch
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MEILI_ENABLED=FALSE
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MEILI_NO_ANALYTICS=TRUE
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MEILI_HOST=http://meilisearch:7700
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MEILI_MASTER_KEY=
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# --- LLM CONFIGURATION ---
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# Providers: 'local', 'openai', 'anthropic', 'deepseek', 'google', 'kimi'
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DEFAULT_LLM_PROVIDER=local
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# Local LLM (Ollama)
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OLLAMA_BASE_URL=http://ollama:11434
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OLLAMA_MODEL=llama3.3:latest
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# --- API KEYS (OPTIONAL) ---
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# Fill only the ones you intend to use.
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ANTHROPIC_API_KEY=
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DEEPSEEK_API_KEY=
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GOOGLE_API_KEY=
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KIMI_API_KEY=
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OPENAI_API_KEY=
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# --- RAG ENGINE ---
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# Enable/Disable the Retrieval Augmented Generation system
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RAG_ENABLED=TRUE
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RAG_API_URL=http://rag_api:8000
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RAG_API_PORT=8000
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EMBEDDINGS_PROVIDER=ollama
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EMBEDDINGS_MODEL=nomic-embed-text
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