Merge pull request #1 from Varming73/claude/help-needed-018WdFNrzNu1oZfobErALfdP
Add .env.unraid.example for personal use
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Lars Varming
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.env.unraid.example
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.env.unraid.example
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###########################################################################################
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### LightRAG Configuration - Optimized for Books, Articles, and Podcast Transcripts
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###
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### FILL IN THE FOLLOWING PLACEHOLDERS:
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### - YOUR_OPENAI_API_KEY_HERE (for GPT-5-mini)
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### - YOUR_VOYAGE_API_KEY_HERE (for voyage-3-large embeddings)
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### - YOUR_JINA_API_KEY_HERE (for Jina reranker)
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### - Neo4j connection details (URI, username, password, database)
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### - Postgres connection details (host, port, user, password, database)
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###########################################################################################
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###########################
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### Server Configuration
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###########################
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HOST=0.0.0.0
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PORT=9621
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WEBUI_TITLE='My Graph KB'
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WEBUI_DESCRIPTION="Knowledge Graph for Books, Articles & Podcasts"
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### Worker Configuration
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### Optimized for Dell T140 (6-core Xeon E-2226G, 32GB RAM)
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WORKERS=3
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TIMEOUT=180
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### Directory Configuration
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### Uncomment and set if you want custom paths (otherwise defaults to ./inputs and ./rag_storage)
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# INPUT_DIR=/path/to/your/inputs
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# WORKING_DIR=/path/to/your/working_dir
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### Logging
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LOG_LEVEL=INFO
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# LOG_DIR=/path/to/logs
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#####################################
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### Authentication (Optional)
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#####################################
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### Uncomment if you want to secure your API
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# LIGHTRAG_API_KEY=your-secure-api-key-here
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# AUTH_ACCOUNTS='admin:admin123'
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# TOKEN_SECRET=Your-Secret-Key-For-JWT
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# TOKEN_EXPIRE_HOURS=48
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######################################################################################
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### Query Configuration
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### Optimized for long-form content (books, articles, transcripts)
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######################################################################################
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ENABLE_LLM_CACHE=true
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### Increase these for better context with long documents
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TOP_K=50
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CHUNK_TOP_K=25
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MAX_ENTITY_TOKENS=8000
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MAX_RELATION_TOKENS=10000
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MAX_TOTAL_TOKENS=40000
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### Use VECTOR method for better semantic matching
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KG_CHUNK_PICK_METHOD=VECTOR
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#########################################################
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### Reranking Configuration - JINA AI
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#########################################################
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RERANK_BINDING=jina
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RERANK_BY_DEFAULT=true
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RERANK_MODEL=jina-reranker-v2-base-multilingual
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RERANK_BINDING_HOST=https://api.jina.ai/v1/rerank
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RERANK_BINDING_API_KEY=YOUR_JINA_API_KEY_HERE
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### Keep all chunks for reranking (let Jina do the filtering)
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MIN_RERANK_SCORE=0.0
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########################################
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### Document Processing Configuration
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########################################
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ENABLE_LLM_CACHE_FOR_EXTRACT=true
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SUMMARY_LANGUAGE=English
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### Chunk size optimized for long-form content
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CHUNK_SIZE=1200
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CHUNK_OVERLAP_SIZE=100
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### Entity types optimized for mixed content (books, articles, podcasts)
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ENTITY_TYPES='["Person", "Organization", "Location", "Event", "Concept", "Method", "Theory", "Technology", "Product", "Research", "Topic"]'
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### Summary configuration
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FORCE_LLM_SUMMARY_ON_MERGE=8
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SUMMARY_MAX_TOKENS=1500
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SUMMARY_LENGTH_RECOMMENDED_=800
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SUMMARY_CONTEXT_SIZE=15000
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### Related chunks per entity/relation
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RELATED_CHUNK_NUMBER=5
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###############################
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### Concurrency Configuration
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### Optimized for Dell T140 (6-core Xeon E-2226G, 32GB RAM)
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###############################
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MAX_ASYNC=6
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MAX_PARALLEL_INSERT=2
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EMBEDDING_FUNC_MAX_ASYNC=12
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EMBEDDING_BATCH_NUM=10
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###########################################################################
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### LLM Configuration - OpenAI GPT-5-mini
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###########################################################################
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LLM_BINDING=openai
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LLM_MODEL=gpt-5-mini
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LLM_BINDING_HOST=https://api.openai.com/v1
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LLM_BINDING_API_KEY=YOUR_OPENAI_API_KEY_HERE
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LLM_TIMEOUT=180
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### GPT-5 configuration
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OPENAI_LLM_MAX_COMPLETION_TOKENS=9000
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OPENAI_LLM_TEMPERATURE=0.7
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#######################################################################################
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### Embedding Configuration - Voyage AI (voyage-3-large)
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### ⚠️ IMPORTANT: Do NOT change after processing your first file!
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#######################################################################################
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EMBEDDING_BINDING=openai
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EMBEDDING_MODEL=voyage-3-large
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EMBEDDING_DIM=2048
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EMBEDDING_SEND_DIM=false
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EMBEDDING_TOKEN_LIMIT=32000
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EMBEDDING_BINDING_HOST=https://api.voyageai.com/v1
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EMBEDDING_BINDING_API_KEY=YOUR_VOYAGE_API_KEY_HERE
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EMBEDDING_TIMEOUT=60
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####################################################################
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### Workspace Configuration (Optional)
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### Uncomment to isolate data between different LightRAG instances
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####################################################################
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# WORKSPACE=my_knowledge_base
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############################
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### Storage Configuration
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############################
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### Using Neo4j for graph + Postgres for everything else
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### Graph Storage
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LIGHTRAG_GRAPH_STORAGE=Neo4JStorage
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### Vector, KV, and Doc Status Storage (All in Postgres)
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LIGHTRAG_VECTOR_STORAGE=PGVectorStorage
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LIGHTRAG_KV_STORAGE=PGKVStorage
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LIGHTRAG_DOC_STATUS_STORAGE=PGDocStatusStorage
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###########################
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### PostgreSQL Configuration
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### FILL IN YOUR DETAILS:
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### - POSTGRES_HOST: Docker container name (e.g., 'postgres') or IP address
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### - POSTGRES_PORT: Usually 5432
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### - POSTGRES_USER: Your postgres username
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### - POSTGRES_PASSWORD: Your postgres password
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### - POSTGRES_DATABASE: Database name (e.g., 'lightrag')
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###########################
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POSTGRES_HOST=YOUR_POSTGRES_HOST_HERE
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POSTGRES_PORT=5432
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POSTGRES_USER=YOUR_POSTGRES_USER_HERE
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POSTGRES_PASSWORD='YOUR_POSTGRES_PASSWORD_HERE'
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POSTGRES_DATABASE=YOUR_POSTGRES_DATABASE_HERE
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POSTGRES_MAX_CONNECTIONS=20
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### PostgreSQL Vector Storage - HNSW index (best for quality)
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POSTGRES_VECTOR_INDEX_TYPE=HNSW
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POSTGRES_HNSW_M=16
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POSTGRES_HNSW_EF=200
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### Connection retry configuration (good for Docker networking)
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POSTGRES_CONNECTION_RETRIES=3
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POSTGRES_CONNECTION_RETRY_BACKOFF=0.5
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POSTGRES_CONNECTION_RETRY_BACKOFF_MAX=5.0
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POSTGRES_POOL_CLOSE_TIMEOUT=5.0
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###########################
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### Neo4j Configuration
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### FILL IN YOUR DETAILS:
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### - NEO4J_URI: Connection string
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### Examples:
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### bolt://neo4j:7687 (Docker, no SSL)
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### neo4j://neo4j:7687 (Docker, no SSL)
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### neo4j+s://your-instance.databases.neo4j.io (Cloud with SSL)
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### - NEO4J_USERNAME: Usually 'neo4j'
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### - NEO4J_PASSWORD: Your neo4j password
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### - NEO4J_DATABASE: Database name (usually 'neo4j')
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###########################
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NEO4J_URI=YOUR_NEO4J_URI_HERE
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NEO4J_USERNAME=neo4j
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NEO4J_PASSWORD='YOUR_NEO4J_PASSWORD_HERE'
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NEO4J_DATABASE=neo4j
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### Neo4j connection pool settings (optimized for your Dell T140)
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NEO4J_MAX_CONNECTION_POOL_SIZE=75
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NEO4J_CONNECTION_TIMEOUT=30
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NEO4J_CONNECTION_ACQUISITION_TIMEOUT=30
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NEO4J_MAX_TRANSACTION_RETRY_TIME=30
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NEO4J_MAX_CONNECTION_LIFETIME=300
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NEO4J_LIVENESS_CHECK_TIMEOUT=30
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NEO4J_KEEP_ALIVE=true
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###########################################################################################
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### WORKER CONFIGURATION - OPTIMIZED FOR YOUR DELL T140
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###########################################################################################
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### Current settings optimized for:
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### - CPU: Intel Xeon E-2226G (6 cores @ 3.40GHz)
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### - RAM: 32GB (currently 65% utilized, ~11GB free)
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### - Current load: 10-15% CPU usage
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###
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### Settings:
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### WORKERS=3 - 3 Gunicorn server processes (good for 6-core CPU)
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### MAX_ASYNC=6 - 6 concurrent LLM API requests (balanced for API limits)
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### MAX_PARALLEL_INSERT=2 - 2 documents processed in parallel (prevents API overload)
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### EMBEDDING_FUNC_MAX_ASYNC=12 - 12 concurrent embedding requests (embeddings are lighter)
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###
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### These settings utilize ~40-50% of your CPU during heavy processing, leaving plenty
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### of headroom for your other Unraid services. API rate limits are the bottleneck, not
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### your server hardware.
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###
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### If you experience OpenAI rate limit errors, reduce MAX_ASYNC to 4.
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### If your server load stays very low (<20%), you can increase MAX_ASYNC to 8.
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###########################################################################################
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###########################################################################################
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### QUICK START CHECKLIST
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###########################################################################################
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### 1. Fill in API keys:
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### - YOUR_OPENAI_API_KEY_HERE (for GPT-5-mini)
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### - YOUR_VOYAGE_API_KEY_HERE (for voyage-3-large embeddings)
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### - YOUR_JINA_API_KEY_HERE (for reranking)
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###
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### 2. Fill in Postgres details:
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### - POSTGRES_HOST (e.g., 'postgres' if Docker container name)
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### - POSTGRES_USER
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### - POSTGRES_PASSWORD
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### - POSTGRES_DATABASE (create a database named 'lightrag' or similar)
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###
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### 3. Fill in Neo4j details:
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### - NEO4J_URI (e.g., bolt://neo4j:7687 or neo4j://neo4j:7687)
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### - NEO4J_PASSWORD
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### - NEO4J_DATABASE (usually 'neo4j')
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###
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### 4. Verify your Postgres has pgvector extension enabled:
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### Run in psql: CREATE EXTENSION IF NOT EXISTS vector;
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###
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### 5. Start LightRAG and verify it connects to both databases
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###
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### 6. (Optional) Adjust WORKERS/MAX_ASYNC based on your server specs (see guide above)
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###
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### 7. (Optional) Enable authentication by uncommenting LIGHTRAG_API_KEY
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###########################################################################################
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