LIGHTRAG_GRAPH_STORAGE=Neo4JStorage ### Neo4j Configuration NEO4J_URI=bolt://neo4j:7687 NEO4J_USERNAME=neo4j NEO4J_PASSWORD=Root@123 NEO4J_DATABASE=neo4j NEO4J_MAX_CONNECTION_POOL_SIZE=100 NEO4J_CONNECTION_TIMEOUT=300 NEO4J_CONNECTION_ACQUISITION_TIMEOUT=30 NEO4J_MAX_TRANSACTION_RETRY_TIME=30 NEO4J_MAX_CONNECTION_LIFETIME=300 NEO4J_LIVENESS_CHECK_TIMEOUT=30 NEO4J_KEEP_ALIVE=true ### DB specific workspace should not be set, keep for compatible only ### NEO4J_WORKSPACE=forced_workspace_name
### Milvus Configuration MILVUS_URI=http://localhost:19530 MILVUS_DB_NAME=lightrag # MILVUS_USER=root # MILVUS_PASSWORD=your_password # MILVUS_TOKEN=your_token ### DB specific workspace should not be set, keep for compatible only ### MILVUS_WORKSPACE=forced_workspace_name
### Qdrant QDRANT_URL=http://localhost:6333 # QDRANT_API_KEY=your-api-key ### DB specific workspace should not be set, keep for compatible only ### QDRANT_WORKSPACE=forced_workspace_name
### Redis REDIS_URI=redis://localhost:6379 REDIS_SOCKET_TIMEOUT=30 REDIS_CONNECT_TIMEOUT=10 REDIS_MAX_CONNECTIONS=100 REDIS_RETRY_ATTEMPTS=3 ### DB specific workspace should not be set, keep for compatible only ### REDIS_WORKSPACE=forced_workspace_name
### Memgraph Configuration MEMGRAPH_URI=bolt://localhost:7687 MEMGRAPH_USERNAME= MEMGRAPH_PASSWORD= MEMGRAPH_DATABASE=memgraph ### DB specific workspace should not be set, keep for compatible only ### MEMGRAPH_WORKSPACE=forced_workspace_name OPENAI_API_KEY=token-abc123
########################### ### Server Configuration ########################### HOST=0.0.0.0 PORT=9621 WEBUI_TITLE='Boer Graph KB' WEBUI_DESCRIPTION="Simple and Fast Graph Based RAG System" # WORKERS=2 ### gunicorn worker timeout(as default LLM request timeout if LLM_TIMEOUT is not set) TIMEOUT=15000 # CORS_ORIGINS=http://localhost:3000,http://localhost:8080
### Directory Configuration (defaults to current working directory) ### Default value is ./inputs and ./rag_storage # INPUT_DIR=<absolute_path_for_doc_input_dir> # WORKING_DIR=<absolute_path_for_working_dir>
### Tiktoken cache directory (Store cached files in this folder for offline deployment) # TIKTOKEN_CACHE_DIR=/app/data/tiktoken
### Ollama Emulating Model and Tag # OLLAMA_EMULATING_MODEL_NAME=lightrag OLLAMA_EMULATING_MODEL_TAG=latest
### Max nodes for graph retrieval (Ensure WebUI local settings are also updated, which is limited to this value) # MAX_GRAPH_NODES=1000
### Logging level # LOG_LEVEL=INFO # VERBOSE=False # LOG_MAX_BYTES=10485760 # LOG_BACKUP_COUNT=5 ### Logfile location (defaults to current working directory) # LOG_DIR=/path/to/log/directory
### API-Key to access LightRAG Server API ### Use this key in HTTP requests with the 'X-API-Key' header ### Example: curl -H "X-API-Key: your-secure-api-key-here" http://localhost:9621/query # LIGHTRAG_API_KEY=your-secure-api-key-here # WHITELIST_PATHS=/health,/api/*
###################################################################################### ### Query Configuration ### ### How to control the context length sent to LLM: ### MAX_ENTITY_TOKENS + MAX_RELATION_TOKENS < MAX_TOTAL_TOKENS ### Chunk_Tokens = MAX_TOTAL_TOKENS - Actual_Entity_Tokens - Actual_Relation_Tokens ###################################################################################### # LLM response cache for query (Not valid for streaming response) ENABLE_LLM_CACHE=true # COSINE_THRESHOLD=0.2 ### Number of entities or relations retrieved from KG # TOP_K=40 ### Maximum number or chunks for naive vector search # CHUNK_TOP_K=20 ### control the actual entities send to LLM # MAX_ENTITY_TOKENS=6000 ### control the actual relations send to LLM # MAX_RELATION_TOKENS=8000 ### control the maximum tokens send to LLM (include entities, relations and chunks) # MAX_TOTAL_TOKENS=30000
### chunk selection strategies ### VECTOR: Pick KG chunks by vector similarity, delivered chunks to the LLM aligning more closely with naive retrieval ### WEIGHT: Pick KG chunks by entity and chunk weight, delivered more solely KG related chunks to the LLM ### If reranking is enabled, the impact of chunk selection strategies will be diminished. # KG_CHUNK_PICK_METHOD=VECTOR
######################################################### ### Reranking configuration ### RERANK_BINDING type: null, cohere, jina, aliyun ### For rerank model deployed by vLLM use cohere binding ######################################################### RERANK_BINDING=null ### Enable rerank by default in query params when RERANK_BINDING is not null # RERANK_BY_DEFAULT=True ### rerank score chunk filter(set to 0.0 to keep all chunks, 0.6 or above if LLM is not strong enough) # MIN_RERANK_SCORE=0.0
### For local deployment with vLLM # RERANK_MODEL=BAAI/bge-reranker-v2-m3 # RERANK_BINDING_HOST=http://localhost:8000/v1/rerank # RERANK_BINDING_API_KEY=your_rerank_api_key_here
### Default value for Cohere AI # RERANK_MODEL=rerank-v3.5 # RERANK_BINDING_HOST=https://api.cohere.com/v2/rerank # RERANK_BINDING_API_KEY=your_rerank_api_key_here ### Cohere rerank chunking configuration (useful for models with token limits like ColBERT) # RERANK_ENABLE_CHUNKING=true # RERANK_MAX_TOKENS_PER_DOC=480
### Default value for Jina AI # RERANK_MODEL=jina-reranker-v2-base-multilingual # RERANK_BINDING_HOST=https://api.jina.ai/v1/rerank # RERANK_BINDING_API_KEY=your_rerank_api_key_here
### Default value for Aliyun # RERANK_MODEL=gte-rerank-v2 # RERANK_BINDING_HOST=https://dashscope.aliyuncs.com/api/v1/services/rerank/text-rerank/text-rerank # RERANK_BINDING_API_KEY=your_rerank_api_key_here
### PDF decryption password for protected PDF files # PDF_DECRYPT_PASSWORD=your_pdf_password_here
### Entity types that the LLM will attempt to recognize # ENTITY_TYPES='["Person", "Creature", "Organization", "Location", "Event", "Concept", "Method", "Content", "Data", "Artifact", "NaturalObject"]'
### Chunk size for document splitting, 500~1500 is recommended # CHUNK_SIZE=1200 # CHUNK_OVERLAP_SIZE=100
### Number of summary segments or tokens to trigger LLM summary on entity/relation merge (at least 3 is recommended) # FORCE_LLM_SUMMARY_ON_MERGE=8 ### Max description token size to trigger LLM summary # SUMMARY_MAX_TOKENS = 1200 ### Recommended LLM summary output length in tokens # SUMMARY_LENGTH_RECOMMENDED_=600 ### Maximum context size sent to LLM for description summary # SUMMARY_CONTEXT_SIZE=12000
### control the maximum chunk_ids stored in vector and graph db # MAX_SOURCE_IDS_PER_ENTITY=300 # MAX_SOURCE_IDS_PER_RELATION=300 ### control chunk_ids limitation method: FIFO, KEEP ### FIFO: First in first out ### KEEP: Keep oldest (less merge action and faster) # SOURCE_IDS_LIMIT_METHOD=FIFO
# Maximum number of file paths stored in entity/relation file_path field (For displayed only, does not affect query performance) # MAX_FILE_PATHS=100
### maximum number of related chunks per source entity or relation ### The chunk picker uses this value to determine the total number of chunks selected from KG(knowledge graph) ### Higher values increase re-ranking time # RELATED_CHUNK_NUMBER=5
############################### ### Concurrency Configuration ############################### ### Max concurrency requests of LLM (for both query and document processing) MAX_ASYNC=4 ### Number of parallel processing documents(between 2~10, MAX_ASYNC/3 is recommended) MAX_PARALLEL_INSERT=2 ### Max concurrency requests for Embedding # EMBEDDING_FUNC_MAX_ASYNC=8 ### Num of chunks send to Embedding in single request EMBEDDING_BATCH_NUM=25
########################################################################### ### LLM Configuration ### LLM_BINDING type: openai, ollama, lollms, azure_openai, aws_bedrock, gemini ### LLM_BINDING_HOST: host only for Ollama, endpoint for other LLM service ### If LightRAG deployed in Docker: ### uses host.docker.internal instead of localhost in LLM_BINDING_HOST ########################################################################### ### LLM request timeout setting for all llm (0 means no timeout for Ollma) LLM_TIMEOUT=3000
### Azure OpenAI example ### Use deployment name as model name or set AZURE_OPENAI_DEPLOYMENT instead # AZURE_OPENAI_API_VERSION=2024-08-01-preview # LLM_BINDING=azure_openai # LLM_BINDING_HOST=https://xxxx.openai.azure.com/ # LLM_BINDING_API_KEY=your_api_key # LLM_MODEL=my-gpt-mini-deployment
### use the following command to see all support options for OpenAI, azure_openai or OpenRouter ### lightrag-server --llm-binding gemini --help ### Gemini Specific Parameters # GEMINI_LLM_MAX_OUTPUT_TOKENS=9000 # GEMINI_LLM_TEMPERATURE=0.7 ### Enable Thinking # GEMINI_LLM_THINKING_CONFIG='{"thinking_budget": -1, "include_thoughts": true}' ### Disable Thinking # GEMINI_LLM_THINKING_CONFIG='{"thinking_budget": 0, "include_thoughts": false}'
### use the following command to see all support options for OpenAI, azure_openai or OpenRouter ### lightrag-server --llm-binding openai --help ### OpenAI Specific Parameters # OPENAI_LLM_REASONING_EFFORT=minimal ### OpenRouter Specific Parameters # OPENAI_LLM_EXTRA_BODY='{"reasoning": {"enabled": false}}' ### Qwen3 Specific Parameters deploy by vLLM # OPENAI_LLM_EXTRA_BODY='{"chat_template_kwargs": {"enable_thinking": false}}'
### OpenAI Compatible API Specific Parameters ### Increased temperature values may mitigate infinite inference loops in certain LLM, such as Qwen3-30B. # OPENAI_LLM_TEMPERATURE=0.9 ### Set the max_tokens to mitigate endless output of some LLM (less than LLM_TIMEOUT * llm_output_tokens/second, i.e. 9000 = 180s * 50 tokens/s) ### Typically, max_tokens does not include prompt content ### For vLLM/SGLang deployed models, or most of OpenAI compatible API provider # OPENAI_LLM_MAX_TOKENS=9000 ### For OpenAI o1-mini or newer modles utilizes max_completion_tokens instead of max_tokens OPENAI_LLM_MAX_COMPLETION_TOKENS=9000
### use the following command to see all support options for Ollama LLM ### lightrag-server --llm-binding ollama --help ### Ollama Server Specific Parameters ### OLLAMA_LLM_NUM_CTX must be provided, and should at least larger than MAX_TOTAL_TOKENS + 2000 OLLAMA_LLM_NUM_CTX=32768 ### Set the max_output_tokens to mitigate endless output of some LLM (less than LLM_TIMEOUT * llm_output_tokens/second, i.e. 9000 = 180s * 50 tokens/s) # OLLAMA_LLM_NUM_PREDICT=9000 ### Stop sequences for Ollama LLM # OLLAMA_LLM_STOP='["</s>", "<|EOT|>"]'
### Bedrock Specific Parameters # BEDROCK_LLM_TEMPERATURE=1.0
####################################################################################### ### Embedding Configuration (Should not be changed after the first file processed) ### EMBEDDING_BINDING: ollama, openai, azure_openai, jina, lollms, aws_bedrock ### EMBEDDING_BINDING_HOST: host only for Ollama, endpoint for other Embedding service ### If LightRAG deployed in Docker: ### uses host.docker.internal instead of localhost in EMBEDDING_BINDING_HOST ####################################################################################### # EMBEDDING_TIMEOUT=30
### Control whether to send embedding_dim parameter to embedding API ### IMPORTANT: Jina ALWAYS sends dimension parameter (API requirement) - this setting is ignored for Jina ### For OpenAI: Set to 'true' to enable dynamic dimension adjustment ### For OpenAI: Set to 'false' (default) to disable sending dimension parameter ### Note: Automatically ignored for backends that don't support dimension parameter (e.g., Ollama)
# Ollama embedding # EMBEDDING_BINDING=ollama # EMBEDDING_MODEL=bge-m3:latest # EMBEDDING_DIM=1024 # EMBEDDING_BINDING_API_KEY=your_api_key ### If LightRAG deployed in Docker uses host.docker.internal instead of localhost # EMBEDDING_BINDING_HOST=http://localhost:11434
### Optional for Azure embedding ### Use deployment name as model name or set AZURE_EMBEDDING_DEPLOYMENT instead # AZURE_EMBEDDING_API_VERSION=2024-08-01-preview # EMBEDDING_BINDING=azure_openai # EMBEDDING_BINDING_HOST=https://xxxx.openai.azure.com/ # EMBEDDING_API_KEY=your_api_key # EMBEDDING_MODEL==my-text-embedding-3-large-deployment # EMBEDDING_DIM=3072
### Jina AI Embedding # EMBEDDING_BINDING=jina # EMBEDDING_BINDING_HOST=https://api.jina.ai/v1/embeddings # EMBEDDING_MODEL=jina-embeddings-v4 # EMBEDDING_DIM=2048 # EMBEDDING_BINDING_API_KEY=your_api_key
### Optional for Ollama embedding OLLAMA_EMBEDDING_NUM_CTX=8192 ### use the following command to see all support options for Ollama embedding ### lightrag-server --embedding-binding ollama --help
#################################################################### ### WORKSPACE sets workspace name for all storage types ### for the purpose of isolating data from LightRAG instances. ### Valid workspace name constraints: a-z, A-Z, 0-9, and _ #################################################################### # WORKSPACE=space1
############################ ### Data storage selection ############################ ### Default storage (Recommended for small scale deployment) # LIGHTRAG_KV_STORAGE=JsonKVStorage # LIGHTRAG_DOC_STATUS_STORAGE=JsonDocStatusStorage # LIGHTRAG_GRAPH_STORAGE=NetworkXStorage # LIGHTRAG_VECTOR_STORAGE=NanoVectorDBStorage
### Redis Storage (Recommended for production deployment) # LIGHTRAG_KV_STORAGE=RedisKVStorage # LIGHTRAG_DOC_STATUS_STORAGE=RedisDocStatusStorage
### Vector Storage (Recommended for production deployment) # LIGHTRAG_VECTOR_STORAGE=MilvusVectorDBStorage # LIGHTRAG_VECTOR_STORAGE=QdrantVectorDBStorage # LIGHTRAG_VECTOR_STORAGE=FaissVectorDBStorage
### Graph Storage (Recommended for production deployment) LIGHTRAG_GRAPH_STORAGE=Neo4JStorage # LIGHTRAG_GRAPH_STORAGE=MemgraphStorage
### MongoDB (Vector storage only available on Atlas Cloud) # LIGHTRAG_KV_STORAGE=MongoKVStorage # LIGHTRAG_DOC_STATUS_STORAGE=MongoDocStatusStorage # LIGHTRAG_GRAPH_STORAGE=MongoGraphStorage # LIGHTRAG_VECTOR_STORAGE=MongoVectorDBStorage
### PostgreSQL Configuration #POSTGRES_HOST=localhost #POSTGRES_PORT=5432 #POSTGRES_USER=your_username #POSTGRES_PASSWORD='your_password' #POSTGRES_DATABASE=your_database #POSTGRES_MAX_CONNECTIONS=12 ### DB specific workspace should not be set, keep for compatible only ### POSTGRES_WORKSPACE=forced_workspace_name
### PostgreSQL Server Settings (for Supabase Supavisor) # Use this to pass extra options to the PostgreSQL connection string. # For Supabase, you might need to set it like this: # POSTGRES_SERVER_SETTINGS="options=reference%3D[project-ref]"
# Default is 100 set to 0 to disable # POSTGRES_STATEMENT_CACHE_SIZE=100
### Neo4j Configuration NEO4J_URI=bolt://neo4j:7687 NEO4J_USERNAME=neo4j NEO4J_PASSWORD=Root@123 NEO4J_DATABASE=neo4j NEO4J_MAX_CONNECTION_POOL_SIZE=100 NEO4J_CONNECTION_TIMEOUT=300 NEO4J_CONNECTION_ACQUISITION_TIMEOUT=30 NEO4J_MAX_TRANSACTION_RETRY_TIME=30 NEO4J_MAX_CONNECTION_LIFETIME=300 NEO4J_LIVENESS_CHECK_TIMEOUT=30 NEO4J_KEEP_ALIVE=true ### DB specific workspace should not be set, keep for compatible only ### NEO4J_WORKSPACE=forced_workspace_name
### Milvus Configuration MILVUS_URI=http://localhost:19530 MILVUS_DB_NAME=lightrag # MILVUS_USER=root # MILVUS_PASSWORD=your_password # MILVUS_TOKEN=your_token ### DB specific workspace should not be set, keep for compatible only ### MILVUS_WORKSPACE=forced_workspace_name
### Qdrant QDRANT_URL=http://localhost:6333 # QDRANT_API_KEY=your-api-key ### DB specific workspace should not be set, keep for compatible only ### QDRANT_WORKSPACE=forced_workspace_name
### Redis REDIS_URI=redis://localhost:6379 REDIS_SOCKET_TIMEOUT=30 REDIS_CONNECT_TIMEOUT=10 REDIS_MAX_CONNECTIONS=100 REDIS_RETRY_ATTEMPTS=3 ### DB specific workspace should not be set, keep for compatible only ### REDIS_WORKSPACE=forced_workspace_name
### Memgraph Configuration MEMGRAPH_URI=bolt://localhost:7687 MEMGRAPH_USERNAME= MEMGRAPH_PASSWORD= MEMGRAPH_DATABASE=memgraph ### DB specific workspace should not be set, keep for compatible only ### MEMGRAPH_WORKSPACE=forced_workspace_name
########################################################### ### Langfuse Observability Configuration ### Only works with LLM provided by OpenAI compatible API ### Install with: pip install lightrag-hku[observability] ### Sign up at: https://cloud.langfuse.com or self-host ########################################################### # LANGFUSE_SECRET_KEY="" # LANGFUSE_PUBLIC_KEY="" # LANGFUSE_HOST="https://cloud.langfuse.com" # 或您的自托管实例地址 # LANGFUSE_ENABLE_TRACE=true
############################ ### Evaluation Configuration ############################ ### RAGAS evaluation models (used for RAG quality assessment) ### ⚠️ IMPORTANT: Both LLM and Embedding endpoints MUST be OpenAI-compatible ### Default uses OpenAI models for evaluation
### LLM Configuration for Evaluation #EVAL_LLM_MODEL=Qwen2.5-32B-Instruct-AWQ ### API key for LLM evaluation (fallback to OPENAI_API_KEY if not set) #EVAL_LLM_BINDING_API_KEY=token-abc123 ### Custom OpenAI-compatible endpoint for LLM evaluation (optional) ##EVAL_LLM_BINDING_HOST=http://192.168.11.15:8000/v1 OPENAI_API_KEY=token-abc123
### Embedding Configuration for Evaluation #EVAL_EMBEDDING_MODEL=Qwen3-Embedding-0.6B ### API key for embeddings (fallback: EVAL_LLM_BINDING_API_KEY -> OPENAI_API_KEY) #EVAL_EMBEDDING_BINDING_API_KEY=token-abc123 ### Custom OpenAI-compatible endpoint for embeddings (fallback: EVAL_LLM_BINDING_HOST) #EVAL_EMBEDDING_BINDING_HOST=http://192.168.11.15:11520/v1
### Performance Tuning ### Number of concurrent test case evaluations ### Lower values reduce API rate limit issues but increase evaluation time # EVAL_MAX_CONCURRENT=2 ### TOP_K query parameter of LightRAG (default: 10) ### Number of entities or relations retrieved from KG # EVAL_QUERY_TOP_K=10 ### LLM request retry and timeout settings for evaluation # EVAL_LLM_MAX_RETRIES=5 # EVAL_LLM_TIMEOUT=180
