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env.example
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yangdx
🔧 chore(env): update postgres max connections default value
29 мар 2026, 20:44
29 мар 2026, 20:44
3ed6e12
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### All configurable environment variable must show up in this sample file in active or comment out status ### Setup tool `make env-*` uses this file to generate final .env file ### These are placeholders and setup tool should not be substituted with actual values in this lines. ### Target environment of this env file: host/compose (compose is for Dokcer or Kubernetes) # LIGHTRAG_RUNTIME_TARGET=host ########################### ### Server Configuration ########################### HOST=0.0.0.0 PORT=9621 WEBUI_TITLE='My 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=150 # CORS_ORIGINS=http://localhost:3000,http://localhost:8080 ### Optional SSL Configuration ### Docker note: generated compose files mount staged certs at /app/data/certs/ inside the container # SSL=true # SSL_CERTFILE=/path/to/cert.pem # SSL_KEYFILE=/path/to/key.pem ### 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 # LIGHTRAG_PERFORMANCE_TIMING_LOGS=false ##################################### ### Login and API-Key Configuration ##################################### # AUTH_ACCOUNTS='admin:admin123,user1:{bcrypt}$2b$12$S8Yu.gCbuAbNTJFB.231gegTwr5pgrFxc8H9kXQ4/sduFBHkhM8Ka' # TOKEN_SECRET=lightrag-jwt-default-secret-key! # JWT_ALGORITHM=HS256 # TOKEN_EXPIRE_HOURS=48 # GUEST_TOKEN_EXPIRE_HOURS=24 ### Token Auto-Renewal Configuration (Sliding Window Expiration) ### Enable automatic token renewal to prevent active users from being logged out ### When enabled, tokens will be automatically renewed when remaining time < threshold # TOKEN_AUTO_RENEW=true ### Token renewal threshold (0.0 - 1.0) ### Renew token when remaining time < (total time * threshold) ### Default: 0.5 (renew when 50% time remaining) ### Examples: ### 0.5 = renew when 24h token has 12h left ### 0.25 = renew when 24h token has 6h left # TOKEN_RENEW_THRESHOLD=0.5 ### Note: Token renewal is automatically skipped for certain endpoints: ### - /health: Health check endpoint (no authentication required) ### - /documents/paginated: Frequently polled by client (5-30s interval) ### - /documents/pipeline_status: Very frequently polled by client (2s interval) ### - Rate limit: Minimum 60 seconds between renewals for same user ### 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 ### 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 ######################################################### ### Reranking configuration ### RERANK_BINDING type: null, cohere, jina, aliyun ### For rerank model deployed by vLLM use cohere binding ### If LightRAG deployed in Docker: ### uses host.docker.internal instead of localhost in RERANK_BINDING_HOST ######################################################### RERANK_BINDING=null # RERANK_MODEL=BAAI/bge-reranker-v2-m3 # RERANK_BINDING_HOST=http://localhost:8000/rerank # RERANK_BINDING_API_KEY=your_rerank_api_key_here ### 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 ### Enable rerank by default in query params when RERANK_BINDING is not null # RERANK_BY_DEFAULT=True ### 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 ### Aliyun Dashscope # # 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 ### 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 ### For local deployment Embedding and Reranker with vLLM (OpenAI-compatible API) ### Wizard metadata used to preserve the chosen deployment provider across setup reruns # LIGHTRAG_SETUP_EMBEDDING_PROVIDER=vllm # LIGHTRAG_SETUP_RERANK_PROVIDER=vllm # VLLM_EMBED_MODEL=BAAI/bge-m3 # VLLM_EMBED_PORT=8001 # VLLM_EMBED_DEVICE=cpu ### VLLM_EMBED_API_KEY is passed as --api-key to vLLM; synced to EMBEDDING_BINDING_API_KEY; auto-generated if blank # VLLM_EMBED_API_KEY= # VLLM_EMBED_EXTRA_ARGS= # VLLM_RERANK_MODEL=BAAI/bge-reranker-v2-m3 # VLLM_RERANK_PORT=8000 # VLLM_RERANK_DEVICE=cuda ### VLLM_RERANK_API_KEY is passed as --api-key to vLLM; synced to RERANK_BINDING_API_KEY; auto-generated if blank # VLLM_RERANK_API_KEY= ### Use float16 for GPU mode. CPU mode uses the official vLLM CPU image. # VLLM_USE_CPU=1 ### Set to 1 for CPU mode, unset for GPU mode # CUDA_VISIBLE_DEVICES=-1 ### Set to -1 to disable CUDA (CPU mode), or specific GPU IDs for GPU mode # NVIDIA_VISIBLE_DEVICES=0 ### Optional Docker runtime equivalent; generated GPU compose honors either variable. # VLLM_RERANK_EXTRA_ARGS= ######################################## ### Document processing configuration ######################################## ENABLE_LLM_CACHE_FOR_EXTRACT=true ### Document processing output language: English, Chinese, French, German ... SUMMARY_LANGUAGE=English ### File upload size limit (in bytes) ### Default: 104857600 (100MB) ### Set to 0 or None for unlimited upload size ### Examples: ### 52428800 = 50MB ### 104857600 = 100MB (default) ### 209715200 = 200MB ### Note: If using Nginx as reverse proxy, also configure client_max_body_size # MAX_UPLOAD_SIZE=104857600 ### 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 ### Maximum token size allowed for entity extraction input context # MAX_EXTRACT_INPUT_TOKENS=20480 ### 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 ### PDF decryption password for protected PDF files # PDF_DECRYPT_PASSWORD=your_pdf_password_here ############################### ### 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=10 ########################################################################### ### LLM Configuration ### LLM_BINDING type: openai, ollama, lollms, azure_openai, aws_bedrock, gemini ### LLM_BINDING_HOST: Service endpoint (left empty if using default endpoint provided by openai or gemini SDK) ### LLM_BINDING_API_KEY: api key ### 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=180 LLM_BINDING=openai LLM_BINDING_HOST=https://api.openai.com/v1 LLM_BINDING_API_KEY=your_api_key LLM_MODEL=gpt-5-mini ### 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 ### 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 ### Openrouter example # # LLM_BINDING=openai # # LLM_BINDING_HOST=https://openrouter.ai/api/v1 # # LLM_BINDING_API_KEY=your_api_key # # LLM_MODEL=google/gemini-2.5-flash ### Google Gemini example (AI Studio) # # LLM_BINDING=gemini # # LLM_BINDING_API_KEY=your_gemini_api_key # # LLM_BINDING_HOST=https://generativelanguage.googleapis.com # # LLM_MODEL=gemini-flash-latest ### 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 or disable thinking # GEMINI_LLM_THINKING_CONFIG='{"thinking_budget": -1, "include_thoughts": true}' # # GEMINI_LLM_THINKING_CONFIG='{"thinking_budget": 0, "include_thoughts": false}' ### Google Vertex AI example ### Vertex AI use GOOGLE_APPLICATION_CREDENTIALS instead of API-KEY for authentication ### LLM_BINDING_HOST=DEFAULT_GEMINI_ENDPOINT means select endpoit based on project and location automatically # # LLM_BINDING=gemini # # LM_BINDING_HOST=https://aiplatform.googleapis.com ### or use DEFAULT_GEMINI_ENDPOINT to select endpoint based on project and location automatically # # LLM_BINDING_HOST=DEFAULT_GEMINI_ENDPOINT # # LLM_MODEL=gemini-2.5-flash # GOOGLE_GENAI_USE_VERTEXAI=true # GOOGLE_CLOUD_PROJECT='your-project-id' # GOOGLE_CLOUD_LOCATION='us-central1' # GOOGLE_APPLICATION_CREDENTIALS='/Users/xxxxx/your-service-account-credentials-file.json' ### Ollama example # # LLM_BINDING=ollama # # LLM_BINDING_HOST=http://localhost:11434 # # LLM_MODEL=qwen3.5:9b ### 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 # OLLAMA_LLM_TEMPERATURE=0.85 ### Stop sequences for Ollama LLM # OLLAMA_LLM_STOP='["</s>", "<|EOT|>"]' ### Bedrock Specific Parameters ### Bedrock uses AWS credentials from the environment / AWS credential chain. ### It does not use LLM_BINDING_API_KEY. # # LLM_BINDING=aws_bedrock # # LLM_MODEL=anthropic.claude-3-5-sonnet-20241022-v2:0 # AWS_ACCESS_KEY_ID=your_aws_access_key_id # AWS_SECRET_ACCESS_KEY=your_aws_secret_access_key # AWS_SESSION_TOKEN=your_optional_aws_session_token # AWS_REGION=us-east-1 # 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: Service endpoint (left empty if using default endpoint provided by openai or gemini SDK) ### EMBEDDING_BINDING_API_KEY: api key ### If LightRAG deployed in Docker: ### uses host.docker.internal instead of localhost in EMBEDDING_BINDING_HOST ### Control whether to send embedding_dim parameter to embedding API ### For OpenAI: Set EMBEDDING_SEND_DIM=true to enable dynamic dimension adjustment ### For OpenAI: Set EMBEDDING_SEND_DIM=false (default) to disable sending dimension parameter ### For Gemini: Allways set EMBEDDING_SEND_DIM=true ####################################################################################### # EMBEDDING_TIMEOUT=30 ### OpenAI compatible embedding EMBEDDING_BINDING=openai EMBEDDING_BINDING_HOST=https://api.openai.com/v1 EMBEDDING_BINDING_API_KEY=your_api_key EMBEDDING_MODEL=text-embedding-3-large EMBEDDING_DIM=3072 EMBEDDING_TOKEN_LIMIT=8192 EMBEDDING_SEND_DIM=false ### Optional for Azure Embedding ### Use deployment name as model name or set AZURE_EMBEDDING_DEPLOYMENT instead # # 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 # AZURE_EMBEDDING_API_VERSION=2024-08-01-preview ### Gemini embedding # # EMBEDDING_BINDING=gemini # # EMBEDDING_MODEL=gemini-embedding-001 # # EMBEDDING_DIM=1536 # # EMBEDDING_TOKEN_LIMIT=2048 # # EMBEDDING_BINDING_HOST=https://generativelanguage.googleapis.com # # EMBEDDING_BINDING_API_KEY=your_api_key ### Gemini embedding requires sending dimension to server # # EMBEDDING_SEND_DIM=true ### Ollama embedding # # EMBEDDING_BINDING=ollama # # EMBEDDING_BINDING_HOST=http://localhost:11434 # # EMBEDDING_BINDING_API_KEY=your_api_key # # EMBEDDING_MODEL=qwen3-embedding:4b # # EMBEDDING_DIM=2560 ### 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 ### Bedrock embedding ### Bedrock uses AWS credentials from the environment / AWS credential chain. ### It does not use EMBEDDING_BINDING_API_KEY. # # EMBEDDING_BINDING=aws_bedrock # # EMBEDDING_MODEL=amazon.titan-embed-text-v2:0 # # EMBEDDING_DIM=1024 # AWS_ACCESS_KEY_ID=your_aws_access_key_id # AWS_SECRET_ACCESS_KEY=your_aws_secret_access_key # AWS_SESSION_TOKEN=your_optional_aws_session_token # AWS_REGION=us-east-1 ### 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 #################################################################### ### 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= ############################ ### Data storage selection ############################ ### Default storage: JSON/Nano/NetworkX (Recommended for test deployment) LIGHTRAG_KV_STORAGE=JsonKVStorage LIGHTRAG_DOC_STATUS_STORAGE=JsonDocStatusStorage LIGHTRAG_GRAPH_STORAGE=NetworkXStorage LIGHTRAG_VECTOR_STORAGE=NanoVectorDBStorage ### Wizard metadata used to preserve env-storage Docker deployment defaults across setup reruns # LIGHTRAG_SETUP_POSTGRES_DEPLOYMENT=docker # LIGHTRAG_SETUP_NEO4J_DEPLOYMENT=docker # LIGHTRAG_SETUP_MONGODB_DEPLOYMENT=docker # LIGHTRAG_SETUP_MONGODB_DEPLOYMENT=atlas-capable # LIGHTRAG_SETUP_REDIS_DEPLOYMENT=docker # LIGHTRAG_SETUP_MILVUS_DEPLOYMENT=docker # LIGHTRAG_SETUP_QDRANT_DEPLOYMENT=docker # LIGHTRAG_SETUP_MEMGRAPH_DEPLOYMENT=docker # LIGHTRAG_SETUP_OPENSEARCH_DEPLOYMENT=docker ### PostgreSQL Configuration POSTGRES_HOST=localhost POSTGRES_PORT=5432 POSTGRES_USER=your_username POSTGRES_PASSWORD='your_password' POSTGRES_DATABASE=rag POSTGRES_MAX_CONNECTIONS=25 ### DB specific workspace should not be set, keep for compatible only # POSTGRES_WORKSPACE=forced_workspace_name ### PostgreSQL Vector Storage Configuration ### Enable/disable vector features (default: true for backward compatibility) ### Set to false to disable pgvector extension and vector operations when using PostgreSQL ### only for KV/Graph/DocStatus storage with a different vector backend (e.g., Milvus, Qdrant) POSTGRES_ENABLE_VECTOR=true ### Use HNSW_HALFVEC for large embeddings (2000+ dim). ### Requires pgvector extension >= 0.7.0. ### Vector storage type: HNSW, HNSW_HALFVEC, IVFFlat, VCHORDRQ POSTGRES_VECTOR_INDEX_TYPE=HNSW POSTGRES_HNSW_M=16 POSTGRES_HNSW_EF=200 POSTGRES_IVFFLAT_LISTS=100 POSTGRES_VCHORDRQ_BUILD_OPTIONS= POSTGRES_VCHORDRQ_PROBES= POSTGRES_VCHORDRQ_EPSILON=1.9 ### PostgreSQL Connection Retry Configuration (Network Robustness) ### NEW DEFAULTS (v1.4.10+): Optimized for HA deployments with ~30s switchover time ### These defaults provide out-of-the-box support for PostgreSQL High Availability setups ### ### Number of retry attempts (1-100, default: 10) ### - Default 10 attempts allows ~225s total retry time (sufficient for most HA scenarios) ### - For extreme cases: increase up to 20-50 ### Initial retry backoff in seconds (0.1-300.0, default: 3.0) ### - Default 3.0s provides reasonable initial delay for switchover detection ### - For faster recovery: decrease to 1.0-2.0 ### Maximum retry backoff in seconds (must be >= backoff, max: 600.0, default: 30.0) ### - Default 30.0s matches typical switchover completion time ### - For longer switchovers: increase to 60-90 ### Connection pool close timeout in seconds (1.0-30.0, default: 5.0) # POSTGRES_CONNECTION_RETRIES=10 # POSTGRES_CONNECTION_RETRY_BACKOFF=3.0 # POSTGRES_CONNECTION_RETRY_BACKOFF_MAX=30.0 # POSTGRES_POOL_CLOSE_TIMEOUT=5.0 ### PostgreSQL SSL Configuration (Optional) # POSTGRES_SSL_MODE=require # POSTGRES_SSL_CERT=/path/to/client-cert.pem # POSTGRES_SSL_KEY=/path/to/client-key.pem # POSTGRES_SSL_ROOT_CERT=/path/to/ca-cert.pem # POSTGRES_SSL_CRL=/path/to/crl.pem ### 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=neo4j+s://xxxxxxxx.databases.neo4j.io NEO4J_USERNAME=neo4j NEO4J_PASSWORD='your_password' NEO4J_DATABASE=neo4j NEO4J_MAX_CONNECTION_POOL_SIZE=100 NEO4J_CONNECTION_TIMEOUT=30 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 ### MongoDB Configuration # For MongoVectorDBStorage, MONGO_URI must point to a MongoDB endpoint with # Atlas Search / Vector Search support, such as MongoDB Atlas or Atlas local. MONGO_URI=mongodb://localhost:27017/ MONGO_DATABASE=LightRAG ### DB specific workspace should not be set, keep for compatible only # MONGODB_WORKSPACE=forced_workspace_name # Community/local Docker MongoDB example for KV, graph, or doc-status storage only: # MONGO_URI=mongodb://localhost:27017/ ### OpenSearch Configuration ### OpenSearch can be used for all storage types: KV, Vector, Graph, DocStatus ### Connection settings (comma-separated host:port entries; do not include http:// or https://) ### This setup wizard supports authenticated OpenSearch clusters only. ### OPENSEARCH_USE_SSL controls whether those hosts are reached over TLS. OPENSEARCH_HOSTS=localhost:9200 OPENSEARCH_USER=admin OPENSEARCH_PASSWORD=LightRAG2026_!@ OPENSEARCH_USE_SSL=true OPENSEARCH_VERIFY_CERTS=false # OPENSEARCH_TIMEOUT=30 # OPENSEARCH_MAX_RETRIES=3 ### Index Settings (for 3-AZ Amazon OpenSearch Service, set replicas to 2) # OPENSEARCH_NUMBER_OF_SHARDS=1 # OPENSEARCH_NUMBER_OF_REPLICAS=0 ### k-NN Settings for Vector Storage (HNSW algorithm) # OPENSEARCH_KNN_EF_CONSTRUCTION=200 # OPENSEARCH_KNN_M=16 # OPENSEARCH_KNN_EF_SEARCH=100 ### PPL graphlookup for server-side graph traversal (auto-detected if not set) # OPENSEARCH_USE_PPL_GRAPHLOOKUP=true ### DB specific workspace should not be set, keep for compatible only # OPENSEARCH_WORKSPACE=forced_workspace_name ### Milvus Configuration MILVUS_URI=http://localhost:19530 MILVUS_DB_NAME=lightrag # MILVUS_DEVICE=cpu # MILVUS_USER=root # MILVUS_PASSWORD=your_password # MILVUS_TOKEN=your_token # Required for the bundled Docker Milvus stack; may come from .env or exported shell variables. # MINIO_ACCESS_KEY_ID=minioadmin # MINIO_SECRET_ACCESS_KEY=minioadmin ### DB specific workspace should not be set, keep for compatible only # MILVUS_WORKSPACE=forced_workspace_name ### Milvus Vector Index Configuration ### Index type: AUTOINDEX (default), HNSW, HNSW_SQ, HNSW_PQ, IVF_FLAT, IVF_SQ8, DISKANN # MILVUS_INDEX_TYPE=AUTOINDEX ### Metric type: COSINE (default), L2, IP # MILVUS_METRIC_TYPE=COSINE ### HNSW / HNSW_SQ / HNSW_PQ Parameters (aligned with Milvus 2.4+ defaults) ### M: Maximum number of connections per node [2-2048], default 16 # MILVUS_HNSW_M=16 ### efConstruction: Size of dynamic candidate list during build [8-512], default 360 # MILVUS_HNSW_EF_CONSTRUCTION=360 ### ef: Size of dynamic candidate list during search, default 200 # MILVUS_HNSW_EF=200 ### HNSW_SQ Specific Parameters (requires Milvus 2.6.8+) ### sq_type: Scalar quantization type - SQ4U, SQ6, SQ8 (default), BF16, FP16 # MILVUS_HNSW_SQ_TYPE=SQ8 ### refine: Enable refinement step for higher precision, default false # MILVUS_HNSW_SQ_REFINE=false ### refine_type: Refinement precision (must be higher than sq_type) - SQ6, SQ8, BF16, FP16, FP32 # MILVUS_HNSW_SQ_REFINE_TYPE=FP32 ### refine_k: Refinement expansion factor, default 10 # MILVUS_HNSW_SQ_REFINE_K=10 ### IVF_FLAT / IVF_SQ8 Parameters ### nlist: Number of cluster units [1-65536], recommended sqrt(n) for n>1M, default 1024 # MILVUS_IVF_NLIST=1024 ### nprobe: Number of units to query [1-nlist], default 16 # MILVUS_IVF_NPROBE=16 ### Qdrant QDRANT_URL=http://localhost:6333 # QDRANT_DEVICE=cpu # QDRANT_API_KEY=your-api-key ### Qdrant upsert batching (enabled by default) ### Split large upserts by estimated JSON payload size and point count ### Default 16MB keeps safe headroom below common 32MB gateway/request limits # QDRANT_UPSERT_MAX_PAYLOAD_BYTES=16777216 # QDRANT_UPSERT_MAX_POINTS_PER_BATCH=128 ### 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' # 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=gpt-4o-mini ### API key for LLM evaluation (fallback to OPENAI_API_KEY if not set) # EVAL_LLM_BINDING_API_KEY=your_api_key ### Custom OpenAI-compatible endpoint for LLM evaluation (optional) # EVAL_LLM_BINDING_HOST=https://api.openai.com/v1 ### Embedding Configuration for Evaluation # EVAL_EMBEDDING_MODEL=text-embedding-3-large ### API key for embeddings (fallback: EVAL_LLM_BINDING_API_KEY -> OPENAI_API_KEY) # EVAL_EMBEDDING_BINDING_API_KEY=your_embedding_api_key ### Custom OpenAI-compatible endpoint for embeddings (fallback: EVAL_LLM_BINDING_HOST) # EVAL_EMBEDDING_BINDING_HOST=https://api.openai.com/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 ########################################################################## ### ----- Preserved custom environment variables from previous .env ----- ### ----- Comments in this session will persist across regenerations ----- ### (This must be the final session; ensure the preceding lines unchanged) ########################################################################## ### Default Storage (Recommended for test deployment) # LIGHTRAG_KV_STORAGE=JsonKVStorage # LIGHTRAG_DOC_STATUS_STORAGE=JsonDocStatusStorage # LIGHTRAG_GRAPH_STORAGE=NetworkXStorage # LIGHTRAG_VECTOR_STORAGE=NanoVectorDBStorage ### Production Storage # LIGHTRAG_KV_STORAGE=RedisKVStorage # LIGHTRAG_DOC_STATUS_STORAGE=RedisDocStatusStorage # LIGHTRAG_VECTOR_STORAGE=QdrantVectorDBStorage # LIGHTRAG_GRAPH_STORAGE=MemgraphStorage ### Select OpenSearch for all storages # LIGHTRAG_KV_STORAGE=OpenSearchKVStorage # LIGHTRAG_DOC_STATUS_STORAGE=OpenSearchDocStatusStorage # LIGHTRAG_GRAPH_STORAGE=OpenSearchGraphStorage # LIGHTRAG_VECTOR_STORAGE=OpenSearchVectorDBStorage ### Select PostgreSQL for all storages # LIGHTRAG_KV_STORAGE=PGKVStorage # LIGHTRAG_DOC_STATUS_STORAGE=PGDocStatusStorage # LIGHTRAG_GRAPH_STORAGE=PGGraphStorage # LIGHTRAG_VECTOR_STORAGE=PGVectorStorage ### Select MongoDB for all storage (Vector storage requires an Atlas-capable deployment) # LIGHTRAG_KV_STORAGE=MongoKVStorage # LIGHTRAG_DOC_STATUS_STORAGE=MongoDocStatusStorage # LIGHTRAG_GRAPH_STORAGE=MongoGraphStorage # LIGHTRAG_VECTOR_STORAGE=MongoVectorDBStorage ### ----- Extra setting from previous .env -----