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.env

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+### This is sample file of .env
+
+###########################
+### 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
+# 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 return from graph retrieval in webui
+# 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
+
+#####################################
+### Login and API-Key Configuration
+#####################################
+# AUTH_ACCOUNTS='admin:admin123,user1:pass456'
+# TOKEN_SECRET=Your-Key-For-LightRAG-API-Server
+# TOKEN_EXPIRE_HOURS=48
+# GUEST_TOKEN_EXPIRE_HOURS=24
+# JWT_ALGORITHM=HS256
+
+### API-Key to access LightRAG Server API
+# 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
+
+### 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
+
+### 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
+
+### 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
+
+########################################
+### Document processing configuration
+########################################
+ENABLE_LLM_CACHE_FOR_EXTRACT=true
+
+### Document processing output language: English, Chinese, French, German ...
+SUMMARY_LANGUAGE=English
+
+### 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
+
+###############################
+### 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
+###########################################################
+### LLM request timeout setting for all llm (0 means no timeout for Ollma)
+# LLM_TIMEOUT=180
+
+LLM_BINDING=openai
+LLM_MODEL=gpt-4o
+LLM_BINDING_HOST=https://api.openai.com/v1
+LLM_BINDING_API_KEY=your_api_key
+
+### Optional for Azure
+# AZURE_OPENAI_API_VERSION=2024-08-01-preview
+# AZURE_OPENAI_DEPLOYMENT=gpt-4o
+
+### Openrouter example
+# LLM_MODEL=google/gemini-2.5-flash
+# LLM_BINDING_HOST=https://openrouter.ai/api/v1
+# LLM_BINDING_API_KEY=your_api_key
+# LLM_BINDING=openai
+
+### 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, though some models, such as Gemini Models, are exceptions
+### For vLLM/SGLang deployed models, or most of OpenAI compatible API provider
+# OPENAI_LLM_MAX_TOKENS=9000
+### For OpenAI o1-mini or newer modles
+OPENAI_LLM_MAX_COMPLETION_TOKENS=9000
+
+#### OpenAI's new API utilizes max_completion_tokens instead of max_tokens
+# OPENAI_LLM_MAX_COMPLETION_TOKENS=9000
+
+### 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}}'
+
+### 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_TIMEOUT=30
+EMBEDDING_BINDING=ollama
+EMBEDDING_MODEL=bge-m3:latest
+EMBEDDING_DIM=1024
+EMBEDDING_BINDING_API_KEY=your_api_key
+# If the embedding service is deployed within the same Docker stack, use host.docker.internal instead of localhost
+EMBEDDING_BINDING_HOST=http://localhost:11434
+
+### OpenAI compatible (VoyageAI embedding openai compatible)
+# EMBEDDING_BINDING=openai
+# EMBEDDING_MODEL=text-embedding-3-large
+# EMBEDDING_DIM=3072
+# EMBEDDING_BINDING_HOST=https://api.openai.com/v1
+# EMBEDDING_BINDING_API_KEY=your_api_key
+
+### Optional for Azure
+# AZURE_EMBEDDING_DEPLOYMENT=text-embedding-3-large
+# AZURE_EMBEDDING_API_VERSION=2023-05-15
+# AZURE_EMBEDDING_ENDPOINT=your_endpoint
+# AZURE_EMBEDDING_API_KEY=your_api_key
+
+### 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
+
+### PostgreSQL
+# LIGHTRAG_KV_STORAGE=PGKVStorage
+# LIGHTRAG_DOC_STATUS_STORAGE=PGDocStatusStorage
+# LIGHTRAG_GRAPH_STORAGE=PGGraphStorage
+# LIGHTRAG_VECTOR_STORAGE=PGVectorStorage
+
+### 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
+# POSTGRES_WORKSPACE=forced_workspace_name
+
+### PostgreSQL Vector Storage Configuration
+### Vector storage type: HNSW, IVFFlat
+POSTGRES_VECTOR_INDEX_TYPE=HNSW
+POSTGRES_HNSW_M=16
+POSTGRES_HNSW_EF=200
+POSTGRES_IVFFLAT_LISTS=100
+
+### PostgreSQL Connection Retry Configuration (Network Robustness)
+### Number of retry attempts (1-10, default: 3)
+### Initial retry backoff in seconds (0.1-5.0, default: 0.5)
+### Maximum retry backoff in seconds (backoff-60.0, default: 5.0)
+### Connection pool close timeout in seconds (1.0-30.0, default: 5.0)
+# POSTGRES_CONNECTION_RETRIES=3
+# POSTGRES_CONNECTION_RETRY_BACKOFF=0.5
+# POSTGRES_CONNECTION_RETRY_BACKOFF_MAX=5.0
+# 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
+# NEO4J_WORKSPACE=forced_workspace_name
+
+### MongoDB Configuration
+MONGO_URI=mongodb://root:root@localhost:27017/
+#MONGO_URI=mongodb+srv://xxxx
+MONGO_DATABASE=LightRAG
+# MONGODB_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
+# MILVUS_WORKSPACE=forced_workspace_name
+
+### Qdrant
+QDRANT_URL=http://localhost:6333
+# QDRANT_API_KEY=your-api-key
+# 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
+# REDIS_WORKSPACE=forced_workspace_name
+
+### Memgraph Configuration
+MEMGRAPH_URI=bolt://localhost:7687
+MEMGRAPH_USERNAME=
+MEMGRAPH_PASSWORD=
+MEMGRAPH_DATABASE=memgraph
+# MEMGRAPH_WORKSPACE=forced_workspace_name