env.example

### 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"
OLLAMA_EMULATING_MODEL_TAG=latest
# WORKERS=2
# 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>

### Max nodes return from grap 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
########################
# LLM responde cache for query (Not valid for streaming response
ENABLE_LLM_CACHE=true
# HISTORY_TURNS=3
# COSINE_THRESHOLD=0.2
### Number of entities or relations retrieved from KG
# TOP_K=40
### Maxmium number or chunks plan to send to LLM
# CHUNK_TOP_K=10
### control the actual enties send to LLM
# MAX_ENTITY_TOKENS=10000
### control the actual relations send to LLM
# MAX_RELATION_TOKENS=10000
### control the maximum tokens send to LLM (include entities, raltions and chunks)
# MAX_TOTAL_TOKENS=32000

### Reranker configuration (Set ENABLE_RERANK to true in reranking model is configed)
ENABLE_RERANK=False
# RERANK_MODEL=BAAI/bge-reranker-v2-m3
# RERANK_BINDING_HOST=https://api.your-rerank-provider.com/v1/rerank
# RERANK_BINDING_API_KEY=your_rerank_api_key_here

########################################
### Document processing configuration
########################################
### Language: English, Chinese, French, German ...
SUMMARY_LANGUAGE=English
ENABLE_LLM_CACHE_FOR_EXTRACT=true
### MAX_TOKENS: max tokens send to LLM for entity relation summaries (less than context size of the model)
MAX_TOKENS=32000
### Chunk size for document splitting, 500~1500 is recommended
# CHUNK_SIZE=1200
# CHUNK_OVERLAP_SIZE=100
### Entity and relation summarization configuration
### Number of duplicated entities/edges to trigger LLM re-summary on merge ( at least 3 is recommented)
# FORCE_LLM_SUMMARY_ON_MERGE=4
### Maximum number of entity extraction attempts for ambiguous content
# MAX_GLEANING=1

###############################
### 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/4 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
#######################
### Time out in seconds for LLM, None for infinite timeout
TIMEOUT=240
### Some models like o1-mini require temperature to be set to 1
TEMPERATURE=0
### LLM Binding type: openai, ollama, lollms, azure_openai
LLM_BINDING=openai
LLM_MODEL=gpt-4o
LLM_BINDING_HOST=https://api.openai.com/v1
LLM_BINDING_API_KEY=your_api_key

### Set as num_ctx option for Ollama LLM
# OLLAMA_NUM_CTX=32768

### Optional for Azure
# AZURE_OPENAI_API_VERSION=2024-08-01-preview
# AZURE_OPENAI_DEPLOYMENT=gpt-4o

####################################################################################
### Embedding Configuration (Should not be changed after the first file processed)
####################################################################################
### Embedding Binding type: openai, ollama, lollms, azure_openai
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
### Maximum tokens sent to Embedding for each chunk (no longer in use?)
# MAX_EMBED_TOKENS=8192

### 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

############################
### 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

####################################################################
### WORKSPACE setting workspace name for all storage types
### in the purpose of isolating data from LightRAG instances.
### Valid workspace name constraints: a-z, A-Z, 0-9, and _
####################################################################
# WORKSPACE=space1

### 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

### Neo4j Configuration
NEO4J_URI=neo4j+s://xxxxxxxx.databases.neo4j.io
NEO4J_USERNAME=neo4j
NEO4J_PASSWORD='your_password'
# 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_WORKSPACE=forced_workspace_name

### Memgraph Configuration
MEMGRAPH_URI=bolt://localhost:7687
MEMGRAPH_USERNAME=
MEMGRAPH_PASSWORD=
MEMGRAPH_DATABASE=memgraph
# MEMGRAPH_WORKSPACE=forced_workspace_name