Update env sample file

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yangdx
2025-05-14 13:22:03 +08:00
parent c6e943976a
commit d15d3d78e3

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@@ -1,12 +1,25 @@
### This is sample file of .env
### Server Configuration
# HOST=0.0.0.0
# PORT=9621
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
WEBUI_TITLE='Graph RAG Engine'
WEBUI_DESCRIPTION="Simple and Fast Graph Based RAG System"
### Login 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/*
### Optional SSL Configuration
# SSL=true
@@ -14,11 +27,10 @@ WEBUI_DESCRIPTION="Simple and Fast Graph Based RAG System"
# SSL_KEYFILE=/path/to/key.pem
### Directory Configuration (defaults to current working directory)
# WORKING_DIR=<absolute_path_for_working_dir>
### Should be set if deploy by docker (Set by Dockerfile instead of .env)
### Default value is ./inputs and ./rag_storage
# INPUT_DIR=<absolute_path_for_doc_input_dir>
### Ollama Emulating Model Tag
# OLLAMA_EMULATING_MODEL_TAG=latest
# WORKING_DIR=<absolute_path_for_working_dir>
### Max nodes return from grap retrieval
# MAX_GRAPH_NODES=1000
@@ -39,82 +51,57 @@ WEBUI_DESCRIPTION="Simple and Fast Graph Based RAG System"
# MAX_TOKEN_RELATION_DESC=4000
# MAX_TOKEN_ENTITY_DESC=4000
### Settings for document indexing
### Entity and ralation summarization configuration
### Language: English, Chinese, French, German ...
SUMMARY_LANGUAGE=English
### Number of duplicated entities/edges to trigger LLM re-summary on merge ( at least 3 is recommented)
# FORCE_LLM_SUMMARY_ON_MERGE=6
### Max tokens for entity/relations description after merge
# MAX_TOKEN_SUMMARY=500
### Number of parallel processing documents(Less than MAX_ASYNC/2 is recommended)
# MAX_PARALLEL_INSERT=2
### Chunk size for document splitting, 500~1500 is recommended
# CHUNK_SIZE=1200
# CHUNK_OVERLAP_SIZE=100
### Number of parallel processing documents in one patch
# MAX_PARALLEL_INSERT=2
### Max tokens for entity/relations description after merge
# MAX_TOKEN_SUMMARY=500
### Number of entities/edges to trigger LLM re-summary on merge ( at least 3 is recommented)
# FORCE_LLM_SUMMARY_ON_MERGE=6
### LLM Configuration
ENABLE_LLM_CACHE=true
ENABLE_LLM_CACHE_FOR_EXTRACT=true
### 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
### Max concurrency requests of LLM
MAX_ASYNC=4
### Max tokens send to LLM for entity relation summaries (less than context size of the model)
MAX_TOKENS=32768
### LLM Binding type: openai, ollama, lollms
LLM_BINDING=openai
LLM_MODEL=gpt-4o
LLM_BINDING_HOST=https://api.openai.com/v1
LLM_BINDING_API_KEY=your_api_key
### Embedding Configuration
### Embedding Binding type: openai, ollama, lollms
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
### Num of chunks send to Embedding in single request
# EMBEDDING_BATCH_NUM=32
### Max concurrency requests for Embedding
# EMBEDDING_FUNC_MAX_ASYNC=16
### Maximum tokens sent to Embedding for each chunk (no longer in use?)
# MAX_EMBED_TOKENS=8192
### LLM Configuration
### Time out in seconds for LLM, None for infinite timeout
TIMEOUT=150
### Some models like o1-mini require temperature to be set to 1
TEMPERATURE=0.5
### Max concurrency requests of LLM
MAX_ASYNC=4
### Max tokens send to LLM (less than context size of the model)
MAX_TOKENS=32768
ENABLE_LLM_CACHE=true
ENABLE_LLM_CACHE_FOR_EXTRACT=true
### Ollama example (For local services installed with docker, you can use host.docker.internal as host)
LLM_BINDING=ollama
LLM_MODEL=mistral-nemo:latest
LLM_BINDING_API_KEY=your_api_key
LLM_BINDING_HOST=http://localhost:11434
### OpenAI alike example
# LLM_BINDING=openai
# LLM_MODEL=gpt-4o
# LLM_BINDING_HOST=https://api.openai.com/v1
# LLM_BINDING_API_KEY=your_api_key
### lollms example
# LLM_BINDING=lollms
# LLM_MODEL=mistral-nemo:latest
# LLM_BINDING_HOST=http://localhost:9600
# LLM_BINDING_API_KEY=your_api_key
### Embedding Configuration (Use valid host. For local services installed with docker, you can use host.docker.internal)
EMBEDDING_MODEL=bge-m3:latest
EMBEDDING_DIM=1024
# EMBEDDING_BINDING_API_KEY=your_api_key
### ollama example
EMBEDDING_BINDING=ollama
EMBEDDING_BINDING_HOST=http://localhost:11434
### OpenAI alike example
# EMBEDDING_BINDING=openai
# EMBEDDING_BINDING_HOST=https://api.openai.com/v1
### Lollms example
# EMBEDDING_BINDING=lollms
# EMBEDDING_BINDING_HOST=http://localhost:9600
### Optional for Azure (LLM_BINDING_HOST, LLM_BINDING_API_KEY take priority)
# AZURE_OPENAI_API_VERSION=2024-08-01-preview
# AZURE_OPENAI_DEPLOYMENT=gpt-4o
# AZURE_OPENAI_API_KEY=your_api_key
# AZURE_OPENAI_ENDPOINT=https://myendpoint.openai.azure.com
# AZURE_EMBEDDING_DEPLOYMENT=text-embedding-3-large
# AZURE_EMBEDDING_API_VERSION=2023-05-15
### Data storage selection
LIGHTRAG_KV_STORAGE=JsonKVStorage
LIGHTRAG_VECTOR_STORAGE=NanoVectorDBStorage
LIGHTRAG_GRAPH_STORAGE=NetworkXStorage
LIGHTRAG_DOC_STATUS_STORAGE=JsonDocStatusStorage
# LIGHTRAG_KV_STORAGE=PGKVStorage
# LIGHTRAG_VECTOR_STORAGE=PGVectorStorage
# LIGHTRAG_DOC_STATUS_STORAGE=PGDocStatusStorage
# LIGHTRAG_GRAPH_STORAGE=Neo4JStorage
### TiDB Configuration (Deprecated)
# TIDB_HOST=localhost
@@ -135,22 +122,22 @@ POSTGRES_MAX_CONNECTIONS=12
### separating all data from difference Lightrag instances(deprecating)
# POSTGRES_WORKSPACE=default
### Neo4j Configuration
NEO4J_URI=neo4j+s://xxxxxxxx.databases.neo4j.io
NEO4J_USERNAME=neo4j
NEO4J_PASSWORD='your_password'
### Independent AGM Configuration(not for AMG embedded in PostreSQL)
AGE_POSTGRES_DB=
AGE_POSTGRES_USER=
AGE_POSTGRES_PASSWORD=
AGE_POSTGRES_HOST=
# AGE_POSTGRES_DB=
# AGE_POSTGRES_USER=
# AGE_POSTGRES_PASSWORD=
# AGE_POSTGRES_HOST=
# AGE_POSTGRES_PORT=8529
# AGE Graph Name(apply to PostgreSQL and independent AGM)
### AGE_GRAPH_NAME is precated
# AGE_GRAPH_NAME=lightrag
### Neo4j Configuration
NEO4J_URI=neo4j+s://xxxxxxxx.databases.neo4j.io
NEO4J_USERNAME=neo4j
NEO4J_PASSWORD='your_password'
### MongoDB Configuration
MONGO_URI=mongodb://root:root@localhost:27017/
MONGO_DATABASE=LightRAG
@@ -170,14 +157,3 @@ QDRANT_URL=http://localhost:16333
### Redis
REDIS_URI=redis://localhost:6379
### For JWT Auth
# 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/*