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Raphaël MANSUY
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1 changed files with 47 additions and 150 deletions
197
env.example
197
env.example
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@ -23,13 +23,13 @@ WEBUI_DESCRIPTION="Simple and Fast Graph Based RAG System"
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# WORKING_DIR=<absolute_path_for_working_dir>
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### Tiktoken cache directory (Store cached files in this folder for offline deployment)
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# TIKTOKEN_CACHE_DIR=/app/data/tiktoken
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# TIKTOKEN_CACHE_DIR=./temp/tiktoken
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### Ollama Emulating Model and Tag
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# OLLAMA_EMULATING_MODEL_NAME=lightrag
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OLLAMA_EMULATING_MODEL_TAG=latest
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### Max nodes for graph retrieval (Ensure WebUI local settings are also updated, which is limited to this value)
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### Max nodes return from graph retrieval in webui
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# MAX_GRAPH_NODES=1000
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### Logging level
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@ -50,8 +50,6 @@ OLLAMA_EMULATING_MODEL_TAG=latest
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# JWT_ALGORITHM=HS256
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### API-Key to access LightRAG Server API
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### Use this key in HTTP requests with the 'X-API-Key' header
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### Example: curl -H "X-API-Key: your-secure-api-key-here" http://localhost:9621/query
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# LIGHTRAG_API_KEY=your-secure-api-key-here
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# WHITELIST_PATHS=/health,/api/*
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@ -76,6 +74,11 @@ ENABLE_LLM_CACHE=true
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### control the maximum tokens send to LLM (include entities, relations and chunks)
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# MAX_TOTAL_TOKENS=30000
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### maximum number of related chunks per source entity or relation
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### The chunk picker uses this value to determine the total number of chunks selected from KG(knowledge graph)
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### Higher values increase re-ranking time
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# RELATED_CHUNK_NUMBER=5
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### chunk selection strategies
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### VECTOR: Pick KG chunks by vector similarity, delivered chunks to the LLM aligning more closely with naive retrieval
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### WEIGHT: Pick KG chunks by entity and chunk weight, delivered more solely KG related chunks to the LLM
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@ -121,9 +124,6 @@ ENABLE_LLM_CACHE_FOR_EXTRACT=true
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### Document processing output language: English, Chinese, French, German ...
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SUMMARY_LANGUAGE=English
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### PDF decryption password for protected PDF files
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# PDF_DECRYPT_PASSWORD=your_pdf_password_here
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### Entity types that the LLM will attempt to recognize
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# ENTITY_TYPES='["Person", "Creature", "Organization", "Location", "Event", "Concept", "Method", "Content", "Data", "Artifact", "NaturalObject"]'
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@ -140,22 +140,6 @@ SUMMARY_LANGUAGE=English
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### Maximum context size sent to LLM for description summary
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# SUMMARY_CONTEXT_SIZE=12000
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### control the maximum chunk_ids stored in vector and graph db
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# MAX_SOURCE_IDS_PER_ENTITY=300
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# MAX_SOURCE_IDS_PER_RELATION=300
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### control chunk_ids limitation method: FIFO, KEEP
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### FIFO: First in first out
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### KEEP: Keep oldest (less merge action and faster)
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# SOURCE_IDS_LIMIT_METHOD=FIFO
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# Maximum number of file paths stored in entity/relation file_path field (For displayed only, does not affect query performance)
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# MAX_FILE_PATHS=100
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### maximum number of related chunks per source entity or relation
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### The chunk picker uses this value to determine the total number of chunks selected from KG(knowledge graph)
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### Higher values increase re-ranking time
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# RELATED_CHUNK_NUMBER=5
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###############################
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### Concurrency Configuration
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###############################
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@ -168,13 +152,10 @@ MAX_PARALLEL_INSERT=2
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### Num of chunks send to Embedding in single request
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# EMBEDDING_BATCH_NUM=10
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###########################################################################
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###########################################################
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### LLM Configuration
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### LLM_BINDING type: openai, ollama, lollms, azure_openai, aws_bedrock, gemini
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### LLM_BINDING_HOST: host only for Ollama, endpoint for other LLM service
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### If LightRAG deployed in Docker:
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### uses host.docker.internal instead of localhost in LLM_BINDING_HOST
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###########################################################################
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### LLM_BINDING type: openai, ollama, lollms, azure_openai, aws_bedrock
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###########################################################
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### LLM request timeout setting for all llm (0 means no timeout for Ollma)
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# LLM_TIMEOUT=180
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@ -183,13 +164,9 @@ LLM_MODEL=gpt-4o
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LLM_BINDING_HOST=https://api.openai.com/v1
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LLM_BINDING_API_KEY=your_api_key
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### Azure OpenAI example
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### Use deployment name as model name or set AZURE_OPENAI_DEPLOYMENT instead
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### Optional for Azure
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# AZURE_OPENAI_API_VERSION=2024-08-01-preview
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# LLM_BINDING=azure_openai
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# LLM_BINDING_HOST=https://xxxx.openai.azure.com/
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# LLM_BINDING_API_KEY=your_api_key
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# LLM_MODEL=my-gpt-mini-deployment
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# AZURE_OPENAI_DEPLOYMENT=gpt-4o
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### Openrouter example
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# LLM_MODEL=google/gemini-2.5-flash
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@ -197,21 +174,18 @@ LLM_BINDING_API_KEY=your_api_key
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# LLM_BINDING_API_KEY=your_api_key
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# LLM_BINDING=openai
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### Gemini example
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# LLM_BINDING=gemini
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# LLM_MODEL=gemini-flash-latest
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# LLM_BINDING_API_KEY=your_gemini_api_key
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# LLM_BINDING_HOST=https://generativelanguage.googleapis.com
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### OpenAI Compatible API Specific Parameters
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### Increased temperature values may mitigate infinite inference loops in certain LLM, such as Qwen3-30B.
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# OPENAI_LLM_TEMPERATURE=0.9
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### 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)
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### Typically, max_tokens does not include prompt content, though some models, such as Gemini Models, are exceptions
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### For vLLM/SGLang deployed models, or most of OpenAI compatible API provider
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# OPENAI_LLM_MAX_TOKENS=9000
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### For OpenAI o1-mini or newer modles
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OPENAI_LLM_MAX_COMPLETION_TOKENS=9000
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### use the following command to see all support options for OpenAI, azure_openai or OpenRouter
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### lightrag-server --llm-binding gemini --help
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### Gemini Specific Parameters
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# GEMINI_LLM_MAX_OUTPUT_TOKENS=9000
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# GEMINI_LLM_TEMPERATURE=0.7
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### Enable Thinking
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# GEMINI_LLM_THINKING_CONFIG='{"thinking_budget": -1, "include_thoughts": true}'
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### Disable Thinking
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# GEMINI_LLM_THINKING_CONFIG='{"thinking_budget": 0, "include_thoughts": false}'
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#### OpenAI's new API utilizes max_completion_tokens instead of max_tokens
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# OPENAI_LLM_MAX_COMPLETION_TOKENS=9000
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### use the following command to see all support options for OpenAI, azure_openai or OpenRouter
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### lightrag-server --llm-binding openai --help
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@ -222,16 +196,6 @@ LLM_BINDING_API_KEY=your_api_key
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### Qwen3 Specific Parameters deploy by vLLM
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# OPENAI_LLM_EXTRA_BODY='{"chat_template_kwargs": {"enable_thinking": false}}'
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### OpenAI Compatible API Specific Parameters
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### Increased temperature values may mitigate infinite inference loops in certain LLM, such as Qwen3-30B.
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# OPENAI_LLM_TEMPERATURE=0.9
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### 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)
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### Typically, max_tokens does not include prompt content
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### For vLLM/SGLang deployed models, or most of OpenAI compatible API provider
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# OPENAI_LLM_MAX_TOKENS=9000
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### For OpenAI o1-mini or newer modles utilizes max_completion_tokens instead of max_tokens
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OPENAI_LLM_MAX_COMPLETION_TOKENS=9000
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### use the following command to see all support options for Ollama LLM
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### lightrag-server --llm-binding ollama --help
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### Ollama Server Specific Parameters
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@ -245,56 +209,30 @@ OLLAMA_LLM_NUM_CTX=32768
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### Bedrock Specific Parameters
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# BEDROCK_LLM_TEMPERATURE=1.0
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#######################################################################################
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####################################################################################
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### Embedding Configuration (Should not be changed after the first file processed)
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### EMBEDDING_BINDING: ollama, openai, azure_openai, jina, lollms, aws_bedrock
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### EMBEDDING_BINDING_HOST: host only for Ollama, endpoint for other Embedding service
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### If LightRAG deployed in Docker:
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### uses host.docker.internal instead of localhost in EMBEDDING_BINDING_HOST
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#######################################################################################
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####################################################################################
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# EMBEDDING_TIMEOUT=30
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### Control whether to send embedding_dim parameter to embedding API
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### IMPORTANT: Jina ALWAYS sends dimension parameter (API requirement) - this setting is ignored for Jina
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### For OpenAI: Set to 'true' to enable dynamic dimension adjustment
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### For OpenAI: Set to 'false' (default) to disable sending dimension parameter
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### Note: Automatically ignored for backends that don't support dimension parameter (e.g., Ollama)
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# Ollama embedding
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# EMBEDDING_BINDING=ollama
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# EMBEDDING_MODEL=bge-m3:latest
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# EMBEDDING_DIM=1024
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# EMBEDDING_BINDING_API_KEY=your_api_key
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### If LightRAG deployed in Docker uses host.docker.internal instead of localhost
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# EMBEDDING_BINDING_HOST=http://localhost:11434
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### OpenAI compatible embedding
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EMBEDDING_BINDING=openai
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EMBEDDING_MODEL=text-embedding-3-large
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EMBEDDING_DIM=3072
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EMBEDDING_SEND_DIM=false
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EMBEDDING_TOKEN_LIMIT=8192
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EMBEDDING_BINDING_HOST=https://api.openai.com/v1
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EMBEDDING_BINDING=ollama
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EMBEDDING_MODEL=bge-m3:latest
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EMBEDDING_DIM=1024
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EMBEDDING_BINDING_API_KEY=your_api_key
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# If the embedding service is deployed within the same Docker stack, use host.docker.internal instead of localhost
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EMBEDDING_BINDING_HOST=http://localhost:11434
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### Optional for Azure embedding
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### Use deployment name as model name or set AZURE_EMBEDDING_DEPLOYMENT instead
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# AZURE_EMBEDDING_API_VERSION=2024-08-01-preview
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# EMBEDDING_BINDING=azure_openai
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# EMBEDDING_BINDING_HOST=https://xxxx.openai.azure.com/
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# EMBEDDING_API_KEY=your_api_key
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# EMBEDDING_MODEL==my-text-embedding-3-large-deployment
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### OpenAI compatible (VoyageAI embedding openai compatible)
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# EMBEDDING_BINDING=openai
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# EMBEDDING_MODEL=text-embedding-3-large
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# EMBEDDING_DIM=3072
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### Gemini embedding
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# EMBEDDING_BINDING=gemini
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# EMBEDDING_MODEL=gemini-embedding-001
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# EMBEDDING_DIM=1536
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# EMBEDDING_TOKEN_LIMIT=2048
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# EMBEDDING_BINDING_HOST=https://generativelanguage.googleapis.com
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# EMBEDDING_BINDING_HOST=https://api.openai.com/v1
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# EMBEDDING_BINDING_API_KEY=your_api_key
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### Gemini embedding requires sending dimension to server
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# EMBEDDING_SEND_DIM=true
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### Optional for Azure
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# AZURE_EMBEDDING_DEPLOYMENT=text-embedding-3-large
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# AZURE_EMBEDDING_API_VERSION=2023-05-15
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# AZURE_EMBEDDING_ENDPOINT=your_endpoint
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# AZURE_EMBEDDING_API_KEY=your_api_key
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### Jina AI Embedding
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# EMBEDDING_BINDING=jina
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POSTGRES_PASSWORD='your_password'
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POSTGRES_DATABASE=your_database
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POSTGRES_MAX_CONNECTIONS=12
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### DB specific workspace should not be set, keep for compatible only
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### POSTGRES_WORKSPACE=forced_workspace_name
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# POSTGRES_WORKSPACE=forced_workspace_name
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### PostgreSQL Vector Storage Configuration
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### Vector storage type: HNSW, IVFFlat, VCHORDRQ
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### Vector storage type: HNSW, IVFFlat
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POSTGRES_VECTOR_INDEX_TYPE=HNSW
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POSTGRES_HNSW_M=16
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POSTGRES_HNSW_EF=200
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POSTGRES_IVFFLAT_LISTS=100
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POSTGRES_VCHORDRQ_BUILD_OPTIONS=
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POSTGRES_VCHORDRQ_PROBES=
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POSTGRES_VCHORDRQ_EPSILON=1.9
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### PostgreSQL Connection Retry Configuration (Network Robustness)
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### Number of retry attempts (1-10, default: 3)
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@ -406,8 +340,7 @@ NEO4J_MAX_TRANSACTION_RETRY_TIME=30
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NEO4J_MAX_CONNECTION_LIFETIME=300
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NEO4J_LIVENESS_CHECK_TIMEOUT=30
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NEO4J_KEEP_ALIVE=true
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### DB specific workspace should not be set, keep for compatible only
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### NEO4J_WORKSPACE=forced_workspace_name
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# NEO4J_WORKSPACE=forced_workspace_name
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### MongoDB Configuration
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MONGO_URI=mongodb://root:root@localhost:27017/
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@ -421,14 +354,12 @@ MILVUS_DB_NAME=lightrag
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# MILVUS_USER=root
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# MILVUS_PASSWORD=your_password
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# MILVUS_TOKEN=your_token
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### DB specific workspace should not be set, keep for compatible only
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### MILVUS_WORKSPACE=forced_workspace_name
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# MILVUS_WORKSPACE=forced_workspace_name
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### Qdrant
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QDRANT_URL=http://localhost:6333
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# QDRANT_API_KEY=your-api-key
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### DB specific workspace should not be set, keep for compatible only
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### QDRANT_WORKSPACE=forced_workspace_name
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# QDRANT_WORKSPACE=forced_workspace_name
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### Redis
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REDIS_URI=redis://localhost:6379
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REDIS_CONNECT_TIMEOUT=10
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REDIS_MAX_CONNECTIONS=100
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REDIS_RETRY_ATTEMPTS=3
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### DB specific workspace should not be set, keep for compatible only
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### REDIS_WORKSPACE=forced_workspace_name
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# REDIS_WORKSPACE=forced_workspace_name
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### Memgraph Configuration
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MEMGRAPH_URI=bolt://localhost:7687
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MEMGRAPH_USERNAME=
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MEMGRAPH_PASSWORD=
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MEMGRAPH_DATABASE=memgraph
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### DB specific workspace should not be set, keep for compatible only
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### MEMGRAPH_WORKSPACE=forced_workspace_name
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############################
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### Evaluation Configuration
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############################
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### RAGAS evaluation models (used for RAG quality assessment)
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### ⚠️ IMPORTANT: Both LLM and Embedding endpoints MUST be OpenAI-compatible
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### Default uses OpenAI models for evaluation
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### LLM Configuration for Evaluation
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# EVAL_LLM_MODEL=gpt-4o-mini
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### API key for LLM evaluation (fallback to OPENAI_API_KEY if not set)
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# EVAL_LLM_BINDING_API_KEY=your_api_key
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### Custom OpenAI-compatible endpoint for LLM evaluation (optional)
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# EVAL_LLM_BINDING_HOST=https://api.openai.com/v1
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### Embedding Configuration for Evaluation
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# EVAL_EMBEDDING_MODEL=text-embedding-3-large
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### API key for embeddings (fallback: EVAL_LLM_BINDING_API_KEY -> OPENAI_API_KEY)
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# EVAL_EMBEDDING_BINDING_API_KEY=your_embedding_api_key
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### Custom OpenAI-compatible endpoint for embeddings (fallback: EVAL_LLM_BINDING_HOST)
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# EVAL_EMBEDDING_BINDING_HOST=https://api.openai.com/v1
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### Performance Tuning
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### Number of concurrent test case evaluations
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### Lower values reduce API rate limit issues but increase evaluation time
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# EVAL_MAX_CONCURRENT=2
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### TOP_K query parameter of LightRAG (default: 10)
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### Number of entities or relations retrieved from KG
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# EVAL_QUERY_TOP_K=10
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### LLM request retry and timeout settings for evaluation
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# EVAL_LLM_MAX_RETRIES=5
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# EVAL_LLM_TIMEOUT=180
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# MEMGRAPH_WORKSPACE=forced_workspace_name
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