wehub-resource-sync
1151 lines
55 KiB
Plaintext
1151 lines
55 KiB
Plaintext
### All configurable environment variable must show up in this sample file in active or comment out status
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### Setup tool `make env-*` uses this file to generate final .env file
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### Target environment of this env file: host/compose (compose is for Docker or Kubernetes)
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# LIGHTRAG_RUNTIME_TARGET=host
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###########################
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### Server Configuration
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###########################
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### HOST binds to all network interfaces (0.0.0.0) by default.
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### SECURITY: only expose 0.0.0.0 together with LIGHTRAG_API_KEY or AUTH_ACCOUNTS
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### (see "Login and API-Key Configuration" below). Without authentication, a
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### server on 0.0.0.0 grants anyone on the network full access to your documents
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### and knowledge graph. Bind to 127.0.0.1 for local-only access.
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HOST=0.0.0.0
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PORT=9621
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WEBUI_TITLE='My Graph KB'
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WEBUI_DESCRIPTION='Simple and Fast Graph Based RAG System'
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# WORKERS=2
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### gunicorn worker timeout(as default LLM request timeout if LLM_TIMEOUT is not set)
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# TIMEOUT=150
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### CORS allowed origins for browser cross-origin requests. Defaults to "*"
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### (any origin). The bundled WebUI is served same-origin and does not need
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### this; set an explicit allowlist only when a different-origin web app calls
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### the API from a browser. Credentialed (cookie) cross-origin requests are
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### only enabled for an explicit allowlist, never for the "*" wildcard.
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# CORS_ORIGINS=http://localhost:3000,http://localhost:8080
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### Path Prefix Configuration (Optional)
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### Used to host multiple LightRAG instances on one host behind a reverse
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### proxy that routes by site prefix. Leave unset (or empty) for a
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### single-instance deployment.
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###
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### - LIGHTRAG_API_PREFIX : reverse-proxy prefix the upstream proxy strips
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### before forwarding (passed to FastAPI as root_path).
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###
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### See docs/MultiSiteDeployment.md for end-to-end examples.
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# LIGHTRAG_API_PREFIX=/site01
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### Optional SSL Configuration
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### Docker note: generated compose files mount staged certs at /app/data/certs/ inside the container
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# SSL=true
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# SSL_CERTFILE=/path/to/cert.pem
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# SSL_KEYFILE=/path/to/key.pem
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### Directory Configuration (defaults to current working directory)
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### Default value is: ./inputs ./rag_storage
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# INPUT_DIR=<absolute_path_for_doc_input_dir>
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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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### 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_GRAPH_NODES=1000
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### Logging level
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# LOG_LEVEL=INFO
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# VERBOSE=False
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# LOG_MAX_BYTES=10485760
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# LOG_BACKUP_COUNT=5
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### Logfile location (defaults to current working directory)
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# LOG_DIR=/path/to/log/directory
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# LIGHTRAG_PERFORMANCE_TIMING_LOGS=false
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#####################################
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### Login and API-Key Configuration
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#####################################
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### SECURITY: If neither AUTH_ACCOUNTS nor LIGHTRAG_API_KEY is set, the server
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### runs with NO authentication and every endpoint is publicly accessible.
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### This is only safe on a loopback bind (HOST=127.0.0.1). Before exposing the
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### server to a network (HOST=0.0.0.0), configure at least one of the two below.
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### NOTE: AUTH_ACCOUNTS additionally requires TOKEN_SECRET to be set to a
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### non-default value, otherwise the server refuses to start.
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### NOTE: even with authentication enabled, the default WHITELIST_PATHS below
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### exempts /api/* so the Ollama-compatible endpoints (/api/chat, /api/generate,
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### ...) stay open by default, matching Ollama's own unauthenticated behavior.
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### Those routes invoke the LLM and read your knowledge base, so if you expose
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### the server to a network and want them protected, set WHITELIST_PATHS=/health
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### (and have your Ollama clients send the API key). See WHITELIST_PATHS below.
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# AUTH_ACCOUNTS='admin:admin123,user1:{bcrypt}$2b$12$S8Yu.gCbuAbNTJFB.231gegTwr5pgrFxc8H9kXQ4/sduFBHkhM8Ka'
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# TOKEN_SECRET=lightrag-jwt-default-secret-key!
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# JWT_ALGORITHM=HS256
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# TOKEN_EXPIRE_HOURS=48
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# GUEST_TOKEN_EXPIRE_HOURS=24
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### Token Auto-Renewal Configuration (Sliding Window Expiration)
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### Enable automatic token renewal to prevent active users from being logged out
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### When enabled, tokens will be automatically renewed when remaining time < threshold
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# TOKEN_AUTO_RENEW=true
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### Token renewal threshold (0.0 - 1.0)
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### Renew token when remaining time < (total time * threshold)
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### Default: 0.5 (renew when 50% time remaining)
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### Examples:
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### 0.5 = renew when 24h token has 12h left
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### 0.25 = renew when 24h token has 6h left
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# TOKEN_RENEW_THRESHOLD=0.5
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### Note: Token renewal is automatically skipped for certain endpoints:
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### - /health: Health check endpoint (no authentication required)
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### - /documents/paginated: Frequently polled by client (5-30s interval)
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### - /documents/pipeline_status: Very frequently polled by client (2s interval)
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### - Rate limit: Minimum 60 seconds between renewals for same user
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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: paths exempt from authentication (prefix match with /*).
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### Default keeps /api/* open for Ollama-client compatibility (Ollama is
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### unauthenticated by default). To require auth on the Ollama routes too when
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### the server is network-exposed, narrow this to /health.
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### NOTE: /health stays whitelisted as a liveness probe, but it no longer leaks
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### configuration to unauthenticated callers: anonymous requests get only
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### liveness signals (status/versions/auth_mode/pipeline_busy), while the full
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### runtime configuration is returned only to authenticated callers (valid JWT
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### or X-API-Key).
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# WHITELIST_PATHS=/health,/api/*
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######################################################################################
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### Query Configuration
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###
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### How to control the context length sent to LLM:
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### MAX_ENTITY_TOKENS + MAX_RELATION_TOKENS < MAX_TOTAL_TOKENS
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### Chunk_Tokens = MAX_TOTAL_TOKENS - Actual_Entity_Tokens - Actual_Relation_Tokens
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######################################################################################
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# LLM response cache for query (default=true，permanently disabled for streaming response)
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ENABLE_LLM_CACHE=false
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# COSINE_THRESHOLD=0.2
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### Number of entities or relations retrieved from KG
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# TOP_K=40
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### Maximum number or chunks for naive vector search
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# CHUNK_TOP_K=20
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### control the actual entities send to LLM
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# MAX_ENTITY_TOKENS=6000
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### control the actual relations send to LLM
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# MAX_RELATION_TOKENS=8000
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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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### 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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### If reranking is enabled, the impact of chunk selection strategies will be diminished.
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# KG_CHUNK_PICK_METHOD=VECTOR
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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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### Append each chunk's heading path (parent headings joined by " → ") as a
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### `content_headings` field in the chunk JSON sent to the LLM. Costs extra tokens.
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ENABLE_CONTENT_HEADINGS=true
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#########################################################
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### Reranking configuration
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### RERANK_BINDING type: null, cohere, jina, aliyun
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### For rerank model deployed by vLLM use cohere binding
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### If LightRAG deployed in Docker:
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### uses host.docker.internal instead of localhost in RERANK_BINDING_HOST
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#########################################################
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RERANK_BINDING=null
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# RERANK_MODEL=BAAI/bge-reranker-v2-m3
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# RERANK_BINDING_HOST=http://localhost:8000/rerank
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# RERANK_BINDING_API_KEY=your_rerank_api_key_here
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### rerank score chunk filter(set to 0.0 to keep all chunks, 0.6 or above if LLM is not strong enough)
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# MIN_RERANK_SCORE=0.0
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### Enable rerank by default in query params when RERANK_BINDING is not null
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# RERANK_BY_DEFAULT=True
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### Rerank concurrency and timeout (independent from base LLM settings)
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### MAX_ASYNC_RERANK falls back to MAX_ASYNC_LLM when unset.
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### RERANK_TIMEOUT has its own default (30s) since reranker calls are
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### typically much shorter than full LLM generation.
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# MAX_ASYNC_RERANK=4
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# RERANK_TIMEOUT=30
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### Cohere AI
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# # RERANK_MODEL=rerank-v3.5
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# # RERANK_BINDING_HOST=https://api.cohere.com/v2/rerank
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# # RERANK_BINDING_API_KEY=your_rerank_api_key_here
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### Cohere rerank chunking configuration (useful for models with token limits like ColBERT)
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# RERANK_ENABLE_CHUNKING=true
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# RERANK_MAX_TOKENS_PER_DOC=480
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### Aliyun Dashscope (gte-rerank-*, qwen3-vl-rerank) — nested input/parameters format
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# # RERANK_BINDING=aliyun
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# # RERANK_MODEL=gte-rerank-v2
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# # RERANK_BINDING_HOST=https://dashscope.aliyuncs.com/api/v1/services/rerank/text-rerank/text-rerank
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# # RERANK_BINDING_API_KEY=your_rerank_api_key_here
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### Aliyun Dashscope qwen3-rerank series — flat (Cohere-style) payload format
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### The qwen3-rerank models expect a flat body {"model", "query", "documents", "top_n", ...}
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### and return top-level "results", identical to the standard Cohere format. They are also served
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### from a DIFFERENT, Cohere-compatible endpoint (/compatible-api/v1/reranks) — NOT the
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### .../text-rerank/text-rerank path used by gte-rerank-*/qwen3-vl-rerank above.
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### So use RERANK_BINDING=cohere (NOT aliyun) and point RERANK_BINDING_HOST at that endpoint.
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### Replace {WorkspaceId} and the region with your own; see the Aliyun Text Rerank API docs:
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### https://help.aliyun.com/zh/model-studio/text-rerank-api
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# # RERANK_BINDING=cohere
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# # RERANK_MODEL=qwen3-rerank
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# # RERANK_BINDING_HOST=https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-api/v1/reranks
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# # RERANK_BINDING_API_KEY=your_rerank_api_key_here
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### Jina AI
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# # RERANK_MODEL=jina-reranker-v2-base-multilingual
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# # RERANK_BINDING_HOST=https://api.jina.ai/v1/rerank
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# # RERANK_BINDING_API_KEY=your_rerank_api_key_here
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### For local deployment Embedding and Reranker with vLLM (OpenAI-compatible API)
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### Wizard metadata used to preserve the chosen deployment provider across setup reruns
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# LIGHTRAG_SETUP_EMBEDDING_PROVIDER=vllm
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# LIGHTRAG_SETUP_RERANK_PROVIDER=vllm
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# VLLM_EMBED_MODEL=BAAI/bge-m3
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# VLLM_EMBED_PORT=8001
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# VLLM_EMBED_DEVICE=cpu
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### VLLM_EMBED_API_KEY is passed as --api-key to vLLM; synced to EMBEDDING_BINDING_API_KEY; auto-generated if blank
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# VLLM_EMBED_API_KEY=
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# VLLM_EMBED_EXTRA_ARGS=
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# VLLM_RERANK_MODEL=BAAI/bge-reranker-v2-m3
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# VLLM_RERANK_PORT=8000
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# VLLM_RERANK_DEVICE=cuda
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### VLLM_RERANK_API_KEY is passed as --api-key to vLLM; synced to RERANK_BINDING_API_KEY; auto-generated if blank
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# VLLM_RERANK_API_KEY=
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### Use float16 for GPU mode. CPU mode uses the official vLLM CPU image.
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# VLLM_USE_CPU=1
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### Set to 1 for CPU mode, unset for GPU mode
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# CUDA_VISIBLE_DEVICES=-1
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### Set to -1 to disable CUDA (CPU mode), or specific GPU IDs for GPU mode
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# NVIDIA_VISIBLE_DEVICES=0
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### Optional Docker runtime equivalent; generated GPU compose honors either variable.
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# VLLM_RERANK_EXTRA_ARGS=
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########################################
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### Document processing configuration
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########################################
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### Document processing output language: English, Chinese, French, German ...
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SUMMARY_LANGUAGE=English
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### Enable JSON-structured output for entity extraction
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### Default behavior: JSON output is disabled when ENTITY_EXTRACTION_USE_JSON is unset
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### JSON output incurs higher latency but delivers improved reliability
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ENTITY_EXTRACTION_USE_JSON=true
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### Optional external YAML profile for entity type guidance and extraction examples
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### Profiles are loaded from PROMPT_DIR/entity_type (PROMPT_DIR defaults to ./prompts).
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### A reference template is shipped at prompts/samples/entity_type_prompt.sample.yml;
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# ENTITY_TYPE_PROMPT_FILE=entity_type_prompt.yml
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# PROMPT_DIR=<absolute_path_for_prompt_dir>
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### Multimodal parsing/analyze integration
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### Optional parser routing rules. Example for VLM & MinerU enabled configuration:
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### LIGHTRAG_PARSER=*:native-iteP;*:mineru-iteP;*:legacy-R
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### Rules are separated with semicolons ';' (recommended) or commas ',';
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### Rules match file suffixes (pdf) are checked left-to-right.
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### If mineru/docling appears in LIGHTRAG_PARSER, the corresponding endpoint
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### below must be configured before server startup.
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###
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### Per-strategy chunk parameters may be attached in parentheses to a chunk
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### selector (F/R/V/P). Inside the parentheses a comma only separates parameters.
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### Supported parameters (alias in brackets):
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### chunk_token_size [chunk_ts] F/R/V/P e.g. R(chunk_ts=800)
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### chunk_overlap_token_size [chunk_ol] F/R/P (V has no overlap)
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### LIGHTRAG_PARSER=pdf:legacy-R(chunk_ts=800,chunk_ol=80);*:legacy-R
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### The same syntax works in a filename hint, e.g. notes.[-R(chunk_ts=800)].md
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### See docs/FileProcessingPipeline.md for detail
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LIGHTRAG_PARSER=*:native-teP;*:legacy-R
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### Native Markdown (.md / .textpack) remote image handling
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### External http(s) images in markdown are downloaded and embedded into the
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### sidecar assets by default (SSRF-guarded: private/loopback/link-local hosts
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### are refused; the socket is pinned to the validated IP so a DNS rebind cannot
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### redirect it to an internal host, and any ambient HTTP(S)_PROXY is ignored).
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### Set ENABLED=false to instead DROP external images (no sidecar entry), in
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### which case a doc whose only images are external links produces no drawings.json.
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NATIVE_MD_IMAGE_DOWNLOAD_ENABLED=true
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### When downloading is enabled, REQUIRED=true fails the document on a download
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### error; false (default) keeps the image as an external link and warns.
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### (Base64 and .textpack file-reference images are always embedded regardless
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### of this switch; SVG images are rasterized to PNG via cairosvg.)
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# NATIVE_MD_IMAGE_DOWNLOAD_REQUIRED=false
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# NATIVE_MD_IMAGE_DOWNLOAD_TIMEOUT=30
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### Per-image size ceiling: caps a remote download AND a single bundled
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### (.textpack) asset, so one oversized image cannot be read into memory.
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# NATIVE_MD_IMAGE_MAX_BYTES=26214400
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### SVG render budget: an SVG whose declared canvas (width*height or viewBox)
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### exceeds this pixel count is skipped BEFORE rasterization
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# NATIVE_MD_IMAGE_MAX_SVG_PIXELS=16000000
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### Escape hatch for the SSRF guard: only globally-routable IPs are allowed by
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### default. To permit specific non-public ranges (e.g. an internal image host),
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### list comma-separated CIDRs/IPs. Applies to DNS-resolved IPs and redirects.
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# NATIVE_MD_IMAGE_ALLOWED_NON_PUBLIC_CIDRS=10.0.0.0/8,192.168.1.5
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### Downloaded external images are cached in a `<file>.native_raw/` sidecar dir
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### so a re-parse of an unchanged file reuses them instead of re-downloading.
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### Set the following env var true to force a re-download (discard the cache)
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# LIGHTRAG_FORCE_REPARSE_NATIVE=false
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### Async parser service protocol (optional)
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### Configure these when using remote MinerU/Docling async services
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### ---- MinerU shared parameters (both local and official modes) ----
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### MinerU API protocol. Choose one active mode.
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### - official: MinerU precision API v4. Requires MINERU_API_TOKEN.
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### - local: self-hosted mineru-api / mineru-router base URL.
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MINERU_API_MODE=local
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# MINERU_POLL_INTERVAL_SECONDS=2
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# MINERU_MAX_POLLS=600
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# MINERU_LANGUAGE=ch
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# MINERU_ENABLE_TABLE=true
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# MINERU_ENABLE_FORMULA=true
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# MINERU_PAGE_RANGES=
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### MINERU_PAGE_RANGES semantics differ by mode:
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### - official: forwarded verbatim, supports e.g. "1-3,5,7-9".
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### - local: only a single page ("3") or simple range ("1-10"); comma
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### lists are rejected at startup.
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### When switching modes, double-check this constraint.
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### Per-file override: a hint / rule may set page_range on the engine token,
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### e.g. notes.[mineru(page_range=1-3,page_range=5)].pdf — inside the parens a
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### comma only separates parameters, so a multi-segment list REPEATS the key
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### (and requires MINERU_API_MODE=official). Likewise language / local_parse_method.
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### ---- MinerU local-only (MINERU_API_MODE=local) ----
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MINERU_LOCAL_ENDPOINT=http://127.0.0.1:8000
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### MINERU_LOCAL_BACKEND: which mineru-api backend handles the parse.
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### Accepted values (per mineru-api POST /tasks form parameter `backend`):
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### hybrid-auto-engine - pipeline + VLM combo with auto-selected local
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### engine (mineru-api's default). GPU required.
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### pipeline - CPU-friendly traditional pipeline; no VLM step.
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### vlm-auto-engine - VLM with auto-selected local inference engine
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### (sglang-engine / vllm-engine if GPU is available);
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### requires the matching engine extra preinstalled
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### on the mineru-api side, plus model weights.
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### We ship `hybrid-auto-engine` -- requires the target mineru-api
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### deployment to have a GPU plus the matching inference engine
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### (sglang / vllm) and model weights installed. Switch to `pipeline`
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### for CPU-only deployments without those dependencies.
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MINERU_LOCAL_BACKEND=hybrid-auto-engine
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### MINERU_LOCAL_PARSE_METHOD: parsing strategy for the pipeline component.
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### Accepted values:
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### auto - auto-detect embedded text-layer vs OCR per page (default).
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### txt - extract text from the embedded text layer only; fastest,
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### but yields empty output on scanned PDFs without a text layer.
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### ocr - force OCR on every page regardless of text-layer quality;
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### slowest, reliable on scanned or low-quality PDFs.
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### Only consumed when MINERU_LOCAL_BACKEND is `pipeline` or
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### `hybrid-auto-engine` (the pipeline arm of the hybrid pipeline).
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### Pure VLM backends (`vlm-auto-engine`, `vlm-http-client`) ignore this
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### parameter -- the VLM model handles layout/OCR natively.
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MINERU_LOCAL_PARSE_METHOD=auto
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### MINERU_LOCAL_IMAGE_ANALYSIS: enable VLM image/chart analysis pass for
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### better caption an footnote recognition.
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### Only consumed by `vlm-auto-engine`, `vlm-http-client`,
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### `hybrid-auto-engine`, `hybrid-http-client`. The `pipeline` backend
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### silently drops this flag -- its `_process_pipeline` does not accept
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### the kwarg, so setting `false` under pipeline does NOT speed parsing
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||
### up; pipeline never invokes the VLM image pass to begin with.
|
||
### Disable (`false`) on VLM / hybrid backends to skip the extra VLM
|
||
### round, trading image / chart semantic descriptions for faster parsing
|
||
### and lower GPU cost.
|
||
MINERU_LOCAL_IMAGE_ANALYSIS=false
|
||
# MINERU_LOCAL_START_PAGE_ID=0
|
||
# MINERU_LOCAL_END_PAGE_ID=99999
|
||
|
||
### ---- MinerU official-only (MINERU_API_MODE=official) ----
|
||
# MINERU_API_TOKEN=your-api-key
|
||
# MINERU_OFFICIAL_ENDPOINT=https://mineru.net
|
||
# MINERU_MODEL_VERSION=vlm
|
||
# MINERU_IS_OCR=false
|
||
|
||
### Force re-upload of file to MinerU on every retry after failure
|
||
### Disables caching of result outcomes
|
||
# LIGHTRAG_FORCE_REPARSE_MINERU=false
|
||
|
||
### Docling parser (docling-serve v1 / async API).
|
||
###
|
||
### Endpoint: base URL only — the client appends /v1/convert/file/async,
|
||
### /v1/status/poll/{task_id}?wait=<DOCLING_POLL_INTERVAL_SECONDS>,
|
||
### /v1/result/{task_id} itself.
|
||
### Pipeline shape (pipeline=standard, target_type=zip,
|
||
### to_formats=[json,md], image_export_mode=referenced) is fixed in
|
||
### code so the sidecar flow stays self-consistent — flipping any of
|
||
### these would break the adapter and is therefore not exposed as env.
|
||
###
|
||
### OCR tunables:
|
||
### - DOCLING_DO_OCR: master switch; when false the engine relies only on
|
||
### text-layer extraction.
|
||
### - DOCLING_FORCE_OCR: when true, OCR every page regardless of text-layer
|
||
### quality (slower, useful for scanned PDFs with bad text layers).
|
||
### - DOCLING_OCR_ENGINE: explicit engine selection (DEPRECATED in the
|
||
### docling-serve OpenAPI but still honored for older deployments).
|
||
### - DOCLING_OCR_PRESET: recommended replacement for DOCLING_OCR_ENGINE.
|
||
### - DOCLING_OCR_LANG: JSON array (e.g. ["en","zh"]) or comma-separated
|
||
### list. Empty (default) lets the OCR engine pick its default.
|
||
### - DOCLING_DO_FORMULA_ENRICHMENT: when true, the code-formula model runs
|
||
### and `texts[*].label="formula"` items carry LaTeX in `text`. Default
|
||
### false because the model may not be present on every deployment;
|
||
### adapter falls back to plain-text formulas when disabled.
|
||
###
|
||
### Polling budget (server-side long-poll; client does NOT add extra sleep):
|
||
### - DOCLING_POLL_INTERVAL_SECONDS: ``?wait=N`` value sent to
|
||
### /v1/status/poll/{task_id}. Larger N = fewer round trips per parse;
|
||
### bound by your reverse-proxy idle timeout. Default 5.
|
||
### - DOCLING_MAX_POLLS: max polling rounds before raising TimeoutError.
|
||
### Worst-case wall-clock budget ≈
|
||
### DOCLING_POLL_INTERVAL_SECONDS × DOCLING_MAX_POLLS. Default 240
|
||
### (≈ 20 minutes at wait=5s); raise for very large PDFs.
|
||
###
|
||
### Bundle cache controls:
|
||
### - DOCLING_ENGINE_VERSION: recorded in <base>.docling_raw/_manifest.json.
|
||
### Mismatch with the recorded value forces a cache miss → re-download.
|
||
### Leave empty to skip this check.
|
||
### - LIGHTRAG_FORCE_REPARSE_DOCLING: when truthy ("1"/"true"), bypass the
|
||
### docling raw cache and re-upload on every parse_docling call.
|
||
### - DOCLING_BBOX_ATTRIBUTES: override the doc-level bbox_attributes
|
||
### written into <base>.blocks.jsonl meta. Default
|
||
### {"origin":"LEFTBOTTOM"} matches docling's default coordinate system.
|
||
DOCLING_ENDPOINT=http://localhost:5001
|
||
DOCLING_DO_OCR=true
|
||
### DOCLING_FORCE_OCR can be overridden per file via a hint / rule on the engine
|
||
### token, e.g. scan.[docling(force_ocr=true)].pdf
|
||
DOCLING_FORCE_OCR=true
|
||
DOCLING_DO_FORMULA_ENRICHMENT=false
|
||
# DOCLING_OCR_ENGINE=auto
|
||
# DOCLING_OCR_PRESET=auto
|
||
# DOCLING_OCR_LANG=
|
||
# DOCLING_POLL_INTERVAL_SECONDS=5
|
||
# DOCLING_MAX_POLLS=240
|
||
# DOCLING_BBOX_ATTRIBUTES={"origin":"LEFTBOTTOM"}
|
||
### Force re-upload of file to Docling on every retry after failure
|
||
### Disables caching of result outcomes
|
||
# LIGHTRAG_FORCE_REPARSE_DOCLING=false
|
||
|
||
### 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
|
||
|
||
### Global chunk size, 500~1500 is recommended.
|
||
### Chunker inherits the global value here only when its own var is unset.
|
||
### Exception: P never inherits CHUNK_SIZE — it uses CHUNK_P_SIZE (default 2000).
|
||
# CHUNK_SIZE=1200
|
||
# CHUNK_OVERLAP_SIZE=100
|
||
|
||
### Fixed-token chunker (process_options=F, default) settings
|
||
### CHUNK_F_SIZE: per-strategy chunk_token_size override; falls back to CHUNK_SIZE when unset
|
||
### CHUNK_F_OVERLAP_SIZE: token overlap; falls back to CHUNK_OVERLAP_SIZE when unset
|
||
### CHUNK_F_SPLIT_BY_CHARACTER: optional separator string; pre-segment before token windowing
|
||
### CHUNK_F_SPLIT_BY_CHARACTER_ONLY: when true, raise on oversize segment instead of token re-split
|
||
# CHUNK_F_SIZE=1200
|
||
# CHUNK_F_OVERLAP_SIZE=100
|
||
# CHUNK_F_SPLIT_BY_CHARACTER=
|
||
# CHUNK_F_SPLIT_BY_CHARACTER_ONLY=false
|
||
|
||
### Recursive character chunker (process_options=R) settings
|
||
### CHUNK_R_SIZE: per-strategy chunk_token_size override; falls back to CHUNK_SIZE when unset
|
||
### CHUNK_R_OVERLAP_SIZE: token overlap between adjacent chunks; falls back to CHUNK_OVERLAP_SIZE when unset
|
||
### CHUNK_R_SEPARATORS: JSON array of cascaded separators tried by RecursiveCharacterTextSplitter.
|
||
### Default includes CJK sentence-ending punctuation so Chinese / mixed-language
|
||
### documents split at semantic boundaries. Order: paragraph (\n\n) > line (\n) >
|
||
### Chinese sentence-end (。！？) > Chinese semi-clause (；，) > space > char.
|
||
### English ".?!" are intentionally omitted (literal match would split "0.95" /
|
||
### "e.g."); the English path falls through space / char as before.
|
||
# CHUNK_R_SIZE=1200
|
||
# CHUNK_R_OVERLAP_SIZE=100
|
||
# CHUNK_R_SEPARATORS=["\n\n","\n","。","！","？","；","，"," ",""]
|
||
|
||
### Semantic vector chunker (process_options=V) settings
|
||
### CHUNK_V_SIZE: per-strategy chunk_token_size hard cap (oversized pieces are
|
||
### re-split via R before being emitted); falls back to CHUNK_SIZE when unset
|
||
### CHUNK_V_BREAKPOINT_THRESHOLD_TYPE: percentile | standard_deviation | interquartile | gradient
|
||
### CHUNK_V_BREAKPOINT_THRESHOLD_AMOUNT: leave empty to use the LangChain per-type default (e.g. 95 for percentile)
|
||
### CHUNK_V_BUFFER_SIZE: number of adjacent sentences combined when computing distances
|
||
### CHUNK_V_SENTENCE_SPLIT_REGEX: regex fed to LangChain SemanticChunker for the
|
||
### initial sentence split. Default extends the upstream English-only pattern
|
||
### with CJK sentence-end punctuation (。？！). Override if you need a
|
||
### different language mix. Note: env value is the raw regex string, no JSON
|
||
### quoting.
|
||
# CHUNK_V_SIZE=1200
|
||
# CHUNK_V_BREAKPOINT_THRESHOLD_TYPE=percentile
|
||
# CHUNK_V_BREAKPOINT_THRESHOLD_AMOUNT=
|
||
# CHUNK_V_BUFFER_SIZE=1
|
||
# CHUNK_V_SENTENCE_SPLIT_REGEX=(?<=[.?!])\s+|(?<=[。？！])
|
||
|
||
### Paragraph semantic chunker (process_options=P) settings
|
||
### CHUNK_P_SIZE: per-strategy chunk_token_size override; defaults to 2000 when unset
|
||
### (does NOT fall back to CHUNK_SIZE — paragraph-semantic merging needs more
|
||
### headroom than the global default to keep related paragraphs together).
|
||
### CHUNK_P_OVERLAP_SIZE: overlap for prose fallback and table-bridge context;
|
||
### falls back to CHUNK_OVERLAP_SIZE when unset
|
||
### CHUNK_P_DROP_REFERENCES: drop the trailing reference section before chunking.
|
||
### Global default switch; overridable per-file via the hint param
|
||
### drop_references (alias drop_rf), e.g. paper.[-P(drop_rf=true)].pdf. Frozen
|
||
### into the document's chunk_options at enqueue and recorded in
|
||
### doc_status.metadata['chunk_opts'].
|
||
### CHUNK_P_REFERENCES_TAIL_N: a reference block is only dropped when it sits in
|
||
### the last N content blocks (safety window; default 2).
|
||
### CHUNK_P_REFERENCES_HEADINGS: pipe-separated reference heading prefixes
|
||
### (default References|Bibliography|参考文献). English words match
|
||
### case-insensitively at a word boundary; 参考文献 matches as a prefix.
|
||
### NOTE: TAIL_N / HEADINGS are read live by the chunker at run time (NOT
|
||
### snapshotted) — editing them changes the behaviour of re-runs.
|
||
# CHUNK_P_SIZE=2000
|
||
# CHUNK_P_OVERLAP_SIZE=100
|
||
# CHUNK_P_DROP_REFERENCES=false
|
||
# CHUNK_P_REFERENCES_TAIL_N=2
|
||
# CHUNK_P_REFERENCES_HEADINGS=References|Bibliography|参考文献
|
||
|
||
### 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
|
||
|
||
### Multimodal surrounding-context budget (per-half token cap for the
|
||
### `leading` / `trailing` text injected into VLM and extract prompts).
|
||
### Computed at analyze_multimodal entry; the two halves are independent
|
||
### so deployments can bias context forward or backward as needed.
|
||
# SURROUNDING_LEADING_MAX_TOKENS=2000
|
||
# SURROUNDING_TRAILING_MAX_TOKENS=2000
|
||
|
||
### Per-response cap on total entity+relationship rows/records emitted by the LLM
|
||
# MAX_EXTRACTION_RECORDS=100
|
||
### Per-response cap on entity rows/objects emitted by the LLM
|
||
# MAX_EXTRACTION_ENTITIES=40
|
||
|
||
### Control the maximum chunk_ids stored in vector and graph db
|
||
### Addresses the hard-coded 64KB size constraint for Milvus dynamic field ($meta)
|
||
# MAX_SOURCE_IDS_PER_ENTITY=200
|
||
# MAX_SOURCE_IDS_PER_RELATION=200
|
||
### control chunk_ids limitation method: KEEP, FIFO,
|
||
### KEEP: Keep oldest (default, less merge action and faster)
|
||
### do not change entity/release description after max_source_ids reached
|
||
### FIFO: First in first out
|
||
# SOURCE_IDS_LIMIT_METHOD=KEEP
|
||
|
||
### Maximum number of file paths stored in entity/relation file_path field
|
||
### For displayed only, does not affect query performance
|
||
# MAX_FILE_PATHS=75
|
||
|
||
### PDF decryption password for protected PDF files
|
||
# PDF_DECRYPT_PASSWORD=your_pdf_password_here
|
||
|
||
########################################
|
||
### Pipeline Concurrency Configuration
|
||
########################################
|
||
### Number of parallel processing documents(between 2~10, MAX_ASYNC_LLM/3 is recommended)
|
||
MAX_PARALLEL_INSERT=3
|
||
### Optional per-stage document pipeline concurrency
|
||
# MAX_PARALLEL_PARSE_NATIVE=5
|
||
# MAX_PARALLEL_PARSE_MINERU=2
|
||
# MAX_PARALLEL_PARSE_DOCLING=2
|
||
# MAX_PARALLEL_ANALYZE=5
|
||
### Optional queue sizes for staged pipeline workers
|
||
# QUEUE_SIZE_PARSE=20
|
||
# QUEUE_SIZE_ANALYZE=100
|
||
# QUEUE_SIZE_INSERT=4
|
||
### Max concurrency requests for Embedding
|
||
# EMBEDDING_FUNC_MAX_ASYNC=8
|
||
### Num of chunks send to Embedding in single request (default is 10)
|
||
EMBEDDING_BATCH_NUM=32
|
||
|
||
###########################################################################
|
||
### Gloabal LLM Configuration
|
||
### LLM_BINDING type: openai, ollama, lollms, azure_openai, bedrock, gemini
|
||
### LLM_BINDING_HOST: Service endpoint (left empty if using the provider SDK default endpoint)
|
||
### 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=240
|
||
|
||
LLM_BINDING=openai
|
||
LLM_BINDING_HOST=https://api.openai.com/v1
|
||
LLM_BINDING_API_KEY=your_api_key
|
||
LLM_MODEL=gpt-5.4-mini
|
||
|
||
### Max concurrency requests of LLM
|
||
### MAX_ASYNC is still accepted as a deprecated alias
|
||
### NOTE: with gunicorn multi-worker (lightrag-gunicorn --workers N) every
|
||
### MAX_ASYNC_* / *_MAX_ASYNC_* setting (LLM roles, embedding, rerank)
|
||
### is enforced BOTH per worker process AND as a cross-worker global
|
||
### cap. Under normal operation this keeps total in-process provider
|
||
### calls clamped to MAX_ASYNC, similar to single-process mode. Slots
|
||
### held by crashed workers (kill -9 / OOM) are reclaimed automatically
|
||
### via lease heartbeats; if a worker is terminated externally while its
|
||
### provider request is still pending, replacement work may briefly make
|
||
### provider-side concurrency exceed the cap until the abandoned request
|
||
### times out or closes.
|
||
### Runtime caveat: changing a role's max_async through the API
|
||
### updates only that worker's local limit — the cross-worker cap
|
||
### keeps the value read at startup.
|
||
MAX_ASYNC_LLM=4
|
||
|
||
###########################################################################
|
||
### Role-specific LLM/VLM overrides
|
||
### Available roles: EXTRACT, KEYWORD, QUERY, VLM
|
||
### If unset, each role falls back to gloabal LLM configuration above.
|
||
### For detail information, refer to:
|
||
### docs/RoleSpecificLLMConfiguration.md
|
||
### docs/RoleSpecificLLMConfiguration-zh.md
|
||
###########################################################################
|
||
# KEYWORD_LLM_MODEL=gpt-5.4-nano
|
||
KEYWORD_MAX_ASYNC_LLM=4
|
||
# KEYWORD_LLM_TIMEOUT=60
|
||
# KEYWORD_LLM_BINDING=openai
|
||
# KEYWORD_LLM_BINDING_HOST=https://api.openai.com/v1
|
||
# KEYWORD_LLM_BINDING_API_KEY=your_api_key
|
||
|
||
# QUERY_LLM_MODEL=gpt-5.4
|
||
QUERY_MAX_ASYNC_LLM=4
|
||
# QUERY_LLM_TIMEOUT=240
|
||
# QUERY_LLM_BINDING=openai
|
||
# QUERY_LLM_BINDING_HOST=https://api.openai.com/v1
|
||
# QUERY_LLM_BINDING_API_KEY=your_api_key
|
||
|
||
# VLM_LLM_MODEL=gpt-5.4-mini
|
||
# VLM_MAX_ASYNC_LLM=4
|
||
# VLM_LLM_TIMEOUT=240
|
||
# VLM_LLM_BINDING=openai
|
||
# VLM_LLM_BINDING_HOST=https://api.example.com/v1
|
||
# VLM_LLM_BINDING_API_KEY=your_vlm_api_key
|
||
|
||
### Master switch for VLM multimodal analysis (i/t/e items).
|
||
### When false, multimodal item is skipped regardless of document process_options
|
||
### When true, VLM_LLM_BINDING (or the base LLM_BINDING) must be vision-capable
|
||
### lollms is rejected at startup
|
||
VLM_PROCESS_ENABLE=false
|
||
### Maximum image bytes sent to VLM (5242880=5MB)
|
||
VLM_MAX_IMAGE_BYTES=5242880
|
||
### Minimum image side (width or height) in pixels accepted for VLM analysis.
|
||
### Images with a smaller width or height are treated as decorative (icons,
|
||
### separators, etc.) and skipped instead of sent to the VLM.
|
||
VLM_MIN_IMAGE_PIXEL=64
|
||
|
||
###########################################################################
|
||
### Provider sepecific LLM options
|
||
### Increasing the temperature setting may help mitigate infinite inference
|
||
### loops during entity/elation extraction, particularly when using
|
||
### models with more limited capabilities, such as Qwen3-30B
|
||
### Set a max output token limit to prevent endless output from certain LLMs,
|
||
### which may trigger timeout errors during entity and relation extraction.
|
||
### max_output_token < LLM_TIMEOUT * llm_tokens_per_second
|
||
### i.e. max_output_token = 9000 < 240s * 50 tokens/s
|
||
### Sample commands to list all supported options specific LLM_BINDING:
|
||
### lightrag-server --llm-binding openai --help
|
||
### lightrag-server --llm-binding bedrock --help
|
||
### lightrag-server --llm-binding gemini --help
|
||
###########################################################################
|
||
### OpenAI Specific Parameters (Openrouter of other OpenAI compatible API):
|
||
### LLM_BINDING=openai
|
||
### LLM_BINDING_HOST=https://openrouter.ai/api/v1
|
||
### LLM_MODEL=google/gemini-2.5-flash
|
||
# OPENAI_LLM_TEMPERATURE=0.9
|
||
### For vLLM/SGLang and 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
|
||
### For OpenAI reason control
|
||
# OPENAI_LLM_REASONING_EFFORT=minimal
|
||
### For OpenRouter reasoning control
|
||
# OPENAI_LLM_EXTRA_BODY='{"reasoning": {"enabled": false}}'
|
||
### For Qwen3 reasoning control deploy by vLLM
|
||
# OPENAI_LLM_EXTRA_BODY='{"chat_template_kwargs": {"enable_thinking": false}}'
|
||
|
||
### Azure OpenAI Specific Parameters:
|
||
### 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
|
||
### You may use deployment name for LLM_MODEL or set AZURE_OPENAI_DEPLOYMENT instead
|
||
# AZURE_OPENAI_DEPLOYMEN=my—deplyment-name
|
||
# AZURE_OPENAI_API_VERSION=2024-08-01-preview
|
||
|
||
### Google AI Studio Gemini Specific Parameters:
|
||
### DEFAULT_GEMINI_ENDPOINT means selecting endpoit by SDK automatically
|
||
### LLM_BINDING=gemini
|
||
### LLM_BINDING_HOST=DEFAULT_GEMINI_ENDPOINT
|
||
### LLM_BINDING_API_KEY=your_gemini_api_key
|
||
### LLM_MODEL=gemini-flash-latest
|
||
# GEMINI_LLM_TEMPERATURE=0.7
|
||
# GEMINI_LLM_MAX_OUTPUT_TOKENS=9000
|
||
### Enable or disable thinking
|
||
### GEMINI_LLM_THINKING_CONFIG='{"thinking_budget": -1, "include_thoughts": true}'
|
||
### GEMINI_LLM_THINKING_CONFIG='{"thinking_budget": 0, "include_thoughts": false}'
|
||
# GEMINI_LLM_THINKING_CONFIG='{"thinking_budget": 0, "include_thoughts": false}'
|
||
|
||
### Google Vertex AI Gemini Specific Parameters:
|
||
### Vertex AI use GOOGLE_APPLICATION_CREDENTIALS instead of API-KEY for authentication
|
||
# 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'
|
||
|
||
### Bedrock Specific Parameters:
|
||
### LLM_BINDING=bedrock
|
||
### LLM_BINDING_HOST=DEFAULT_BEDROCK_ENDPOINT
|
||
### LLM_MODEL=us.amazon.nova-lite-v1:0
|
||
### Region is required for all three modes (Bedrock endpoints are regional).
|
||
# AWS_REGION=us-west-1
|
||
### Bedrock Authentication (choose ONE of the following three approaches):
|
||
### Bedrock API key (bearer token). Bedrock ignores LLM_BINDING_API_KEY;
|
||
### set AWS_BEARER_TOKEN_BEDROCK directly before startup. This is a
|
||
### process-level AWS SDK setting and cannot be overridden per role.
|
||
# AWS_BEARER_TOKEN_BEDROCK=your_bedrock_api_key
|
||
### SigV4 credentials (classic IAM user / STS / instance profile).
|
||
# 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
|
||
### Ambient credentials (AWS SDK default credential chain).
|
||
### To use this mode, leave AWS_BEARER_TOKEN_BEDROCK, AWS_ACCESS_KEY_ID,
|
||
### AWS_SECRET_ACCESS_KEY, and AWS_SESSION_TOKEN above commented out — the
|
||
### AWS SDK will then resolve credentials from ~/.aws/credentials, IAM role,
|
||
### instance profile, SSO, or environment variables outside .env.
|
||
### Activating any of the lines above forces that explicit mode and bypasses
|
||
### the credential chain.
|
||
# BEDROCK_LLM_TEMPERATURE=1.0
|
||
# BEDROCK_LLM_MAX_TOKENS=9000
|
||
# BEDROCK_LLM_TOP_P=1.0
|
||
# BEDROCK_LLM_STOP_SEQUENCES='["</s>"]'
|
||
### Bedrock model reasoning control
|
||
# BEDROCK_LLM_EXTRA_FIELDS='{"reasoningConfig": {"type": "enabled", "maxReasoningEffort": "low"}}'
|
||
|
||
### Ollama Specific Parameters:
|
||
### LLM_BINDING=ollama
|
||
### LLM_BINDING_HOST=http://localhost:11434
|
||
### LLM_MODEL=qwen3.5:9b
|
||
### OLLAMA_LLM_NUM_CTX must be provided, and should at least larger than MAX_TOTAL_TOKENS + 2000
|
||
OLLAMA_LLM_NUM_CTX=32768
|
||
# OLLAMA_LLM_NUM_PREDICT=9000
|
||
# OLLAMA_LLM_TEMPERATURE=0.85
|
||
# OLLAMA_LLM_STOP='["</s>", "<|EOT|>"]'
|
||
|
||
#######################################################################################
|
||
### Embedding Configuration (Should not be changed after the first file processed)
|
||
### EMBEDDING_BINDING: ollama, openai, azure_openai, jina, lollms, 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
|
||
### Control whether to use base64 encoding format for embeddings (improves performance for OpenAI)
|
||
### For OpenAI: Set EMBEDDING_USE_BASE64=true (default) to use base64 encoding
|
||
### For Yandex Cloud and other providers that don't support it: Set EMBEDDING_USE_BASE64=false
|
||
#######################################################################################
|
||
# 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
|
||
EMBEDDING_USE_BASE64=true
|
||
|
||
### Optional: asymmetric embeddings (query/document behavior split)
|
||
### Leave EMBEDDING_ASYMMETRIC unset or set false to keep symmetric behavior.
|
||
### Set true only when the selected embedding backend supports asymmetric mode.
|
||
# EMBEDDING_ASYMMETRIC=true
|
||
### Provider-task bindings such as Jina/Gemini/VoyageAI use provider parameters
|
||
### and should not configure the prefix variables below.
|
||
### Prefix-based models such as BGE/E5/GTE require both prefix variables.
|
||
### Wrap non-empty values with quotes if there are trailing spaces.
|
||
# EMBEDDING_DOCUMENT_PREFIX="search_document: "
|
||
### Use NO_PREFIX for a side that should intentionally have no prefix.
|
||
### EMBEDDING_DOCUMENT_PREFIX=NO_PREFIX
|
||
# EMBEDDING_QUERY_PREFIX="search_query: "
|
||
|
||
###########################################################################
|
||
### Provider sepecific Embedding options
|
||
### Increasing the temperature setting may help mitigate infinite inference
|
||
### loops during entity/elation extraction, particularly when using
|
||
### models with more limited capabilities, such as Qwen3-30B
|
||
### Set a max output token limit to prevent endless output from certain LLMs,
|
||
### which may trigger timeout errors during entity and relation extraction.
|
||
### max_output_token < LLM_TIMEOUT * llm_tokens_per_second
|
||
### i.e. max_output_token = 9000 < 240s * 50 tokens/s
|
||
### Sample commands to list all supported options specific EMBEDDING_BINDING:
|
||
### lightrag-server --embedding-binding openai --help
|
||
### lightrag-server --embedding-binding ollama --help
|
||
### lightrag-server --embedding-binding bedrock --help
|
||
###########################################################################
|
||
### Azure Embedding Specific Parameters:
|
||
### 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
|
||
|
||
### Ollama Embedding Specific Parameters:
|
||
### EMBEDDING_BINDING=ollama
|
||
### EMBEDDING_BINDING_HOST=http://localhost:11434
|
||
### EMBEDDING_BINDING_API_KEY=your_api_key
|
||
### EMBEDDING_MODEL=qwen3-embedding:4b
|
||
### EMBEDDING_DIM=2560
|
||
### Ollama should set num_ctx option inaddition to EMBEDDING_TOKEN_LIMIT
|
||
OLLAMA_EMBEDDING_NUM_CTX=8192
|
||
|
||
### Gemini Embedding Specific Parameters:
|
||
### DEFAULT_GEMINI_ENDPOINT means selecting endpoit by SDK automatically
|
||
### Gemini embedding requires sending dimension to server
|
||
### EMBEDDING_BINDING=gemini
|
||
### EMBEDDING_BINDING_HOST=DEFAULT_GEMINI_ENDPOINT
|
||
### EMBEDDING_BINDING_API_KEY=your_api_key
|
||
### EMBEDDING_MODEL=gemini-embedding-001
|
||
### EMBEDDING_DIM=1536
|
||
### EMBEDDING_TOKEN_LIMIT=2048
|
||
### EMBEDDING_SEND_DIM=true
|
||
|
||
### Bedrock Embedding Specific Parameters:
|
||
### EMBEDDING_BINDING=bedrock
|
||
### EMBEDDING_BINDING_HOST=DEFAULT_BEDROCK_ENDPOINT
|
||
### EMBEDDING_MODEL=amazon.titan-embed-text-v2:0
|
||
### EMBEDDING_DIM=1024
|
||
### Share the same region and authentication settings as LLMs, no reconfiguration here
|
||
### AWS_REGION=us-west-1
|
||
### AWS_BEARER_TOKEN_BEDROCK=your_bedrock_api_key
|
||
### 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
|
||
|
||
### Jina AI Embedding Specific Parameters:
|
||
### 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
|
||
|
||
### 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
|
||
|
||
### Batch write limits for KV/Vector/DocStatus (split a single executemany / ANY($2) delete; non-positive disables that dimension)
|
||
# POSTGRES_UPSERT_MAX_PAYLOAD_BYTES=16777216
|
||
# POSTGRES_UPSERT_MAX_RECORDS_PER_BATCH=200
|
||
# POSTGRES_DELETE_MAX_RECORDS_PER_BATCH=1000
|
||
|
||
### 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
|
||
# Flush-time bulk_write batching limits for MongoDB upsert paths (KV, vector, graph).
|
||
# (non-positive disables that dimension; DELETE cap applies to MongoVectorDBStorage)
|
||
# MONGO_UPSERT_MAX_PAYLOAD_BYTES=16777216
|
||
# MONGO_UPSERT_MAX_RECORDS_PER_BATCH=128
|
||
# MONGO_DELETE_MAX_RECORDS_PER_BATCH=1000
|
||
|
||
# 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
|
||
### Bulk batching limits (split a single async_bulk request; non-positive disables that dimension)
|
||
# OPENSEARCH_UPSERT_MAX_PAYLOAD_BYTES=104857600
|
||
# OPENSEARCH_UPSERT_MAX_RECORDS_PER_BATCH=128
|
||
# OPENSEARCH_DELETE_MAX_RECORDS_PER_BATCH=1000
|
||
### 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 upsert/delete batching (enabled by default)
|
||
### Split large flushes by estimated JSON payload size and record count to stay
|
||
### under the server-side 64MB gRPC message limit. A single record larger than the
|
||
### byte budget is sent as its own batch instead of failing.
|
||
# MILVUS_UPSERT_MAX_PAYLOAD_BYTES=33554432
|
||
# MILVUS_UPSERT_MAX_RECORDS_PER_BATCH=128
|
||
# MILVUS_DELETE_MAX_RECORDS_PER_BATCH=1000
|
||
|
||
### Milvus schema-migration resilience (enabled by default)
|
||
### On a transient connection failure the migration is retried from scratch with
|
||
### a rebuilt client and exponential backoff. Set MAX_RETRIES=0 to fail fast.
|
||
### Lower the iterator batch size to reduce write pressure on a small server.
|
||
# MILVUS_MIGRATION_MAX_RETRIES=5
|
||
# MILVUS_MIGRATION_RETRY_BACKOFF=5
|
||
# MILVUS_MIGRATION_RETRY_MAX_BACKOFF=60
|
||
# MILVUS_MIGRATION_ITERATOR_BATCH_SIZE=2000
|
||
|
||
### 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/delete batching (enabled by default)
|
||
### Split large upserts by estimated JSON payload size and point count, and
|
||
### large deletes by point count, to stay under the server/gateway request limit.
|
||
### Default 16MB keeps safe headroom below common 32MB gateway/request limits.
|
||
### A single point larger than the byte budget is sent as its own batch instead of failing.
|
||
# QDRANT_UPSERT_MAX_PAYLOAD_BYTES=16777216
|
||
# QDRANT_UPSERT_MAX_POINTS_PER_BATCH=128
|
||
# QDRANT_DELETE_MAX_POINTS_PER_BATCH=1000
|
||
### 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)
|
||
##########################################################################
|
||
### The "make env*" wizard will leave the following lines unchanged
|
||
### You may add additional env vars or commnets here for your own purpose
|
||
##########################################################################
|
||
|
||
### AWS Bedrock
|
||
# LLM_BINDING=bedrock
|
||
# LLM_BINDING_HOST=DEFAULT_BEDROCK_ENDPOINT
|
||
# LLM_MODEL=us.anthropic.claude-haiku-4-5-20251001-v1:0
|
||
# KEYWORD_LLM_MODEL=us.anthropic.claude-haiku-4-5-20251001-v1:0
|
||
# QUERY_LLM_MODEL=us.anthropic.claude-haiku-4-5-20251001-v1:0
|
||
# VLM_LLM_MODEL=us.anthropic.claude-haiku-4-5-20251001-v1:0
|
||
|
||
### ----- Extra setting from previous .env -----
|