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lightrag/env.example
yangdx 484e441d0c Optimize Postgres retry logic for HA switchover 
- Increase default retries and backoff
- Raise connection retry parameter caps
- Update env example with HA defaults
- Extend frontend timeouts for updates
- Update integration test limits
2025-12-31 16:03:46 +08:00

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### This is sample file of .env
###########################
### Server Configuration
###########################
HOST=0.0.0.0
PORT=9621
WEBUI_TITLE='My Graph KB'
WEBUI_DESCRIPTION="Simple and Fast Graph Based RAG System"
# WORKERS=2
### gunicorn worker timeout(as default LLM request timeout if LLM_TIMEOUT is not set)
# TIMEOUT=150
# CORS_ORIGINS=http://localhost:3000,http://localhost:8080
### Optional SSL Configuration
# SSL=true
# SSL_CERTFILE=/path/to/cert.pem
# SSL_KEYFILE=/path/to/key.pem
### Directory Configuration (defaults to current working directory)
### Default value is ./inputs and ./rag_storage
# INPUT_DIR=<absolute_path_for_doc_input_dir>
# WORKING_DIR=<absolute_path_for_working_dir>
### Tiktoken cache directory (Store cached files in this folder for offline deployment)
# TIKTOKEN_CACHE_DIR=/app/data/tiktoken
### Ollama Emulating Model and Tag
# OLLAMA_EMULATING_MODEL_NAME=lightrag
OLLAMA_EMULATING_MODEL_TAG=latest
### Max nodes for graph retrieval (Ensure WebUI local settings are also updated, which is limited to this value)
# MAX_GRAPH_NODES=1000
### Logging level
# LOG_LEVEL=INFO
# VERBOSE=False
# LOG_MAX_BYTES=10485760
# LOG_BACKUP_COUNT=5
### Logfile location (defaults to current working directory)
# LOG_DIR=/path/to/log/directory
#####################################
### Login and API-Key Configuration
#####################################
# AUTH_ACCOUNTS='admin:admin123,user1:pass456'
# TOKEN_SECRET=Your-Key-For-LightRAG-API-Server
# TOKEN_EXPIRE_HOURS=48
# GUEST_TOKEN_EXPIRE_HOURS=24
# JWT_ALGORITHM=HS256
### Token Auto-Renewal Configuration (Sliding Window Expiration)
### Enable automatic token renewal to prevent active users from being logged out
### When enabled, tokens will be automatically renewed when remaining time < threshold
# TOKEN_AUTO_RENEW=true
### Token renewal threshold (0.0 - 1.0)
### Renew token when remaining time < (total time * threshold)
### Default: 0.5 (renew when 50% time remaining)
### Examples:
### 0.5 = renew when 24h token has 12h left
### 0.25 = renew when 24h token has 6h left
# TOKEN_RENEW_THRESHOLD=0.5
### Note: Token renewal is automatically skipped for certain endpoints:
### - /health: Health check endpoint (no authentication required)
### - /documents/paginated: Frequently polled by client (5-30s interval)
### - /documents/pipeline_status: Very frequently polled by client (2s interval)
### - Rate limit: Minimum 60 seconds between renewals for same user
### API-Key to access LightRAG Server API
### Use this key in HTTP requests with the 'X-API-Key' header
### Example: curl -H "X-API-Key: your-secure-api-key-here" http://localhost:9621/query
# LIGHTRAG_API_KEY=your-secure-api-key-here
# WHITELIST_PATHS=/health,/api/*
######################################################################################
### Query Configuration
###
### How to control the context length sent to LLM:
### MAX_ENTITY_TOKENS + MAX_RELATION_TOKENS < MAX_TOTAL_TOKENS
### Chunk_Tokens = MAX_TOTAL_TOKENS - Actual_Entity_Tokens - Actual_Relation_Tokens
######################################################################################
# LLM response cache for query (Not valid for streaming response)
ENABLE_LLM_CACHE=true
# COSINE_THRESHOLD=0.2
### Number of entities or relations retrieved from KG
# TOP_K=40
### Maximum number or chunks for naive vector search
# CHUNK_TOP_K=20
### control the actual entities send to LLM
# MAX_ENTITY_TOKENS=6000
### control the actual relations send to LLM
# MAX_RELATION_TOKENS=8000
### control the maximum tokens send to LLM (include entities, relations and chunks)
# MAX_TOTAL_TOKENS=30000
### chunk selection strategies
### VECTOR: Pick KG chunks by vector similarity, delivered chunks to the LLM aligning more closely with naive retrieval
### WEIGHT: Pick KG chunks by entity and chunk weight, delivered more solely KG related chunks to the LLM
### If reranking is enabled, the impact of chunk selection strategies will be diminished.
# KG_CHUNK_PICK_METHOD=VECTOR
#########################################################
### Reranking configuration
### RERANK_BINDING type: null, cohere, jina, aliyun
### For rerank model deployed by vLLM use cohere binding
#########################################################
RERANK_BINDING=null
### Enable rerank by default in query params when RERANK_BINDING is not null
# RERANK_BY_DEFAULT=True
### rerank score chunk filter(set to 0.0 to keep all chunks, 0.6 or above if LLM is not strong enough)
# MIN_RERANK_SCORE=0.0
### For local deployment with vLLM
# RERANK_MODEL=BAAI/bge-reranker-v2-m3
# RERANK_BINDING_HOST=http://localhost:8000/v1/rerank
# RERANK_BINDING_API_KEY=your_rerank_api_key_here
### Default value for Cohere AI
# RERANK_MODEL=rerank-v3.5
# RERANK_BINDING_HOST=https://api.cohere.com/v2/rerank
# RERANK_BINDING_API_KEY=your_rerank_api_key_here
### Cohere rerank chunking configuration (useful for models with token limits like ColBERT)
# RERANK_ENABLE_CHUNKING=true
# RERANK_MAX_TOKENS_PER_DOC=480
### Default value for Jina AI
# RERANK_MODEL=jina-reranker-v2-base-multilingual
# RERANK_BINDING_HOST=https://api.jina.ai/v1/rerank
# RERANK_BINDING_API_KEY=your_rerank_api_key_here
### Default value for Aliyun
# RERANK_MODEL=gte-rerank-v2
# RERANK_BINDING_HOST=https://dashscope.aliyuncs.com/api/v1/services/rerank/text-rerank/text-rerank
# RERANK_BINDING_API_KEY=your_rerank_api_key_here
########################################
### Document processing configuration
########################################
ENABLE_LLM_CACHE_FOR_EXTRACT=true
### Document processing output language: English, Chinese, French, German ...
SUMMARY_LANGUAGE=English
### PDF decryption password for protected PDF files
# PDF_DECRYPT_PASSWORD=your_pdf_password_here
### Entity types that the LLM will attempt to recognize
# ENTITY_TYPES='["Person", "Creature", "Organization", "Location", "Event", "Concept", "Method", "Content", "Data", "Artifact", "NaturalObject"]'
### Chunk size for document splitting, 500~1500 is recommended
# CHUNK_SIZE=1200
# CHUNK_OVERLAP_SIZE=100
### Number of summary segments or tokens to trigger LLM summary on entity/relation merge (at least 3 is recommended)
# FORCE_LLM_SUMMARY_ON_MERGE=8
### Max description token size to trigger LLM summary
# SUMMARY_MAX_TOKENS = 1200
### Recommended LLM summary output length in tokens
# SUMMARY_LENGTH_RECOMMENDED_=600
### Maximum context size sent to LLM for description summary
# SUMMARY_CONTEXT_SIZE=12000
### control the maximum chunk_ids stored in vector and graph db
# MAX_SOURCE_IDS_PER_ENTITY=300
# MAX_SOURCE_IDS_PER_RELATION=300
### control chunk_ids limitation method: FIFO, KEEP
### FIFO: First in first out
### KEEP: Keep oldest (less merge action and faster)
# SOURCE_IDS_LIMIT_METHOD=FIFO
# Maximum number of file paths stored in entity/relation file_path field (For displayed only, does not affect query performance)
# MAX_FILE_PATHS=100
### maximum number of related chunks per source entity or relation
### The chunk picker uses this value to determine the total number of chunks selected from KG(knowledge graph)
### Higher values increase re-ranking time
# RELATED_CHUNK_NUMBER=5
###############################
### Concurrency Configuration
###############################
### Max concurrency requests of LLM (for both query and document processing)
MAX_ASYNC=4
### Number of parallel processing documents(between 2~10, MAX_ASYNC/3 is recommended)
MAX_PARALLEL_INSERT=2
### Max concurrency requests for Embedding
# EMBEDDING_FUNC_MAX_ASYNC=8
### Num of chunks send to Embedding in single request
# EMBEDDING_BATCH_NUM=10
###########################################################################
### LLM Configuration
### LLM_BINDING type: openai, ollama, lollms, azure_openai, aws_bedrock, gemini
### LLM_BINDING_HOST: host only for Ollama, endpoint for other LLM service
### 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=180
LLM_BINDING=openai
LLM_MODEL=gpt-4o
LLM_BINDING_HOST=https://api.openai.com/v1
LLM_BINDING_API_KEY=your_api_key
### Azure OpenAI example
### Use deployment name as model name or set AZURE_OPENAI_DEPLOYMENT instead
# AZURE_OPENAI_API_VERSION=2024-08-01-preview
# 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
### Openrouter example
# LLM_MODEL=google/gemini-2.5-flash
# LLM_BINDING_HOST=https://openrouter.ai/api/v1
# LLM_BINDING_API_KEY=your_api_key
# LLM_BINDING=openai
### Google Gemini example (AI Studio)
# LLM_BINDING=gemini
# LLM_MODEL=gemini-flash-latest
# LLM_BINDING_API_KEY=your_gemini_api_key
# LLM_BINDING_HOST=https://generativelanguage.googleapis.com
### use the following command to see all support options for OpenAI, azure_openai or OpenRouter
### lightrag-server --llm-binding gemini --help
### Gemini Specific Parameters
# GEMINI_LLM_MAX_OUTPUT_TOKENS=9000
# GEMINI_LLM_TEMPERATURE=0.7
### Enable Thinking
# GEMINI_LLM_THINKING_CONFIG='{"thinking_budget": -1, "include_thoughts": true}'
### Disable Thinking
# GEMINI_LLM_THINKING_CONFIG='{"thinking_budget": 0, "include_thoughts": false}'
### Google Vertex AI example
### Vertex AI use GOOGLE_APPLICATION_CREDENTIALS instead of API-KEY for authentication
### LLM_BINDING_HOST=DEFAULT_GEMINI_ENDPOINT means select endpoit based on project and location automatically
# LLM_BINDING=gemini
# LLM_BINDING_HOST=https://aiplatform.googleapis.com
### or use DEFAULT_GEMINI_ENDPOINT to select endpoint based on project and location automatically
# LLM_BINDING_HOST=DEFAULT_GEMINI_ENDPOINT
# LLM_MODEL=gemini-2.5-flash
# 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'
### use the following command to see all support options for OpenAI, azure_openai or OpenRouter
### lightrag-server --llm-binding openai --help
### OpenAI Specific Parameters
# OPENAI_LLM_REASONING_EFFORT=minimal
### OpenRouter Specific Parameters
# OPENAI_LLM_EXTRA_BODY='{"reasoning": {"enabled": false}}'
### Qwen3 Specific Parameters deploy by vLLM
# OPENAI_LLM_EXTRA_BODY='{"chat_template_kwargs": {"enable_thinking": false}}'
### OpenAI Compatible API Specific Parameters
### Increased temperature values may mitigate infinite inference loops in certain LLM, such as Qwen3-30B.
# OPENAI_LLM_TEMPERATURE=0.9
### Set the max_tokens to mitigate endless output of some LLM (less than LLM_TIMEOUT * llm_output_tokens/second, i.e. 9000 = 180s * 50 tokens/s)
### Typically, max_tokens does not include prompt content
### For vLLM/SGLang deployed models, or 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
### use the following command to see all support options for Ollama LLM
### lightrag-server --llm-binding ollama --help
### Ollama Server Specific Parameters
### OLLAMA_LLM_NUM_CTX must be provided, and should at least larger than MAX_TOTAL_TOKENS + 2000
OLLAMA_LLM_NUM_CTX=32768
### Set the max_output_tokens to mitigate endless output of some LLM (less than LLM_TIMEOUT * llm_output_tokens/second, i.e. 9000 = 180s * 50 tokens/s)
# OLLAMA_LLM_NUM_PREDICT=9000
### Stop sequences for Ollama LLM
# OLLAMA_LLM_STOP='["</s>", "<|EOT|>"]'
### Bedrock Specific Parameters
# BEDROCK_LLM_TEMPERATURE=1.0
#######################################################################################
### Embedding Configuration (Should not be changed after the first file processed)
### EMBEDDING_BINDING: ollama, openai, azure_openai, jina, lollms, aws_bedrock
### EMBEDDING_BINDING_HOST: host only for Ollama, endpoint for other Embedding service
### If LightRAG deployed in Docker:
### uses host.docker.internal instead of localhost in EMBEDDING_BINDING_HOST
#######################################################################################
# EMBEDDING_TIMEOUT=30
### Control whether to send embedding_dim parameter to embedding API
### IMPORTANT: Jina ALWAYS sends dimension parameter (API requirement) - this setting is ignored for Jina
### For OpenAI: Set to 'true' to enable dynamic dimension adjustment
### For OpenAI: Set to 'false' (default) to disable sending dimension parameter
### Note: Automatically ignored for backends that don't support dimension parameter (e.g., Ollama)
# Ollama embedding
# EMBEDDING_BINDING=ollama
# EMBEDDING_MODEL=bge-m3:latest
# EMBEDDING_DIM=1024
# EMBEDDING_BINDING_API_KEY=your_api_key
### If LightRAG deployed in Docker uses host.docker.internal instead of localhost
# EMBEDDING_BINDING_HOST=http://localhost:11434
### OpenAI compatible embedding
EMBEDDING_BINDING=openai
EMBEDDING_MODEL=text-embedding-3-large
EMBEDDING_DIM=3072
EMBEDDING_SEND_DIM=false
EMBEDDING_TOKEN_LIMIT=8192
EMBEDDING_BINDING_HOST=https://api.openai.com/v1
EMBEDDING_BINDING_API_KEY=your_api_key
### Optional for Azure embedding
### Use deployment name as model name or set AZURE_EMBEDDING_DEPLOYMENT instead
# AZURE_EMBEDDING_API_VERSION=2024-08-01-preview
# 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
### Gemini embedding
# EMBEDDING_BINDING=gemini
# EMBEDDING_MODEL=gemini-embedding-001
# EMBEDDING_DIM=1536
# EMBEDDING_TOKEN_LIMIT=2048
# EMBEDDING_BINDING_HOST=https://generativelanguage.googleapis.com
# EMBEDDING_BINDING_API_KEY=your_api_key
### Gemini embedding requires sending dimension to server
# EMBEDDING_SEND_DIM=true
### Jina AI Embedding
# EMBEDDING_BINDING=jina
# EMBEDDING_BINDING_HOST=https://api.jina.ai/v1/embeddings
# EMBEDDING_MODEL=jina-embeddings-v4
# EMBEDDING_DIM=2048
# EMBEDDING_BINDING_API_KEY=your_api_key
### Optional for Ollama embedding
OLLAMA_EMBEDDING_NUM_CTX=8192
### use the following command to see all support options for Ollama embedding
### lightrag-server --embedding-binding ollama --help
####################################################################
### WORKSPACE sets workspace name for all storage types
### for the purpose of isolating data from LightRAG instances.
### Valid workspace name constraints: a-z, A-Z, 0-9, and _
####################################################################
# WORKSPACE=space1
############################
### Data storage selection
############################
### Default storage (Recommended for small scale deployment)
# LIGHTRAG_KV_STORAGE=JsonKVStorage
# LIGHTRAG_DOC_STATUS_STORAGE=JsonDocStatusStorage
# LIGHTRAG_GRAPH_STORAGE=NetworkXStorage
# LIGHTRAG_VECTOR_STORAGE=NanoVectorDBStorage
### Redis Storage (Recommended for production deployment)
# LIGHTRAG_KV_STORAGE=RedisKVStorage
# LIGHTRAG_DOC_STATUS_STORAGE=RedisDocStatusStorage
### Vector Storage (Recommended for production deployment)
# LIGHTRAG_VECTOR_STORAGE=MilvusVectorDBStorage
# LIGHTRAG_VECTOR_STORAGE=QdrantVectorDBStorage
# LIGHTRAG_VECTOR_STORAGE=FaissVectorDBStorage
### Graph Storage (Recommended for production deployment)
# LIGHTRAG_GRAPH_STORAGE=Neo4JStorage
# LIGHTRAG_GRAPH_STORAGE=MemgraphStorage
### PostgreSQL
# LIGHTRAG_KV_STORAGE=PGKVStorage
# LIGHTRAG_DOC_STATUS_STORAGE=PGDocStatusStorage
# LIGHTRAG_GRAPH_STORAGE=PGGraphStorage
# LIGHTRAG_VECTOR_STORAGE=PGVectorStorage
### MongoDB (Vector storage only available on Atlas Cloud)
# LIGHTRAG_KV_STORAGE=MongoKVStorage
# LIGHTRAG_DOC_STATUS_STORAGE=MongoDocStatusStorage
# LIGHTRAG_GRAPH_STORAGE=MongoGraphStorage
# LIGHTRAG_VECTOR_STORAGE=MongoVectorDBStorage
### PostgreSQL Configuration
POSTGRES_HOST=localhost
POSTGRES_PORT=5432
POSTGRES_USER=your_username
POSTGRES_PASSWORD='your_password'
POSTGRES_DATABASE=your_database
POSTGRES_MAX_CONNECTIONS=12
### DB specific workspace should not be set, keep for compatible only
### POSTGRES_WORKSPACE=forced_workspace_name
### PostgreSQL Vector Storage Configuration
### Vector storage type: HNSW, 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
### 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
MONGO_URI=mongodb://root:root@localhost:27017/
#MONGO_URI=mongodb+srv://xxxx
MONGO_DATABASE=LightRAG
# MONGODB_WORKSPACE=forced_workspace_name
### Milvus Configuration
MILVUS_URI=http://localhost:19530
MILVUS_DB_NAME=lightrag
# MILVUS_USER=root
# MILVUS_PASSWORD=your_password
# MILVUS_TOKEN=your_token
### DB specific workspace should not be set, keep for compatible only
### MILVUS_WORKSPACE=forced_workspace_name
### Qdrant
QDRANT_URL=http://localhost:6333
# QDRANT_API_KEY=your-api-key
### 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" # 或您的自托管实例地址
# 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
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