env.example

### All configurable environment variable must show up in this sample file in active or comment out status
### Setup tool `make env-*` uses this file to generate final .env file

### Target environment of this env file: host/compose (compose is for Docker or Kubernetes)
# LIGHTRAG_RUNTIME_TARGET=host

###########################
### Server Configuration
###########################
### HOST binds to all network interfaces (0.0.0.0) by default.
### SECURITY: only expose 0.0.0.0 together with LIGHTRAG_API_KEY or AUTH_ACCOUNTS
### (see "Login and API-Key Configuration" below). Without authentication, a
### server on 0.0.0.0 grants anyone on the network full access to your documents
### and knowledge graph. Bind to 127.0.0.1 for local-only access.
HOST=0.0.0.0
PORT=9621
### Deployment name shown in WebUI headers, on the login page, and as the browser tab title.
### Leave unset to keep the default 'LightRAG'.
WEBUI_TITLE='My Graph KB'
WEBUI_DESCRIPTION='Simple and Fast Graph Based RAG System'
### Show an "AI-generated content" notice on every answer in the WebUI
### retrieval panel (/webui) and the query entry (/workspace). It is appended
### to the response-time line below the answer, not given a line of its own.
### Enable it where machine-generated output must be labelled. The notice is a
### UI element only: it is never added to the API response or to the stored
### chat history, and its wording follows the interface language.
# ENABLE_AI_CONTENT_NOTICE=false
# WORKERS=2
### gunicorn worker timeout(as default LLM request timeout if LLM_TIMEOUT is not set)
# TIMEOUT=150
### Ceiling on how long one knowledge-graph admin edit (entity/relation
### create, edit, delete, merge; custom KG insert) may hold the workspace
### admin lock and the pipeline busy reservation. Applies only with
### LIGHTRAG_GRAPH_STORAGE=NetworkXStorage, where such an edit defers a
### pipeline start for its duration; server-backed graph stores are not
### gated. On expiry the edit fails with HTTP 500 and both are released.
### The failure does not mean the edit was undone: a commit already in flight
### is allowed to finish, so re-read the object before retrying.
### Defaults to max(180, 6 x EMBEDDING_TIMEOUT) -- 180 at the default
### embedding timeout of 30. Derived rather than fixed because the embedding
### round-trip runs inside the hold: raising EMBEDDING_TIMEOUT alone would
### otherwise leave a ceiling sized for the old value, killing every retry.
### One hold has to cover an embedding retry storm (3 x EMBEDDING_TIMEOUT plus
### 8s of backoff = 98s at the default) plus a whole-graph GraphML commit
### (~17s at 200k nodes -- the entire graph is rewritten however small the edit
### was), so ~115s, with the rest as headroom for the edges an edit touches.
### Resolved per LightRAG instance from its own embedding timeout, so a
### direct LightRAG(default_embedding_timeout=...) caller is followed without
### setting anything here. Setting this BELOW the embedding timeout is refused
### at startup.
### Prefer raising it over lowering it: too high only defers ingestion, which
### self-heals through the sticky rescan request, while too low kills edits
### whose commit may already have landed.
# LIGHTRAG_ADMIN_WRITE_MAX_HOLD_SECONDS=180
### How long a second concurrent knowledge-graph admin edit waits behind the
### first before giving up with HTTP 409 ("Another knowledge graph edit is in
### progress"). Same NetworkXStorage-only scope as the ceiling above. This is
### the only bound on that wait, and it is a responsiveness knob, NOT a
### derivative of the ceiling: it asks how long a caller should wait before
### being told to retry, so when the edit ahead runs long the queue is meant
### to fail fast rather than to wait longer. A value above
### LIGHTRAG_ADMIN_WRITE_MAX_HOLD_SECONDS is NOT pointless: the admin lock is
### taken before the ceiling starts and released after it ends, and an
### in-flight commit runs past the expiry, so the lock is always held longer
### than the ceiling and a longer wait can still be rewarded.
# LIGHTRAG_ADMIN_WRITE_LOCK_ACQUIRE_TIMEOUT=30
### CORS allowed origins for browser cross-origin requests. Defaults to "*"
### (any origin). The bundled WebUI is served same-origin and does not need
### this; set an explicit allowlist only when a different-origin web app calls
### the API from a browser. Credentialed (cookie) cross-origin requests are
### only enabled for an explicit allowlist, never for the "*" wildcard.
# CORS_ORIGINS=http://localhost:3000,http://localhost:8080

### Interactive API documentation (Swagger UI /docs, ReDoc /redoc and the
### /openapi.json schema). Set to false to disable all of them (each returns
### 404) — recommended for hardened production deployments. The WebUI hides
### its API-docs entry point automatically based on /health.
# ENABLE_API_DOCS=true

### Default UI entry for the root path '/'.
### The server hosts two WebUI entries: /webui (admin: documents, knowledge
### graph, query debugging) and /workspace (query-only entry for everyday
### users). This variable controls EXACTLY ONE behavior — which entry '/'
### redirects to. Both entries stay mounted regardless. Allowed values:
### webui (default) or workspace; anything else fails startup.
### Query parameters on /workspace are inherited, not editable: each query
### uses the settings that /webui saved in the SAME BROWSER (frontend
### defaults when none were saved). This is per-browser local state, not a
### server-wide policy — an end user opening /workspace on their own device
### gets the frontend defaults, not the parameters an admin saved elsewhere.
# LIGHTRAG_DEFAULT_UI=webui

### Path Prefix Configuration (Optional)
### Used to host multiple LightRAG instances on one host behind a reverse
### proxy that routes by site prefix. Leave unset (or empty) for a
### single-instance deployment.
###
### - LIGHTRAG_API_PREFIX  : reverse-proxy prefix the upstream proxy strips
###                          before forwarding (passed to FastAPI as root_path).
###
### See docs/MultiSiteDeployment.md for end-to-end examples.
# LIGHTRAG_API_PREFIX=/site01

### Optional read-only UI customization bundle: the welcome page, the query
### empty state, the login-page text and user agreement, the copyright line,
### and the brand logo (multi-language). Unset means "no customization": the
### frontend shows its built-in branding. A directory that holds no
### manifest.json yet means the same thing (logged as a warning at startup).
### Once manifest.json is there, the bundle must be complete and valid
### (manifest.json + locales/ + assets/) or the server refuses to start. This
### value is for host/source runs: the compose files set the container path in
### their environment: block, which overrides the value here, so one .env
### serves both deployments. Changes require a restart of all workers. See
### docs/UserDefinedUI.md for the full guide, and docs/ui_templates_example/
### for a ready-to-copy example bundle.
# UI_TEMPLATES_DIR=./lightrag_webui/ui_templates

### Optional SSL Configuration
### Docker note: generated compose files mount staged certs at /app/data/certs/ inside the container
# 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 ./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)
### Default: ~/.tiktoken_cache if that directory exists, else tiktoken's temp-dir fallback
# 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
# LIGHTRAG_PERFORMANCE_TIMING_LOGS=false

#####################################
### Login and API-Key Configuration
#####################################
### SECURITY: If neither AUTH_ACCOUNTS nor LIGHTRAG_API_KEY is set, the server
### runs with NO authentication and every endpoint is publicly accessible.
### This is only safe on a loopback bind (HOST=127.0.0.1). Before exposing the
### server to a network (HOST=0.0.0.0), configure at least one of the two below.
### NOTE: AUTH_ACCOUNTS additionally requires TOKEN_SECRET to be set to a
### non-default value, otherwise the server refuses to start. Use a random
### value of at least 32 bytes, e.g. `openssl rand -hex 32`: a shorter key
### weakens the HS256 signature and makes PyJWT emit InsecureKeyLengthWarning.
### NOTE: even with authentication enabled, the default WHITELIST_PATHS below
### exempts /api/* so the Ollama-compatible endpoints (/api/chat, /api/generate,
### ...) stay open by default, matching Ollama's own unauthenticated behavior.
### Those routes invoke the LLM and read your knowledge base, so if you expose
### the server to a network and want them protected, set WHITELIST_PATHS=/health
### (and have your Ollama clients send the API key). See WHITELIST_PATHS below.
# AUTH_ACCOUNTS='admin:admin123,user1:{bcrypt}$2b$12$S8Yu.gCbuAbNTJFB.231gegTwr5pgrFxc8H9kXQ4/sduFBHkhM8Ka'
# TOKEN_SECRET=lightrag-jwt-default-secret-key!
# JWT_ALGORITHM=HS256
# TOKEN_EXPIRE_HOURS=48
# GUEST_TOKEN_EXPIRE_HOURS=24

### Login brute-force protection (POST /login).
### After LOGIN_MAX_FAILED_ATTEMPTS failed attempts from the same client IP for
### the same username within LOGIN_LOCKOUT_WINDOW_SECONDS, further attempts are
### rejected with HTTP 429 until the window passes; a successful login resets it.
### Set LOGIN_MAX_FAILED_ATTEMPTS=0 to disable. Counters are per server process
### (in-memory): under gunicorn with N workers the effective limit is N x the
### value below; use a reverse-proxy / WAF rate limit for strict enforcement.
# LOGIN_MAX_FAILED_ATTEMPTS=5
# LOGIN_LOCKOUT_WINDOW_SECONDS=300

### 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: paths exempt from authentication. A /* suffix matches on
### path-segment boundaries, so /api/* covers /api and everything under /api/
### (it does NOT match a sibling like /apikeys).
### Entries are internal route paths and must NOT include LIGHTRAG_API_PREFIX:
### the mount prefix is removed before matching, so /health here exempts
### /site01/health as the browser sees it (see docs/MultiSiteDeployment.md).
### Default keeps /api/* open for Ollama-client compatibility (Ollama is
### unauthenticated by default). To require auth on the Ollama routes too when
### the server is network-exposed, narrow this to /health.
### NOTE: /health stays whitelisted as a liveness probe, but it no longer leaks
### configuration to unauthenticated callers: anonymous requests get only
### liveness signals (status/versions/auth_mode/pipeline_busy), while the full
### runtime configuration is returned only to authenticated callers (valid JWT
### or X-API-Key).
# 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 (default=true，permanently disabled for streaming response)
ENABLE_LLM_CACHE=false
### LLM response cache for document processing: entity/relation extraction,
### merge summaries and multimodal analysis (default=true). It makes re-processing
### an unchanged document nearly free; set false only to force fresh LLM calls
### (e.g. when benchmarking throughput).
# ENABLE_LLM_CACHE_FOR_EXTRACT=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

### 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

### Append each chunk's heading path (parent headings joined by " → ") as a
### `content_headings` field in the chunk JSON sent to the LLM. Costs extra tokens.
ENABLE_CONTENT_HEADINGS=true

### Global instructions prepended to every request's user_prompt (the
### "Additional Instructions" section of the answer prompt). Empty by default.
### A request can opt out with the API field `disable_user_prompt_prefix`.
###   - If a request's user_prompt is empty, this prefix alone becomes the
###     instructions sent to the LLM. Only the API field above suppresses it.
###   - Concatenated VERBATIM, with no separator inserted. End the value with
###     \n\n yourself so it does not run into the request's own prompt.
###   - Single line only. Use DOUBLE quotes with \n for line breaks; inside
###     single quotes a \n stays literal.
###   - `${...}` is substituted from the environment in BOTH quote styles and
###     cannot be escaped. If your text contains `${`, use the file below.
# USER_PROMPT_PREFIX="Please use Mermaid format for diagrams; the delimiter for LaTeX is $$. For inline citations, use the footnote marker syntax `[^1]`, where the `^` preceding the identifier indicates a footnote reference. When multiple citations are required at a single location, each ID should be enclosed in separate footnote markers (e.g., `[^1][^2][^3]`).\n"

### Read the global prefix from PROMPT_DIR/user_prompt/<file> instead
### (.md or .txt, UTF-8). Takes precedence over USER_PROMPT_PREFIX, and is the
### way to configure a long or multi-paragraph prefix.
### File name only, no directory separators. PROMPT_DIR defaults to ./prompts
# USER_PROMPT_PREFIX_FILE=user_prompt_prefix.md

#########################################################
### Reranking configuration
### RERANK_BINDING type: null, cohere, jina, aliyun
### For rerank model deployed by vLLM use cohere binding
### If LightRAG deployed in Docker:
###    uses host.docker.internal instead of localhost in RERANK_BINDING_HOST
#########################################################
RERANK_BINDING=null
# RERANK_MODEL=BAAI/bge-reranker-v2-m3
# RERANK_BINDING_HOST=http://localhost:8000/rerank
# RERANK_BINDING_API_KEY=your_rerank_api_key_here

### 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
### Enable rerank by default in query params when RERANK_BINDING is not null
# RERANK_BY_DEFAULT=True

### Rerank concurrency and timeout (independent from base LLM settings)
### MAX_ASYNC_RERANK falls back to MAX_ASYNC_LLM when unset.
### RERANK_TIMEOUT has its own default (30s) since reranker calls are
### typically much shorter than full LLM generation.
# MAX_ASYNC_RERANK=4
# RERANK_TIMEOUT=30

### 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_MAX_TOKENS_PER_DOC must be an integer >= 1; the server refuses to start otherwise.
### Defaults to 4096 (Cohere rerank-v3.5) when unset; 480 shown below suits 512-token models.
# RERANK_ENABLE_CHUNKING=true
# RERANK_MAX_TOKENS_PER_DOC=480

### Aliyun Dashscope (gte-rerank-*, qwen3-vl-rerank) — nested input/parameters format
# # RERANK_BINDING=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

### Aliyun Dashscope qwen3-rerank series — flat (Cohere-style) payload format
### The qwen3-rerank models expect a flat body {"model", "query", "documents", "top_n", ...}
### and return top-level "results", identical to the standard Cohere format. They are also served
### from a DIFFERENT, Cohere-compatible endpoint (/compatible-api/v1/reranks) — NOT the
### .../text-rerank/text-rerank path used by gte-rerank-*/qwen3-vl-rerank above.
### So use RERANK_BINDING=cohere (NOT aliyun) and point RERANK_BINDING_HOST at that endpoint.
### Replace {WorkspaceId} and the region with your own; see the Aliyun Text Rerank API docs:
### https://help.aliyun.com/zh/model-studio/text-rerank-api
# # RERANK_BINDING=cohere
# # RERANK_MODEL=qwen3-rerank
# # RERANK_BINDING_HOST=https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-api/v1/reranks
# # RERANK_BINDING_API_KEY=your_rerank_api_key_here

### 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

### For local deployment Embedding and Reranker with vLLM (OpenAI-compatible API)
### Wizard metadata used to preserve the chosen deployment provider across setup reruns
# LIGHTRAG_SETUP_EMBEDDING_PROVIDER=vllm
# LIGHTRAG_SETUP_RERANK_PROVIDER=vllm
# VLLM_EMBED_MODEL=BAAI/bge-m3
# VLLM_EMBED_PORT=8001
# VLLM_EMBED_DEVICE=cpu
### VLLM_EMBED_API_KEY is passed as --api-key to vLLM; synced to EMBEDDING_BINDING_API_KEY; auto-generated if blank
# VLLM_EMBED_API_KEY=
# VLLM_EMBED_EXTRA_ARGS=
# VLLM_RERANK_MODEL=BAAI/bge-reranker-v2-m3
# VLLM_RERANK_PORT=8000
# VLLM_RERANK_DEVICE=cuda
### VLLM_RERANK_API_KEY is passed as --api-key to vLLM; synced to RERANK_BINDING_API_KEY; auto-generated if blank
# VLLM_RERANK_API_KEY=
### Use float16 for GPU mode. CPU mode uses the official vLLM CPU image.
# VLLM_USE_CPU=1
### Set to 1 for CPU mode, unset for GPU mode
# CUDA_VISIBLE_DEVICES=-1
### Set to -1 to disable CUDA (CPU mode), or specific GPU IDs for GPU mode
# NVIDIA_VISIBLE_DEVICES=0
### Optional Docker runtime equivalent; generated GPU compose honors either variable.
# VLLM_RERANK_EXTRA_ARGS=

########################################
### Document processing configuration
########################################
### Document processing output language: English, Chinese, French, German ...
SUMMARY_LANGUAGE=English

### Enable JSON-structured output for entity extraction
### Default behavior: JSON output is disabled when ENTITY_EXTRACTION_USE_JSON is unset
### JSON output incurs higher latency but delivers improved reliability
ENTITY_EXTRACTION_USE_JSON=true

### Optional external YAML profile for entity type guidance and extraction examples
### Profiles are loaded from PROMPT_DIR/entity_type (PROMPT_DIR defaults to ./prompts).
### A reference template is shipped at prompts/samples/entity_type_prompt.sample.yml;
# ENTITY_TYPE_PROMPT_FILE=entity_type_prompt.yml
# PROMPT_DIR=<absolute_path_for_prompt_dir>

### Multimodal parsing/analyze integration
### Optional parser routing rules. Example for VLM & MinerU enabled configuration:
###     LIGHTRAG_PARSER=*:native-iteP(drop_rf),xlsx:legacy-R,*:mineru-iteP(drop_rf),*:legacy-R
### Rules are separated with semicolons ';' or commas ',';
### Rules match file suffixes (pdf) are checked left-to-right.
### If mineru/docling appears in LIGHTRAG_PARSER, the corresponding endpoint
### below must be configured before server startup.
###
### Per-strategy chunk parameters may be attached in parentheses to a chunk
### selector (F/R/V/P/C). Inside the parentheses a comma only separates parameters.
### Supported parameters (alias in brackets):
###     chunk_token_size [chunk_ts]          F/R/V/P/C   e.g. R(chunk_ts=800)
###     chunk_overlap_token_size [chunk_ol]  F/R/P/C     (V has no overlap)
###     LIGHTRAG_PARSER=pdf:legacy-R(chunk_ts=800,chunk_ol=80);*:legacy-R
### The same syntax works in a filename hint, e.g. notes.[-R(chunk_ts=800)].md
### Boolean parameters may be written bare as a flag, on a chunk selector as
### well as on an engine token: P(drop_rf) means drop_rf=true, and
### docx:native(smart_heading) means native(smart_heading=true).
### See docs/FileProcessingPipeline.md for detail
LIGHTRAG_PARSER=*:native-teP,*:legacy-R

### Decompression budget for the native DOCX engine (advanced). A .docx is a
### ZIP, so what parsing costs is its UNCOMPRESSED size — MAX_UPLOAD_SIZE
### bounds the compressed file on disk and MAX_REQUEST_BODY_BYTES bounds the
### request body, so neither of them bounds this. Both quantities are read
### from the ZIP central directory before anything is decompressed; a .docx
### over either limit is rejected and the document is recorded FAILED.
### The same budget also bounds the legacy engine's .pptx and .xlsx (both are
### the identical OPC/ZIP bomb class), enforced before those parse locally.
### The ratio gate is applied archive-wide and to the cumulative expansion by
### which individual members exceed their own ratio budgets, so member
### splitting cannot dilute it. That excess receives a fixed
### DOCX_RATIO_FLOOR_BYTES allowance plus a 1:1 allowance for archive bytes
### outside those members. This permits repetitive XML backed by real stored
### media without letting padding buy another ratio-cap multiple. Raise the
### three gates
### (DOCX_MAX_UNCOMPRESSED_BYTES / DOCX_MAX_COMPRESSION_RATIO / DOCX_MAX_ENTRIES)
### only if a legitimate document is being refused; a non-positive value
### disables that gate. DOCX_RATIO_FLOOR_BYTES is the opposite — it is the
### small-file EXEMPTION threshold, so a non-positive value does NOT disable
### the ratio gate, it removes the exemption and makes the ratio gate strictest.
# DOCX_MAX_UNCOMPRESSED_BYTES=536870912
# DOCX_MAX_COMPRESSION_RATIO=100
# DOCX_RATIO_FLOOR_BYTES=16777216
# DOCX_MAX_ENTRIES=10000

### Native DOCX embedded-image export budgets. Images are copied from the
### archive in 1 MiB chunks, with a 25 MiB ceiling for one image and a 64 MiB
### cumulative ceiling for one document. An over-budget image is skipped with
### a parse warning; document text continues. Defaults are sized against the
### five native parse workers so attacker-controlled output remains bounded.
### Raise these only when retaining unusually large embedded images matters
### more than the memory/disk ceiling. A zero or negative value is NOT
### unlimited; it falls back to the safe default.
# NATIVE_DOCX_IMAGE_MAX_BYTES=26214400
# NATIVE_DOCX_IMAGE_MAX_TOTAL_BYTES=67108864

### Zip-bomb budget for the result BUNDLE an external parser engine (docling,
### mineru) returns — a zip fetched from the configured server and extracted
### locally. Defense-in-depth against a compromised/misbehaving endpoint. The
### bundle size scales with your source document and the engine's rendering
### settings, so raise (or disable) these if a legitimate result is refused.
### A non-positive value disables that gate. Both are read live per parse.
# PARSER_RESULT_BUNDLE_MAX_ENTRIES=10000
# PARSER_RESULT_BUNDLE_MAX_TOTAL_BYTES=536870912

### Overall wall-clock budget (seconds) for downloading that result bundle,
### on top of the per-read timeout each client already sets. A per-read
### timeout only bounds a single socket operation, so a peer trickling one
### byte per interval can reset it indefinitely; this bounds the whole
### download regardless of how it stalls. A non-positive value disables the
### deadline. Read live per parse.
# PARSER_RESULT_BUNDLE_DOWNLOAD_TIMEOUT=300

### Cap on the raw response bytes received while streaming that download,
### checked before the bytes ever reach the zip-bomb checks above. Separate
### from PARSER_RESULT_BUNDLE_MAX_TOTAL_BYTES: that one bounds the
### *uncompressed* size a zip declares, this bounds the *compressed* bytes
### actually transferred — tune independently. A non-positive value
### disables this gate. Read live per parse.
# PARSER_RESULT_BUNDLE_DOWNLOAD_MAX_BYTES=536870912

### Global default for the native docx smart_heading engine parameter.
### When true, .docx files routed to the native engine get smart_heading
### enabled without per-file declaration; opt out per file/rule with an
### explicit native(smart_heading=false). Enabling this (or carrying
### native(smart_heading=true) in a LIGHTRAG_PARSER rule) makes the server
### verify the pinned spaCy models at startup and fail fast if missing
### (install: lightrag-download-cache --spacy-install; the main
### Docker image ships them). Applies to new uploads only — already-ingested
### documents keep their persisted engine parameters on re-parse.
# DOCX_SMART_HEADING=true

### smart_heading tuning (advanced). These apply only when smart_heading is
### active (DOCX_SMART_HEADING above, or a per-file/rule opt-in). The defaults
### suit most documents — the values shown ARE the defaults; uncomment to change.

### Skip smart_heading for WHOLE documents shorter than this many tokens; they
### keep the plain outline-based headings. Kept below CHUNK_P_SIZE (2000): a
### document that fits inside one paragraph-chunk needs no heading splitting.
### Lower it to run the extra analysis on short documents (e.g. 红头 notices).
# DOCX_SMART_MIN_TOKENS=1800
### Once a document clears the gate above, a single SUB-document shorter than
### this many tokens falls back to outline-only levels instead of size-based
### leveling. Defaults to min(1000, DOCX_SMART_MIN_TOKENS): lowering the
### whole-document gate to run smart on short documents also pulls this floor
### down, so their sub-documents are not silently left on outline-only. Set it
### explicitly to override (e.g. level only big sections of a long document).
# DOCX_SMART_SUBDOC_MIN_TOKENS=1000

### Heading-detection sensitivity. The engine drops a line from the heading set
### when it looks like body text; these tune how strict that is.
###   DENSITY_MAX             largest share of paragraphs allowed to be headings
###                           (0-1) before the engine decides it over-detected
###                           and recomputes the body font size.
###   DENSITY_BASELINE_MARGIN for a document with a rich built-in outline, the
###                           ceiling rises by this much above the document's own
###                           outline ratio (percentage points).
###   MIN_INTER_HEADING_CHARS fewest body characters expected between two adjacent
###                           headings (a Chinese char counts as 3); a denser run
###                           also triggers the recompute.
###   HEADING_MAX_CHARS       a line longer than this (Chinese char = 3) is body,
###                           never a heading. Also caps the merged main+sub doc
###                           title. FATAL if non-integer or < 3 (too small to
###                           hold the "..." truncation marker): startup and
###                           parsing both reject it; a value below the title-line
###                           width (90) is accepted but warns (most headings
###                           would be demoted to body).
### Raise the ceiling / lower the minimums if real headings are being dropped;
### do the reverse if body text is leaking in as headings.
# DOCX_SMART_DENSITY_MAX=0.40
# DOCX_SMART_DENSITY_BASELINE_MARGIN=0.10
# DOCX_SMART_MIN_INTER_HEADING_CHARS=200
# DOCX_SMART_HEADING_MAX_CHARS=180

### Table of contents (TOC) retention. A detected TOC keeps its first N visible
### lines as body text (so a 目录 heading is not orphaned from its entries) and
### collapses the remainder to a single "……". Counted globally by visible line
### (a soft-break line counts as one). 0 keeps none (one "……" replaces the whole
### TOC); a negative value is treated as 0; a very large value keeps it all.
# DOCX_SMART_TOC_KEEP_LINES=5

### Lines starting with one of these words (followed by a number) are figure /
### table captions, never headings. Comma-separated; localize for your corpus.
# DOCX_SMART_CAPTION_PREFIXES=图,表,公式,Figure,Table,Fig.,Eq.,Chart

### 公文版记 (imprint) ANCHORS — openers like 抄送：/ 主题词：(colon class) start a
### 版记 region: they are body, never headings, and veto title-block membership
### for themselves and the 2 preceding non-blank paragraphs. An anchor may also
### be middle content of another anchor's region (主题词 then 抄送). Comma-
### separated; localize for your corpus.
# DOCX_SMART_IMPRINT_COLON_PREFIXES=抄送,主题词

### 版记 region CLOSERS (印发-family — the issuing-organ / print line that ENDS a
### 版记), recognized ONLY within FORWARD_PARAS non-blank paragraphs after an
### anchor above (so a body line ending in 印发 cannot false-fire alone). Prefix
### form: 印发：XX / 印发 XX / 印发机关 XX (印发机关 is a closer, not an anchor — the
### old DOCX_SMART_IMPRINT_SPACE_PREFIXES knob is gone); trailing form: a line
### ENDING with 印发 (某某办公室 2026年6月30日 印发, the GB/T layout). A found 抄送…印发
### span (middle lines included) is barred from title blocks; when a valid title
### block immediately follows it (a 公文汇编 boundary), the span is force-demoted
### to body. Comma-separated; localize for your corpus.
# DOCX_SMART_IMPRINT_CLOSER_PREFIXES=印发,印发机关
# DOCX_SMART_IMPRINT_CLOSER_TRAILING=印发
# DOCX_SMART_IMPRINT_FORWARD_PARAS=3

### Document-title detection and its LLM cost. Only the first eligible paragraph
### can be a single-line title candidate; it must be at least this many points
### larger than the body font. The LLM input remains bounded by a token window.
# DOCX_SMART_TITLE_BLOCK_MIN_DELTA=2.0
# DOCX_SMART_LLM_WINDOW_TOKENS=1000
### Mid-document title-window gate: a multi/table window may open freely only
### in the document head zone — fewer than this many content records before it
### AND before the first body signal (a sentence-punctuated paragraph, a real
### outline/numbered heading, or a data table that cannot be cover material).
### Past the zone it needs a 版记 tail or 附件 marker as boundary evidence.
# DOCX_SMART_TITLE_HEAD_ZONE_RECORDS=8
### Further DOCX_SMART_* thresholds exist (circuit-breaker ratios, confidence
### ratio, numbering sequence break, TOC detection minimum). They are algorithm
### internals rather than a supported tuning surface: see lightrag/constants.py
### for their names and defaults, and prefer reporting a misclassification over
### tuning them.

### Native Markdown (.md / .textpack) remote image handling
### External http(s) images in markdown are downloaded and embedded into the
### sidecar assets by default (SSRF-guarded: private/loopback/link-local hosts
### are refused; the socket is pinned to the validated IP so a DNS rebind cannot
### redirect it to an internal host, and any ambient HTTP(S)_PROXY is ignored).
### Set ENABLED=false to instead DROP external images (no sidecar entry), in
### which case a doc whose only images are external links produces no drawings.json.
NATIVE_MD_IMAGE_DOWNLOAD_ENABLED=true
### When downloading is enabled, REQUIRED=true fails the document on a download
### error; false (default) keeps the image as an external link and warns.
### (Base64 and .textpack file-reference images are always embedded regardless
### of this switch; SVG images are rasterized to PNG via cairosvg.)
# NATIVE_MD_IMAGE_DOWNLOAD_REQUIRED=false
### Wall-clock deadline for ONE image request, covering connect, TLS, headers,
### body and every redirect hop (DNS resolution is the one phase outside it).
# NATIVE_MD_IMAGE_DOWNLOAD_TIMEOUT=30
### Per-image size ceiling: caps a remote download AND a single bundled
### (.textpack) asset, so one oversized image cannot be read into memory.
# NATIVE_MD_IMAGE_MAX_BYTES=26214400
### SVG render budget: an SVG whose declared canvas (width*height or viewBox)
### exceeds this pixel count is skipped BEFORE rasterization
# NATIVE_MD_IMAGE_MAX_SVG_PIXELS=16000000
### Per-DOCUMENT image ceilings. The three above bound ONE image; these bound
### a whole document, and they are what stops a small upload referencing a
### large number of images from exhausting the host.
### Over-budget images do NOT fail the document: a remote one degrades to an
### external link and a base64 / .textpack one is dropped, both with a parse
### warning. DOWNLOAD_REQUIRED=true turns an over-budget REMOTE image into a
### document failure, matching how it treats any other failed download; base64
### and .textpack images are outside that switch, as they always have been.
### 0 or a negative value is NOT "unlimited" — it falls back to the default.
### For effectively unlimited, set a very large number.
###
### Total image bytes RETAINED for one document, across every source (base64,
### .textpack file, download, and cache reuse). This is the memory bound; the
### default is derived from MAX_PARALLEL_PARSE_NATIVE (5), so all parse workers
### together hold at most ~320 MiB of retained image data.
### Live image data peaks a little higher while one image is in flight, since
### assembling a download into one immutable buffer holds the pieces and the
### result together for a moment. The bound on live image ALLOCATIONS is this
### value plus min(MAX_BYTES, this value) — ~89 MiB at the defaults — plus a
### small constant for the read buffer. Lowering MAX_BYTES lowers it. Process
### RSS follows the allocator's high-water mark and can sit above that.
# NATIVE_MD_IMAGE_MAX_TOTAL_BYTES=67108864
### Remote image fetch ATTEMPTS per document. Counts every redirect hop, and
### counts attempts that fail in DNS or the SSRF guard without a packet leaving
### the host, so it is an upper bound on outbound HTTP requests rather than an
### exact count. A `.native_raw/` cache hit issues no request and is not
### counted. Base64 and .textpack file references are not counted either.
# NATIVE_MD_IMAGE_MAX_REQUESTS=100
### Wall-clock budget for ALL image downloads in one document, in seconds
### (DOWNLOAD_TIMEOUT above is per request). Starts at the first download.
### Once spent, the remaining external images degrade to links with no request
### issued. Successful downloads are cached, so a re-parse resumes where the
### previous one stopped rather than starting over.
# NATIVE_MD_IMAGE_DOWNLOAD_TOTAL_TIMEOUT=120
### Escape hatch for the SSRF guard: only globally-routable IPs are allowed by
### default. To permit specific non-public ranges (e.g. an internal image host),
### list comma-separated CIDRs/IPs. Applies to DNS-resolved IPs and redirects.
# NATIVE_MD_IMAGE_ALLOWED_NON_PUBLIC_CIDRS=10.0.0.0/8,192.168.1.5
### Downloaded external images are cached in a `<file>.native_raw/` sidecar dir
### so a re-parse of an unchanged file reuses them instead of re-downloading.
### Set the following env var true to force a re-download (discard the cache)
# LIGHTRAG_FORCE_REPARSE_NATIVE=false

### Async parser service protocol (optional)
### Configure these when using remote MinerU/Docling async services

### ---- MinerU shared parameters (both local and official modes) ----
### MinerU API protocol. Choose one active mode.
### - official: MinerU precision API v4. Requires MINERU_API_TOKEN.
### - local: self-hosted mineru-api / mineru-router base URL.
MINERU_API_MODE=local
# MINERU_POLL_INTERVAL_SECONDS=2
# MINERU_MAX_POLLS=600
# MINERU_LANGUAGE=ch
# MINERU_ENABLE_TABLE=true
# MINERU_ENABLE_FORMULA=true
# MINERU_PAGE_RANGES=
### MINERU_PAGE_RANGES semantics differ by mode:
### - official: forwarded verbatim, supports e.g. "1-3,5,7-9".
### - local:    only a single page ("3") or simple range ("1-10"); comma
###             lists are rejected at startup.
### When switching modes, double-check this constraint.
### Per-file override: a hint / rule may set page_range on the engine token,
### e.g. notes.[mineru(page_range=1-3,page_range=5)].pdf — inside the parens a
### comma only separates parameters, so a multi-segment list REPEATS the key
### (and requires MINERU_API_MODE=official). Likewise language / local_parse_method.
# MINERU_ADDITIONAL_SUFFIXES=doc,xls,ppt
### MINERU_ADDITIONAL_SUFFIXES: suffixes your MinerU endpoint can handle on top
### of the baseline set (pdf docx pptx xlsx png jpg jpeg jp2 webp gif bmp).
### MinerU converts legacy Office formats through LibreOffice on its own side,
### so which of them work is a property of your deployment, not of LightRAG.
### Format: bare lowercase suffixes separated by ',' — a leading dot and
### surrounding whitespace are tolerated (" .DOC " = doc); anything else
### ('*.doc' written out of glob habit, or a ';'-separated list) is rejected at
### startup rather than silently ignored.
### Semantics differ by mode: this describes the endpoint of the CURRENTLY
### selected MINERU_API_MODE — official coverage is fixed by the service, local
### coverage depends on your container. When switching modes, re-check it.
### NOTE: this only makes a suffix *routable to mineru*; it does not by itself
### make a bare 'x.doc' uploadable. Pair it with a routing rule
### (LIGHTRAG_PARSER=doc:mineru) or a per-file hint (x.[mineru].doc), otherwise
### such files still fall through to the default legacy engine and are rejected
### as unsupported. Conversely, a 'doc:mineru' rule without this variable fails
### startup validation, since doc is not among mineru's capabilities.

### ---- MinerU local-only (MINERU_API_MODE=local) ----
MINERU_LOCAL_ENDPOINT=http://127.0.0.1:8000
### MINERU_LOCAL_BACKEND: which mineru-api backend handles the parse.
###   Accepted values (per mineru-api POST /tasks form parameter `backend`):
###     hybrid-auto-engine - pipeline + VLM combo with auto-selected local
###                          engine (mineru-api's default). GPU required.
###     pipeline           - CPU-friendly traditional pipeline; no VLM step.
###     vlm-auto-engine    - VLM with auto-selected local inference engine
###                          (sglang-engine / vllm-engine if GPU is available);
###                          requires the matching engine extra preinstalled
###                          on the mineru-api side, plus model weights.
###   We ship `hybrid-auto-engine` -- requires the target mineru-api
###   deployment to have a GPU plus the matching inference engine
###   (sglang / vllm) and model weights installed. Switch to `pipeline`
###   for CPU-only deployments without those dependencies.
MINERU_LOCAL_BACKEND=hybrid-auto-engine
### MINERU_LOCAL_PARSE_METHOD: parsing strategy for the pipeline component.
###   Accepted values:
###     auto - auto-detect embedded text-layer vs OCR per page (default).
###     txt  - extract text from the embedded text layer only; fastest,
###            but yields empty output on scanned PDFs without a text layer.
###     ocr  - force OCR on every page regardless of text-layer quality;
###            slowest, reliable on scanned or low-quality PDFs.
###   Only consumed when MINERU_LOCAL_BACKEND is `pipeline` or
###   `hybrid-auto-engine` (the pipeline arm of the hybrid pipeline).
###   Pure VLM backends (`vlm-auto-engine`, `vlm-http-client`) ignore this
###   parameter -- the VLM model handles layout/OCR natively.
MINERU_LOCAL_PARSE_METHOD=auto
### MINERU_LOCAL_IMAGE_ANALYSIS: enable VLM image/chart analysis pass for
###   better caption an footnote recognition.
###   Only consumed by `vlm-auto-engine`, `vlm-http-client`,
###   `hybrid-auto-engine`, `hybrid-http-client`. The `pipeline` backend
###   silently drops this flag -- its `_process_pipeline` does not accept
###   the kwarg, so setting `false` under pipeline does NOT speed parsing
###   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

### ---- MinerU raw-bundle cache (<base>.mineru_raw/) ----
### Engine version recorded in the bundle manifest; changing it invalidates the
### cache. Leave empty to skip the version check.
# MINERU_ENGINE_VERSION=
### Default coordinate system for MinerU layout boxes, written into the
### sidecar meta. NOTE the default differs from DOCLING_BBOX_ATTRIBUTES.
# MINERU_BBOX_ATTRIBUTES={"origin":"LEFTTOP","max":1000}

### 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.
###
### Optional formats:
### - DOCLING_ADDITIONAL_SUFFIXES: comma-separated suffixes your
###     docling-serve deployment can actually handle on top of the baseline
###     set (pdf docx pptx xlsx md html xhtml png jpg jpeg tiff webp bmp).
###     Docling's legacy Office support (doc/xls/ppt) needs LibreOffice on
###     the docling-serve side, so it is opted in per deployment rather than
###     advertised globally. Bare lowercase suffixes only — 'doc,ppt,xls',
###     not '*.doc' or 'doc;ppt' (the server refuses to start otherwise).
###     NOTE: this only makes the suffix *routable to docling*; it does not
###     by itself make bare 'x.doc' uploadable. Pair it with a routing rule
###     (LIGHTRAG_PARSER=doc:docling) or a per-file hint (x.[docling].doc),
###     otherwise such files still fall through to the default legacy engine
###     and are rejected as unsupported.
###
### 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_ADDITIONAL_SUFFIXES=doc,ppt,xls
# 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"}
# DOCLING_ENGINE_VERSION=
### 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
### Note: /documents/upload derives its raw request-body ceiling from this value
###   (plus 1 MiB of multipart overhead), so 0/None leaves that route with no
###   body ceiling at all and the server warns about it at startup.
# 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

### Optional installed third-party chunker (lightrag.chunkers entry points).
### Bare registered name only, not module:func or a file path. CLI: --custom-chunker.
### Unset preserves the built-in callback. Selection serves C AND no-selector
### inserts instance-wide; explicit F/R/V/P still use their built-in strategies.
### No-selector inserts then lose source-span sidecar backfill, exactly as a
### constructor-supplied chunking_func does -- this changes default ingestion.
### Unknown/duplicate selected name or invalid implementation fails startup.
### Authoring, diagnostics and executor opt-in: docs/ThirdPartyChunker.md
# CUSTOM_CHUNKER=

### Overlap (in tokens) borrowed from the previous chunk's tail when the
### embedding hard fallback still has to token-window-split a chunk that
### remains over the embedding model's context limit after chunking.
### Independent of CHUNK_OVERLAP_SIZE above (which some chunker strategies,
### e.g. V, deliberately zero out for unrelated reasons) — 0 disables this
### fallback's overlap; negative values are rejected at startup.
# EMBEDDING_CHUNK_OVERLAP_TOKEN_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.
###       Bounded at 64 entries of at most 256 characters each. The splitter
###       re-scans the whole text once per remaining separator, so an oversized
###       cascade costs O(len(separators) x len(text)) for no extra splitting.
###       The two limits do NOT behave the same way:
###         - an entry longer than 256 characters is DROPPED, not shortened. A
###           lone 300-character separator disappears; it does not fall back to
###           matching its first 256 characters.
###         - a list longer than 64 entries is TRUNCATED to 64, keeping the
###           trailing char-level "" sentinel when the original had one.
###       If nothing survives, the fallback differs by consumer and is NOT this
###       variable's default: the R chunker uses the splitter's own four-entry
###       cascade ("\n\n", "\n", " ", ""), while multimodal surrounding-context
###       extraction uses the CJK-aware default above minus the sentinel.
###       A valid-but-out-of-bounds configured value is corrected and logged once
###       when its configuration is loaded/cached, rather than once per document.
###       A value that is not a JSON array of strings falls back to the default
###       cascade above instead of being bounded, so a bare string can never
###       become 64 single-character separators.
# 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.
###       This env var (or the SDK addon_params) is the ONLY way to set the
###       pattern: /documents/text and /documents/texts reject a
###       "sentence_split_regex" key in the chunking params with HTTP 422.  An
###       attacker-supplied pattern is a ReDoS vector — it is applied to the
###       request's own text and CPython's regex engine holds the GIL while
###       backtracking, so one request can freeze the worker process
###       (GHSA-32jh-39m7-8x84).  Keep this value under operator control and
###       prefer anchored, non-ambiguous patterns.
###     V embeds one item per sentence window while chunking, so the size of
###       those requests is governed by EMBEDDING_BATCH_NUM (see the Embedding
###       section), not by CHUNK_V_SIZE.  Per-window token length is capped on
###       a best-effort basis at the embedding function's declared token limit:
###       EMBEDDING_TOKEN_LIMIT when set, otherwise the binding's own default
###       (8192 for openai/azure_openai/ollama/jina/bedrock/lollms, 32000 for
###       voyageai, 2048 for gemini) — every built-in binding declares one, so
###       on the API server path CHUNK_V_SIZE is never the budget.  It becomes
###       the fallback only for an SDK caller whose EmbeddingFunc declares no
###       max_token_size (or 0).  Truncation there only affects boundary
###       detection, never chunk content.
# 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 matching reference blocks 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: 0 scans all content blocks for reference
###       headings (default); a positive value scans only the last N blocks.
###     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=0
# 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
### Floor on the multimodal item's own content budget. If the surrounding
### budgets above leave less than this for the item itself, startup warns and
### names this variable as the knob to raise (or lower SURROUNDING_* instead).
# MM_EXTRACT_CONTENT_MIN_TOKENS=100

### 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).
### This does not set per-document chunk extraction or graph-merge task limits.
MAX_PARALLEL_INSERT=3
### For each document, MAX_ASYNC_LLM caps chunk entity/relation extraction tasks;
### each entity-merge or relation-merge phase caps tasks at 2 * MAX_ASYNC_LLM.
### EXTRACT_MAX_ASYNC_LLM limits the actual Extract-role LLM requests and does
### not change those pipeline task limits.
### Optional per-stage document pipeline concurrency
# MAX_PARALLEL_PARSE_NATIVE=5
# MAX_PARALLEL_PARSE_MINERU=1
# MAX_PARALLEL_PARSE_DOCLING=1
# MAX_PARALLEL_ANALYZE=5
### Optional queue sizes for staged pipeline workers
# QUEUE_SIZE_PARSE=20
# QUEUE_SIZE_ANALYZE=100
# QUEUE_SIZE_INSERT=4
### Bounded scheduling page size: the scheduler sweeps the doc_status backlog
### through keyset pages of this many records so memory grows with page-size +
### inflight instead of the whole backlog. 0 disables paging (legacy single
### scan). Default 500.
# PIPELINE_SCHEDULING_PAGE_SIZE=500
### /documents/scan discovery is a single streaming pass; this bounds how many
### newly claimed files one batch holds before it is written to doc_status, so
### scan memory grows with the batch instead of with the input directory. Must be
### positive (there is no "disabled" value — the server refuses to start on 0).
### Default 100.
# SCAN_ENQUEUE_BATCH_SIZE=100
### Directory for /documents/scan's disposable candidate spool — the disk-backed
### index that orders discovered files oldest-first without holding them in RAM.
### One fixed-name database per workspace subdirectory, deleted when the scan
### ends and reclaimed by the next scan after a crash. Default: an empty value
### means WORKING_DIR/scan_spool. Set it when WORKING_DIR is a network volume.
### It must be writable local disk — if it cannot be used the scan FAILS rather
### than relocating to the OS temp dir, which is a RAM-backed tmpfs on many
### hosts and would defeat the memory bound. Never point it at INPUT_DIR.
# SCAN_SPOOL_DIR=
### Refuse to start when the configured doc_status backend is missing a strict
### capability (active count / source-conflict listing / source-conflict repair /
### strict point reads). Default false: the gaps are logged loudly at startup and
### reported by /health under "capabilities", and the affected features fail closed
### (admission 503, conflicts 501). Set true if you would rather not start at all.
# PIPELINE_REQUIRE_STRICT_STORAGE_READS=false
### Admission capacity: refuse new uploads / text inserts with HTTP 429 once this
### many documents are already active (PENDING/PARSING/ANALYZING/PROCESSING) or
### reserved by an in-flight request. Manual retries and /documents/scan may
### exceed it on purpose; the rows they create make ordinary uploads wait.
### 0 disables admission control (default).
# MAX_PENDING_DOCUMENTS=0
### Ceiling on how many texts ONE /documents/texts request may carry, refused
### with 413 before any per-text storage lookup. Bounds the fan-out of a single
### request, unlike MAX_PENDING_DOCUMENTS which bounds the whole backlog and says
### "retry later" — no amount of waiting makes an oversized batch fit.
### 0 disables (default).
# MAX_TEXTS_PER_REQUEST=0
### Per-workspace ceiling on manual retry requests (/documents/reprocess_failed,
### /documents/scan) that have been published but not yet acknowledged by their
### exclusive FAILED->PENDING reset. The channel is sticky, so an over-capacity
### publish is refused with 429 rather than dropped. Default 64.
# MAX_UNACKED_MANUAL_RETRIES=64
### Hard ceiling on the raw request body, counted as it streams through ASGI (so
### a body that lies about or omits Content-Length is still cut off with 413).
### Applies to EVERY route, and is layered because routes differ by orders of
### magnitude in what they legitimately carry:
###   - ordinary routes (/query, /api/chat, ...):  this value, default 1 MiB
###   - /documents/text and /documents/texts:      50 MiB built in, ONLY while
###                                                this variable is left unset
###   - /documents/upload:                         MAX_UPLOAD_SIZE + 1 MiB of
###                                                multipart overhead
### Setting this at all makes it govern every non-upload route, ingestion
### included; there is no separate knob for the ingestion tier. That holds even
### when the value equals the 1 MiB default, so uncommenting the line below as-is
### does change behaviour: it drops /documents/text(s) from 50 MiB to 1 MiB.
### Setting it to 0 turns off every ceiling, including the derived upload one.
### Distinct from MAX_UPLOAD_SIZE, which bounds one uploaded FILE after multipart
### parsing: this bounds the bytes the server agrees to read at all.
# MAX_REQUEST_BODY_BYTES=1048576

###########################################################################
### Gloabal LLM Configuration
###   LLM_BINDING type: openai, openai_responses, 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-6-luna

### Base maximum concurrency for LLM roles and file-pipeline task scheduling.
### Per document, it caps chunk entity/relation extraction tasks; each entity
### or relation merge phase uses twice this task limit. EXTRACT_MAX_ASYNC_LLM
### can separately limit actual Extract-role requests without changing them.
### 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 global LLM configuration above.
### For detail information, refer to:
###   docs/RoleSpecificLLMConfiguration.md
###   docs/RoleSpecificLLMConfiguration-zh.md
###########################################################################
# EXTRACT_LLM_MODEL=gpt-6-luna
### Overrides only the actual Extract-role LLM request limit; file-pipeline
### chunk and merge task limits above still use MAX_ASYNC_LLM.
# EXTRACT_MAX_ASYNC_LLM=4
# EXTRACT_LLM_TIMEOUT=240
# EXTRACT_LLM_BINDING=openai
# EXTRACT_LLM_BINDING_HOST=https://api.openai.com/v1
# EXTRACT_LLM_BINDING_API_KEY=your_api_key

# KEYWORD_LLM_MODEL=gpt-6-luna
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-6-sol
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-6-luna
# VLM_MAX_ASYNC_LLM=4
# VLM_LLM_TIMEOUT=300
# 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 analysis of IMAGE items (the `i` process option).
### Table (`t`) and equation (`e`) items are analyzed by the EXTRACT role and
### are NOT affected by this switch.
###
### This switch is only ever consulted for a document whose process_options
### include `i`. A document without `i` never enters image analysis, so
### leaving this false is the normal setup for e.g. LIGHTRAG_PARSER=*:native-teP
### -- such documents keep processing regardless of how many images they carry.
###
### For a document WITH `i` and this set to false: the first image that
### survives the pre-filters (file present, raster format, both sides >=
### VLM_MIN_IMAGE_PIXEL) FAILS that document rather than being skipped --
### analysis raises and the document lands in FAILED with "VLM analysis
### required but VLM role is not available". Images dropped by those
### pre-filters, and documents whose drawings sidecar is absent, are
### unaffected and keep processing.
###
### 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
### OrcaRouter (OpenRouter-style AI gateway, OpenAI-compatible API):
###     LLM_BINDING=openai
###     LLM_BINDING_HOST=https://api.orcarouter.ai/v1
###     LLM_BINDING_API_KEY=sk-orca-...
###     LLM_MODEL=your-model
# 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 reasoning control. GPT-6 models (including the default
### gpt-6-luna) accept none/low/medium/high/xhigh/max and default to medium,
### which makes per-chunk extraction slow and costly: low is recommended.
### Older models use their own levels (e.g. minimal for GPT-5).
### Override one role with a prefix, e.g. KEYWORD_OPENAI_LLM_REASONING_EFFORT=none
# OPENAI_LLM_REASONING_EFFORT=low
### 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}}'

### Role-specific + Provider-sepecific LLM options
# VLM_OPENAI_LLM_MAX_TOKENS=20000

### OpenAI Responses API Specific Parameters (/v1/responses):
###     LLM_BINDING=openai_responses
###     LLM_BINDING_HOST=https://api.openai.com/v1
###     LLM_MODEL=gpt-5.6
### A sibling of the `openai` binding, not a replacement. Chat Completions
### remains supported; Responses is worth choosing for better prompt-cache
### utilisation on reasoning models, or for a gateway that exposes only
### /v1/responses. Many OpenAI-compatible servers do not implement it, so the
### `openai` binding stays the right default for those.
### Options use their own OPENAI_RESPONSES_LLM_* namespace: the Responses API
### spells these differently and does not accept the Chat Completions forms.
# OPENAI_RESPONSES_LLM_MAX_OUTPUT_TOKENS=9000
# OPENAI_RESPONSES_LLM_REASONING_EFFORT=minimal
### Request reasoning summaries so <think> output is populated (auto, concise, detailed).
### Only for reasoning models; models without reasoning reject reasoning.summary.
# OPENAI_RESPONSES_LLM_REASONING_SUMMARY=auto
# OPENAI_RESPONSES_LLM_TEMPERATURE=0.9
### Whether OpenAI retains the response server-side. LightRAG sends false unless
### this is set to true (the API's own default is to retain every request).
# OPENAI_RESPONSES_LLM_STORE=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 caps the OUTPUT budget. A response cut off by it is
### kept (and reported in doc_status.metadata.llm_truncation); one cut off
### before emitting any content fails the document instead of indexing an empty
### graph — raise this value, or disable thinking mode, if that happens.
# OLLAMA_LLM_NUM_PREDICT=9000
# OLLAMA_LLM_TEMPERATURE=0.85
# OLLAMA_LLM_STOP='["</s>", "<|EOT|>"]'
### OLLAMA_LLM_THINK also accepts a reasoning level (low/medium/high, requires
### an Ollama server that supports levels). Leave it unset entirely to
### follow the model's own default -- an empty value means false, not unset, and
### setting true for a model without thinking support is rejected by Ollama.
# OLLAMA_LLM_THINK=low
### If a thinking-capable model silently returns empty entities/relations during
### extraction (issue #3597), it spent its whole budget on hidden reasoning --
### turn thinking off for the roles that need deterministic output:
# EXTRACT_OLLAMA_LLM_THINK=false
# KEYWORD_OLLAMA_LLM_THINK=false

#######################################################################################
### Embedding Configuration (Should not be changed after the first file processed)
### EMBEDDING_MODEL is REQUIRED: the server refuses to start without it. Every
###    vector storage records the model's name beside its vectors, and that is
###    the only thing that detects a later switch to a different model of the
###    same dimension. Changing it means stopping the server, running
###    lightrag-rebuild-vdb and starting again -- a rolling change is not
###    supported.
### 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

### Max concurrency requests for Embedding
# EMBEDDING_FUNC_MAX_ASYNC=8
### Num of chunks send to Embedding in single request (default is 10)
### Also caps the items per request when the V chunker embeds sentence windows
EMBEDDING_BATCH_NUM=32

### 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_GRAPH_STORAGE options: NetworkXStorage, Neo4JStorage, PGGraphStorage,
###   PGTableGraphStorage, MongoGraphStorage, MemgraphStorage, OpenSearchGraphStorage
### PGTableGraphStorage runs the graph on plain PostgreSQL 14+ tables with no
###   Apache AGE and no extensions, so it works on managed PostgreSQL
###   (RDS / Cloud SQL / Supabase / Neon), and in Docker the official
###   pgvector/pgvector:pg18 image is sufficient. Use PGGraphStorage only if you
###   need AGE — that is the only option requiring the AGE-bundled image
###   gzdaniel/postgres-for-rag:pg18-age-pgvector.
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

### Vector index type: HNSW, HNSW_HALFVEC, IVFFlat, VCHORDRQ
### HNSW and IVFFlat support at most 2000 dimensions; HNSW_HALFVEC supports
### up to 4000 and requires pgvector >= 0.7.0. The default embedding model
### above (text-embedding-3-large, 3072 dims) therefore needs HNSW_HALFVEC;
### the server refuses to start on an index type that cannot hold the
### embedding dimension. Switching an existing table to HNSW_HALFVEC converts
### its stored vectors to 16-bit floats in place, which cannot be undone.
### Several workspaces sharing one database (one vector table): prefer HNSW or
### HNSW_HALFVEC with pgvector >= 0.8. The vector index is global and the
### workspace filter is applied after it, so a workspace holding a small share
### of the rows can get no dense-search results at all. With pgvector >= 0.8
### LightRAG enables iterative index scan for HNSW (strict order) and IVFFlat
### (relaxed order, re-sorted); VCHORDRQ is not covered, and pgvector < 0.8
### has no remedy. Many or very uneven workspaces: QdrantVectorDBStorage keeps
### one HNSW graph per workspace and is not exposed to this at all.
### Details: docs/design/PgVectorFilteredSearch.md
POSTGRES_VECTOR_INDEX_TYPE=HNSW_HALFVEC
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

### Apache AGE version (PGGraphStorage only)
# PGGraphStorage refuses to start on Apache AGE 1.8.0 and newer: id() began
# returning graphid there, which breaks get_knowledge_graph and can terminate the
# PostgreSQL backend with SIGSEGV, taking the whole instance through crash
# recovery. See https://github.com/apache/age/issues/2500
# Verified good: AGE 1.7.0. Verified bad: AGE 1.8.0, and AGE master.
# AGE reports its version per release rather than per commit, so a source build
# that fixes the crash still reports an unsupported number; set this to skip the
# check for such a build, at your own risk. Default is false.
# POSTGRES_AGE_ALLOW_UNSUPPORTED_VERSION=false

### Neo4j Configuration
### Use neo4j:// or neo4j+s:// for Aura and clusters (routing); use bolt:// for a single
### instance or a single Docker container to avoid "Unable to retrieve routing information"
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
### NOTE: OpenSearch index names must be lowercase, so the workspace is
### lowercased (and any character outside [a-z0-9_-] folded to '_') when the
### index name is built. Two workspaces that differ only in case or
### punctuation therefore map to the SAME indexes. When several LightRAG
### deployments share one OpenSearch cluster, give them workspace names that
### differ by more than case -- a colliding deployment refuses to start.

### 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
### LightRAG keeps doc_status / full_docs / the scheduling index in Redis as a
### system of record, and none of those keys carry a TTL. Startup therefore
### REFUSES an instance with maxmemory>0 plus an `allkeys-*` maxmemory-policy,
### because eviction there drops documents and scheduling state silently and
### undetectably. Use `noeviction` (or a `volatile-*` policy), or give LightRAG
### its own instance/db. Set this to true only to accept that data loss.
# REDIS_ALLOW_EVICTION_POLICY=false
### 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 -----