LLM and Embedding Provider Options Reference

This document is the complete reference for provider-specific generation options — the OPENAI_LLM_*, OLLAMA_LLM_*, GEMINI_LLM_*, BEDROCK_LLM_*, OLLAMA_EMBEDDING_*, and GEMINI_EMBEDDING_* families. These control sampling, output length, thinking/reasoning behavior, and provider pass-through payloads.

It does not cover binding selection and credentials (LLM_BINDING, LLM_MODEL, LLM_BINDING_HOST, LLM_BINDING_API_KEY, LLM_TIMEOUT, MAX_ASYNC_LLM) — those are described in LightRAG Server and WebUI — nor role-level inheritance rules, which are described in Role-Specific LLM/VLM Configuration Guide.

The single source of truth for the field lists below is lightrag/llm/binding_options.py. This document is intentionally English-only: it is a technical reference, not a tutorial.

Contents

• 1. Naming and resolution
• 2. Coverage matrix
• 3. OpenAI / Azure OpenAI LLM options
• 4. Ollama LLM options
• 5. Gemini LLM options
• 6. Bedrock LLM options
• 7. Ollama embedding options
• 8. Gemini embedding options
• 9. Where the values end up
• 10. Provider options and the LLM cache
• 11. Common tasks
• 12. Inspecting the options yourself

1. Naming and resolution

1.1 Name forms

Every option exists under one prefix per provider and per direction (LLM vs embedding). The three usable forms are:

Scope	Form	Example
Base, environment / .env	{PREFIX}_{FIELD}	OPENAI_LLM_MAX_COMPLETION_TOKENS=9000
Base, command line	--{prefix-in-lowercase-dashes}-{field}	--openai-llm-max_completion_tokens 9000
Role, environment / .env only	{ROLE}_{PREFIX}_{FIELD}	QUERY_OPENAI_LLM_MAX_COMPLETION_TOKENS=9000

Note the CLI spelling: the prefix uses dashes, the field keeps its underscores (--ollama-llm-num_ctx, not --ollama-llm-num-ctx).

ROLE is one of EXTRACT, KEYWORD, QUERY, VLM. Role-level provider options have no command-line equivalent — they are read from the environment only.

Precedence, highest first: base command-line argument, base environment variable, option unset. A role-level environment variable overlays whatever the base resolved to (for a same-provider role) or an empty set (for a cross-provider role); see RoleSpecificLLMConfiguration.md.

1.2 Unset means "not sent"

Every option is registered with argparse.SUPPRESS as its default. An option you do not configure is absent from the outgoing request, so the provider applies its own default. Consequences worth internalizing:

• The default values visible in lightrag/llm/binding_options.py (and in the generated .env sample of §12) are documentation of the provider's typical default, not values LightRAG ships. OLLAMA_LLM_THINK shows True and OPENAI_LLM_REASONING_EFFORT shows medium, yet neither is sent unless you set it.
• lightrag-server --llm-binding openai --help therefore prints no default values.
• Removing an option means commenting the line out, not setting it to an empty value (see §1.4).

1.3 Only the selected binding's options are read at base level

Base-level options are registered for the binding actually selected by LLM_BINDING / EMBEDDING_BINDING (lightrag/api/config.py). With LLM_BINDING=ollama, every OPENAI_LLM_* variable in your .env is ignored — silently, because it is never read. This is a frequent source of "my setting has no effect".

Role-level variables are read straight from the environment instead, so a cross-provider role does honor {ROLE}_{PREFIX}_* for its own provider — but it does not inherit the base provider's options (a cross-provider role starts from an empty option set by design).

1.4 Value syntax

Field type	Accepted spelling	Notes
bool	true/1/yes/t/on → true; anything else → false	Case-insensitive.
bool-or-level (OLLAMA_LLM_THINK only)	true/false/… or low/medium/high	An unrecognized word is a startup error, not a silent false. An empty value means false, not "unset".
int / float	plain number	An empty value is a startup error: argument --openai-llm-max_tokens: invalid int value: ''.
list	JSON array, e.g. '["</s>", "\n\n"]'	Quote it in .env / the shell.
dict	JSON object, e.g. '{"reasoning": {"enabled": false}}'	Quote it in .env / the shell.
str	verbatim	An empty value is a value: OPENAI_LLM_SERVICE_TIER= sends service_tier: "" to the provider. Comment the line out instead.

Malformed JSON behaves differently by scope, which matters when debugging:

• Base level (OPENAI_LLM_EXTRA_BODY=not json): the option is dropped and the server starts as if it were unset.
• Role level (QUERY_OPENAI_LLM_EXTRA_BODY=not json): the raw string is kept and forwarded, so the failure surfaces later as a provider-side request error.

2. Coverage matrix

Not every binding has a provider option set. Bindings absent from the LLM column below accept no generation options at all.

Binding	LLM options	Embedding options	Notes
openai	OPENAI_LLM_*	none	Also the binding for OpenAI-compatible servers: vLLM, SGLang, OpenRouter, and similar.
azure_openai	OPENAI_LLM_* (same option set)	none	AZURE_OPENAI_API_VERSION / AZURE_OPENAI_DEPLOYMENT are separate process-level variables, not provider options.
ollama	OLLAMA_LLM_*	OLLAMA_EMBEDDING_*
gemini	GEMINI_LLM_*	GEMINI_EMBEDDING_*
bedrock	BEDROCK_LLM_*	none	The legacy alias aws_bedrock is normalized to bedrock.
lollms	see note	none	Base level: no options at all. lightrag-server --llm-binding lollms --help prints no option group, and the base binding is wired with an empty options payload. Role level maps lollms to the Ollama option set, so {ROLE}_OLLAMA_LLM_* is read — but the lollms driver only consumes temperature, top_k, top_p, repeat_penalty, repeat_last_n, and seed; every other field is ignored.
jina	— (embedding-only binding)	none
voyageai	— (embedding-only binding)	none

3. OpenAI / Azure OpenAI LLM options

Prefix OPENAI_LLM_ — shared by the openai and azure_openai bindings.

Environment variable	Type	Description
OPENAI_LLM_TEMPERATURE	float	Controls randomness (0.0-2.0, higher = more creative).
OPENAI_LLM_TOP_P	float	Nucleus sampling parameter (0.0-1.0, lower = more focused).
OPENAI_LLM_MAX_COMPLETION_TOKENS	int	Maximum number of tokens to generate. The current parameter for OpenAI reasoning-capable models.
OPENAI_LLM_MAX_TOKENS	int	Maximum number of tokens to generate. Deprecated by OpenAI in favor of max_completion_tokens, but still the correct field for most OpenAI-compatible servers (vLLM, SGLang, many gateways).
OPENAI_LLM_FREQUENCY_PENALTY	float	Penalty for token frequency (-2.0 to 2.0, positive values discourage repetition).
OPENAI_LLM_PRESENCE_PENALTY	float	Penalty for token presence (-2.0 to 2.0, positive values encourage new topics).
OPENAI_LLM_REASONING_EFFORT	str	Reasoning effort for reasoning-capable models. Forwarded verbatim, so the accepted words are the provider's, not LightRAG's — OpenAI currently accepts minimal/low/medium/high, other gateways add their own (e.g. none).
OPENAI_LLM_STOP	JSON list	Stop sequences, e.g. '["</s>", "\n\n"]'.
OPENAI_LLM_SERVICE_TIER	str	Service tier for API usage.
OPENAI_LLM_SAFETY_IDENTIFIER	str	Safety identifier for content filtering.
OPENAI_LLM_EXTRA_BODY	JSON dict	Extra top-level request-body fields, merged into the request. The escape hatch for anything not in this table — vLLM/SGLang chat-template switches, OpenRouter routing and reasoning controls.

Notes:

• These options are merged into the Chat Completions request as keyword arguments and forwarded as-is. LightRAG does not validate them against the target model, so a parameter your endpoint does not implement surfaces as a provider-side error.
• A local OpenAI-compatible server that rejects reasoning_effort is the usual reason to keep that option role-level (EXTRACT_OPENAI_LLM_REASONING_EFFORT=…) rather than global.

4. Ollama LLM options

Prefix OLLAMA_LLM_. All fields except think travel to the Ollama server inside the request's options payload.

4.1 Context and output length

Environment variable	Type	Description
OLLAMA_LLM_NUM_CTX	int	Context window size in tokens. Must be larger than MAX_TOTAL_TOKENS + 2000; env.example ships it set to 32768 because the Ollama default is usually too small for LightRAG's prompts.
OLLAMA_LLM_NUM_PREDICT	int	Maximum number of tokens to predict — the output budget. Use it to stop runaway extraction output.
OLLAMA_LLM_NUM_KEEP	int	Number of tokens to keep from the initial prompt.
OLLAMA_LLM_STOP	JSON list	Stop sequences, e.g. '["</s>", "\n\n"]'.
OLLAMA_LLM_PENALIZE_NEWLINE	bool	Penalize newline tokens.

4.2 Sampling

Environment variable	Type	Description
OLLAMA_LLM_TEMPERATURE	float	Controls randomness (0.0-2.0, higher = more creative).
OLLAMA_LLM_TOP_K	int	Top-k sampling parameter (0 = disabled).
OLLAMA_LLM_TOP_P	float	Top-p (nucleus) sampling parameter (0.0-1.0).
OLLAMA_LLM_MIN_P	float	Minimum probability threshold (0.0 = disabled).
OLLAMA_LLM_TYPICAL_P	float	Typical probability mass (1.0 = disabled).
OLLAMA_LLM_TFS_Z	float	Tail free sampling parameter (1.0 = disabled).
OLLAMA_LLM_SEED	int	Random seed for generation (-1 for random).

4.3 Repetition control and Mirostat

Environment variable	Type	Description
OLLAMA_LLM_REPEAT_PENALTY	float	Penalty for repetition (1.0 = no penalty).
OLLAMA_LLM_REPEAT_LAST_N	int	Number of tokens to consider for the repetition penalty.
OLLAMA_LLM_PRESENCE_PENALTY	float	Penalty for token presence (-2.0 to 2.0).
OLLAMA_LLM_FREQUENCY_PENALTY	float	Penalty for token frequency (-2.0 to 2.0).
OLLAMA_LLM_MIROSTAT	int	Mirostat sampling algorithm (0 = disabled, 1 = Mirostat 1.0, 2 = Mirostat 2.0).
OLLAMA_LLM_MIROSTAT_TAU	float	Mirostat target entropy.
OLLAMA_LLM_MIROSTAT_ETA	float	Mirostat learning rate.

4.4 Thinking / reasoning

Environment variable	Type	Description
OLLAMA_LLM_THINK	bool or low/medium/high	Enables the model's extended-thinking trace. LLM only — the embedding option set has no such field.

think is the one Ollama option that does not travel inside options; it is a top-level request field. Details that matter:

• Leaving it unset follows the model's own default. Setting true for a model without thinking support makes Ollama reject the request.
• Reasoning levels (low/medium/high) additionally require an Ollama server that supports levels; think in any form requires ollama>=0.5.4 on the LightRAG side, and a misconfiguration fails at server startup rather than mid-pipeline.
• An empty value (OLLAMA_LLM_THINK=) means false, not "unset".
• A thinking-capable model can spend its whole generation budget on hidden reasoning and return empty entities/relations during extraction (issue #3597). The fix is EXTRACT_OLLAMA_LLM_THINK=false (and KEYWORD_OLLAMA_LLM_THINK=false if keyword extraction is affected too) rather than a global switch, because disabling thinking measurably hurts extraction quality on some models.

4.5 Hardware and memory

These are Ollama server-side runtime knobs. Set them only if you know why.

Environment variable	Type	Description
OLLAMA_LLM_NUMA	bool	Enable NUMA optimization.
OLLAMA_LLM_NUM_BATCH	int	Batch size for processing.
OLLAMA_LLM_NUM_GPU	int	Number of GPUs to use (-1 for auto).
OLLAMA_LLM_MAIN_GPU	int	Main GPU index.
OLLAMA_LLM_LOW_VRAM	bool	Optimize for low VRAM.
OLLAMA_LLM_NUM_THREAD	int	Number of CPU threads (0 for auto).
OLLAMA_LLM_F16_KV	bool	Use half-precision for the key/value cache.
OLLAMA_LLM_USE_MMAP	bool	Use memory mapping for model files.
OLLAMA_LLM_USE_MLOCK	bool	Lock the model in memory.
OLLAMA_LLM_VOCAB_ONLY	bool	Only load the vocabulary.
OLLAMA_LLM_LOGITS_ALL	bool	Return logits for all tokens.
OLLAMA_LLM_EMBEDDING_ONLY	bool	Only use for embeddings.

5. Gemini LLM options

Prefix GEMINI_LLM_. Each field maps onto the Google GenAI GenerateContentConfig field of the same name.

Environment variable	Type	Description
GEMINI_LLM_TEMPERATURE	float	Controls randomness (0.0-2.0, higher = more creative).
GEMINI_LLM_TOP_P	float	Nucleus sampling parameter (0.0-1.0).
GEMINI_LLM_TOP_K	int	Limits sampling to the top K tokens (1 disables the limit).
GEMINI_LLM_MAX_OUTPUT_TOKENS	int	Maximum tokens generated in the response.
GEMINI_LLM_CANDIDATE_COUNT	int	Number of candidates returned per request.
GEMINI_LLM_PRESENCE_PENALTY	float	Penalty for token presence (-2.0 to 2.0).
GEMINI_LLM_FREQUENCY_PENALTY	float	Penalty for token frequency (-2.0 to 2.0).
GEMINI_LLM_STOP_SEQUENCES	JSON list	Stop sequences, e.g. '["END"]'.
GEMINI_LLM_SEED	int	Random seed for reproducible generation.
GEMINI_LLM_THINKING_CONFIG	JSON dict	Thinking configuration, e.g. '{"thinking_budget": 1024}', '{"include_thoughts": true}', or '{"thinking_budget": 0, "include_thoughts": false}' to turn thinking off. thinking_budget: -1 selects Gemini's dynamic budget (the model chooses).
GEMINI_LLM_SAFETY_SETTINGS	JSON dict	Gemini safety-settings overrides.

Note: entries whose value is None or an empty string are dropped before the config object is built, so an empty setting cannot produce a type error at request time.

6. Bedrock LLM options

Prefix BEDROCK_LLM_. These map onto the Converse API.

Environment variable	Type	Description
BEDROCK_LLM_TEMPERATURE	float	Controls randomness (0.0-1.0 for most Bedrock models).
BEDROCK_LLM_MAX_TOKENS	int	Maximum tokens generated in the response → inferenceConfig.maxTokens.
BEDROCK_LLM_TOP_P	float	Nucleus sampling parameter (0.0-1.0) → inferenceConfig.topP.
BEDROCK_LLM_STOP_SEQUENCES	JSON list	Stop sequences → inferenceConfig.stopSequences.
BEDROCK_LLM_EXTRA_FIELDS	JSON dict	Model-specific request fields forwarded as additionalModelRequestFields, e.g. '{"reasoningConfig": {"type": "enabled", "maxReasoningEffort": "low"}}'. The Bedrock counterpart of OPENAI_LLM_EXTRA_BODY.

Note: the driver builds inferenceConfig from exactly the four fields above. Anything model-specific must go through BEDROCK_LLM_EXTRA_FIELDS.

Bedrock authentication uses SigV4 or AWS_BEARER_TOKEN_BEDROCK, never LLM_BINDING_API_KEY; see RoleSpecificLLMConfiguration.md.

7. Ollama embedding options

Prefix OLLAMA_EMBEDDING_. The field set is identical to §4 minus think — num_ctx, num_predict, num_keep, seed, temperature, top_k, top_p, tfs_z, typical_p, min_p, repeat_last_n, repeat_penalty, presence_penalty, frequency_penalty, mirostat, mirostat_tau, mirostat_eta, numa, num_batch, num_gpu, main_gpu, low_vram, num_thread, f16_kv, logits_all, vocab_only, use_mmap, use_mlock, embedding_only, penalize_newline, and stop.

In practice only the runtime knobs are meaningful for an embedding request:

Environment variable	Type	Description
OLLAMA_EMBEDDING_NUM_CTX	int	Context window of the embedding model. Ollama needs this set in addition to EMBEDDING_TOKEN_LIMIT; env.example ships 8192.
OLLAMA_EMBEDDING_NUM_GPU / _MAIN_GPU / _NUM_THREAD / _NUM_BATCH / _LOW_VRAM / _USE_MMAP / _USE_MLOCK / _NUMA / _F16_KV	int / bool	Same meaning as their OLLAMA_LLM_* counterparts in §4.5.

The sampling and repetition fields are accepted for symmetry with the LLM option set but have no effect on an embedding response.

Changing the embedding model or its effective dimension invalidates all stored vectors. See the embedding-model warning in LightRAG Server and WebUI.

8. Gemini embedding options

Prefix GEMINI_EMBEDDING_.

Environment variable	Type	Description
GEMINI_EMBEDDING_TASK_TYPE	str	Task type for embedding optimization. If unset, it is derived from context (RETRIEVAL_QUERY for queries, RETRIEVAL_DOCUMENT for documents). Supported values: RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY, SEMANTIC_SIMILARITY, CLASSIFICATION, CLUSTERING, CODE_RETRIEVAL_QUERY, QUESTION_ANSWERING, FACT_VERIFICATION.

Pinning a single task_type disables the query/document distinction that asymmetric embedding relies on; see Asymmetric Embedding Configuration.

9. Where the values end up

Binding	Destination	Silently dropped
openai, azure_openai	Merged into the Chat Completions request as keyword arguments.	Nothing — unsupported fields reach the provider and become provider errors.
ollama	The request's options payload, plus top-level think.	Nothing.
gemini	GenerateContentConfig(**options).	Entries whose value is None or "".
bedrock	inferenceConfig (temperature, maxTokens, topP, stopSequences) plus additionalModelRequestFields from extra_fields.	Any field outside that set.
lollms	Top-level request fields.	Every field except temperature, top_k, top_p, repeat_penalty, repeat_last_n, seed.

10. Provider options and the LLM cache

The LLM cache key is partitioned by a non-secret identity consisting of the role, binding, model, and host. api_key and provider options are deliberately excluded, so cache keys stay safe to persist. The practical consequence:

• Changing LLM_MODEL, LLM_BINDING, or LLM_BINDING_HOST produces new cache keys, so new calls are made.
• Changing a provider option — temperature, think, reasoning_effort, max_tokens, … — does not. A cached extraction or query result is still served with the old value.

When you need a tuning change to take effect on content that has already been processed, clear the relevant cache explicitly (/documents/clear_cache, or ENABLE_LLM_CACHE=false while experimenting on queries). Note that clearing the LLM cache drops the extraction cache too, which is what entity/relation rebuild after a document delete relies on.

11. Common tasks

Cap output length (prevents endless extraction output)

Set an output cap so a runaway response is truncated before the request times out. A usable ceiling is LLM_TIMEOUT * output_tokens_per_second (e.g. 240s * 50 tok/s, so stay under 12000).

# OpenAI-compatible servers (vLLM/SGLang/most gateways)
OPENAI_LLM_MAX_TOKENS=9000
# OpenAI reasoning-capable models
OPENAI_LLM_MAX_COMPLETION_TOKENS=9000
# Ollama
OLLAMA_LLM_NUM_PREDICT=9000
# Gemini
GEMINI_LLM_MAX_OUTPUT_TOKENS=9000
# Bedrock
BEDROCK_LLM_MAX_TOKENS=9000

Turn thinking off for extraction and keyword generation

Pick the line that matches your provider — these are alternatives, not a block to paste as a whole (a repeated variable would just keep its last value).

# Ollama
EXTRACT_OLLAMA_LLM_THINK=false
KEYWORD_OLLAMA_LLM_THINK=false

# Gemini
EXTRACT_GEMINI_LLM_THINKING_CONFIG='{"thinking_budget": 0, "include_thoughts": false}'

# OpenAI reasoning-capable models
EXTRACT_OPENAI_LLM_REASONING_EFFORT=minimal

# OpenRouter (one EXTRA_BODY per role — choose one of the two forms below)
EXTRACT_OPENAI_LLM_EXTRA_BODY='{"reasoning": {"enabled": false}}'
# Qwen-style models served by vLLM
# EXTRACT_OPENAI_LLM_EXTRA_BODY='{"chat_template_kwargs": {"enable_thinking": false}}'

# Bedrock — the field name and accepted values belong to the target model, not to
# LightRAG; check that model's Converse API documentation. Example shape:
# EXTRACT_BEDROCK_LLM_EXTRA_FIELDS='{"reasoningConfig": {"type": "enabled", "maxReasoningEffort": "low"}}'

Keep a provider option out of one role

There is no "unset an inherited option" syntax. Either override the option for that role with a value the role's model accepts, or keep the option role-level from the start instead of setting it globally. A local OpenAI-compatible endpoint sharing the openai binding with the official API is the usual case:

LLM_BINDING=openai
LLM_MODEL=gpt-5-mini
# Do NOT set OPENAI_LLM_REASONING_EFFORT globally here — the local server rejects it.
QUERY_OPENAI_LLM_REASONING_EFFORT=medium
KEYWORD_OPENAI_LLM_MAX_TOKENS=2048

12. Inspecting the options yourself

The --help output lists the option group of the currently selected binding only, so pass the binding you want to inspect:

lightrag-server --llm-binding openai --help
lightrag-server --llm-binding ollama --help
lightrag-server --llm-binding gemini --help
lightrag-server --llm-binding bedrock --help
lightrag-server --embedding-binding ollama --help
lightrag-server --embedding-binding gemini --help

To dump every binding's options at once as a commented .env block:

python -m lightrag.llm.binding_options

Both outputs are generated from lightrag/llm/binding_options.py, so they are always current. Remember that the values shown in the generated .env sample are the providers' typical defaults, not values LightRAG sends (§1.2) — the lines are commented out for exactly that reason.