cognee.config

Static class for configuring Cognee’s runtime settings. All setters persist for the duration of the process (or until overridden).

Configuration Types

LLM Configuration

cognee.config.set_llm_provider("openai")          # "openai", "anthropic", "ollama", "gemini", "mistral", "bedrock"
cognee.config.set_llm_model("gpt-4o-mini")
cognee.config.set_llm_api_key("sk-...")
cognee.config.set_llm_endpoint("https://custom-endpoint.example.com")

# Or set all at once — keys must match LLMConfig attribute names exactly
cognee.config.set_llm_config({
    "llm_provider": "openai",
    "llm_model": "gpt-4o",
    "llm_api_key": "sk-...",
})

set_llm_config() keys must match the internal attribute names on LLMConfig. The exact internal attributes name are displayed in the table below.

Internal LLM Configuration attributes

Key	Type	Default	Description
`llm_provider`	`str`	`"openai"`	Provider: `"openai"`, `"anthropic"`, `"ollama"`, `"gemini"`, `"mistral"`, `"bedrock"`, `"azure"`, `"custom"`
`llm_model`	`str`	`"openai/gpt-5-mini"`	Model identifier
`llm_api_key`	`str`	`None`	API key for the provider
`llm_endpoint`	`str`	`""`	Custom endpoint URL (required for Ollama, vLLM, etc.)
`llm_api_version`	`str`	`None`	API version (required for Azure)
`llm_temperature`	`float`	`0.0`	Response temperature (0.0–2.0)
`llm_streaming`	`bool`	`False`	Enable streaming responses
`llm_max_completion_tokens`	`int`	`16384`	Maximum tokens in the response
`llm_args`	`dict`	`{}`	Arbitrary provider-specific kwargs merged into every LLM call. Set as a JSON string in `.env` (e.g. `LLM_ARGS='{"top_p": 0.9}'`).
`llm_rate_limit_enabled`	`bool`	`False`	Enable client-side rate limiting for LLM calls
`llm_rate_limit_requests`	`int`	`60`	Max LLM requests allowed per interval
`llm_rate_limit_interval`	`int`	`60`	Duration of the rate limit window in seconds
`llm_rate_limit_tokens`	`int`	`0`	Max tokens per interval (`0` = disabled)

Embedding Configuration

cognee.config.set_embedding_provider("fastembed")
cognee.config.set_embedding_model("BAAI/bge-small-en-v1.5")
cognee.config.set_embedding_dimensions(384)

# Or set all at once — keys must match EmbeddingConfig attribute names exactly
cognee.config.set_embedding_config({
    "embedding_provider": "fastembed",
    "embedding_model": "BAAI/bge-small-en-v1.5",
    "embedding_dimensions": 384,
})

Internal Embedding Configuration attributes

Key	Type	Default	Description
`embedding_provider`	`str`	`"openai"`	Provider: `"openai"`, `"ollama"`, `"fastembed"`, `"gemini"`, `"mistral"`, `"bedrock"`, `"custom"`
`embedding_model`	`str`	`"openai/text-embedding-3-large"`	Model identifier
`embedding_dimensions`	`int`	`3072`	Vector dimension size (must match your vector store)
`embedding_api_key`	`str`	`None`	API key (falls back to `LLM_API_KEY` if unset)
`embedding_endpoint`	`str`	`None`	Custom endpoint URL
`embedding_api_version`	`str`	`None`	API version
`embedding_max_completion_tokens`	`int`	`8191`	Maximum tokens for embedding input
`embedding_batch_size`	`int`	`36`	Batch size for embedding requests
`huggingface_tokenizer`	`str`	`None`	HuggingFace Hub model ID for token counting with Ollama
`embedding_rate_limit_enabled`	`bool`	`False`	Enable client-side rate limiting for embedding calls
`embedding_rate_limit_requests`	`int`	`60`	Max embedding requests allowed per interval
`embedding_rate_limit_interval`	`int`	`60`	Duration of the rate limit window in seconds
`embedding_rate_limit_tokens`	`int`	`0`	Max tokens per interval (`0` = disabled)

Graph Database Configuration

cognee.config.set_graph_database_provider("kuzu")  # "kuzu", "neo4j", "kuzu-remote", "neptune", "neptune_analytics"

# Keys must match GraphConfig attribute names exactly
cognee.config.set_graph_db_config({
    "graph_database_provider": "neo4j",
    "graph_database_url": "bolt://localhost:7687",
    "graph_database_username": "neo4j",
    "graph_database_password": "password",
})

Vector Database Configuration

cognee.config.set_vector_db_provider("lancedb")    # "lancedb", "pgvector", "qdrant", "chromadb"
cognee.config.set_vector_db_url("http://localhost:6333")
cognee.config.set_vector_db_key("your-key")

# Keys must match VectorConfig attribute names exactly
cognee.config.set_vector_db_config({
    "vector_db_provider": "qdrant",
    "vector_db_url": "http://localhost:6333",
    "vector_db_key": "...",
})

Chunking Configuration

cognee.config.set_chunk_size(1024)
cognee.config.set_chunk_overlap(128)
cognee.config.set_chunk_strategy("PARAGRAPH")      # "EXACT", "PARAGRAPH", "SENTENCE", "CODE"
cognee.config.set_chunk_engine("DEFAULT_ENGINE")    # "DEFAULT_ENGINE", "LANGCHAIN_ENGINE"

Model Configuration

cognee.config.set_classification_model(MyClassifier)
cognee.config.set_summarization_model(MySummarizer)
cognee.config.set_graph_model(MyGraphModel)

Other Settings

cognee.config.system_root_directory("/custom/path")
cognee.config.data_root_directory("/data/path")
cognee.config.set_translation_provider("google")  # "llm", "google", "azure"
cognee.config.set_translation_target_language("en")

# Generic setter for supported keys
cognee.config.set("llm_model", "openai/gpt-5-mini")

cognee.config.set(key, value) is not a free-form setter. It supports a fixed set of keys plus valid embedding config attributes. Unsupported keys raise InvalidConfigAttributeError.

All Configuration Methods

Configuration Methods

Method	Description
`set_llm_provider(provider)`	Set the LLM provider
`set_llm_model(model)`	Set the LLM model name
`set_llm_api_key(key)`	Set the LLM API key
`set_llm_endpoint(url)`	Set a custom LLM endpoint
`set_llm_config(dict)`	Set all LLM config at once
`set_embedding_provider(provider)`	Set the embedding provider
`set_embedding_model(model)`	Set the embedding model name
`set_embedding_dimensions(dimensions)`	Set embedding vector dimensions
`set_embedding_endpoint(url)`	Set a custom embedding endpoint
`set_embedding_api_key(key)`	Set the embedding API key
`set_embedding_config(dict)`	Set all embedding config at once
`set_graph_database_provider(provider)`	Set the graph DB provider
`set_relational_db_config(dict)`	Set relational DB config
`set_migration_db_config(dict)`	Set migration DB config
`set_graph_db_config(dict)`	Set all graph DB config
`set_vector_db_provider(provider)`	Set the vector DB provider
`set_vector_db_url(url)`	Set the vector DB URL
`set_vector_db_key(key)`	Set the vector DB API key
`set_vector_db_config(dict)`	Set all vector DB config
`set_chunk_size(size)`	Set chunk size in tokens
`set_chunk_overlap(overlap)`	Set chunk overlap
`set_chunk_strategy(strategy)`	Set chunking strategy
`set_chunk_engine(engine)`	Set chunking engine
`set_classification_model(model)`	Set classification model
`set_summarization_model(model)`	Set summarization model
`set_graph_model(model)`	Set graph extraction model
`system_root_directory(path)`	Set system root directory
`data_root_directory(path)`	Set data root directory
`monitoring_tool(tool)`	Set the monitoring tool
`set_translation_provider(provider)`	Set translation provider
`set_translation_target_language(lang)`	Set translation target language
`set_translation_config(dict)`	Set translation config
`set(key, value)`	Generic config setter

Environment Variables

These environment variables are read at startup and can be overridden with the config methods above:

Variable	Default	Description
`LLM_API_KEY`	—	(Required) API key for the LLM provider
`LLM_PROVIDER`	`"openai"`	LLM provider name
`LLM_MODEL`	`"openai/gpt-5-mini"`	LLM model identifier
`LLM_ENDPOINT`	—	Custom LLM endpoint URL
`LLM_ARGS`	`"{}"`	JSON string of extra kwargs merged into every LLM call (e.g. `'{"top_p": 0.9}'`)
`LLM_RATE_LIMIT_ENABLED`	`false`	Enable client-side rate limiting for LLM calls
`LLM_RATE_LIMIT_REQUESTS`	`60`	Max LLM requests per interval
`LLM_RATE_LIMIT_INTERVAL`	`60`	Rate limit window in seconds
`LLM_RATE_LIMIT_TOKENS`	`0`	Max LLM tokens per interval (`0` = disabled)
`EMBEDDING_RATE_LIMIT_ENABLED`	`false`	Enable client-side rate limiting for embedding calls
`EMBEDDING_RATE_LIMIT_REQUESTS`	`60`	Max embedding requests per interval
`EMBEDDING_RATE_LIMIT_INTERVAL`	`60`	Rate limit window in seconds
`EMBEDDING_RATE_LIMIT_TOKENS`	`0`	Max embedding tokens per interval (`0` = disabled)
`GRAPH_DATABASE_PROVIDER`	`"kuzu"`	Graph database provider
`VECTOR_DB_PROVIDER`	`"lancedb"`	Vector database provider
`LOG_LEVEL`	`"INFO"`	Logging level

Reference

​cognee.config

​Configuration Types

​All Configuration Methods

​Environment Variables

cognee.config

Configuration Types

All Configuration Methods

Environment Variables