cognee.config

Static class for configuring Cognee’s runtime settings. All setters persist for the duration of the process (or until overridden). Use cognee.config.set(...) for supported runtime-safe settings inside a Python process. Cognee can also be configured through .env or process environment variables before import. Use those for process-level settings such as auth, logging, cache backend, storage backend, telemetry, API server settings, and deployment credentials. For the full environment variable reference and precedence rules, see Setup Configuration.

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. Use it for supported runtime-safe settings such as LLMs, embeddings, graph/vector databases, chunking, model overrides, and root directories. Use .env, shell variables, deployment variables, or pre-import os.environ for process-level settings such as auth, logging, cache backend, storage backend, API server settings, telemetry, and cloud credentials.

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

​cognee.config

​Configuration Types

​All Configuration Methods

cognee.config

Configuration Types

All Configuration Methods