Getting Started
==============

This guide will help you get started with CortexFlow.

Basic Usage
----------

Here's a simple example of how to use CortexFlow:

.. code-block:: python

    from cortexflow import CortexFlowManager, CortexFlowConfig, MemoryConfig, LLMConfig

    # Configure with custom settings (nested config)
    config = CortexFlowConfig(
        memory=MemoryConfig(active_token_limit=2048, working_token_limit=4096, archive_token_limit=8192),
        llm=LLMConfig(backend="ollama", default_model="llama3"),
    )

    # Create the context manager
    context_manager = CortexFlowManager(config)

    # Add messages to the context
    context_manager.add_message("system", "You are a helpful AI assistant.")
    context_manager.add_message("user", "What is the capital of France?")

    # Generate a response
    response = context_manager.generate_response()
    print(f"Assistant: {response}")

    # Explicitly store important information
    context_manager.remember_knowledge("The user's name is Alice and she lives in Boston.")

    # Clean up when done
    context_manager.close()

Using the ConfigBuilder
----------------------

CortexFlow also supports a fluent builder pattern for configuration:

.. code-block:: python

    from cortexflow import CortexFlowManager, ConfigBuilder

    config = (ConfigBuilder()
        .with_memory(active_token_limit=2000, working_token_limit=4000, archive_token_limit=6000)
        .with_llm(default_model="llama3")
        .build())

    manager = CortexFlowManager(config)

Advanced Features
---------------

Chain of Agents
~~~~~~~~~~~~~~

CortexFlow includes a Chain of Agents framework that breaks down complex reasoning into specialized roles:

.. code-block:: python

    from cortexflow import CortexFlowManager, ConfigBuilder

    # Enable Chain of Agents via the builder
    config = (ConfigBuilder()
        .with_agents(use_chain_of_agents=True, chain_complexity_threshold=5)
        .with_llm(default_model="llama3")
        .build())

    # Create the manager with this configuration
    manager = CortexFlowManager(config)

    # Add a complex query
    manager.add_message("user", "What connection exists between quantum physics and consciousness?")

    # Generate response (automatically uses Chain of Agents for complex queries)
    response = manager.generate_response()

See the :doc:`configuration` guide for more details on all available options.