# Large Language Model (LLM) Engine

This directory contains the language model engine for the ADAM system. The LLM engine provides natural language processing capabilities, such as text generation, summarization, and question answering.

## Base Class

All LLM engine implementations should inherit from the `BaseLLMEngine` class in `base_llm_engine.py`. This class defines the common interface for all LLM engines, including:

*   **`__init__(self, config)`:** Initializes the LLM engine with its configuration.
*   **`generate(self, prompt, **kwargs)`:** Generates text based on the given prompt and optional parameters.
*   **`summarize(self, text, **kwargs)`:** Summarizes the given text.
*   **`answer_question(self, question, context, **kwargs)`:** Answers a question based on the given context.

## Advanced LLM Techniques

In addition to the basic capabilities of the LLM engine, there are several advanced techniques that can be used to improve the performance and quality of the generated text.

### Prompt Chaining

Prompt chaining is a technique in which the output of one prompt is used as the input for another prompt. This can be used to create more complex and sophisticated text generation pipelines. For example, you could use one prompt to generate a summary of a news article, and then use another prompt to generate a list of key takeaways from the summary.

### Fine-Tuning

Fine-tuning is a technique in which a pre-trained language model is further trained on a smaller, task-specific dataset. This can be used to adapt the language model to a specific domain or task, such as generating financial reports or answering questions about a particular industry.

### Retrieval-Augmented Generation (RAG)

Retrieval-augmented generation (RAG) is a technique in which a language model is combined with a retrieval system. The retrieval system is used to find relevant documents from a knowledge base, and then the language model is used to generate text that is conditioned on the retrieved documents. This can be used to improve the accuracy and relevance of the generated text, especially for tasks that require domain-specific knowledge.

## Available Engines

*   **`dummy_llm_engine.py`:** A dummy implementation of the LLM engine that can be used for testing and development.
*   **`openai_llm_engine.py`:** An implementation of the LLM engine that uses the OpenAI API.

## Adding a New Engine

To add a new LLM engine, follow these steps:

1.  **Create a new Python file** in the `engines/` subdirectory. The file name should be descriptive of the engine (e.g., `my_new_llm_engine.py`).
2.  **Import the `BaseLLMEngine` class** from `base_llm_engine.py`.
3.  **Create a new class** that inherits from the `BaseLLMEngine` class.
4.  **Implement the `__init__` method** to initialize the engine with its configuration. This should include any API keys or other credentials required to access the engine.
5.  **Implement the `generate`, `summarize`, and `answer_question` methods** to provide the core functionality of the engine.
6.  **Add the new engine to the `config/llm_plugin.yaml` file.** This will make the engine available to the rest of the system.

## Configuration

The configuration for the LLM engine is stored in the `config/llm_plugin.yaml` file. This file contains the necessary information to connect to and authenticate with the selected LLM engine, such as API keys, model names, and other parameters.

By following these guidelines, you can help to ensure that the LLM engine in the ADAM system is flexible, extensible, and easy to use.