Welcome to Rakam Systems' Documentation!
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**Rakam Systems** is a Python library designed to provide an integrated solution for creating **Vector Stores** and building **Retrieval-Augmented Generation (RAG)** systems. It offers a modular architecture to support search, content extraction, and large language model (LLM)-driven tasks, such as text classification and RAG-enabled prompt generation.

Check out the :doc:`usage` section for further information, including
how to :ref:`installation` the project.

.. note::

   This project is under active development.

Contents
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.. toctree::
   :maxdepth: 2
   :caption: Documentation Contents

   usage
   api
   installation
   features
   key_components
   advanced_features
   contributing

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Key Features
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- **Vector Store Management**: Create, manage, and search through vector stores using embeddings for efficient retrieval.
- **Retrieval-Augmented Generation (RAG)**: Combines vector store retrieval and large language model (LLM) generation.
- **Content Extraction**: Extracts content from various file formats such as PDF, URLs, and JSON files.
- **Node Processing**: Processes text data for optimized storage and retrieval.
- **Modular Action-Based Agents**: Supports actions like query classification and custom RAG generation using agents.

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Installation
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Before getting started, ensure you have installed the necessary dependencies. See the :doc:`installation` section for instructions.

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Usage Guide
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This library supports a variety of operations, including:

- **Creating and managing Vector Stores** for fast, vector-based content retrieval.
- **Using Retrieval-Augmented Generation (RAG)** to generate LLM-based responses.
- **Extracting content** from PDFs, URLs, and JSON files for processing.
- **Processing content into nodes** for efficient querying and storage.

Refer to the :doc:`usage` section for detailed examples and code snippets on how to use these features.

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Contributing
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We welcome contributions! Check out the :doc:`contributing` section to get started.

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License
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This project is licensed under the MIT License.

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Support
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For any issues, questions, or suggestions, please contact mohammed@rakam.ai.