yaraa

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #typescript #llm #android #machine-learning #deep-learning #linux #awesome #awesome-list #pytorch #security

(★ 25)

Yaraa (Yet Another Rag Automation Attempt) is a library that tackles the boring aspects of managing Rag pipelines, so you don't have to.

yaraa Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

**YARAA** (Yet Another Rag Automation Attempt) is a library that simplifies the development of RAG (Retrieval-Augmented Generation) pipelines. You can build your vector database in any way you prefer, and YARAA will assist you in testing, evaluating, and optimizing the best configurations.

![tutorial (2)](https://github.com/user-attachments/assets/78de2620-fe94-4f2e-9582-72e7a0757de0)

# Installation

To install the library, follow these steps:

1. **Clone the repository:**

```bash
   git clone https://github.com/khalilbenkhaled/yaraa
   ```

2. **Navigate to the project directory:**

```bash
   cd yaraa
   ```

3. **Create a virtual environment (optional but recommended):**

```bash
   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
   ```

4. **Install the library:**

```bash
   pip install .
   ```

**Optional**: To install specific optional libraries, use:

```bash
   pip install .[NAME_OF_LIBRARY1, NAME_OF_LIBRARY2]
   #example:
   pip install .[ollama,chromadb,streamlit,fastapi]
   ```

Supported options include:

- `transformers`
   - `ollama`
   - `server` (for the OpenAI library)
   - `chromadb`
   - `streamlit`
   - `fastapi`

Choose the options you need and include them in the command above.

## Usage

### Quick Start (for Fast Testing or New Users)

1. **Configure Your Settings:**

- Locate the file named `example1.yaml` in the repository.
   - Edit this file to specify your hyperparameters, LLM, the query encoder, and the path to your vector database directory.

*Note:* Currently, the library offers limited configuration options, but more features (like multiple vector stores and additional hyperparameter controls) will be added soon.

2. **Run the Inference:**

Execute the following command:

```bash
   python inference.py example1.yaml
   ```

This command will launch a browser window with a chat interface for interacting with your RAG pipeline.

3. **File Information:**

- **`example1.yaml`**: Controls inference settings.
   - **`example2.yaml`**: Shows how to use the YAML file for evaluating your RAG pipeline.

For inference, the library currently supports:
   - Streamlit
   - FastAPI

For evaluation, the only supported metric is:
   - Answer relevance by RAGAS.

### Advanced Usage (for Experienced Developers and Large Projects)

- For detailed usage and full control, explore the `examples` folder in the repository. It provides various examples to help you understand how to utilize the library in different scenarios.

yaraa

yaraa Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

yaraa

yaraa Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README