AMAR: Ask Me Anything with RAG

Objective
Repository Structure
Virtual Environment
1. Tools used
Running the Web App
1. Demo
Future Work

1. Objective

The objective of this project is to create a simple and easy to use tool that allows users to ask questions about local data. It works by serving local LLM models and a RAG model to answer questions about the data.

The following diagram shows the architecture of the project:

2. Repository Structure

The repository is structured as follows:

├── README.md <- The top-level README for developers using this project.
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├── app.py <- Code to deploy a web app with Streamlit
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├── *-environment.yaml <- Virtual environment configuration files for pro* and dev*
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├── tmp <- Folder for temporary files (uploads)
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├── configs <- Folder for app configuration files
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├── notebooks <- Folder where jupyter notebooks are located. Development purposes
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└── src  <- Source code for use in this project.
    ├── processing <- Data processing code. Reading and chunking
    ├── database <- Database related code
    ├── embeddings <- Embedding related code
    ├── llm <- LLM related code
    └── helpers <- Various helper functions

3. Virtual Environment

To ensure the reproducibility of the project, it is recommended to create a virtual environment. Conda is a package manager that allows you to create virtual environments with different Python versions and install the required dependencies.

The following commands can be used to create a virtual environment using conda:

conda env create -f dev-environment.yaml

3.1. Tools used

The following tools were used in the development of the project:

SuryaOCR: OCR tool used to extract text from scanned documents.
Ollama: Get up and running with large language models locally. Used for embedding text data and LLMs serving.
ChromaDB: Embedding database used to store and retrieve embeddings.
Streamlit: Streamlit turns data scripts into shareable web apps in minutes. All in pure Python. No front‑end experience required.

4. Running the Web App

We prepared a simple web app to interact with the models. To run the app, execute the following command:

streamlit run app.py

4.1. Demo

The following is a demo of the web app:

demo.mp4

5. Future Work

Add more models to the app: Hugging Face models, OpenAI models, etc.
Prevent inserting the same document multiple times in the database.

MarioProjects / amar