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https://github.com/chdl17/ai-document-retrieval-using-llama

This project implements an AI-powered pipeline to retrieve relevant document chunks and answer user queries based on PDF documents.
https://github.com/chdl17/ai-document-retrieval-using-llama

chromadb huggingface langchain llama3 meta streamlit

Last synced: 14 days ago
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This project implements an AI-powered pipeline to retrieve relevant document chunks and answer user queries based on PDF documents.

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README 

        
# AI Document Retrieval and Question-Answering Pipeline



This project implements an AI-powered pipeline to retrieve relevant document chunks and answer user queries based on PDF documents. It leverages **LangChain**, **Hugging Face**, and **Chroma** for document processing, embedding generation, and question-answering tasks using a **Llama** language model. The solution is built as a web application using **Streamlit**.



### Table of Contents

- [Overview](#overview)

- [Project Flow](#project-flow)

- [Technologies](#technologies)

- [Installation](#installation)

- [Usage](#usage)

- [File Structure](#file-structure)

- [License](#license)



### Overview



This pipeline takes a PDF file as input, processes it to extract relevant information, and stores the data in vector embeddings. Then, a **Llama language model** is used to answer user queries based on the extracted documents. The key components of the pipeline include:



1. **PDF Document Loading**: Load PDF documents and split them into manageable chunks.

2. **Document Vectorization**: Convert the chunks into vector embeddings using Hugging Face’s model.

3. **Vector Store Creation**: Store these embeddings in a Chroma vector store for efficient retrieval.

4. **Retrieval-Based Question Answering**: A **RetrievalQA** pipeline is used to retrieve relevant document chunks and answer user queries.

5. **Interactive Streamlit UI**: Users can interact with the system to upload PDFs and query the model via a simple Streamlit web interface.



### Project Flow



#### Step-by-Step Process:



1. **PDF Document Upload**:

    - The user uploads a PDF document to the system via the **Streamlit interface**.

    - The PDF is loaded using the **PyPDFLoader** class from LangChain.



2. **Document Chunking**:

    - The loaded PDF content is split into smaller, manageable chunks using **RecursiveCharacterTextSplitter**. This ensures that each chunk fits within the model's input constraints.



3. **Embedding Generation**:

    - The text chunks are embedded using the **Hugging Face embeddings** model.

    - The embeddings are then stored in **Chroma**, a vector store, to allow for efficient retrieval during the question-answering phase.



4. **Model Initialization**:

    - The **Llama model** is loaded using **Hugging Face Transformers** and moved to either the CPU or GPU for processing.

    - The tokenizer and model are initialized.



5. **Retrieval QA Setup**:

    - The **RetrievalQA** chain is created, linking the **Llama model** and the **Chroma vector store**.

    - This setup ensures that the model can query the vector store for the most relevant document chunks when answering user questions.



6. **User Query and Answer Generation**:

    - When the user inputs a query, the **RetrievalQA** chain searches for the most relevant document chunks using cosine similarity.

    - The retrieved document chunks are fed into the **Llama model**, which generates the final answer based on the information retrieved.



7. **Streamlit Interface**:

    - The user interacts with the pipeline through a simple web-based interface built using **Streamlit**.

    - Users can upload PDFs and input their queries, receiving answers directly in the browser.



### Technologies



- **LangChain**: For managing document loading, text splitting, and question-answering chain.

- **Hugging Face**: For using pre-trained models (Llama, embeddings).

- **Chroma**: A vector store for efficient retrieval of document embeddings.

- **Streamlit**: For building the interactive web interface.

- **PyTorch**: For running and managing the Llama model.



### Installation



1. Clone the repository:



    ```bash

    git clone https://github.com/chdl17/AI-Document-Retrieval-QA.git

    cd AI-Document-Retrieval-QA

    ```



2. Create and activate a virtual environment:



    ```bash

    python -m venv venv

    source venv/bin/activate   # On Windows, use venv\Scripts\activate

    ```



3. Install dependencies:



    ```bash

    pip install -r requirements.txt

    ```



4. **Set up environment variables**:

   - Create a `.env` file in the root directory of the project with the following content (ensure it does not contain any sensitive tokens):



    ```bash

    MODEL_NAME="your_model_name_here"

    HUGGINGFACE="your_huggingface_token_here"

    ```



### Usage



1. **Run the Streamlit application**:



    ```bash

    streamlit run app.py

    ```



2. **Upload a PDF**:

   - Open your browser and navigate to the Streamlit app.

   - Upload a PDF document.



3. **Ask a question**:

   - Once the document is loaded and processed, input a query related to the content in the uploaded PDF.



4. **View the answer**:

   - The model will return an answer based on the content of the document, sourced from the relevant sections.



### File Structure



```bash

.

├── app.py                      # Streamlit app entry point

├── rag_pipeline.py             # Pipeline logic and functions

├── requirements.txt            # Python dependencies

├── .env                        # Environment variables (MODEL_NAME, HUGGINGFACE token)

└── README.md                   # Project documentation