https://github.com/alinababer/document-analysis-identification-with-rag-vector-database-and-mistral-llm

This Document Analysis pipeline is a comprehensive document analysis system, designed to automate the processing and analysis of documents from acquisition to consumption. It integrates advanced machine learning & AI models like RAG (Retrieval Augmented Generation) & Mistral LLM to efficiently extract, match, enrich, process document
https://github.com/alinababer/document-analysis-identification-with-rag-vector-database-and-mistral-llm

document-analysis document-analysis-recognition document-pipeline document-uploader llm mistral paddleocr python rag tesseract

Last synced: about 2 months ago
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This Document Analysis pipeline is a comprehensive document analysis system, designed to automate the processing and analysis of documents from acquisition to consumption. It integrates advanced machine learning & AI models like RAG (Retrieval Augmented Generation) & Mistral LLM to efficiently extract, match, enrich, process document

• Host: GitHub
• URL: https://github.com/alinababer/document-analysis-identification-with-rag-vector-database-and-mistral-llm
• Owner: AlinaBaber
• Created: 2024-09-17T07:56:26.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-10-29T12:10:26.000Z (7 months ago)
• Last Synced: 2025-02-09T00:47:03.681Z (3 months ago)
• Topics: document-analysis, document-analysis-recognition, document-pipeline, document-uploader, llm, mistral, paddleocr, python, rag, tesseract
• Language: Python
• Homepage:
• Size: 14.8 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Document Analysis and identification with RAG Vector Database and Mistral LLM

## Overview

This project is a comprehensive document analysis system, designed to automate the processing and analysis of documents from acquisition to consumption. It integrates advanced machine learning and AI models like **RAG (Retrieval Augmented Generation) Vector Databases** and **Mistral LLM** to efficiently extract, match, enrich, and process document information. The pipeline covers:

- **Document Acquisition**

- **Document OCR (Optical Character Recognition)**

- **Document Preprocessing**

- **Document Information and Title Extraction**

- **Document Matching and Enrichment**

- **Document Consumption and Uploading**

## Features

- **Document Acquisition**: Automated gathering of documents from specified sources.

- **OCR Integration**: Converts scanned images or PDFs into machine-readable text using Optical Character Recognition (OCR).

- **Preprocessing**: Cleans and prepares documents for downstream analysis (removal of noise, handling of incomplete documents).

- **Information Extraction**: Title and relevant document information extraction using **RAG vector database** and **Mistral LLM**.

- **Document Matching & Enrichment**: Leverages AI models to match and enrich documents with metadata and other contextual information.

- **Consumption & Uploading**: Final processed document output, ready for consumption, and automatically uploaded to a specified location.

## Architecture

1. **Document Acquisition**:

   - Connects to a variety of data sources (local, cloud storage, web scraping).

   

2. **Document OCR**:

   - Uses popular OCR libraries (e.g., Tesseract) for converting images or scanned PDFs into editable text.

   

3. **Preprocessing**:

   - Text cleaning, noise reduction, normalization, and segmentation.

   

4. **Information Extraction**:

   - Uses **RAG** and **Mistral LLM** for extracting meaningful information like document title, summary, and other details.

   

5. **Matching and Enrichment**:

   - Matches documents with existing records and enriches them with metadata using contextual AI models.

   

6. **Document Uploading**:

   - Final step to upload or store the enriched documents in a specified repository or database.

## Getting Started

### Prerequisites

- Python 3.x

- Libraries: `pandas`, `numpy`, `spacy`, `torch`, `transformers`, `tesseract`, `opencv`, `sentence-transformers`

- Access to a RAG-compatible Vector Database

- **Mistral LLM** API Key

### Installation

1. Clone the repository:

    ```bash

    git clone https://github.com/username/Document-Analysis-Pipeline-LLM-RAG.git

    ```

2. Navigate to the project directory:

    ```bash

    cd Document-Analysis-Pipeline-LLM-RAG

    ```

3. Install the required dependencies:

    ```bash

    pip install -r requirements.txt

    ```

### Usage

1. **Configure Sources:**

   - Define the document sources for acquisition (local storage, cloud storage, etc.).

   

2. **Run the Pipeline**:

    ```bash

    python main.py --source [path_to_documents] --output [output_path]

    ```

3. **OCR & Preprocessing**:

   - The system will automatically run OCR on documents and clean the text.

4. **Information Extraction**:

   - Extract document titles and key information using the integrated **Mistral LLM** and **RAG** models.

5. **Enrichment & Matching**:

   - The processed documents will be enriched with additional metadata and matched to existing datasets if applicable.

6. **Final Output**:

   - The final output is ready for upload or further analysis.

### Configuration

- **Document Sources**: Define paths to documents in `config.json`.

- **Vector Database**: Set up the connection details for the **RAG Vector Database** in `rag_config.yaml`.

- **Mistral LLM**: Provide API keys for **Mistral LLM** integration in `ml_config.yaml`.

### Contributor

### License

This project is licensed under the MIT License.