wizit-context-ingestor 0.2.5b3

Contextual Rag with Cloud Solutions

wizit_context_ingestor

A powerful document processing and ingestion system that leverages AI services for document transcription, analysis, and semantic chunking.

Features

• Document transcription using AWS and Google Cloud AI services
• Semantic chunking of documents for better context understanding
• Vector storage integration with PostgreSQL
• Support for both local and cloud storage (S3)
• Synthetic data generation capabilities
• RAG (Retrieval-Augmented Generation) implementation

Prerequisites

• Python 3.11 or higher
• Poetry for dependency management
• AWS credentials (for AWS services)
• Google Cloud credentials (for GCP services)
• PostgreSQL database (for vector storage)
• Supabase account (for data storage)

Installation

1. Clone the repository:

git clone https://github.com/yourusername/mega-ingestor.git
cd mega-ingestor

2. Install dependencies using Poetry:

poetry install

3. Set up your environment variables by copying the example.env file:

cp example.env .env

4. Fill in your environment variables in the .env file with your credentials and configuration.

Usage

The project provides several main functionalities:

Document Transcription

from main import transcribe_document

# Transcribe a document using AWS services
transcribe_document("your-document.pdf")

# Transcribe a document using Google Cloud services
cloud_transcribe_document("your-document.pdf")

Context Chunking

from main import context_chunks_in_document

# Get semantic chunks from a document
context_chunks_in_document("your-document.pdf")

Running Memory Profiler

To run the memory profiler, use the following command:

python -m memray run test_redis.py

Project Structure

mega-ingestor/
├── src/
│   ├── application/
│   ├── infra/
│   └── ...
├── data/
├── credentials/
├── main.py
├── app.py
└── pyproject.toml

Dependencies

• llama-parse
• langchain-experimental
• langchain-google-vertexai
• pymupdf
• supabase
• vecs
• langchain-postgres
• boto3
• langchain-aws

GENERATE THE PACKAGE WITH POETRY

    poetry build

PUBLISH PACKAGE

    poetry config repositories.tbbcmegaingestor https://aws:$[e-mail]/pypi/tbbc-mega-ingestor-lib/

    export CODEARTIFACT_AUTH_TOKEN=`aws codeartifact get-authorization-token --domain tbbc-mega-ingestor --domain-owner 411728455297 --region us-east-1 --query authorizationToken --output text --profile <your-profile>`

Finally

    poetry publish -r tbbcmegaingestor

License

This project is licensed under the Apache License - see the LICENSE file for details.

TODO

• Do not transcribe logos
• Support for more cloud providers

Authors

(Daniel Quesada)[https://github.com/daquesada] (Jeison Patiño)[https://github.com/jeison-patino] (Javier Fernandez)[https://github.com/javimaufermu] (Esteban Cerón)[https://github.com/estebance]