BubbleAI

BubbleAI is a sophisticated AI-driven platform designed to ingest, process, and analyze documents, leveraging the power of OpenAI's embeddings and a robust FastAPI backend. It's built to handle a variety of tasks, including document ingestion, text extraction, and querying for insights using advanced natural language processing techniques. BubbleAI serves as the backend for AssistAI, a macOS application that utilizes its endpoints for RAG, ingestion and interference.

Features

• Document Ingestion: Securely upload documents to be processed and analyzed.
• Text Extraction: Extract and process text from a variety of document formats.
• Vector Embedding: Utilize OpenAI's embeddings to convert text into vector representations for advanced analysis.
• Query System: Ask questions and receive insights based on the ingested and processed documents.
• Scalable Architecture: Deployed using Docker and AWS services for scalability and reliability.

Stack

• FastAPI
• Uvicorn (server)
• Gunicorn (process manager for Uvicorn)
• NGINX forwards 80 to 8000 (uvicorn)
• AWS CodeDeploy

Getting Started

Prerequisites

• Docker and Docker Compose
• AWS CLI configured with access to S3, SQS, and ECR
• Python 3.11.3 or higher
• An OpenAI API key

Installation

1. Clone the repository

git clone https://github.com/CocoaPriest/bubbleai.git
cd bubbleai

2. Set up environment variables

Create a .env file in the root directory and populate it with your AWS and OpenAI credentials:

OPENAI_API_KEY=your_openai_api_key
aws_access_key_id=your_aws_access_key_id
aws_secret_access_key=your_aws_secret_access_key
aws_region=your_aws_region
DATABASE_URL=your_database_url

3. Install dependencies

pip install -r requirements.txt

4. Build and run the Docker containers

docker-compose up --build

Usage

Ingesting Documents

To ingest a document, send a POST request to /ingest with the document file, its full path, and the machine ID.

curl -X 'POST' \
  'http://localhost:8000/ingest' \
  -H 'accept: application/json' \
  -H 'Content-Type: multipart/form-data' \
  -F 'file=@path/to/your/document.pdf;type=application/pdf' \
  -F 'full_path=/documents/document.pdf' \
  -F 'machine_id=your_machine_id'

Asking Questions

To ask a question based on the ingested documents, send a POST request to /ask with your question.

curl -X 'POST' \
  'http://localhost:8000/ask' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{"question": "What is the main topic of the ingested documents?"}'

Deployment

The provided Makefile includes commands for AWS login, Docker login, and deployment. Use these commands to deploy the application to an AWS EC2 instance.

make login
make docker_login
make docker_deploy

SSH

ssh -i ~/.ssh/fastapi_key.pem [email protected]

NGINX

sudo vim /etc/nginx/sites-enabled/fastapi_nginx

Docker on ec2

Pull

docker pull 246532218018.dkr.ecr.eu-central-1.amazonaws.com/fastapi-bubble:latest

Run

docker run -t -p 80:8000 246532218018.dkr.ecr.eu-central-1.amazonaws.com/fastapi-bubble:latest

Run locally

1. source ~/.virtualenvs/bubble/bin/activate.fish
2. Start docker: make docker_up 2a. Now, I want to stop docker's web and start my local: a. docker stop web b. make local_web

Login into

docker exec -it [name/id] /bin/bash

Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue for any bugs or feature requests.

License

Distributed under the MIT License. See LICENSE for more information.

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This README is a brief overview of BubbleAI. For more detailed documentation, please refer to the individual files and code comments within the project.