https://github.com/mazzasaverio/fastapi-langchain-rag

(Let's start with a) Scalable question-answering system utilizing FastAPI, LangChain (LCEL), and PGVector, featuring an ingestion pipeline. Deployed on GCP Cloud Run via Terraform.
https://github.com/mazzasaverio/fastapi-langchain-rag

chatbot cloud-run fastapi langchain pgvector question-answering rag

Last synced: about 1 month ago
JSON representation

(Let's start with a) Scalable question-answering system utilizing FastAPI, LangChain (LCEL), and PGVector, featuring an ingestion pipeline. Deployed on GCP Cloud Run via Terraform.

• Host: GitHub
• URL: https://github.com/mazzasaverio/fastapi-langchain-rag
• Owner: mazzasaverio
• Created: 2024-03-27T10:10:30.000Z (6 months ago)
• Default Branch: master
• Last Pushed: 2024-04-10T11:18:45.000Z (5 months ago)
• Last Synced: 2024-04-10T18:27:20.872Z (5 months ago)
• Topics: chatbot, cloud-run, fastapi, langchain, pgvector, question-answering, rag
• Language: Python
• Homepage:
• Size: 229 KB
• Stars: 4
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• jimsghstars - mazzasaverio/fastapi-langchain-rag - (Let's start with a) Scalable question-answering system utilizing FastAPI, LangChain (LCEL), and PGVector, featuring an ingestion pipeline. Deployed on GCP Cloud Run via Terraform. (Python)

README

        
# Project Overview

The goal of developing this repository is to create a scalable project based on RAG operations of a vector database (Postgres with pgvector), and to expose a question-answering system developed with LangChain and FastAPI on a Next.js frontend.

The entire system will be deployed in a serverless manner, both on the backend (a Terraform submodule for setting up a cloud run with CloudSQL and Redis) and on the frontend (deployment via Vercel).

Additionally, a layer will be added to limit the app's usage through a subscription plan via Stripe

## Setting Up the Infrastructure

### Import Terraform Submodule

Refer to the following guide for adding and managing submodules:

[Adding a Submodule and Committing Changes: Git, Terraform, FastAPI](https://medium.com/@saverio3107/adding-a-submodule-and-committing-changes-git-terraform-fastapi-6fe9cf7c9ba7?sk=595dafdaa36427a2d6efee8c08940ee9)

**Steps to Initialize Terraform:**

Navigate to the Terraform directory and initialize the configuration:

```bash

cd terraform

terraform init

terraform apply

```

## Configuring the Application

### Set Environment Variables

Duplicate the `.env.example` file and set the required variables:

```bash

cp .env.example .env

```

### Backend Setup

- **Navigate to the backend directory:**

```bash

cd backend

```

- **Install dependencies using Poetry:**

```bash

poetry install

poetry shell

```

### Database Connection

Connect to the database using the Cloud SQL Proxy. Instructions are available in the Terraform README.

```bash

./cloud-sql-proxy ...

```

### Initialize Database

Run the initialization script to set up the database. This script adds the pgvector extension and creates a superuser:

```bash

python app/init_db.py

```

### Data Ingestion

Place your PDF files in `data/raw` and run the following script to populate the database:

```bash

python app/ingestion/run.py

```

## Accessing the Application

### API Documentation

Access live-generated API documentation at:

```

https://cloudrun-service-upr23soxia-uc.a.run.app/api/v1/docs

```

### Obtaining an Access Token

Generate an access token using the `/api/v1/login/access-token` endpoint with credentials specified in your `.env` file.

## Connecting the Frontend

### Generate an Access Token

Obtain an access token using the login endpoint:

```javascript

const token = "your_generated_access_token_here"; // Replace with actual token

```

### Example Frontend Integration

Utilize the access token in your Next.js application as follows:

```javascript

const headers = new Headers({

  Authorization: "Bearer " + token,

  "Content-Type": "application/json",

});

async function chatAnswer() {

  const res = await fetch(

    `${process.env.NEXT_PUBLIC_API_BASE_URL}/api/v1/qa/chat`,

    {

      method: "POST",

      headers: headers,

      body: JSON.stringify({ message: "Your query here" }),

    }

  );

  return res.json();

}

```

## Subscription Management

Integrate Stripe to manage subscriptions and limit usage based on the chosen plan. Follow Stripe's official documentation to set up the billing and subscription logic.

## Contributing

Contributions are welcome! For major changes, please open an issue first to discuss what you would like to change.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.