FastAPI

Demonstration

https://www.loom.com/share/cd1b617cc86b46838e469e908a075ae3?sid=9c269a9a-f0d2-423e-b87f-61682c9553b1

scraping content from a given URL

storing it in a database using SQLAlchemy, within the context of a FastAPI application. It incorporates the following key functionalities:

URL Scraping:

• Extracts content from a specified URL using the requests library.
• Cleans the extracted content by removing scripts and styles (if desired).
• 

SQLAlchemy Integration:

• Creates a database engine and defines a SQLAlchemy model to represent the scraped data.
• Creates a database table based on the model.
• Uses a database session to add and commit scraped content to the database.

FastAPI Endpoint:

• Exposes a POST endpoint (/process_url) that accepts a URL as input.

• Processes the URL, scrapes the content, and stores it in the database.

• Returns a response with a unique chat ID and a success message.

Error Handling:

• Implements basic error handling for HTTP requests and database operations.

Extract text from uploaded pdf

Q&A Chatbot with Document Upload

• This is a FastAPI application for a simple Q&A chatbot that leverages uploaded documents.

Features:

• Supports uploading documents through URLs or PDFs.
• Extracts text content from uploaded documents.
• Processes user chat requests with a specific chat ID.
• Finds the most relevant section within the uploaded document based on the user's question using cosine similarity (Note: currently uses a dummy embedding function, needs replacement).

FastAPI Chatbot with Document Upload

This project implements a simple chatbot that retrieves relevant information from uploaded documents.

Features:

• Supports uploading documents through URLs or PDFs.
• Extracts text content from uploaded documents.
• Processes user questions and identifies the most relevant section based on cosine similarity.

Technologies:

• FastAPI: Web framework for building APIs
• Pydantic: Data validation and serialization
• requests: Making HTTP requests
• BeautifulSoup: Parsing HTML documents
• pdfminer: Extracting text from PDFs

Usage:

Run the application:

$Bash

$ uvicorn main:app --host 127.0.0.1 --port 8000

Upload documents:

Upload URL:

$ Bash $ curl -X POST http://localhost:8000/upload_url/ -F chat_id=user1 -F url=https://www.example.com/article.html

Upload PDF: (using tools like curl or Postman)

Set chat_id in the form data. Send a multipart request with the PDF file as file.

Chat interaction:

$ Bash $ curl -X POST http://localhost:8000/chat/ -H 'Content-Type: application/json' -d '{"chat_id": "user1", "question": "What is the capital of France?"}'

Return

This will return a JSON response containing the most relevant section from the uploaded document for user1 based on the question.