---
title: Chatpdf
emoji: 🚀
colorFrom: purple
colorTo: gray
sdk: gradio
sdk_version: 3.23.0
app_file: app.py
pinned: false
license: mit
---

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

# pdfGPT
### Problem Description : 
1. When you pass a large text to Open AI, it suffers from a 4K token limit. It cannot take an entire pdf file as an input
2. Open AI sometimes becomes overtly chatty and returns irrelevant response not directly related to your query. This is because Open AI uses poor embeddings.
3. ChatGPT cannot directly talk to external data. 
4. There are a number of solutions like https://www.chatpdf.com, https://www.bespacific.com/chat-with-any-pdf/, filechat.io but none of them is open source. In addition, their navigation is not just one step and to the point. Moreover, the content quality is not good due to usage of OpenAI embeddings which are not very good.

### Solution: What is PDF GPT ?
1. PDF GPT allows you to chat with an uploaded PDF file using GPT functionalities.
2. The application intelligently breaks the document into smaller chunks and employs a powerful Deep Averaging Network Encoder to generate embeddings.
3. A semantic search is first performed on your pdf content and the most relevant embeddings are passed to the Open AI.
4. A custom logic generates precise responses. The returned response can even cite the page number in square brackets([]) where the information is located, adding credibility to the responses and helping to locate pertinent information quickly. The Responses are much better than the naive responses by Open AI.

### Demo
Demo URL: https://bit.ly/41ZXBJM

**NOTE**: Please star this project if you like it!
### UML
```mermaid
sequenceDiagram
    participant User
    participant System

    User->>System: Enter API Key
    User->>System: Upload PDF/PDF URL
    User->>System: Ask Question
    User->>System: Submit Call to Action

    System->>System: Blank field Validations
    System->>System: Convert PDF to Text
    System->>System: Decompose Text to Chunks (150 word length)
    System->>System: Check if embeddings file exists
    System->>System: If file exists, load embeddings and set the fitted attribute to True
    System->>System: If file doesn't exist, generate embeddings, fit the recommender, save embeddings to file and set fitted attribute to True
    System->>System: Perform Semantic Search and return Top 5 Chunks with KNN
    System->>System: Load Open AI prompt
    System->>System: Embed Top 5 Chunks in Open AI Prompt
    System->>System: Generate Answer with Davinci

    System-->>User: Return Answer
```

### Flowchart
```mermaid
flowchart TB
A[Input] --> B[URL]
A -- Upload File manually --> C[Parse PDF]
B --> D[Parse PDF] -- Preprocess --> E[Dynamic Text Chunks]
C -- Preprocess --> E[Dynamic Text Chunks with citation history]
E --Fit-->F[Generate text embedding with Deep Averaging Network Encoder on each chunk]
F -- Query --> G[Get Top Results]
G -- K-Nearest Neighbour --> K[Get Nearest Neighbour - matching citation references]
K -- Generate Prompt --> H[Generate Answer]
H -- Output --> I[Output]
```