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title: Chatpdf | |
emoji: π | |
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] | |
``` | |