- 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
- Open AI sometimes becomes overtly chatty and returns irrelevant response not directly related to your query. This is because Open AI uses poor embeddings.
- ChatGPT cannot directly talk to external data.
- PDF GPT allows you to chat with an uploaded PDF file using GPT functionalities.
- The application intelligently breaks the document into smaller chunks and employs a powerful Deep Averaging Network Encoder to generate embeddings.
- A semantic search is first performed on your pdf content and the most relevant embeddings are passed to the Open AI.
- 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 URL: https://sortoite-pdfchatgpt.hf.space
NOTE: Please star this project if you like it!
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 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]
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