pdfGPT

Problem Description :

• 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.

Solution: What is PDF GPT ?

• 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

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UML

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

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]