Overview

Overview

The AI Chat with PDF feature in Nextjs Supabase AI Template allows users to upload PDF files, parse the contents, and extract insights by interacting with the uploaded files as a knowledge base. This feature leverages AI-powered technology to answer queries related to the PDF content, providing a user experience similar to ChatGPT but tailored to the user's specific document.

---

Key Features

• PDF Upload and Parsing: Users can upload PDF files, which are automatically parsed, split into manageable sections, and processed to generate embeddings for AI-powered querying.
• Choose AI Model: Users can change the AI Model during chat to OpenAI, Anthropic and Gemini.
• Interactive Chat: Users can type queries related to the uploaded PDF and receive accurate answers based on the document's content.
• Chat History:
  • Filters: View chat histories by the past 24 hours or all time.
  • Accessibility: All organization members can view chat histories.
• PDF Preview: Users can preview the uploaded document while interacting with it.
• Customization: The system currently supports one chat per PDF upload but can be customized for multiple PDFs or additional workflows.

---

How It Works

PDF Upload and Parsing Workflow

1. Upload:

   • A user uploads a PDF file to the Supabase file storage bucket named files mentioned in app configuration file storage.filesBucketName.

2. Processing:

   • The PDF is parsed, and its content is split into smaller, manageable chunks using LangChain's text splitter.
   • Embeddings are created for each chunk, enabling the AI to reference and analyze the document's content efficiently.

PDF Moderation using OpenAI omni-moderation model:

By default we provide PDF moderation support with a config flag enableModeration. It will restrict PDFs which has been flagged for moderation.

Querying and Chat Workflow

1. Query Input:
   • The user enters a query in the chat panel.
2. Message Storage:
   • All user queries are stored in the supabase message table for future reference.
3. Answer Generation:
   • The AI uses the embeddings created from the uploaded PDF to find similar content based on the query.
   • It calculates similarity scores to ensure accurate responses.
   • The answer is returned to the user, providing clear and concise insights from the document.

Technical Architecture

• Embeddings: Representations of the document's content are generated to facilitate efficient querying. We use the OpenAI text-embedding-3-small model, which generates embeddings with 1536 dimensions. If you plan to switch to a different embedding model, ensure that the embedding dimensionality is updated accordingly in the embeddings table.
• Vercel AI SDK Integration: Queries are processed using Vercel AI SDK, with the uploaded PDF serving as the knowledge base. User can choose between the supported models such as OpenAI, Anthropic and Gemini.
• Supabase Data Tables:
  • PDF Data: Parsed content and embeddings are stored for efficient retrieval.
  • Message Table: Stores user queries for persistent chat histories.

Benefits

• Enhanced Productivity: Quickly extract insights from complex documents without manual searching.
• Collaboration: Share document insights with all organization members.
• Customization: Tailor the feature for specific workflows or multi-document querying.