Multimodal PDF RAG

What is Multimodal PDF RAG ?

Multimodal PDF RAG is an AI-powered tool designed to extract text from scanned PDF documents and enable conversational interactions to gain insights. It combines advanced Optical Character Recognition (OCR) and Retrieval-Augmented Generation (RAG) technologies to process and analyze PDF content efficiently. This tool is particularly useful for extracting readable text from scanned or image-based PDFs and generating relevant responses based on the extracted content.

Features

• Text Extraction from Scanned PDFs: Automatically converts scanned or image-based PDFs into readable text without manual typing.
• Conversational Search: Enables users to ask questions or request information directly from the extracted text, leveraging RAG technology.
• No OCR Software Required: Handles the OCR process internally, streamlining the extraction workflow.
• Insight Generation: Provides meaningful insights and responses based on the content of the PDF.

How to use Multimodal PDF RAG ?

1. Install or Access the Tool: Download or access the Multimodal PDF RAG application or API.
2. Upload a PDF File: Select or input the scanned PDF document you wish to process.
3. Extract Text: Run the extraction process to convert the scanned PDF into readable text.
4. Interact with the Content: Use the extracted text to ask questions, generate summaries, or retrieve specific information.
5. Review Responses: Analyze the insights or responses provided by the tool.

Frequently Asked Questions

What file formats are supported?
Multimodal PDF RAG supports PDF files, including scanned or image-based PDFs. Other formats may require conversion before use.

Can I manually correct the extracted text?
Yes, most versions of the tool allow manual editing of the extracted text to correct any OCR errors.

How long does the extraction process take?
The processing time depends on the size and complexity of the PDF. Scanned documents with clear text typically process faster than those with complex layouts or low-quality images.