The slide introduces "PDF QnA," a tool that unlocks insights from PDF documents using AI. Its subtitle highlights querying documents via RAG for accurate, context-aware answers.

Unlock PDF Insights with AI

Query documents using RAG for accurate, context-aware answers

Retrieval-Augmented Generation (RAG) integrates retrieval from a knowledge base with generative AI to create reliable, context-aware responses that minimize hallucinations by grounding outputs in factual information. It excels at processing domain-specific data, such as PDFs, and boosts accuracy for specialized queries.

Overview of RAG

• Combines retrieval from knowledge base with generative AI
• Produces reliable and context-aware responses
• Reduces AI hallucinations through grounded information
• Handles domain-specific data like PDFs effectively
• Enhances accuracy for specialized queries

The slide outlines the document ingestion and chunking process, starting with a user uploading a PDF for initial parsing using specialized tools to extract text. It then describes dividing the text into efficient 500-token segments, visualized as a flow from the full document to ready chunks.

Document Ingestion and Chunking

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• User uploads PDF document for initial ingestion process.
• Parse extracted text from PDF using specialized tools.
• Chunk text into 500-token segments for efficiency.
• Visualize flow from full doc to ready chunks.

Source: Retrieval-augmented generation

The slide explains the embedding process, where document chunks are transformed into dense vectors using models like BERT to capture semantic meaning and enable efficient similarity comparisons in high-dimensional space. It also covers vector database storage, such as in Pinecone, for scalable indexing and fast similarity searches like cosine similarity to retrieve relevant chunks during RAG retrieval based on query vectors.

Embedding and Vector Database

The slide outlines the retrieval and ranking process in information retrieval systems, starting with embedding the user query into a dense vector representation. It then describes conducting a cosine similarity search in a vector database to retrieve the top-k most relevant document chunks, followed by ranking those chunks based on their relevance scores.

Retrieval and Ranking

• Embed user query into dense vector representation
• Conduct cosine similarity search in vector database
• Retrieve top-k most relevant document chunks
• Rank retrieved chunks by relevance scores

The slide on LLM Response Generation explains how retrieved chunks are combined with the user query and fed into the LLM to generate responses, emphasizing prompt engineering for contextual accuracy. An animation on the slide highlights the resulting augmented and precise output.

LLM Response Generation

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• Retrieved chunks fed to LLM with user query
• Prompt engineering for contextual response generation
• Animation highlights accurate, augmented output

Source: Retrieval-augmented generation

The slide presents an end-to-end workflow animation as a timeline with four steps for processing PDF documents using AI. It starts with ingesting and preprocessing a PDF, followed by chunking the text, generating embeddings, and storing them in a vector database; then proceeds to embedding user queries, retrieving relevant chunks, and finally augmenting an LLM prompt with that context to generate accurate responses.

End-to-End Workflow Animation

Step 1: PDF Document Ingestion Upload PDF file, extract and preprocess text content for further pipeline processing. Step 2: Chunking and Embedding Split text into semantic chunks, generate vector embeddings, and store in vector database. Step 3: Query Processing and Retrieval Embed user query, search vector database, retrieve top relevant chunks for context. Step 4: Answer Generation Augment LLM prompt with retrieved context to generate accurate, informed response.