Use Case Overview: Building a Customer Service Copilot Using a RAG Solution with PDF Data

Scenario Overview

Alex, a newly hired customer service representative at a tech company, is tasked with addressing complex customer queries related to software troubleshooting, features, and usage guidelines. The company has a comprehensive PDF manual containing text instructions, screenshots, and diagrams. To empower Alex and provide an enhanced customer experience, the company plans to create a Customer Service Copilot.

Solution Criteria

To meet the needs of both Alex and the customers, the solution must include the following capabilities:

  1. Efficient Information Retrieval

    • The ability to quickly locate and retrieve precise information from large documents, including both text and images, to effectively answer customer queries.

  2. Intuitive and User-Friendly Interface

    • A simple interface where representatives can input queries and receive comprehensive responses, which include visual aids such as screenshots and diagrams.

  3. Trustworthy Citations

    • Clear references to the original sections of the document, ensuring that the provided information is easily verifiable and trusted by both the representative and the customer.

Leveraging Generative AI for Customer Service

In today’s era of Generative AI, businesses can extract valuable insights from their structured and unstructured data to drive innovation and improve customer experience. By integrating AI into their products, companies can create powerful applications that enable users to access the full potential of their data.

The Importance of RAG (Retrieval-Augmented Generation)

For Generative AI applications to work effectively with customer data, implementing a RAG (Retrieval-Augmented Generation) solution is crucial. RAG ensures that the right context is provided to the Large Language Models (LLMs) based on the user’s query. This is particularly important when dealing with large, unstructured documents like PDFs that include both text and visual elements.

Challenges in Parsing PDF Data for RAG Solutions

Many companies rely on PDFs that include text, images, and diagrams containing critical information. However, parsing this content and maintaining relationships between text and visual elements presents several challenges:

  1. Text and Image Relationship

    • Extracting images and their associated text while maintaining their contextual relationship is difficult but essential for providing accurate answers.

  2. Image Referencing in Citations

    • Including images as part of the citations in LLM-generated outputs is challenging if the images are not properly extracted or retrievable.

Addressing the Challenges: A RAG Solution for PDF Data

To overcome these challenges, we propose a simple architecture to build a RAG application that ensures both text and image content from PDFs are searchable, retrievable, and referenced accurately in LLM-generated outputs. This solution maintains the relationship between the textual context and the extracted visual content, enabling the following features:

  1. Seamless Extraction of Text and Images

    • Ensures all relevant information, including embedded figures (screenshots, diagrams, etc.), is extracted and indexed for retrieval.

  2. Citations with Visual References

    • Provides clear and verifiable citations, including references to extracted images when they are part of the answer.

  3. LLM Integration for Enhanced Query Responses

    • Combines the power of retrieval-augmented generation with LLMs to provide rich, contextual answers based on the user’s query.

Proposed Architecture

The architecture focuses on building a RAG application specifically designed for PDF data. Key components include:

  1. Data Ingestion

    • Upload and process PDF documents, extracting both text and visual elements while maintaining their relationships.

  2. Data Storage and Indexing

    • Store the extracted content in a format optimized for retrieval, ensuring both text and images are searchable.

  3. Azure OpenAI Service for LLM Integration

    • Use Azure OpenAI Service to integrate advanced Large Language Models (LLMs) like GPT-4 for generating answers to user queries. These models leverage both textual and visual data retrieved from the indexed PDF content to provide comprehensive and contextual responses.

  4. Search and Retrieval Layer

    • Implement Azure AI Search to enable efficient retrieval of information. This layer ensures that both text and images are indexed and retrievable based on the context of the user’s query.
    • The search layer supports traditional keyword-based and vector-based queries for accurate matching.

  5. Serverless Data Processing

    • Use Azure Functions to preprocess and extract content from PDFs, ensuring a seamless pipeline for text and image extraction. This ensures the extracted data is ready for indexing and retrieval.

  6. User Interface

    • Build an intuitive user interface (UI) where customer service representatives like Alex can input queries and receive detailed responses.
    • The UI displays both textual responses and visual aids (e.g., screenshots, diagrams) along with clear citations to the source sections in the PDF.

Benefits of the Solution

This RAG-based solution offers several key benefits:

  1. Improved Customer Experience

    • By providing quick, accurate, and visually enriched responses, the copilot helps Alex deliver better support to customers.

  2. Efficient Onboarding

    • New representatives like Alex can quickly become productive by relying on the copilot to handle complex queries.

  3. Trustworthy Information

    • Clear citations ensure that both representatives and customers can trust the answers, as they are directly linked to the original document.

  4. Scalability

    • The solution is scalable and can handle large volumes of queries and documents, making it suitable for enterprises of any size.

Conclusion

By leveraging Azure OpenAI Service, Azure AI Search, Azure Blob Storage, and Azure Functions, the proposed architecture delivers a powerful Customer Service Copilot. This copilot not only enriches the onboarding process for representatives like Alex but also ensures an exceptional customer experience through efficient and accurate query handling.

This approach demonstrates how Generative AI and RAG solutions can unlock valuable insights from unstructured data, transforming how businesses interact with their data and customers.

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