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Chat with Your Documents Using GPT & LangChain

In this code-along, Andrea Valenzuela, Computing Engineer at CERN, and Josep Ferrer Sanchez, Data Scientist at the Catalan Tourist Board, will walk you through building an AI system that can query your documents & data using LangChain & the OpenAI API.
Andrea Valenzuela
Josep Ferrer
Andrea Valenzuela, Josep Ferrer

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Google로 계속하기계속 진행하시면 다음 사항에 동의하는 것으로 간주됩니다: 이용약관 및 개인정보 처리방침 그리고 귀하의 데이터가 미국에 저장된다는 점에 동의하는 것으로 간주됩니다.

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A killer application of large language models (LLMs) is answering questions about specific documents & datasets. This enables use cases such as customer service bots, question-answering systems about specific domains, & LLMs that can navigate data tasks.

In this code-along, Andrea Valenzuela, Computing Engineer at CERN, and Josep Ferrer Sanchez, Data Scientist at the Catalan Tourist Board, will walk you through building an AI system that can query your documents & data using LangChain & the OpenAI API. Throughout the code-along, they will share best practices for effectively loading & storing documents using LangChain, building a retrieval augmented generation pipeline for querying data, and building a question-answering bot.

Key Takeaways:

  • Learn how to effectively load & store documents using LangChain
  • Build a retrieval augmented generation pipeline for querying data
  • Build a question-answering bot that answers questions based on your documents

Additional Resources

Andrea’s Code Along from 2023 - [CODE ALONG] Optimizing GPT Prompts for Data Science

A tutorial from Andrea on Chatbots - [TUTORIAL] Building Context-Aware Chatbots: Leveraging LangChain Framework for ChatGPT

[TUTORIAL] A Beginner's Guide to Using the ChatGPT API

[SKILL TRACK] OpenAI Fundamentals

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