LangChain

Q&A Using Vector Databases you own this product

This project is part of the liveProject series Build a Custom Chatbot Using LangChain and ChatGPT
prerequisites
basics of embeddings • basics of vector databases
skills learned
use LangChain for questions answering • create and populate a vector store with your own data
Pablo Elgueta
2-4 hours per week · BEGINNER
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In this liveProject, you'll join InfoHub, an AI startup disrupting corporate knowledge management. They aim to unlock a company’s knowledge base through conversational Q&A-style user interfaces that use breakthrough language models. You'll leverage LangChain, a framework optimized for integrating LLMs into apps, to integrate InfoHub's data, vector stores, and language models into a single solution. You’ll prepare your data, create a vector store to embed your documents, and then use LangChain to combine it with an LLM.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

Pablo Elgueta
Over the past seven years, Pablo has been deeply involved in the tech industry, specifically focusing on software product development for the last four years. He worked at Microsoft, where he expanded his expertise in large-scale technology solutions. His academic journey led him to earn an MSc in Data Science and Machine Learning, where he researched the application of OpenAI’s GPT3 to predict cryptocurrency prices for his dissertation. Pablo has also spoken at the API Days conference, discussing the practical implementation of Large Language Models (LLMs). He has developed 'FanPods,’ an innovative app that turns text into AI-driven audiobooks. As an LLM Engineer at AffiliateAI, Pablo is crafting solutions for chat-based data retrieval using LLMs.

prerequisites

This liveProject is for intermediate-level Python developers. No special tools are required—you can perform everything you need using a normal IDE or Jupyter Notebook.


TOOLS
  • Intermediate Python
  • Basics of pandas
  • Basics of the OpenAI API

TECHNIQUES
  • Basics of tokens
  • Basics of embeddings
  • Basics of LLMs

you will learn

In this liveProject, you’ll learn to use vector databases and large language models to develop a working Q&A chatbot


  • Simplify LLM integration for applications utilizing the LangChain framework
  • Facilitate fast data retrieval, efficiently managing high-dimensional data through a Chroma vector store
  • Convert textual data into numerical embeddings utilizing OpenAI's large language models
  • Securely handle API keys following best practices with environment variable API key management
  • Gain real-time feedback on iterative processes using progress bars from the tqdm library
  • Combine language models with vectorized data to swiftly answer users' queries accurately via RetrievalQA

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book resources
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