LangChain Nodejs Openai TS | Part 4 | Text Loader, Embeddings, Chroma & Faiss Vector Store, Q/A Bot
🚀 Welcome to Part 4 of our comprehensive series on AI development with LangChain, Node.js, OpenAI, and TypeScript! This episode is a deep dive into the advanced techniques of training your own models, creating vector stores, and implementing custom Q/A chatbots. Whether you're an experienced developer or an enthusiastic beginner, this tutorial is packed with valuable insights and hands-on examples to boost your AI programming skills.
👨💻 Continuing the journey from our previous videos, we begin with a thorough walkthrough of the development environment, highlighting the package.json, ts.config, and nodemon.config files. We delve deeper into the Object-Oriented Programming structure, emphasizing constructors and methods that will drive your custom AI solutions.
🛠️ In this installment, we focus on a series of advanced AI development tasks:
1. **Train OpenAI Model with Your Custom Text Data**: Learn the process of training OpenAI models using your own text data. We guide you through preparing and importing custom data files for a more personalized and powerful AI model.
2. **Create Vector Embeddings with OpenAI Embeddings API**: Discover how to utilize OpenAI's Embeddings API to create vector embeddings, a fundamental technique for understanding and organizing large volumes of text data.
3. **Split Loaded Text File with Langchain Splitter**: Master the use of Langchain's splitter tool to effectively divide and manage your text files, preparing them for efficient processing and analysis.
4. **Create Vector Store using Faiss**: Step into the world of vector storage and retrieval by learning how to implement a vector store using the Faiss library, enhancing the speed and accuracy of your AI applications.
5. **Create Vector Store in Remote Docker-Based Self-Hosted Chroma DB**: Elevate your skills in data management by setting up a vector store in a remote, Docker-based, self-hosted Chroma DB environment.
6. **Test That Data with a Custom Question/Answer Based Chat Bot**: Put your vector store to the test by integrating it with a custom Q/A chatbot, demonstrating the practical application and effectiveness of your trained models and data stores.
📘 Throughout this tutorial, we continue to offer detailed explanations, code walkthroughs, and practical examples. We also revisit the Langchain library's online documentation and demonstrate further how to utilize these resources to your advantage.
🌐 As we progress through this series, your knowledge and skills will expand, opening up new horizons in AI development. This tutorial isn't just about coding; it's about understanding the full potential of AI and how to harness it for innovative solutions.
👍 If you're as passionate about AI and programming as we are, this series is tailored for you. Like, share, and subscribe to stay updated with the latest content and continue enhancing your development skills.
💬 Engage with us in the comments section for discussions, queries, or insights. Your interaction enriches the learning experience for everyone involved.
🎓 Stay tuned for more videos in this series, where we'll cover even more advanced topics and provide you with a comprehensive toolkit for AI programming success.
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