This repository is dedicated to my learning journey through the LangChain & Vector Databases in Production course offered by Activeloop. The course is part of the Gen AI 360 Foundational Model Certification and focuses on mastering Large Language Models (LLMs) and Vector Databases.
- LangChain: A robust framework for building applications with LLMs.
- Vector Databases: Understanding Deep Lake, a groundbreaking vector database for all AI data.
- Projects: 10+ practical projects to build real-world applications.
- Duration: 40+ hours of learning content.
- Code Snippets: Various code examples and exercises from the course.
- Projects: Complete projects developed during the course.
- Notes: Personal notes and insights related to LangChain and Vector Databases.
Feel free to explore the code and projects. If you have any questions or suggestions, please open an issue or submit a pull request.
- Course Introduction
- From Zero to Hero
- Large Language Models and LangChain
- Learning How to Prompt
- Keeping Knowledge Organized with Indexes
- Combining Components Together with Chains
- Giving Memory to LLMs
- Making LLMs Interact with the World Using Tools
- Using Language Model as Reasoning Engines with Agents
This project is licensed under the MIT License - see the LICENSE.md file for details.
- Thanks to Activeloop for providing this comprehensive course.
- Special shoutout to the community and fellow learners for the support and collaboration.
Feel free to connect with me on LinkedIn or follow me on GitHub.
Happy Learning!
