Combine MULTIPLE LLMs to build an AI API! (super simple!!!) Langflow | LangChain | Groq | OpenAI

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AI Summary

Video Summary: Build Advanced Chatbots with Multiple LLMs

• Introduction
  • Tutorial on creating advanced chatbots using multiple language learning models (LLMs).
  • Chatbots can scrape websites, adjust files, connect to databases.
  • API code is used to get text responses.
• Using Lang Flow
  • Lang Flow allows drag-and-drop of components like URL and file components.
  • Components are connected to LLMs, databases, etc.
  • Example project: a chatbot for the website CodiAnal.com and its courses.
• Setting Up
  • Download CSV data with course module information.
  • Use Lang Flow to load and vectorize CSV data.
  • Store vector embeddings in DataStax Astra database for similarity searches.
• Building the Chatbot
  • Chat input and output components are used.
  • Text input is vectorized and compared with database embeddings.
  • OpenAI is used to generate human-like text responses.
  • Website data is scraped and passed into the chatbot for more context.
  • Chat memory is used to build on previous questions.
• Using the Playground
  • The playground allows testing of the chatbot within Lang Flow.
  • Users can type questions and receive text responses.
• Connecting to Apps
  • JavaScript API is provided to connect the chatbot to external apps.
  • Users can make API calls to Lang Flow and receive responses.
• Advanced Features
  • Grouping components for better organization.
  • Multimodal flows with different models like Grok.
  • Users can replace OpenAI with Grok for different responses.
• Conclusion
  • Encouragement to build with Lang Flow and share creations.
  • Reminder that the course is free and to star the repo on GitHub.

Additional Notes

• The tutorial is free and the creator asks for a star on the Lang Flow GitHub repo as appreciation.
• The tutorial covers setting up the environment, creating a Python environment, and installing Lang Flow.
• The tutorial also includes instructions on how to get started with Python in Visual Studio Code.
• The chatbot is designed to answer questions about a specific course, including details not readily available on the internet.
• The tutorial demonstrates how to create vector embeddings and store them in a database for similarity searches.
• The chatbot can answer questions about course content, such as whether it teaches object-oriented programming.
• The tutorial shows how to use Lang Flow’s components to build and test the chatbot.
• The chatbot can remember previous interactions and provide streaming text responses.
• The tutorial concludes with instructions on how to connect the chatbot to applications using an API.