How to Build your Own Local AutoGen GPT Tutorial

---

---

AI Summary

• Introduction
  • Creating a GPT model with knowledge about Autogen using Lang chain.
  • Autogen is not known to AI models as it was released after their training data cut-off.
  • Autogen is used for orchestrating agent workflows and conversations.
• Project Setup
  • Create a new project and a directory called “Ducks”.
  • Clone the Autogen GitHub repository into the “Ducks” directory.
  • Install necessary requirements from a provided requirements.txt file.
• Using Lang Chain and Vector Database
  • Lang chain tool “git tool” is used to get Autogen repository for context.
  • The context is stored in a vector database for the language model (LLM).
  • The vector database helps manage and understand the information for the LLM.
• Implementation Details
  • Clone Autogen repo using git clone command.
  • Create a text splitter to manage document size for the vector database.
  • Use OpenAI embeddings and a vector store to save and manage the data.
  • Set up environment variables including OpenAI API key and index name for the vector database.
  • Use Lang chain’s git loader to load and split Autogen repository files.
  • Save the vector store with the index name in the directory.
  • Instantiate OpenAI embeddings and create a retriever for similar search results.
  • Invoke the retriever with a question to get a response from the LLM.
• Testing and Results
  • Test the setup with questions about Autogen.
  • The model successfully returns detailed summaries and Python code for creating a group chat with agents.
  • Some questions may not return results, indicating room for optimization.
• Conclusion
  • The project demonstrates how to give an LLM context about a specific topic using Lang chain.
  • Future uses could include integrating Wikipedia sources, PDFs, and HTML parsing for additional context.
  • The process is a good starting point for those new to Lang chain.
  • The tutorial ends with encouragement to explore more Autogen videos and content.