Multi-Agent Function Calling LLMs - CODE for AI Agents

OpenAI's new function calling and new OpenAI models (like gpt-4-0613) integrated with python CLASS AI Agents in a code example with GPT-4, AI Agents, Function Calling and external APIs. No LangChain anymore.

Discover new OpenAI's model functionality (JSON) for querying OpenAI's API and then external APIs.

Please noted, that simplifications have been introduced (in the verbal explanations and the code) for educational purposes and augmented understanding. Example: I neglected the error checking code if GPT-4 hallucinated any JSON code answer, etc.

For more official code examples and notebooks on function calling:
Please consult the official notebook ("openai-cookbook") that covers how to use the Chat Completions API in combination with external functions to extend the capabilities of GPT models:
https://github.com/openai/openai-cookbook/blob/main/examples/How_to_call_functions_with_chat_models.ipynb

Recommend this notebook for more code examples:
We'll create our agent in this step, including a Conversation class to support multiple turns with the API, and some Python functions to enable interaction between the ChatCompletion API and our knowledge base functions for arXiv conversations:
https://github.com/openai/openai-cookbook/blob/de3bd5843433a8240f9bcb8e275ccb9dac7f7f0a/examples/How_to_call_functions_for_knowledge_retrieval.ipynb

For the displayed scientific literature (cognitive science) during the video:
All rights with authors:
AI and the transformation of social science research
Science, Vol. 380, No. 6650
https://www.science.org/doi/10.1126/science.adi1778

AI-Augmented Surveys: Leveraging Large Language Models
for Opinion Prediction in Nationally Representative Surveys
https://arxiv.org/pdf/2305.09620.pdf


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