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Enhancing LLMs with function calling and the OpenAI API
- Large Language Models (LLMs) are powerful tools in AI, but are limited by existing data sources, prompting the need for technologies like Retrieval-Augmented Generation (RAG).
- RAG augments LLM response generation by incorporating external information sources to enhance the quality of generated responses.
- Two main methods to integrate RAG are the Model Context Protocol (MCP) and function calling, with the latter being less popular but equally capable.
- Function calling involves providing LLMs with lists of functions/tools when interacting via APIs, giving developers more control over invoking these tools.
- While MCP is effective in certain scenarios, function calling offers more transparency and control over actions performed by the model.
- Potential drawbacks of using MCP include opaqueness and overhead, while function calling allows for more controlled interactions.
- Function calling example with the OpenAI API involves implementing a scheduling assistant that can book meetings by checking availability in real time.
- The tutorial covers setting up the project, integrating the OpenAI API, defining functions like 'parse_date' to handle natural language input, and 'schedule_meeting' to book meetings based on extracted data.
- The approach demonstrates the power of LLMs in understanding context from user messages and successfully scheduling meetings based on availability.
- In conclusion, the tutorial emphasizes the benefits of function calling over MCP in certain use cases, offering a more efficient and controlled solution.
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