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Function Calling in Large Language Models: Unlocking Practical Applications with Real-World Cases…
- ChatGLM's function calling feature enhances the model's reasoning and external operation capabilities.
- Tools are an optional parameter in the content generation API for providing function definitions to the model.
- The API generates function parameters based on user specifications without performing actual function calls.
- Developers can use the model's output parameters for executing function calls in applications.
- The tutorial covers building a student grade database, implementing query and data filtering functions, and using LLM function calls for database interactions.
- Non-technical users can interact with the database using natural language through LLM function calls.
- For technical personnel, understanding LLM function calls aids in implementing relevant functions.
- A student exam score Q&A service example demonstrates generating scores for 100 students and saving data to an SQLite database.
- Simple database query operations are implemented to ensure proper database functioning.
- Five functions are defined and structured for LLM selection, including name, description, parameter list, and descriptions.
- Function calling, obtaining return results, and using LLM for question answering are demonstrated.
- The tutorial summarizes the working principle of LLM function calls for practical applications.
- This content focuses on the application of function calling in ChatGLM for various operations and interactions.
- It emphasizes the integration of external function libraries with ChatGLM models.
- The tutorial highlights the importance of understanding LLM function calls for both technical and non-technical users.
- Overall, the tutorial showcases the potential of LLM function calls in enhancing model capabilities and real-world applications.
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