AI Agents - Async Programming and Pytantic Data Validation
Async Sync means one after another. For example, calling 3 functions named f1 , f2 , and f3 . Assume f1 performs some I/O operations like a DB query, API request, or file operation. After f1 is completed, f2 and f3 will be executed. There are some operations that are running on top of the CPU. E.g. def add ( a , b ): return a + b When we call this function, it will be executed by the CPU. And…
Async programming represents a method of executing operations one after the other, as opposed to simultaneously. For instance, consider three functions: f1, f2, and f3. In a synchronous approach, f1 might perform an I/O operation like a database query or API request. After f1 completes, only then would f2 and f3 execute. Conversely, asynchronous programming enables f2 to run concurrently while waiting for f1's response, thereby eliminating the need for waiting (context switching). Python implements async programming via the asyncio package.
Pydantic, a data validation library for Python, aids in structuring the output from language models like LLMs. By default, the output from an LLM is in text format, which needs to be parsed and structured. Pydantic assists in converting this unstructured text into a desired format, such as a structured dictionary with named fields, like {name: string, age: int}.
To do this, Pydantic data validation is applied, ensuring the data conforms to a specified structure. This not only improves data integrity but also enhances code readability and maintainability by avoiding implicit data validation, which can clutter and complicate the code.
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