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 til.simonwillison.net
A simple Python wrapper for the ChatGPT API | Simon Willison's TILs
| | isthisit.nz
Prompting GPT Models to Return JSON
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| | August 2024 Update: Now a solved problem. Use Structured Outputs. Large language models (LLMs) return unstructured output. When we prompt them they respond with one large string. This is fine for applications such as ChatGPT, but in others where we want the LLM to return structured data such as lists or key value pairs, a parseable response is needed. In Building A ChatGPT-enhanced Python REPL I used a technique to prompt the LLM to return output in a text format I could parse.
| | blog.daniemon.com
ChatGPT API Function Calling: A Python Tutorial Daniemon's Blog
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| | How to use ChatGPT function calling to have better control of the API's response, making it easier to use the generated content in your code.
| | blog.mozilla.ai
Introducing any-llm: A unified API to access any LLM provider
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| | When it comes to using LLMs, it's not always a question of which model to use: it's also a matter of choosing who provides the LLM and where it is deployed. Today, we announce the release of any-llm, a Python library that provides a simple unified interface to access the most popular providers.
| | martinschoombee.com
Building a framework for orchestration in Azure Data Factory
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| Most people will describe a framework as the "building blocks" of something. I see it more as the foundation and outer frame of a structure, if you'd like to use the building of a house as an analogy. There are a few rigid pieces (the foundation) that provide restrictions outside of which you are not...