jackmpcollins

9 ms·

1. 1.▲
   by jackmpcollins 1y ago

   Seems to me that integrations will be the most important component of tools like this. As an engineer I get my context from video calls with customers and other engineers, slack messages, emails, docs online, using the product myself, etc.
2. 2.▲
   by jackmpcollins 1y ago

   I've already merged my first Engine PR! Being able to review PRs like normal and it updates its work is very cool.
3. 3.▲
   by jackmpcollins 2y ago

   I believe we'll see two main types of AI/LLM libraries/frameworks emerge like has happened for ORMs (at least in Python). - The "Sqlalchemy" level: just bridge the gap between code and LLM - The "Django" l
4. 4.▲
   by jackmpcollins 2y ago

   I would love some feedback on my library magentic which aims to have the best features of both approaches! https://github.com/jackmpcollins/magentic
5. 5.▲
   by jackmpcollins 2y ago

   If you are using Python, check out the package I've been building, magentic https://github.com/jackmpcollins/magentic It supports structured outputs and streaming, and aims to avoid making unnecessary abstraction
6. 6.▲
   by jackmpcollins 2y ago

   I'm building magentic for use cases like this! https://github.com/jackmpcollins/magentic It's based on pydantic and aims to make writing LLM queries as easy/compact as possible by using type annotations
7. 7.▲
   by jackmpcollins 2y ago

   I'd be really interested to hear what abstractions you would find useful for RAG. I'm building magentic which is focused on structured outputs and streaming, but also enables RAG [0], though currently has no specific abstractions
8. 8.▲
   by jackmpcollins 2y ago

   I've been building magentic [0] around similar ideas! Structured outputs and keeping control flow in python is definitely the way to go to keep things lightweight and easy to integrate. Having state management built in is cool! I'
9. 9.▲
   by jackmpcollins 2y ago

   I'm working on a very abstraction light package for LLMs called magentic. The goal is to handle the main boilerplate of building with LLMs, but leave the abstractions up to the user. I'd be interested to hear your opinion on it vs
10. 10.▲
    by jackmpcollins 2y ago

    I'm building magentic, which aims to be a middle ground between the current "no framework" and "too much framework" options. It handles retries, logging/tracing, concurrency with asyncio, and other boilerplate,
11. 11.▲
    by jackmpcollins 2y ago

    Pydantic also have support for parsing partial JSON. https://docs.pydantic.dev/latest/concepts/json/#partial-json... from pydantic_core import from_json partial_json_data = '["aa", &quot
12. 12.▲
    by jackmpcollins 2y ago

    That gif is really cool! I built a Python package magentic [0] which similarly parses the LLM streamed output and allows it to be used before it is finished being generated. There are plenty of use cases / prompts that can be refactore
13. 13.▲
    by jackmpcollins 2y ago

    It's a great start but there's a little more work to do for full OpenAI API compatibility, namely streaming support and the tool_choice parameter. Making it fully compatible would allow it be swapped in directly to frameworks like
14. 14.▲
    by jackmpcollins 2y ago

    Thanks for the explanation! Do you see auto-optimization as something that is useful for every use case or just some? And what determines when this is useful vs not?
15. 15.▲
    by jackmpcollins 2y ago

    Looks quite like magentic [0] that I've been building, though broader in scope? I'm (clearly) a huge advocate of pydantic, structured outputs, and keeping control flow in python code (rather than inside abstractions / "c
16. 16.▲
    by jackmpcollins 2y ago

    Is AdalFlow also focused on automated prompt optimization or is it broader in scope? It looks like there are also some features around evaluation. I'd be really interested to see a comparison between AdalFlow, DSPy [0], LangChain [1] a
17. 17.▲
    by jackmpcollins 2y ago

    I've built a lightweight package that provides a standard interface to the LLM providers, as well as taking care of boilerplate around structured outputs, function calling, and opentelemetry/tracing. It's hopefully a good com
18. 18.▲
    by jackmpcollins 2y ago

    I haven't used LangGraph myself, but the latest magentic release is compatible with it if you'd like to check out the examples here https://github.com/jackmpcollins/magentic/issues/287
19. 19.▲
    by jackmpcollins 2y ago

    Please try out https://magentic.dev/ ! It is a light wrapper that is standard across LLM providers and handles the boilerplate code related to structured outputs and function calling. It doesn't include data/vecto
20. 20.▲
    by jackmpcollins 2y ago

    I completely agree, and built magentic [0] to cover the common needs (structured output, common abstraction across LLM providers, LLM-assisted retries) while leaving all the prompts up to the package user. [0] https://github.com&
21. 21.▲
    by jackmpcollins 2y ago

    I'm building magentic https://github.com/jackmpcollins/magentic which has basically this syntax, though it queries the LLM to generate the answer rather than writing + running code. from magentic import promp
22. 22.▲
    by jackmpcollins 2y ago

    Does the dashboard/UI support traces? I would love a tool in which to view opentelemetry traces, that can neatly display full prompt and response for the spans that represent LLM queries. I'm planning to add opentelemetry instrume
23. 23.▲
    by jackmpcollins 3y ago

    Update: I've added the ability to add chat messages using a new decorator `@chatprompt` in v0.7.0. See https://github.com/jackmpcollins/magentic/releases/tag/v0.7....
24. 24.▲
    by jackmpcollins 3y ago

    Ellipses is actually used in quite a few places. See the answers and comments on this stackoverflow post[0]. The usage most similar to what I have in the magentic examples is with the `@overload` decorator in the typing module[1]. With that
25. 25.▲
    by jackmpcollins 3y ago

    Right now we just pass a single user prompt to the chat model. Setting the system prompt could also be done in the `@prompt` decorator. I've added a github issue to track https://github.com/jackmpcollins/magentic&#
26. 26.▲
    by jackmpcollins 3y ago

    Yes, I'm working on allowing few-shot examples to be provided as part of defining the prompt-function, which should help in cases like this. Unfortunately from my testing just now it appears that OpenAI ignores examples added to the mo
27. 27.▲
    by jackmpcollins 3y ago

    Thanks for sharing! LocalAI supports function calling[0] so this should work for most or all features of magentic - I'm interested to see if concurrent requests work. I will test this out. [0] https://localai.io/feature
28. 28.▲
    by jackmpcollins 3y ago

    At the moment only those that support the OpenAI Chat API, with function calling for the structured outputs. For example you can use LocalAI[0][1] to run models locally. [0] https://github.com/go-skynet/LocalAI [1] ht
29. 29.▲
    by jackmpcollins 3y ago

    With magentic you could do chain-of-thought in two or more steps: one function that generates a string output containing the chain-of-thought reasoning and answer, and a second that takes that output and converts it to the final answer obje
30. 30.▲
    by jackmpcollins 3y ago

    Thanks! Currently magentic just uses OpenAI function-calling; it provides it a function schema that matches the structure of the output object. So it fails in the same ways as function-calling - struggles to match complex schemas, occasiona