instructor
chatgpt-localfiles
| instructor | chatgpt-localfiles | |
|---|---|---|
| 19 | 3 | |
| 5,520 | 39 | |
| - | - | |
| 9.8 | 5.9 | |
| about 15 hours ago | 11 months ago | |
| Python | Python | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
instructor
- Instructor: Structured Outputs for LLMs
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Anthropic's Haiku Beats GPT-4 Turbo in Tool Use
Ah yes. Have you tried out instructor [0] or Guidance [1]?
[0]: https://github.com/jxnl/instructor/
- Instructor: Structured Data Like JSON from Large Language Models
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Show HN: Fructose, LLM calls as strongly typed functions
Good stuff. How does this compare to Instructor? I’ve been using this extensively
https://jxnl.github.io/instructor/
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Show HN: Ellipsis – Automatic pull request reviews
it's super cool! checkout how the Instructor repo uses it to keep various parts of their docs in sync: https://github.com/jxnl/instructor/blob/main/ellipsis.yaml
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Pushing ChatGPT's Structured Data Support to Its Limits
I've been using the instructor[1] library recently and have found the abstractions simple and extremely helpful for getting great structured outputs from LLMs with pydantic.
1 https://github.com/jxnl/instructor/tree/main
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Efficiently using python in GPTs
Maybe try using jason liu’s instructor package (https://github.com/jxnl/instructor) to structure the outputs with pydantic? It’s explained in his presentation from the AI Engineer summit (https://youtu.be/yj-wSRJwrrc)
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Ask HN: Cheapest way to run local LLMs?
One of the most powerful ways to integrate LLMs with existing systems is constrained generation. Libraries such as outlines[1] and instructor[2] allow structural specification of the expected outputs as regex patterns, simple types, jsonschema or pydantic models.
These outputs often consume significantly fewer tokens than chat or text completion.
[1] https://github.com/outlines-dev/outlines
[2] https://github.com/jxnl/instructor
- OpenAI Function Calls for Humans
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Unbounded Books: Search by ~Vibes
The best GPT-wrapper you’ll see today?
...but this one hasn't raised oodles of cash.
Mike (creator) here, excited to hear what HN-folks think. Anything to add/improve?
Had fun building, extra s/out to Railway, NextJS, and https://github.com/jxnl/instructor
Check it out: https://www.unboundedbooks.com/
chatgpt-localfiles
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Show HN: OpenAI dev assistant GUI with local code interpreter
I built a local developer-mode plugin that gives ChatGPT access to a local directory [0] -- it's pretty basic in terms of functionality (just simple bridge to access/read files) but I've found a surprising amount of value from it [1]!
[0] https://github.com/samrawal/chatgpt-localfiles
[1] https://samrawal.substack.com/p/example-driven-development
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Show HN: Python package for interfacing with ChatGPT with minimized complexity
I wrote a simple ChatGPT plugin that exposes files within a local directory on my computer to ChatGPT [0]. It's been quite helpful for me, both for working with code as well as other material (supports docx and pdf as well). It does require plugin developer access to run, though.
[0] https://github.com/samrawal/chatgpt-localfiles
- Show HN: Access Local Files from ChatGPT
What are some alternatives?
langchainjs - 🦜🔗 Build context-aware reasoning applications 🦜🔗
simpleaichat - Python package for easily interfacing with chat apps, with robust features and minimal code complexity.
PythonGPT - PythonGPT writes and indexes code to implement dynamic code execution using generative models. Younger sibling of DoctorGPT.
httpx - A next generation HTTP client for Python. 🦋
outlines - Structured Text Generation