httpx
simpleaichat
httpx | simpleaichat | |
---|---|---|
53 | 22 | |
12,274 | 3,376 | |
1.2% | - | |
8.9 | 8.7 | |
8 days ago | 4 months ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" 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.
httpx
-
A Retrospective on Requests
For reference, it's a butterfly, not a moth.
Source: https://github.com/encode/httpx/issues/834
-
Show HN: Twitter API Wrapper for Python – No API Keys Needed
Very cool, first I'm hearing of httpx https://www.python-httpx.org/
I think most people would start with trying out requests or something for this kind of work, I'm guessing that didn't work out? You've got a star from me.
-
Harlequin: SQL IDE for Your Terminal
To access 10 different commands at the same time, that is tricky but definitely doable.
First thing that comes to mind, you can use aliases.
To keep it simple, lets use 3 examples instead of 10: harlequin (this project), pgcli (https://www.pgcli.com/) and httpx (https://www.python-httpx.org/)
Setup a main home for all your venvs:
cd ~
-
HTTP Rate Limit
There are already some implementations for Python HTTP clients. One of them is aiometer. But it's not suitable for my use case. Since httpx already has the internal pool, it would be better to reuse the design.
-
Introducing Flama for Robust Machine Learning APIs
Besides, flama also provides support for SQL databases via SQLAlchemy, an SQL toolkit and Object Relational Mapper that gives application developers the full power and flexibility of SQL. Finally, flama also provides support for HTTP clients to perform requests via httpx, a next generation HTTP client for Python.
-
Embracing Modern Python for Web Development
We can use the async HTTP client provided by httpx, a fully featured HTTP client for Python with an API broadly compatible with requests, so it can be used in pretty much the same way in most cases.
-
Didn't want to click on refresh to see updates, this is what I did!
httpx in place of requests library
-
Python Requests 3
The main value of Requests is that it provided an abstract interface on top of HTTP, which was designed well-enough to become a standard. But today it has fallen way behind in its field, and there are much better alternatives such as HTTPX [0].
[0] https://www.python-httpx.org/
-
Unlocking Performance: A Guide to Async Support in Django
HTTPX is a popular Python library that provides an asynchronous HTTP client, and it can be beneficial for enabling async support in Django. While Django itself does not require HTTPX for async support, using HTTPX in combination with Django's async views can bring several advantages:
-
Show HN: Python package for interfacing with ChatGPT with minimized complexity
The underlying library for both sync and async is httpx (https://www.python-httpx.org/) which may be limited from the HTTP Client perspective but it may be possible to add rate limiting at a Session level.
simpleaichat
- Efficient Coding Assistant with Simpleaichat
-
Please Don't Ask If an Open Source Project Is Dead
I checked both the issues mentioned, people have been respectful and showing empathy to author's situation
https://github.com/minimaxir/simpleaichat/issues/91
https://github.com/minimaxir/simpleaichat/issues/92
-
We Built an AI-Powered Magic the Gathering Card Generator
ChatGPT's June updated added support for "function calling", which in practice is structured data I/O marketed very poorly: https://openai.com/blog/function-calling-and-other-api-updat...
Here's an example of using structured data for better output control (lightly leveraging my Python package to reduce LoC: https://github.com/minimaxir/simpleaichat/blob/main/examples... )
-
LangChain Agent Simulation – Multi-Player Dungeons and Dragons
So what are the alternatives to LangChain that the HN crowd uses?
I see two contenders:
https://github.com/minimaxir/simpleaichat/tree/main/simpleai...
https://github.com/griptape-ai/griptape
There is also the llm command line utility that has a very thin underlying library, but which might grow eventually:
-
Custom Instructions for ChatGPT
A fun note is that even with system prompt engineering it may not give the most efficient solution: ChatGPT still outputs the avergage case.
I tested around it and doing two passes (generate code and "make it more efficient") works best, with system prompt engineering to result in less code output: https://github.com/minimaxir/simpleaichat/blob/main/examples...
-
The Problem with LangChain
I played around with simpleaichat for a few minutes just now, and I really like it. Unlike LangChain, I can understand what it does in minutes, and it looks like its primitives are fairly powerful. It looks like it's going to replace the `openai` library for me, it seems like a nice wrapper.
I'm especially looking forward to playing with the structured data models bit: https://github.com/minimaxir/simpleaichat/blob/main/examples...
Well done, Max!
-
How is Langchain's dev experience? Any alternatives?
https://github.com/minimaxir/simpleaichat bills itself as a simpler alternative to langchain. I have not tried it, but it looks interesting.
-
Stanford A.I. Courses
I think you are asking specifically about practical LLM engineering and not the underlying science.
Honestly this is all moving so fast you can do well by reading the news, following a few reddits/substacks, and skimming the prompt engineering papers as they come out every week (!).
https://www.latent.space/p/ai-engineer provides an early manifesto for this nascent layer of the stack.
Zvi writes a good roundup (though he is concerned mostly with alignment so skip if you don’t like that angle): https://thezvi.substack.com/p/ai-18-the-great-debate-debates
Simon W has some good writeups too: https://simonwillison.net/
I strongly recommend playing with the OpenAI APIs and working with langchain in a Colab notebook to get a feel for how these all fit together. Also, the tools here are incredibly simple and easy to understand (very new) so looking at, say, https://github.com/minimaxir/simpleaichat/tree/main/simpleai... or https://github.com/smol-ai/developer and digging in to the prompts, what goes in system vs assistant roles, how you gourde the LLM, etc.
-
Where is the engineering part in "prompt engineer"?
This notebook from the repo I linked to is a concise example, and the reason you would want to optimize prompts.
- Show HN: Python package for interfacing with ChatGPT with minimized complexity
What are some alternatives?
AIOHTTP - Asynchronous HTTP client/server framework for asyncio and Python
lmql - A language for constraint-guided and efficient LLM programming.
Niquests - Requests but with HTTP/3, HTTP/2, Multiplexed Connections, System CAs, Certificate Revocation, DNS over HTTPS / TLS / QUIC or UDP, Async, DNSSEC, and (much) pain removed!
langroid - Harness LLMs with Multi-Agent Programming
requests-html - Pythonic HTML Parsing for Humansâ„¢
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
requests - A simple, yet elegant, HTTP library.
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
Flask - The Python micro framework for building web applications.
gchain - Composable LLM Application framework inspired by langchain
starlette - The little ASGI framework that shines. 🌟
transynthetical-engine - Applied methods of analytical augmentation to build tools using large-language models.