simpleaichat
json-schema-spec
simpleaichat | json-schema-spec | |
---|---|---|
22 | 30 | |
3,386 | 3,237 | |
- | 3.4% | |
8.7 | 7.9 | |
4 months ago | 8 days ago | |
Python | JavaScript | |
MIT License | GNU General Public License v3.0 or later |
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.
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
json-schema-spec
- Python JSON schema
-
TypeSpec: A New Language for API-Centric Development
Yep and that comes from JSON Schema: https://json-schema.org/
I believe recent versions of OpenAPI are "compatible" with JSON Schema (at least they "wanted to be" last I checked as I was implementing some schema converters).
Even TypeScript is not enough to represent all of JSON Schema! But it gets close (perhaps if you remove validation rules and stuff like that it's a full match).
But even something like Java can represent most of it pretty well, specially since sealed interfaces were added. I know because I've done it :).
- JSON Schema Blog
-
Deploy a simple data storage API with very little code using Amazon API Gateway and DynamoDB
models.tf where I centralized all the Data model that API Gateway uses to perform input and output checks. Those use the JSON-schema specification. GitHub - psantus/serverless.api-gateway-dynamodb-integration.terraform
- Unlocking the frontend – a call for standardizing component APIs pt.2
- JSON Schema
-
How to Automatically Consume RESTful APIs in Your Frontend
In the meantime, we are going to expand our backend with two endpoints: one for fetching data and another one for creating data. Fastify provides out-of-the-box support for API serialization and validation through its schema-based approach built on top of JSON Schema. Through the schema option, we can attach a schema definition to each route.
-
A View on Functional Software Architecture
JSON-schema to define templates for request and response contents.
-
Learn serverless on AWS step-by-step: Strong Types!
The syntax used to define the output is called JSON Schema. It is a standard way to define the structure of a JSON object. If you know zod, the spirit is similar. Based on Swarmion's roadmap, it will be possible to use zod schemas to defined contracts in the future, which will be super cool!
- XML is better than YAML
What are some alternatives?
lmql - A language for constraint-guided and efficient LLM programming.
outlines - Structured Text Generation
langroid - Harness LLMs with Multi-Agent Programming
guidance - A guidance language for controlling large language models.
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
uplaybook - A python-centric IT automation system.
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
nix-configs - My Nix{OS} configuration files
gchain - Composable LLM Application framework inspired by langchain
OpenAPI-Specification - The OpenAPI Specification Repository
transynthetical-engine - Applied methods of analytical augmentation to build tools using large-language models.
torch-grammar