JsonGenius
llmware
| JsonGenius | llmware | |
|---|---|---|
| 12 | 9 | |
| 157 | 3,839 | |
| - | 22.9% | |
| 6.1 | 9.8 | |
| 7 months ago | 4 days ago | |
| Go | Python | |
| Apache License 2.0 | Apache License 2.0 |
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.
JsonGenius
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Show HN: SingleAPI – Convert the Internet into your own API
isn’t this just using jsongenius[1]
[1] https://github.com/semanser/JsonGenius
- FLaNK Stack Weekly 16 October 2023
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Show HN: Convert the Internet into your own API in seconds using GPT
The docs are available after getting the early access (which is not idea and I'm aware of that). You can check the basic example here: https://github.com/semanser/jsongenius
- I built an open-source scraping API that returns structured JSON data using GPT.
- Scraping API that returns structured JSON data using GPT
- Show HN: JsonGenius – Open-Source Web Scraping API with GPT
llmware
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More Agents Is All You Need: LLMs performance scales with the number of agents
I couldn't agree more. You should check out LLMWare's SLIM agents (https://github.com/llmware-ai/llmware/tree/main/examples/SLI...). It's focusing on pretty much exactly this and chaining multiple local LLMs together.
A really good topic that ties in with this is the need for deterministic sampling (I may have the terminology a bit incorrect) depending on what the model is indended for. The LLMWare team did a good 2 part video on this here as well (https://www.youtube.com/watch?v=7oMTGhSKuNY)
I think dedicated miniture LLMs are the way forward.
Disclaimer - Not affiliated with them in any way, just think it's a really cool project.
- FLaNK Stack Weekly 19 Feb 2024
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Show HN: LLMWare – Small Specialized Function Calling 1B LLMs for Multi-Step RAG
I've been building upon the LLMWare project - https://github.com/llmware-ai/llmware - for the past 3 months. The ability to run these models locally on standard consumer CPUs, along with the abstraction provided to chop and change between models and different processes is really cool.
I think these SLIM models are the start of something powerful for automating internal business processes and enhancing the use case of LLMs. Still kinda blows my mind that this is all running on my 3900X and also runs on a bog standard Hetzner server with no GPU.
- Show HN: LLMWare – Integrated Solution for RAG in Finance and Legal
- Llmware.ai – AI Tools for Financial, Legal and Compliance
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Open Source Advent Fun Wraps Up!
16. LLMWare by Ai Bloks | Github | tutorial
- FLaNK Stack Weekly 16 October 2023
- Strategy for PDF data extraction and Display
What are some alternatives?
karapace - Karapace - Your Apache Kafka® essentials in one tool
llm-client-sdk - SDK for using LLM
Kouncil - Powerful dashboard for your Kafka. Monitor status, manage groups, topics, send messages and diagnose problems. All in one user friendly web dashboard.
pinferencia - Python + Inference - Model Deployment library in Python. Simplest model inference server ever.
inference - A fast, easy-to-use, production-ready inference server for computer vision supporting deployment of many popular model architectures and fine-tuned models.
CML_AMP_AI_Text_Summarization_with_Amazon_Bedrock - CML_AMP_AI_Text_Summarization_with_Amazon_Bedrock
openstatus - 🏓 The open-source synthetic & real user monitoring platform 🏓
RealtimeSTT - A robust, efficient, low-latency speech-to-text library with advanced voice activity detection, wake word activation and instant transcription.
megabots - 🤖 State-of-the-art, production ready LLM apps made mega-easy, so you don't have to build them from scratch 🤯 Create a bot, now 🫵
kafdrop - Kafka Web UI
SimplyRetrieve - Lightweight chat AI platform featuring custom knowledge, open-source LLMs, prompt-engineering, retrieval analysis. Highly customizable. For Retrieval-Centric & Retrieval-Augmented Generation.