superduperdb
dspy
superduperdb | dspy | |
---|---|---|
24 | 22 | |
4,390 | 11,228 | |
3.2% | 20.5% | |
9.9 | 9.9 | |
5 days ago | 5 days ago | |
Python | Python | |
Apache License 2.0 | 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.
superduperdb
- FLaNK Stack Weekly 12 February 2024
- FLaNK Stack Weekly 11 Dec 2023
- Trending on GitHub top 10 globally for the 4th day in a row: Open-source framework for integrating OpenAI with major databases
- Trending on GitHub top 10 for the 4th day in a row: Open-source framework for integrating AI models and APIs directly with all major SQL databases
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Trending on GitHub top 10 for the 4th day in a row and official technology partner of MongoDB: Open-source framework for integrating AI with MongoDB and MongoDB Atlas
Definitely check it out: https://github.com/SuperDuperDB/superduperdb and find it here: https://cloud.mongodb.com/ecosystem/
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Trending on GitHub top 10 globally for the 4th day in a row: Open-source framework for integrating OpenAI and GPT with major databases
Build a chatbot with OpenAI: https://github.com/SuperDuperDB/superduperdb/blob/main/examples/question_the_docs.ipynb
- SuperDuperDB - how to use it to talk to your documents locally using llama 7B or Mistral 7B?
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Trending on GitHub globally 3 days in a row: SuperDuperDB, a framework for integrating AI with major databases (making them super-duper)
It is for building AI (into your) apps easily without complex pipelines and make your database intelligent (including vector search), definitely check it out: https://github.com/SuperDuperDB/superduperdb
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🔮 SuperDuperDB is #3 on GitHub Trending globally! 🥉
VentureBeat already covered the launch This is our website This is our main GitHub repository
dspy
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Computer Vision Meetup: Develop a Legal Search Application from Scratch using Milvus and DSPy!
Legal practitioners often need to find specific cases and clauses across thousands of dense documents. While traditional keyword-based search techniques are useful, they fail to fully capture semantic content of queries and case files. Vector search engines and large language models provide an intriguing alternative. In this talk, I will show you how to build a legal search application using the DSPy framework and the Milvus vector search engine.
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Pydantic Logfire
I’ve observed that Pydantic - which we’ve used for years in our API stack - has become very popular in LLM applications, for its type-adjacent features. It serves as a foundational technology for prompting libraries like [DSPy](https://github.com/stanfordnlp/dspy) which are abstracting “up the stack” of LLM apps. (some opinions there)
Operating AI apps reveals a big challenge, in that debugging probabilistic code paths requires more than the usual introspective abilities, and in an environment where function calls can have very real monetary impact we have to be able to see what’s happening in the runtime. See LangChain’s hosted solution (can’t recall the name) that allows an operator to see prompts and responses “on the wire”. (It just occurred to me that Langchain and Pydantic have a lot in common here, in approach.)
Having a coupling between Pydantic - which is *just about* the data layer itself - and an observability tool seems very interesting to me, and having this come from the folks who built it does not seem unreasonable. WRT open source and monetization, I would be lying if I said I wasn’t a little worried - given the recent few months - but I am choosing to see this in a positive light, given this team’s “believability weight” (to overuse Dalio) and history of delivering solid and really useful tooling.
- Ask HN: Most efficient way to fine-tune an LLM in 2024?
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Princeton group open sources "SWE-agent", with 12.3% fix rate for GitHub issues
DSPy is the best tool for optimizing prompts [0]: https://github.com/stanfordnlp/dspy
Think of it as a meta-prompt optimizer, it uses a LLM to optimize your prompts, to optimize your LLM.
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Winner of the SF Mistral AI Hackathon: Automated Test Driven Prompting
Isn’t this just a very naive implementation of what DsPY does?
https://github.com/stanfordnlp/dspy
I don’t understand what is exceptional here.
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Show HN: Fructose, LLM calls as strongly typed functions
Have you done any comparison with DSPy ? (https://github.com/stanfordnlp/dspy)
Feels very similiar to DSPy except you dont have optimizations yet. But I like your API and the programming model your are enforcing through this.
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AI Prompt Engineering Is Dead
I'm interested in hearing if anyone has used DSPy (https://github.com/stanfordnlp/dspy) just for prompt optimization for GPT-3.5 or GPT-4. Was it worth the effort and much better than manual prompt iteration? Was the optimized prompt some weird incantation? Any other insights?
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Ask HN: Are you using a GPT to prompt-engineer another GPT?
You should check out x.com/lateinteraction's DSPy — which is like an optimizer for prompts — https://github.com/stanfordnlp/dspy
- SuperDuperDB - how to use it to talk to your documents locally using llama 7B or Mistral 7B?
- FLaNK Stack Weekly for 12 September 2023
What are some alternatives?
ds2 - Easiest way to use AI models without coding (Web UI & API support)
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
best-of-ml-python - 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
open-interpreter - A natural language interface for computers
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
playground - Play with neural networks!
nyc_traffic_flask - Flask App with leaflet.js that can perform NYC Traffic Prediction
MLflow - Open source platform for the machine learning lifecycle
Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials - A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.
FastMJPG - FastMJPG is a command line tool for capturing, sending, receiving, rendering, piping, and recording MJPG video with extremely low latency. It is optimized for running on constrained hardware and battery powered devices.
mlops-python-package - Kickstart your MLOps initiative with a flexible, robust, and productive Python package.
prompt-engine-py - A utility library for creating and maintaining prompts for Large Language Models