sketch
datasloth
sketch | datasloth | |
---|---|---|
20 | 7 | |
2,198 | 198 | |
0.9% | - | |
4.4 | 3.7 | |
3 months ago | 20 days ago | |
Python | Python | |
MIT License | MIT License |
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sketch
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Ask HN: What have you built with LLMs?
We've made a lot of data tooling things based on LLMs, and are in the process of rebranding and launching our main product.
1. sketch (in notebook, ai for pandas) https://github.com/approximatelabs/sketch
2. datadm (open source, "chat with data", with support for the open source LLMs (https://github.com/approximatelabs/datadm)
3. Our main product: julyp. https://julyp.com/ (currently under very active rebrand and cleanup) -- but a "chat with data" style app, with a lot of specialized features. I'm also streaming me using it (and sometimes building it) every weekday on twitch to solve misc data problems (https://www.twitch.tv/bluecoconut)
For your next question, about the stack and deploy:
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Pandas AI β The Future of Data Analysis
This morning I added a "Related Projects" [3] Section to the Buckaroo docs. If Buckaroo doesn't solve your problem, look at one of the other linked projects (like Mito).
[1] https://github.com/approximatelabs/sketch
[2] https://github.com/paddymul/buckaroo
[3] https://buckaroo-data.readthedocs.io/en/latest/FAQ.html
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Ask HN: What's your favorite GPT powered tool?
For GPT/Copilot style help for pandas, in notebooks REPL flow (without needing to install plugins), I built sketch. I genuinely use it every-time I'm working on pandas dataframes for a quick one-off analysis. Just makes the iteration loop so much faster. (Specifically the `.sketch.howto`, anecdotally I actually don't use `.sketch.ask` anymore)
https://github.com/approximatelabs/sketch
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RasaGPT: First headless LLM chatbot built on top of Rasa, Langchain and FastAPI
https://github.com/approximatelabs/lambdaprompt It has served all of my personal use-cases since making it, including powering `sketch` (copilot for pandas) https://github.com/approximatelabs/sketch
Core things it does: Uses jinja templates, does sync and async, and most importantly treats LLM completion endpoints as "function calls", which you can compose and build structures around just with simple python. I also combined it with fastapi so you can just serve up any templates you want directly as rest endpoints. It also offers callback hooks so you can log & trace execution graphs.
All together its only ~600 lines of python.
I haven't had a chance to really push all the different examples out there, but most "complex behaviors", so there aren't many patterns to copy. But if you're comfortable in python, then I think it offers a pretty good interface.
I hope to get back to it sometime in the next week to introduce local-mode (eg. all the open source smaller models are now available, I want to make those first-class)
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[D] The best way to train an LLM on company data
Please look at sketch and langchain pandas/SQL plugins. I have seen excellent results with both of these approaches. Both of these approaches will require you to send metadata to openAI.
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Meet Sketch: An AI code Writing Assistant For Pandas
π Understand your data through questions π Create code from plain text Quick Read: https://www.marktechpost.com/2023/02/01/meet-sketch-an-ai-code-writing-assistant-for-pandas/ Github: https://github.com/approximatelabs/sketch
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Replacing a SQL analyst with 26 recursive GPT prompts
(3) Asking for re-writes of failed queries (happens occasionally) also helps
The main challenge I think with a lot of these "look it works" tools for data applications, is how do you get an interface that actually will be easy to adopt. The chat-bot style shown here (discord and slack integration) I can see being really valuable, as I believe there has been some traction with these style integrations with data catalog systems recently. People like to ask data questions to other people in slack, adding a bot that tries to answer might short-circuit a lot of this!
We built a prototype where we applied similar techniques to the pandas-code-writing part of the stack, trying to help keep data scientists / data analysts "in flow", integrating the code answers in notebooks (similar to how co-pilot puts suggestions in-line) -- and released https://github.com/approximatelabs/sketch a little while ago.
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FLiP Stack Weekly for 21 Jan 2023
Python AI Helper https://github.com/approximatelabs/sketch
- LangChain: Build AI apps with LLMs through composability
- Show HN: Sketch β AI code-writing assistant that understands data content
datasloth
- GPT-3 query to a database
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Show HN: Sketch β AI code-writing assistant that understands data content
Great work, and a really interesting application of GPT3. Some time ago I developed Datasloth [1] which might be a nice complementary feature to Sketch. Ping me if you're interested to bounce ideas :)
[1] https://github.com/ibestvina/datasloth
- DataSloth - Query Pandas using natural language, powered by GPT-3
- DataSloth - Query Pandas using natural language
- Show HN: Query Pandas using natural language
What are some alternatives?
RasaGPT - π¬ RasaGPT is the first headless LLM chatbot platform built on top of Rasa and Langchain. Built w/ Rasa, FastAPI, Langchain, LlamaIndex, SQLModel, pgvector, ngrok, telegram
lambdaprompt - Ξ»prompt - A functional programming interface for building AI systems
lmql - A language for constraint-guided and efficient LLM programming.
pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more [Moved to: https://github.com/pandas-dev/pandas]
gpt_index - LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data. [Moved to: https://github.com/jerryjliu/llama_index]
CodexDB - CodexDB generates code for SQL query processing via OpenAI's GPT-3 Codex model.
pandas-ai - Chat with your database (SQL, CSV, pandas, polars, mongodb, noSQL, etc). PandasAI makes data analysis conversational using LLMs (GPT 3.5 / 4, Anthropic, VertexAI) and RAG.
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
langchain - β‘ Building applications with LLMs through composability β‘ [Moved to: https://github.com/langchain-ai/langchain]
30-Days-Of-Python - 30 days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than100 days, follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw
rasa - π¬ Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants
viper - Simple, expressive pipeline syntax to transform and manipulate data with ease