sketch
phind-for-firefox
sketch | phind-for-firefox | |
---|---|---|
20 | 53 | |
2,198 | 1 | |
0.9% | - | |
4.4 | 10.0 | |
3 months ago | about 1 year ago | |
Python | ||
MIT License | Mozilla Public License 2.0 |
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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
phind-for-firefox
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April 2023
AI search engine for developers (https://www.phind.com/)
- best chatgpt website alternatives (save message π§βπ»)
- BEST ChatGPT Website Alternatives (save message π§βπ»)
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Is ChatGPT incompetent or do I suck at prompt engineering?
Try something like https://www.phind.com which uses resources like Stackoverflow and others to generate more correct answers, itβs not perfect but itβs a very good way to interrogate βsomeoneβ about a topic which is how you should use these tools.
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GPT-4 dissuading me from using it
I use www.phind.com for software engineering assistance. I think it's using something like GPT-4 under the hood.
- Best ChatGPT Alternatives
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AI Co-pilots for SwiftUI: what do you guys recommend? (Iβve been using ChatGPT4 with great success but itβs knowledge cutoff is 2021 ie. only up to iOS 14)
Check out https://www.phind.com, it works great for me. I don't understand fully how it works but I guess it's GPT4 with web search capability below the surface. What's weird is that it's fully free.
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A lawyer used ChatGPT for legal filing. The chatbot cited nonexistent cases it just made up
On its own a search engine no. But it confabulates less when you give it one
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
AutoGPT - AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
lmql - A language for constraint-guided and efficient LLM programming.
Open-Assistant - OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
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]
chatbox - Chatbox is a desktop client for ChatGPT, Claude and other LLMs, available on Windows, Mac, Linux
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.
textSQL
langchain - β‘ Building applications with LLMs through composability β‘ [Moved to: https://github.com/langchain-ai/langchain]
colab-tunnel - Connect to Google Colab VM locally from VSCode [Moved to: https://github.com/amitness/colab-connect]
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
evaporate - This repo contains data and code for the paper "Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes"