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Sketch Alternatives
Similar projects and alternatives to sketch
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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]
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Onboard AI
Learn any GitHub repo in 59 seconds. Onboard AI learns any GitHub repo in minutes and lets you chat with it to locate functionality, understand different parts, and generate new code. Use it for free at www.getonboard.dev.
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RasaGPT
💬 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
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langchain
⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain] (by hwchase17)
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rasa
💬 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
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viper
Simple, expressive pipeline syntax to transform and manipulate data with ease (by aropele)
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lambdaprompt
λprompt - A functional programming interface for building AI systems
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InfluxDB
Collect and Analyze Billions of Data Points in Real Time. Manage all types of time series data in a single, purpose-built database. Run at any scale in any environment in the cloud, on-premises, or at the edge.
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pandas-ai
PandasAI is the Python library that integrates Gen AI into pandas, making data analysis conversational
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phind-for-firefox
Sets phind.com as the default search engine in Firefox
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LiteratureReviewBot
Experiment to use GPT-3 to help write grant proposals.
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lora
Using Low-rank adaptation to quickly fine-tune diffusion models. (by cloneofsimo)
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VToonify
[SIGGRAPH Asia 2022] VToonify: Controllable High-Resolution Portrait Video Style Transfer
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awk-raycaster
Pseudo-3D shooter written completely in gawk using raycasting technique
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
sketch reviews and mentions
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Pandas AI – The Future of Data Analysis
Sketch is similar, but can do code generation (with `.sketch.howto`): https://github.com/approximatelabs/sketch
> This because the function is no longer idempotent, each call to the AI can yield a different result.
Also, it means that processing larger datasets may be more expensive.
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
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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)
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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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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
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LangChain: Build AI apps with LLMs through composability
In terms of applications with it, I have made things like sketch: https://github.com/approximatelabs/sketch
Raw prompt-structure ideas i've worked with:
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Show HN: Sketch – AI code-writing assistant that understands data content
https://github.com/approximatelabs/sketch#sketch-currently-u...
You can run using your own OpenAI key by setting 2 environment variables:
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A note from our sponsor - Onboard AI
getonboard.dev | 7 Dec 2023
Stats
approximatelabs/sketch is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of sketch is Python.