nextjs-openai-doc-search
namegpt
nextjs-openai-doc-search | namegpt | |
---|---|---|
8 | 5 | |
1,487 | 21 | |
1.4% | - | |
5.9 | 4.5 | |
about 2 months ago | about 1 year ago | |
TypeScript | TypeScript | |
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.
nextjs-openai-doc-search
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Creating an advanced search engine with PostgreSQL
https://supabase.com/blog/openai-embeddings-postgres-vector
https://supabase.com/blog/chatgpt-supabase-docs
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Best Authentication Library in 2023 ?
There is already AI built into the docs - just hit cmd+k and ask a question. we were one of the first to do this: https://supabase.com/blog/chatgpt-supabase-docs
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We made a AI powered assistant using OpenAI, ruby and redis
We were inspired by what supabase did with the creation of their own ai powered assistant here: https://supabase.com/blog/chatgpt-supabase-docs but we wanted to make one that used a more standard backend in redis and ruby.
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Show HN: Gromit, the OS, AI powered assistant for your website/app
https://release.com/blog/training-chatgpt-with-custom-librar...
We were inspired by what supabase did with the creation of their own ai powered assistant here: https://supabase.com/blog/chatgpt-supabase-docs but we wanted to make one that used a more standard backend in redis and ruby.
Gromit is super new; please give it a shot and make pull requests, leave comments, we would love to chat with you about it!
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Knowledge retrieval architectures for LLMs (2023)
This is the same approach that Supabase Clippy took: https://supabase.com/blog/chatgpt-supabase-docs
They called it "context injection" but the OpenAI community appears to call it "retrieval-augmented generation".
(Tangent) I will go to the grave continuing to call it Supabase Clippy even though presumably this prediction from the Supabase blog post became true:
> Today, we're doing our part to support the momentum by releasing “Supabase Clippy” for our docs (and we don't expect this name to last long before the lawyers catch on).
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Finetuning Large Language Models
> the trick where you search for relevant content and paste that into a prompt
Supabase Clippy was the first docs site to ship this experience to production as far as I can tell: https://supabase.com/blog/chatgpt-supabase-docs
I believe they called it "context injection" and I have been following suit in my own writing on the topic.
I am prototyping experiences like Supabase Clippy and am also very interested in fine-tuning for docs Q&A. But my main question is: what exactly would the fine-tuning inputs and outputs look like for docs Q&A?
From my blog:
> AI is all about prediction. Given this temperature, this wind, this day of the year, what is the chance of rain? Temperature, wind, and date are your inputs. Chance of rain is your desired output. Now, try to apply this same type of thinking towards documentation. What are your inputs? What’s your output? The page title and code block could be your inputs. Whether or not the code builds could be your output. Or maybe the code block should be the output? This is why I keep saying that applying fine-tuning to docs is tricky. What are the inputs and outputs?
https://technicalwriting.tools/posts/ten-principles-response...
(I am an AI n00b and have not looked deeply into how fine-tuning works but it's high on my list to experiment with OpenAI's fine-tuning API. Please LMK if I am getting any fundamentals wrong.)
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Supabase kit for building ChatGPT apps
Make sure to check out https://supabase.com/blog/chatgpt-supabase-docs!
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A ChatGPT Starterkit with Next.js & Tailwind CSS
Can try this: https://github.com/supabase-community/nextjs-openai-doc-search
namegpt
- NameGPT - Generate Project Names in Seconds
- NameGPT - Generate Project Names in Seconds!
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NameGPT - Get your next project name within 3 clicks.
Also, feel free to take a look at the Github and eventually leave a star, much appreciated!
What are some alternatives?
superprompt - Prompt Development Environment for GPT
llm-code-interpreter - Powered by AI Playgrounds by E2B. Code interpreter on steroids for ChatGPT. Run any language, any terminal process, use filesystem freely. All with access to the internet.
medusa-product-ai-widget - A Medusa Admin widget to improve product descriptions with AI. Built with Medusa UI, OpenAI and Vercel AI SDK.
memex - Your second brain for the web browsing. An AI powered Chrome extension that constructs personal knowledge base for you.
partner-gallery-example - Supabase Partner Gallery Example
ScribeAI - ChatGPT extension for VSCode.
nodejs-api-starter - 💥 Yarn v2 based monorepo template (seed project) pre-configured with GraphQL API, PostgreSQL, React, Relay, and Material UI. [Moved to: https://github.com/kriasoft/relay-starter-kit]
Lobe Chat - LobeChat is a open-source, extensible (Function Calling), high-performance chatbot framework.It supports one-click free deployment of your private ChatGPT/LLM web application.
knowledge - A knowledge daemon to collect ideas and auto organize them, with SQLite
ai-template - Mercury - Train your own custom GPT. Chat with any file, or website.
gromit
gpt-turbo - JavaScript library for OpenAI's Chat Completion API. Comes with conversation history management, parameter configuration, callable functions and plugin support!