nextjs-openai-doc-search
openai-cookbook
nextjs-openai-doc-search | openai-cookbook | |
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8 | 215 | |
1,487 | 55,954 | |
1.4% | 1.0% | |
5.9 | 9.5 | |
about 2 months ago | 4 days ago | |
TypeScript | MDX | |
Apache License 2.0 | MIT License |
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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
openai-cookbook
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Question-Answer System Architectures using LLMs
A pretrained LLM is a closed-book system: It can only access information that it was trained on. With domain fine-tuning, the system manifests additional material. An early prototype of this technique was shown in this OpenAi cookbook: For the target domain, text was embedded using an API, and then when using the LLM, embeddings were retrieved using semantic similarity search to formulate an answer. Although this approach evolved to retrieval-augmented generation, its still a technique to adapt a Gen2 (2020) or Gen3 (2022) LLM into a question-answering system.
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Ask HN: High quality Python scripts or small libraries to learn from
https://github.com/openai/openai-cookbook/blob/main/examples...
- Collection of notebooks showcasing some fun and effective ways of using Claude
- OpenAI Cookbook: Techniques to improve reliability
- OpenAI Cookbooks
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How to fine tune vit/convnet to focus on the layout of the input room image and ignore other things ?
It sounds like you are trying to tweak embeddings for similarity search. Rather than fine-tune the model's layers, you may want to try training a linear transformation the existing model's output embedding. Openai has a cookbook on how to do that. You will need some data though - but I think you can try it with ~20 pieces of synthetically generated data.
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Best base model 1B or 7B for full finetuning
tutorial from OpenAI https://github.com/openai/openai-cookbook/blob/main/examples/Question_answering_using_embeddings.ipynb
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Resources to learn ChatGPT and the OpenAI API
OpenAI Cookbook
- OpenAI Cookbook
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Another Major Outage Across ChatGPT and API
OpenAI community repo with lots of examples: https://github.com/openai/openai-cookbook
What are some alternatives?
superprompt - Prompt Development Environment for GPT
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
namegpt - Generate unique and creative project names in seconds with AI!
gpt4-pdf-chatbot-langchain - GPT4 & LangChain Chatbot for large PDF docs
medusa-product-ai-widget - A Medusa Admin widget to improve product descriptions with AI. Built with Medusa UI, OpenAI and Vercel AI SDK.
chatgpt-retrieval-plugin - The ChatGPT Retrieval Plugin lets you easily find personal or work documents by asking questions in natural language.
partner-gallery-example - Supabase Partner Gallery Example
askai - Command Line Interface for OpenAi ChatGPT
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]
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]
knowledge - A knowledge daemon to collect ideas and auto organize them, with SQLite
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows