openai-cookbook
yt-semantic-search
openai-cookbook | yt-semantic-search | |
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216 | 6 | |
56,923 | 508 | |
1.7% | - | |
9.4 | 3.2 | |
3 days ago | about 1 year ago | |
MDX | TypeScript | |
MIT License | MIT License |
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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
yt-semantic-search
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Dev LLM stack, production LLM stack, example projects, & things you'll discover
The dev LLM stack
- OpenAI + Pinecone + GPT-Index or Langchain
- Perhaps also dust.tt for playing around with prompts, kinda like a more advanced gpt playground --
The production LLM stack
- The dev stack
- OpenAI + Pinecone + GPT-Index or Langchain
- arXiv for finding new research to build on
- Prompt platforms such as Humanloop
- ML frameworks such as PyTorch, Keras, Tensorflow
- MLOps tools such as MLflow, Kubeflow, Metaflow, Airflow, Seldon Core, TFServing
Example OpenAI Projects
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What OpenAI/MSFT should do
- Fund "AI white mirror" -- a tv show that has beautiful visions a future where intelligence costs ~0
Things you'll probably discover
- Embeddings work ok, but not great, from a user perspective. As a developer they're great to work with. As a user, the results aren't ranked quite right. Embeddings use cases will be better with GPT-4 or GPT-4.5.
- All of the obvious gpt apps will be built. We'll get hundreds of basic gpt wrapper apps (and some of them will be big businesses!), hundreds of basic embeddings search apps. If someone can think of the idea and make it without needing specific relationships, credibility, or experience, then it'll probably exist by Summer 2023.
- The developer energy in this space is intense. Adults are going to hackathons to build ai apps. This is awesome.
- Devs using gpt will soon be a large enough market that startups will exist and succeed just by selling to developers that are using gpt-3 in production. We already saw it a little bit, but we'll get many more startups here.
- How could AI not be better than me at all computer based things within 10 years?
- AI is kinda like a kid. When they're young, they're not that smart. Then all of a sudden, they've gotten enough training data, and their brain (compute!) has grown, and they're doing useful stuff. This is related to why people will say that building models can feel frustrating because it doesn't work well for ages and then all of a sudden it works (CEO of Oasis said this, CTO of OpenAI said this, and Instagram co-founder said this).
Would love input and feedback on this. I have similar things that I'm going to submit, covering what builders and engineers should do, what vector database to use, why no one else made ChatGPT before OpenAI, things holding ai powered apps back, and some other stuff like that. If you want a preview and are happy to give feedback, then email is in my profile.
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Show HN: Semantic Search for Any Video
Are you using whisper for transcription?
For https://github.com/transitive-bullshit/yt-semantic-search, I'm using YouTube's built-in transcriptions which definitely aren't as high quality, but they work well enough to power the semantic search.
- Show HN: OpenAI-powered semantic search for the All-In Podcast
- OpenAI-powered semantic search for the All-In Podcast
What are some alternatives?
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
semantic-search-nextjs-pinecone-langchain-chatgpt - Embeds text files into vectors, stores them on Pinecone, and enables semantic search using GPT3 and Langchain in a Next.js UI
gpt4-pdf-chatbot-langchain - GPT4 & LangChain Chatbot for large PDF docs
codesearch - Semantic Code Search tool. Query your codebases using natural language
chatgpt-retrieval-plugin - The ChatGPT Retrieval Plugin lets you easily find personal or work documents by asking questions in natural language.
client-vector-search - A client side vector search library that can embed, store, search, and cache vectors. Works on the browser and node. It outperforms OpenAI's text-embedding-ada-002 and is way faster than Pinecone and other VectorDBs.
askai - Command Line Interface for OpenAi ChatGPT
generate-subtitles - Generate transcripts for audio and video content with a user friendly UI, powered by Open AI's Whisper with automatic translations and download videos automatically with yt-dlp integration
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]
youtube-summarized-browser-extension - YouTube Summarized - Browser extension for summarizing YouTube videos using GPT3 🎥
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
knowledge_gpt - Accurate answers and instant citations for your documents.