openai-cookbook
askai
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215 | 1,752 | |
55,954 | 86 | |
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9.5 | 10.0 | |
6 days ago | over 1 year ago | |
MDX | TypeScript | |
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
askai
- The ChatGPT URL have changed
- Chat.openai.com now redirects (me) to chatgpt.com
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Unofficial ChatGPT API
This API allows you to interact with ChatGPT programmatically, and I've built some cool agents on top of it. Check out the code and let me know what you think! :
ChatGPT unofficial API :
This project is a Node.js application that interacts with the ChatGPT conversational AI model using Puppeteer, a Node.js library for automating web browsers.
Files chatgptv1.js: This file contains the main logic for the ChatGPT bot, including methods for initializing the browser, sending messages, receiving replies, and handling errors.
bart.js: This file contains a function that uses the Cloudflare API to summarize the conversation history when an error occurs, in order to resume the conversation.
twochatbotsconv.js: This file is simple use of the API , which creates two instances of the ChatGPT class, initiates a conversation between them, and saves the conversation history to a file.
.env: This file contains the API token for the Cloudflare API, which is used in the bart.js file.
Dependencies :
puppeteer: A Node.js library for automating web browsers. fs: The built-in file system module in Node.js. winston: A logging library for Node.js. crypto: The built-in cryptography module in Node.js. axios: A popular HTTP client library for Node.js. dotenv: A zero-dependency module that loads environment variables from a .env file.
Usage:
Install the dependencies by running npm install in your project directory. Create a .env file in the project directory and add your Cloudflare API token:
API_TOKEN=YourfreeCloudFlareAPIToken In your code, create a new instance of the ChatGPT class and use the sendMessage and getReply methods to interact with the ChatGPT model:
const ChatGPT = require('./chatgptv1');
const chatgpt = new ChatGPT(); await chatgpt.initializeBrowser();
await chatgpt.sendMessage('Hello, ChatGPT!'); const reply = await chatgpt.getReply(); console.log(reply);
await chatgpt.closeBrowser(); If an error occurs during the conversation, the handleError method will attempt to save the conversation history and resume the conversation using the summarized context.
Before Running :
run Google chrom in the debug mode using 9220 port , run : google-chrome-stable --remote-debugging-port=9222
Customization :
You can customize the behavior of the ChatGPT bot by passing options to the ChatGPT constructor:
chatbotUrl: The URL of the ChatGPT interface (default: 'https://chat.openai.com/'). headless: Whether to run the browser in headless mode (default: false). saveConversationCallback: A callback function that will be called with the conversation summary and the conversation file name when an error occurs.
License:
This project is licensed under the MIT License.
- It's a shame – chat.openai.com redirect to chatgpt.com is broken
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Building a Basic Forex Rate Assistant Using Agents for Amazon Bedrock
After wrestling with it for a bit and eventually giving up, I instead turned to ChatGPT to see if it is smart enough for the task. With my free plan, I asked ChatGPT 3.5 the following:
- Learn to ask for help
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How to build a custom GPT: Step-by-step tutorial
Go to chat.openai.com and log in
- Chat.openai.com no longer requires login
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Integrating Strapi with ChatGPT and Next.js
In this tutorial, we will learn how to use Strapi, ChatGPT, and Next.js to build an app that displays recipes using AI.
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GPT-4 Turbo with Vision is a step backwards for coding
Maybe I am bit dim, but how one can choose GPT-4 Turbo? Is this available from https://chat.openai.com/ ?
What are some alternatives?
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
ChatGPT - 🔮 ChatGPT Desktop Application (Mac, Windows and Linux)
gpt4-pdf-chatbot-langchain - GPT4 & LangChain Chatbot for large PDF docs
gpt-4chan-model
chatgpt-retrieval-plugin - The ChatGPT Retrieval Plugin lets you easily find personal or work documents by asking questions in natural language.
ai-cli - Get answers for CLI commands from ChatGPT right from your terminal
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]
KoboldAI-Client
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
knowledge_gpt - Accurate answers and instant citations for your documents.
civitai - A repository of models, textual inversions, and more