dispatch
chatgpt-retrieval-plugin
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dispatch | chatgpt-retrieval-plugin | |
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20 | 52 | |
4,602 | 20,836 | |
2.1% | 1.1% | |
9.9 | 6.1 | |
about 12 hours ago | 3 days ago | |
Python | Python | |
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.
dispatch
- Netflix Dispatch
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Is there any open source project that uses FasAPI?
They use only sync routes in the project and can’t explain why https://github.com/Netflix/dispatch/issues/1073
- Is it really advisable to try to run fastapi with predominantly sync routes in a real world application?
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How to build a scalable project file structure for a beginner.
By far my favorite production FastAPI app to use as a references of how to use these technologies well is NetFlix Dispatch: https://github.com/Netflix/dispatch
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FastAPI Boilerplate using MongoDB, Motor, Docker
Hey, I have a lot of opinions about this template, but these are just my opinions based on my own experiences being burned by these things so take from them what you will: 1. Your version of poetry is outdated, dependency groups don't work that way anymore and this will fail to install on modern poetry 2. You list pyyaml as a dependency but don't use it anywhere 3. The healthcheck endpoint is interesting, but expensive and a security risk. I like the value this provides, but I don't know if exposing it this way or using it as a healthcheck is a good idea 1. You typically don't want to touch external systems (mongo) as part of a healthcheck as this can cause cascading failure chains that get out of hand quickly 2. You typically don't want to touch the underlying system itself 1. which means you can / should get rid of psutil as a dependency 4. You don't need and shouldn't use pytest-asyncio for a FastAPI project. It comes built-in with its own async test handlers that you should be using 5. Having python-dotenv installed in production has burned me many times. I recommend removing this complete, otherwise just moving it to a dev dep 6. Using the src layout prevents a lot of weird import time problems from cropping up in production, I recommend checking it out 7. The entrypoint for the Docker container should be using 1 worker, as containers really prefer if you have only a single root PID chain and nothing else. Deploying this into k8s would cause a lot of issues 8. Native python logging really isn't great for modern production applications. Structlog or Loguru are great alternatives and much easier to use (which should remove your only dependency on pyyaml) 9. The configuration management may not work the way you want since it is weakly typed. Since FastAPI uses Pydantic, you have access to BaseSettings which is a far superior product for configuration management, especially with environment variables 10. The app and API folder structure is an anti-pattern that doesn't scale past projects the size of a tutorial on how to laern FastAPI. I strongly recommend changing this to move of a vertical slice or folder per feature layout such as is used in https://github.com/Netflix/dispatch/tree/master/src/dispatch 11. FastAPI routes don't need `response_model=` anymore in favor of adding the return type to your function signature such as `async def create_thing() -> Thing:` 12. The uuid_masker function is interesting, but exposing UUIDs in logs usually doesn't pose a security risk and only makes debugging more difficult 13. You have some type lies in your code that could burn you such as https://github.com/alexk1919/fastapi-motor-mongo-template/blob/main/app/db/db.py#L10 . This pattern for the global DB handle has also burned me in the past and I had to go back and refactor out all of them to instead to purely use the FastAPI dependency injection chaining 14. datetime.datetime isn't safe to use as it is in sample_resource_common.py, you need a timezone aware implementation 15. Your test suite is stateful, require a running database, leak a lot of implementation details of the underlying models. This is every anti-pattern in the book for unit testing. And if you are going to do integration tests, then you would be better off with tooling designed for it such as playwright. Again, these are all just my opinions and may alone not be enough to warrant changing anything you have here.
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Python projects with best practices on Github?
Two random examples I found from 30 seconds of googling: Here’s Netflix using it in their crisis management tool, and here’s Uber using it in their deep learning framework.
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Open Source Projects based on FastAPI
netflix dispatch
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As a long time programmer what are some important coding styles ?
As someone who uses FastAPI, I find the https://github.com/Netflix/dispatch code to be a great reference.
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CEO faces backlash after quoting Martin Luther King Jr. in announcing layoffs
Besides that paying $21 to $41 per user for this nuts. Set up a VPS with Dispatch (opensourced by Netflix) and save your company some money.
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Total beginner, use FastAPI?
For production ready code examples I use: https://github.com/Netflix/dispatch
chatgpt-retrieval-plugin
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I replaced 50 lines of code with a single LLM prompt
Many comments are criticizing the usage of LLM for this use case but I do believe this will become more common in the future. For example, OpenAI's retrieval plugin leverages LLM to do PII detection [1] instead of using the traditional libraries [2].
[1] https://github.com/openai/chatgpt-retrieval-plugin/blob/main...
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PMET: Precise Model Editing in a Transformer
There is this
https://github.com/openai/chatgpt-retrieval-plugin
I just use SBERT which has models I can run locally
https://sbert.net/
- FLaNK Stack Weekly for 14 Aug 2023
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Pgvector: Fewer Dimensions Are Better
should be yes, but even in examples from openai, they usually do splitting into chunks
For example in chatgpt-retrieval-plugin[0] repo default chunk size is just 200 tokens
[0] https://github.com/openai/chatgpt-retrieval-plugin/blob/main...
this is anyway a limitation, no doubt, but chunking is pretty often used
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I've changed my mind about Code Interpretor
To begin, visit the retrieval plugin repository.
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Any luck using the ChatGPT Retriever Plugin?
You need to run the chatgpt-retrieval-plugin. The README has a quickstart guide for doing that: https://github.com/openai/chatgpt-retrieval-plugin
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Help me write a GPT4 Program
If the browser plugin for ChatGPT gets better to actually exceed what a web-scraper / summarizer can do, you could use GPT for the summarization part and store the results again in some vector database after creating embeddings. You could then use ChatGPT to "talk to your data", meaning when asked for specific information, it could query the database, retrieve the relevant knowledge and provide a meaningful answer based on your question. This could be done via OpenAIs own retrieval plugin (see https://github.com/openai/chatgpt-retrieval-plugin ) or by creating your own Chat-UI that performs the semantic search, feeds the result into the GPT4 API and returns the answer back to the user. David Shapiro made a nice video with step by step instructions on how to do that: https://www.youtube.com/watch?v=2xNzB7xq8nk&pp=ygUUZ3B0NCBtZW1vcnkgcGluZWNvbmU%3D
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March 2023
22-Mar-2023 Adobe unveils creative generative AI model, Firefly, to aid content creation Google has begun rolling out early access to its Bard chatbot in the US and UK Data Breach At ChatGPT? Users Report Seeing Unknown Conversations On Their Screens GPT-4 is available in preview in Azure OpenAI Service AI-powered coding assistance REPL that pairs GPT-4 (https://github.com/jiggy-ai/pair) Open source alternative to ChatGPT (https://github.com/nichtdax/awesome-totally-open-chatgpt) Run 100B+ language models at home, BitTorrent‑style (https://petals.ml/) Find the most relevant piece of code context. Hover and highlight blocks of code, the tool will point you to the most relevant pieces of information on git, messaging, and ticketing systems. Finally, it provide a summary with the power of GPT.(https://www.watermelontools.com/) Why AI Won't Replace Software Engineers (https://softwarecomplexity.com/why-ai-wont-replace-software-engineers) 23-Mar-2023 'The iPhone Moment of AI' Nvidia to Rent Out Supercomputers Behind ChatGPT to Businesses for $37,000 a Month Bill Gates calls AI revolutionary, says it can reduce some of the world’s worst inequities AI pics of Donald Trump's arrest by 'cop' Joe Biden go viral. Will we no longer be able to tell what’s real vs what’s fake?” - Eluna AI New research shows we can only accurately identify AI writers about 50% of the time. (https://hai.stanford.edu/news/was-written-human-or-ai-tsu) FauxPilot - an open-source GitHub Copilot server(https://github.com/fauxpilot/fauxpilot) Flower , an open-source framework for training AI on distributed data. We move the model to the data instead of moving the data to the model. (https://flower.dev/) OpenAI-Integrated Microsoft Bing Outperforms Google in Page Visits (https://www.gadgets360.com/internet/news/openai-integrated-microsoft-bing-outperforms-google-page-visits-growth-3885069) GitHub Copilot X: GitHub Copilot is evolving to bring chat and voice interfaces, support pull requests, answer questions on docs, and adopt OpenAI’s GPT-4 for a more personalized developer experience. (https://github.blog/2023-03-22-github-copilot-x-the-ai-powered-developer-experience/) Moonshine – open-source, pretrained ML models for satellite (https://github.com/moonshinelabs-ai/moonshine) Mozilla.ai: A startup — and a community — that will build a trustworthy and independent open-source AI ecosystem. Mozilla.ai’s initial focus? Tools that make generative AI safer and more transparent. And, people-centric recommendation systems that don’t misinform or undermine our well-being. (https://blog.mozilla.org/en/mozilla/introducing-mozilla-ai-investing-in-trustworthy-ai/) OpenAI’s policies hinder reproducible research on language models (https://aisnakeoil.substack.com/p/openais-policies-hinder-reproducible) 24-Mar-2023 Adobe has added AI features to Photoshop and Illustrator, while Nvidia has unveiled ‘Picasso’ AI image generation service. ChatGPT-owner OpenAI fixes 'significant issue' exposing user chat titles.A bug in an open-source library caused ChatGPT to leak user conversation titles. Graphic design platform Canva introduces new generative AI tools Gmail for Android, Google Messages to Soon Get Features for AI-Generated Texts Apple: Transformer architecture optimized for Apple Silicon (https://github.com/apple/ml-ane-transformers) ChatGPT plugins, join waitlist (https://openai.com/blog/chatgpt-plugins) Microsoft's paper on OpenAI's GPT-4 had hidden information (https://twitter.com/DV2559106965076/status/1638769434763608064) how to use LoRA to fine-tune LLaMA using Alpaca training data (https://replicate.com/blog/fine-tune-alpaca-with-lora) Helicone: one-line integration logs the prompts, completions, latencies, and costs of your OpenAI requests (https://github.com/Helicone/helicone) RWKV is an RNN with Transformer-level LLM performance, which can also be directly trained like a GPT transformer (parallelizable). (https://github.com/BlinkDL/RWKV-LM) open-source retrieval plugin The open-source retrieval plugin enables ChatGPT to access personal or organizational information sources (with permission). It allows users to obtain the most relevant document snippets from their data sources, such as files, notes, emails or public documentation, by asking questions or expressing needs in natural language. Security considerations The retrieval plugin allows ChatGPT to search a vector database of content, and add the best results into the ChatGPT session. This means it doesn’t have any external effects, and the main risk is data authorization and privacy. Developers should only add content into their retrieval plugin that they are authorized to use and can share in users’ ChatGPT sessions. https://github.com/openai/chatgpt-retrieval-plugin 27-Mar-2023 Autodoc: Toolkit for auto-generating codebase documentation using LLMs (https://github.com/context-labs/autodoc) March 20 ChatGPT outage: Here’s what happened (https://openai.com/blog/march-20-chatgpt-outage) Facebook is going after LLaMA repos with DMCA's (https://twitter.com/theshawwn/status/1638925249709240322) ChatGPT + Wolfram is INSANE! (https://old.reddit.com/r/ChatGPT/comments/1205omc/chatgpt\_wolfram\_is\_insane/) Reproducing the Stanford Alpaca results using low-rank adaptation (LoRA) (https://github.com/chris-alexiuk/alpaca-lora) GOAT, a decentralized way to publish and download AI models.Powered by BitTorrent and Bitcoin.(https://ipfs.io/ipfs/QmYyucgBQVfs9JXZ2MtmkGPAhgUjNgyGE6rcJT1KybQHhp/index.html) Dolly from databricks (https://www.databricks.com/blog/2023/03/24/hello-dolly-democratizing-magic-chatgpt-open-models.html) AI powered Developer Tools 2.0. https://www.sequoiacap.com/article/ai-powered-developer-tools/ Turn your designs into production-ready front-end code for mobile apps and the web (https://www.locofy.ai/) Using ChatGPT Plugins with LLaMA (https://blog.lastmileai.dev/using-openais-retrieval-plugin-with-llama-d2e0b6732f14) 28-Mar-2023 Bing AI now allows 20 prompts per session and can make images for you ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks (https://arxiv.org/abs/2303.15056) ChatGPT or Grammarly? Evaluating ChatGPT on Grammatical Error Correction Benchmark (https://arxiv.org/abs/2303.13648) AI-controlled Linux Containers (https://github.com/fafrd/aquarium) Microsoft reportedly orders AI chatbot rivals to stop using Bing’s search data (https://www.theverge.com/2023/3/25/23656336/microsoft-chatbot-rivals-stop-using-bing-search-index) 29-Mar-2023 Text2Video-Zero Code and Weights Released by Picsart AI Research (12G VRAM).(https://github.com/Picsart-AI-Research/Text2Video-Zero) Pause Giant AI Experiments: An Open Letter. Huggingface's SF Open-Source AI Meetup officially has 2000 people registered. Cerebras open sources seven GPT-3 models from 111 million to 13 billion parameters. Trained using the Chinchilla formula, these models set new benchmarks for accuracy and compute efficiency.(https://www.cerebras.net/blog/cerebras-gpt-a-family-of-open-compute-efficient-large-language-models/) Independent implementation of LLaMA that is fully open source under the Apache 2.0 license (https://github.com/Lightning-AI/lit-llama) Bootstrap knowledge of LLMs (https://gist.github.com/rain-1/eebd5e5eb2784feecf450324e3341c8d) OPENFLAMINGO: AN OPEN-SOURCE FRAMEWORK FOR TRAINING VISION-LANGUAGE MODELS WITH IN-CONTEXT LEARNING (https://laion.ai/blog/open-flamingo/) gpt4all: a chatbot trained on a massive collection of clean assistant data including code, stories and dialogue (https://github.com/nomic-ai/gpt4all) 30-Mar-2022 Microsoft Security Copilot is a new GPT-4 AI assistant for cybersecurity (https://www.theverge.com/2023/3/28/23659711/microsoft-security-copilot-gpt-4-ai-tool-features) UK details ‘pro-innovation’ approach to AI regulation (https://www.artificialintelligence-news.com/2023/03/29/uk-details-pro-innovation-approach-ai-regulation/) Employees Are Feeding Sensitive Biz Data to ChatGPT, Raising Security Fears (https://www.darkreading.com/risk/employees-feeding-sensitive-business-data-chatgpt-raising-security-fears) In the Age of AI, Don't Let Your Skills Atrophy (https://www.cyberdemon.org/2023/03/29/age-of-ai-skill-atrophy.html) Now ChatGPT is being (mis)used to do #PeerReview (https://mstdn.science/@ukrio/110100752908161183) Bing Chat now has Ads! (https://twitter.com/debarghya\_das/status/1640892791923572737) Cerebras-GPT vs LLaMA AI Model Comparison (https://www.lunasec.io/docs/blog/cerebras-gpt-vs-llama-ai-model-comparison/) Arthur C. Clarke about the future of AI. — 21 September 1964 (https://twitter.com/Rainmaker1973/status/1640016339011076097) ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline (https://medium.com/@yangyou\_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) Create and Embed Custom AI Assistants with Libraria (https://libraria.dev/) 31-Mar-2023 Deranged New AI Has No Guardrails Whatsoever, Proudly Praises Hitler (https://futurism.com/deranged-ai-no-guardrails) Midjourney Kills Free AI Image Generator Access After Explosion of Deep Fakes (https://decrypt.co/124972/midjourney-free-ai-image-generation-stopped-over-deepfakes) Judge asks ChatGPT to decide bail in murder trial (https://nypost.com/2023/03/29/judge-asks-chatgpt-for-decision-in-murder-trial/) Should you use OpenAI's embeddings? Probably not, and here's why. (https://iamnotarobot.substack.com/p/should-you-use-openais-embeddings) Visual Studio Code and GitHub Copilot (https://code.visualstudio.com/blogs/2023/03/30/vscode-copilot) Llama Hub (https://llamahub.ai/) Finetuning LLMs on a Single GPU Using Gradient Accumulation (https://lightning.ai/pages/blog/gradient-accumulation/) Open source ETL framework for retrieval augmented generation (RAG). Sync data from your SaaS tools to a vector store, where they can be easily queried by GPT apps (https://github.com/ai-sidekick/sidekick) HALTT4LLM - Hallucination Trivia Test for Large Language Models (https://github.com/manyoso/haltt4llm) Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality (https://vicuna.lmsys.org/) Iterate.ai Brings Generative AI Capabilities to Interplay, the Low-Code Platform Accelerating Customers’ Digital Innovation (https://www.indianweb2.com/2023/03/iterateai-brings-generative-ai.html) RFdiffusion is an open source method for structure generation, with or without conditional information (a motif, target etc). (https://github.com/RosettaCommons/RFdiffusion) Google denies training Bard on ChatGPT chats from ShareGPT
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What do you want GPT-5 to do that GPT-4 can’t?
For things like books or papers, I guess that embedding will stay relevant for a while.
What are some alternatives?
fastapi-best-practices - FastAPI Best Practices and Conventions we used at our startup
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
llama_index - LlamaIndex is a data framework for your LLM applications
full-stack-fastapi-template - Full stack, modern web application template. Using FastAPI, React, SQLModel, PostgreSQL, Docker, GitHub Actions, automatic HTTPS and more.
gpt4all - gpt4all: run open-source LLMs anywhere
fastapi-router-controller - A FastAPI utility to allow Controller Class usage
langchain - 🦜🔗 Build context-aware reasoning applications
opal - Fork of https://github.com/permitio/opal
Milvus - A cloud-native vector database, storage for next generation AI applications
databases - Async database support for Python. 🗄
openai-cookbook - Examples and guides for using the OpenAI API