document-ai-samples
vertex-ai-samples
| document-ai-samples | vertex-ai-samples | |
|---|---|---|
| 5 | 24 | |
| 188 | 1,384 | |
| 4.3% | 6.9% | |
| 8.9 | 9.8 | |
| 4 days ago | 6 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | Apache License 2.0 |
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document-ai-samples
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When Will the GenAI Bubble Burst?
Thanks for the example and that sounds really solid cost savings and definitely agree with the trend that it is here to stay.
For invoice parsing (various formats), are you just using GPT4V? When GPT4V initially came out, i benchmarked it against an out of the box invoice parser from Google Cloud (https://cloud.google.com/document-ai) on 16 documents and it was much better accuracy wise. For ex: i'd get results parsing 10,100 as 101100 (no comma).
Curious if you saw problems like this in your pipeline or if its gotten much better since?
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Based on latest advancements in document transformers, what strategy would you use to parse utility bills?
Google Document AI: Google's generic document processor, found on the Google Cloud Platform, worked ok out of the box. However, it will require significant fine-tuning via manual data labeling for at least 15 to 20 documents before I have a decently accurate processor.
- How to upload hundreds of PDF's and analyze all of them with AI?
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From pixels to information with Document AI
What's next? Well, it's already here, with Document AI, and keeps growing:
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Automate identity document processing with Document AI
The source code for this demo is available in our Document AI sample repository.
vertex-ai-samples
- Gemini 1.5 outshines GPT-4-Turbo-128K on long code prompts, HVM author
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Let's build your first ML app in Google Cloud Run
Google Cloud Platform (GCP) provides a very befitting Machine Learning solution called Vertex Ai that handles Google Cloud's unified platform for building, deploying, and managing machine learning (ML) models. Our goal is to build a simple Machine Learning application that optimizes all that GCP provides plus an implementation of continuous integration and continuous development (CI/CD).
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Google Gemini Pro API Available Through AI Studio
Cross posting some links from another post that HNers found helpful
- https://cloud.google.com/vertex-ai (marketing page)
- https://cloud.google.com/vertex-ai/docs (docs entry point)
- https://console.cloud.google.com/vertex-ai (cloud console)
- https://console.cloud.google.com/vertex-ai/model-garden (all the models)
- https://console.cloud.google.com/vertex-ai/generative (studio / playground)
VertexAI is the umbrella for all of the Google models available through their cloud platform.
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Google Imagen 2
For the peer comments
- https://cloud.google.com/vertex-ai (main page)
- https://cloud.google.com/vertex-ai/docs/start/introduction-u... (docs entry point)
- https://console.cloud.google.com/vertex-ai (cloud console)
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Introducing Gemini: our largest and most capable AI model
Starting on December 13, developers and enterprise customers can access Gemini Pro via the Gemini API in Google AI Studio or Google Cloud Vertex AI.
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How to Use AI/ML Models for Your Projects
Google Cloud Platform (https://cloud.google.com/vertex-ai): Conversely, Google Cloud Platform (GCP) provides a comprehensive suite of AI and machine learning services, including APIs for vision, language, conversation, and structured data analysis. Whether you're analyzing images, interpreting human speech, or diving deep into data patterns, GCP has something for you.
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Create a ChatBot with VertexAI and LibreChat
VertexAI is a machine learning platform available on Google Cloud. It offers a variety of services to train and deploy AI models, including those for Generative AI.
- Tune PaLM 2 with your own RLHF training data
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Any better alternatives to fine-tuning GPT-3 yet to create a custom chatbot persona based on provided knowledge for others to use?
Depending on how much work you want to put into it, you can get started at HuggingFace with their models and datasets, but you'd need compute power, multiple MLOps, etc. I was introduced to the concept in this video, since Google has their Vertex AI tools on Google Cloud, and there's always LangChain but I'm not sure about anything recent.
- Google Cloud Learning Machine
What are some alternatives?
docutron - Docutron Toolkit: detection and segmentation analysis for legal data extraction over documents.
mlops-with-vertex-ai - An end-to-end example of MLOps on Google Cloud using TensorFlow, TFX, and Vertex AI
pdfGPT - PDF GPT allows you to chat with the contents of your PDF file by using GPT capabilities. The most effective open source solution to turn your pdf files in a chatbot!
awesome-mlops - A curated list of references for MLOps
Calliar - A dataset for online Arabic calligraphy. A collection of 2500 annotated calligraphic styles.
MLflow - Open source platform for the machine learning lifecycle
VevestaX - 2 Lines of code to track ML experiments + EDA + check into Github
jina - ☁️ Build multimodal AI applications with cloud-native stack
rasa - 💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants
Micronaut - Micronaut Application Framework
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
searxng - SearXNG is a free internet metasearch engine which aggregates results from various search services and databases. Users are neither tracked nor profiled.