Merge pull request #54 from STRIDES/readme-edit-zy

Readme updates and renamed GenAIStudio tutorial

tutorials/README.md

@@ -38,9 +38,9 @@ You can find a nice tutorial for using managed notebooks [here](https://codelabs
 ## **Artificial Intelligence and Machine Learning** <a name='ml'></a>
 Machine learning is a subfield of artificial intelligence that focuses on the development of algorithms and models that enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed. Machine learning on GCP generally occurs within VertexAI. You can learn more about machine learning on GCP at this [Google Crash Course](https://developers.google.com/machine-learning/crash-course). For hands-on examples, try out [this module](https://github.com/NIGMS/COVIDMachineLearningSFSU) developed by San Francisco State University or [this one from the University of Arkasas](https://github.com/NIGMS/MachineLearningUA) developed for the NIGMS Sandbox Project.
 
-Now that the age of **Generative AI** (Gen AI) has arrived, Google has released a host of Gen AI offerings within the Vertex AI suite. Some examples of what generative AI models are capabile of are extracting wanted information from text, transforming speech into text, generating images from describtions and vice versa, and much more. Vertex AI's [Generative AI Studio](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/generative-ai-studio) console allows the user to rapidly create, test, and train generative AI models on the cloud in a safe and secure setting. See our overview in [this tutorial](/tutorials/notebooks/GenAI/GenAIStudioGCP.ipynb). The studio also has ready-to-use models all contained with in the [Model Garden](https://cloud.google.com/vertex-ai/docs/start/explore-models). These models range from foundation models, fine-tunable models, and task-specific solutions. You are also able to use these models within a jupyter notebook and Google provides many generative AI tutorials hosted on [GitHub](https://github.com/GoogleCloudPlatform/generative-ai/tree/main). Some example they provide are under [language here](https://github.com/GoogleCloudPlatform/generative-ai/tree/main/language).
-
-We created [this tutorial](/tutorials/notebooks/GenAI/langchain_on_vertex.ipynb) that uses [langchain with Vertex AI](https://python.langchain.com/docs/integrations/llms/google_vertex_ai_palm) to walk you through various Gen AI use cases, including programmatically querying a LLM, creating a custom chatbox that queries a scientific article about menopause in :monkey: (sorry no emoji for chimps), and generates code according to a user-specified prompt.
+Now that the age of **Generative AI** (Gen AI) has arrived, Google has released a host of Gen AI offerings within the Vertex AI suite. Some examples of what generative AI models are capable of are extracting wanted information from text, transforming speech into text, generating images from descriptions and vice versa, and much more. Vertex AI's [Vertex AI Studio](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/generative-ai-studio) console allows the user to rapidly create, test, and train generative AI models on the cloud in a safe and secure setting, see our overview in [this tutorial](/tutorials/notebooks/GenAI/VertexAIStudioGCP.ipynb). The studio also has ready-to-use models all contained with in the [Model Garden](https://cloud.google.com/vertex-ai/docs/start/explore-models). These models range from foundation models, fine-tunable models, and task-specific solutions. 
+- To learn more about Gen AI on GCP take a look at our [GenAI tutorials](/tutorials/notebooks/GenAI) that go over several GCP products such as [Gemini](/tutorials/notebooks/GenAI/Gemini_Intro.ipynb) and [Vector Search](/tutorials/notebooks/GenAI/GCP_Pubmed_chatbot.ipynb) and other tools like [Langchain](/tutorials/notebooks/GenAI/langchain_on_vertex.ipynb) and [Huggingface](/tutorials/notebooks/GenAI/GCP_GenAI_Huggingface.ipynb) to deploy, train, prompt, and implement techniques like [Retrieval-Augmented Generation (RAG)](/tutorials/notebooks/GenAI/GCP_Pubmed_chatbot.ipynb) to GenAI models.
+- The Google github also provides many generative AI tutorials hosted on [GitHub](https://github.com/GoogleCloudPlatform/generative-ai/tree/main). Some example they provide are under [language here](https://github.com/GoogleCloudPlatform/generative-ai/tree/main/language).
 
 ## **Medical Image Segmentation** <a name="mi"></a>
 Medical image analysis is the application of computational algorithms and techniques to extract meaningful information from medical images for diagnosis, treatment planning, and research purposes. Medical image analysis requires large image files and often elastic storage and accelerated computing.

tutorials/notebooks/GenAI/GCP_Pubmed_chatbot.ipynb

@@ -1010,7 +1010,7 @@
    "id": "1abcbd48-bb84-4310-b8eb-ad87850a8649",
    "metadata": {},
    "source": [
-    "Running our script in the terminal will require us to export the following global variables before using the command `python NAME_OF_SCRIPT.py`."
+    "Running our script in the terminal will require us to export the following global variables before using the command `python NAME_OF_SCRIPT.py`. You can also check out our **example inference scripts** for the [Pubmed API](/example_scripts/example_langchain_chat_llama_2_zeroshot.py) and [Vertex AI Vector Search](/example_scripts/example_vectorsearch_chat_llama_2_zeroshot.py)."
    ]
   },
   {

tutorials/notebooks/GenAI/GenAIStudioGCP.ipynb → tutorials/notebooks/GenAI/VertexAIStudioGCP.ipynb

@@ -6,7 +6,7 @@
     "id": "Hny4I-ODTIS6"
    },
    "source": [
-    "# Generative AI Studio on GCP - Article Summary\n"
+    "# Vertex AI Studio on GCP - Article Summary\n"
    ]
   },
   {