tested and updated genai tutorials

STRIDES · Apr 5, 2024 · dac89f5 · dac89f5
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diff --git a/images/GCPGenStudio2.png b/images/GCPGenStudio2.png
diff --git a/images/GCPGenStudio3.png b/images/GCPGenStudio3.png
diff --git a/images/GCPGenStudio4.png b/images/GCPGenStudio4.png
diff --git a/notebooks/GenAI/GCP_GenAI_Huggingface.ipynb b/notebooks/GenAI/GCP_GenAI_Huggingface.ipynb
diff --git a/notebooks/GenAI/Gemini_Intro.ipynb b/notebooks/GenAI/Gemini_Intro.ipynb
diff --git a/notebooks/GenAI/Pubmed_RAG_chatbot.ipynb b/notebooks/GenAI/Pubmed_RAG_chatbot.ipynb
diff --git a/notebooks/GenAI/VertexAIStudioGCP.ipynb b/notebooks/GenAI/VertexAIStudioGCP.ipynb
@@ -56,15 +56,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Go to the Generative AI Studio console [here](https://console.cloud.google.com/vertex-ai/generative/language?_ga=2.182664366.923116401.1692009977-1042353744.1691708677).\n",
+    "Go to the Vertex AI Studio console by navigating to Vertex AI via the search bar on the console. On the left side menu scroll down to Vertex AI Studio, click **Language**.\n",
     "\n",
-    "Scroll down to **Summarization** and click on the model **Article Summary**. You will see a prompt session were you will need to enter in the contents of your article as the console does not allow you to upload files. For this tutorial this article is about how gut microbiota affects Alzeheimer's disease because of the gut-brain-microbiota axis network [here](https://www.aging-us.com/article/102930/pdf).\n",
+    " <img src=\"../../images/Gemini_2.png\" width=\"500\" height=\"500\">\n",
     "\n",
-    " <img src=\"../../../images/GCPGenStudio2.png\" width=\"500\" height=\"500\">\n",
     "\n",
-    "To the left you can control the parameters that we have been using before this is a great way to test what each parameter does and how they effect each other. Once you are done click **submit**, you should have a similar output as below. For explainations on the parameters **temperature, token limit max, top p, and top k** see the following article [here](https://cloud.google.com/vertex-ai/docs/generative-ai/text/test-text-prompts#generative-ai-test-text-prompt-drest).\n",
+    "Scroll down **\"Prompt examples\"** then to **Summarization** and click **\"Open\"** on **Article Summary**. You will see a prompt session were you will need to enter in the contents of your article as the console does not allow you to upload files. For this tutorial this article is about how gut microbiota affects Alzeheimer's disease because of the gut-brain-microbiota axis network [here](https://www.aging-us.com/article/102930/pdf).\n",
     "\n",
-    " <img src=\"../../../images/GCPGenStudio3.png\" width=\"600\" height=\"600\">\n",
+    " <img src=\"../../images/GCPGenStudio2.png\" width=\"500\" height=\"500\">\n",
+    "\n",
+    "To the right you can control the parameters that we have been using before this is a great way to test what each parameter does and how they effect each other. Once you are done click **submit**, you should have a similar output as below. For explainations on the parameters **temperature, Output token limit, top p, and top k** see the following article [here](https://cloud.google.com/vertex-ai/docs/generative-ai/text/test-text-prompts#generative-ai-test-text-prompt-drest).\n",
+    "\n",
+    " <img src=\"../../images/GCPGenStudio3.png\" width=\"600\" height=\"600\">\n",
     " \n"
    ]
   },
@@ -79,7 +82,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../images/GCPGenStudio4.png\" width=\"600\" height=\"600\">"
+    "<img src=\"../../images/GCPGenStudio4.png\" width=\"600\" height=\"600\">"
    ]
   },
   {
@@ -125,15 +128,15 @@
    "toc_visible": true
   },
   "environment": {
-   "kernel": "python3",
-   "name": "tf2-gpu.2-11.m108",
+   "kernel": "conda-root-py",
+   "name": "workbench-notebooks.m119",
    "type": "gcloud",
-   "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-11:m108"
+   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/workbench-notebooks:m119"
   },
   "kernelspec": {
-   "display_name": "Python (Local)",
+   "display_name": "Python 3 (ipykernel) (Local)",
    "language": "python",
-   "name": "local-base"
+   "name": "conda-root-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -145,7 +148,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,

diff --git a/notebooks/GenAI/example_scripts/example_langchain_chat_llama_2_zeroshot.py b/notebooks/GenAI/example_scripts/example_langchain_chat_llama_2_zeroshot.py
@@ -1,8 +1,8 @@
-from langchain.retrievers import PubMedRetriever
+from langchain_community.retrievers import PubMedRetriever
 from langchain.chains import ConversationalRetrievalChain
 from langchain.prompts import PromptTemplate
 #from langchain.llms import VertexAIModelGarden
-from langchain.llms import VertexAI
+from langchain_google_vertexai import ChatVertexAI
 import sys
 import json
 import os
@@ -30,7 +30,7 @@ def build_chain():
     #llm = VertexAIModelGarden(project=PROJECT_ID, endpoint_id=ENDPOINT_ID, location=LOCATION_ID)
 
     llm = VertexAI(
-    model_name="text-bison@001",
+    model_name="chat-bison@002",
     max_output_tokens=1024,
     temperature=0.2,
     top_p=0.8,

diff --git a/notebooks/GenAI/example_scripts/example_vectorsearch_chat_llama_2_zeroshot.py b/notebooks/GenAI/example_scripts/example_vectorsearch_chat_llama_2_zeroshot.py
@@ -1,9 +1,9 @@
 from langchain.chains import ConversationalRetrievalChain
 from langchain.prompts import PromptTemplate
-#from langchain.llms import VertexAIModelGarden
-from langchain.embeddings import VertexAIEmbeddings
-from langchain.vectorstores import MatchingEngine
-from langchain.llms import VertexAI
+#from langchain_google_vertexai import VertexAIModelGarden
+from langchain_google_vertexai import VertexAIEmbeddings
+from langchain_google_vertexai import VectorSearchVectorStore
+from langchain_google_vertexai import ChatVertexAI
 import sys
 import json
 import os
@@ -35,23 +35,24 @@ def build_chain():
 
     #llm = VertexAIModelGarden(project=PROJECT_ID, endpoint_id=ENDPOINT_ID, location=LOCATION_ID)
     llm = VertexAI(
-    model_name="text-bison@001",
+    model_name="chat-bison@002",
     max_output_tokens=1024,
     temperature=0.2,
     top_p=0.8,
     top_k=40,
     verbose=True,
 )
-    embeddings = VertexAIEmbeddings()
+    embeddings = VertexAIEmbeddings(model_name="textembedding-gecko@003")
 
-    vector_store = MatchingEngine.from_components(
+    vector_store = VectorSearchVectorStore.from_components(
         project_id=PROJECT_ID,
         region=LOCATION_ID,
         gcs_bucket_name=BUCKET,
         embedding=embeddings,
         index_id=VC_INDEX_ID,
         endpoint_id=VC_ENDPOINT_ID
     )
+
 
     retriever = vector_store.as_retriever(
         search_type="similarity",