Merge branch 'GoogleCloudPlatform:main' into feat-add-conversational-…

…genai-app-template
GoogleCloudPlatform · Sep 17, 2024 · 656d2eb · 656d2eb
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diff --git a/gemini/getting-started/intro_gemini_1_5_flash.ipynb b/gemini/getting-started/intro_gemini_1_5_flash.ipynb
@@ -259,7 +259,7 @@
       },
       "outputs": [],
       "source": [
-        "MODEL_ID = \"gemini-1.5-flash-001\"  # @param {type:\"string\"}\n",
+        "MODEL_ID = \"gemini-1.5-flash\"  # @param {type:\"string\"}\n",
         "\n",
         "model = GenerativeModel(MODEL_ID)"
       ]

diff --git a/gemini/getting-started/intro_gemini_1_5_pro.ipynb b/gemini/getting-started/intro_gemini_1_5_pro.ipynb
@@ -259,7 +259,7 @@
       },
       "outputs": [],
       "source": [
-        "MODEL_ID = \"gemini-1.5-pro-001\"  # @param {type:\"string\"}\n",
+        "MODEL_ID = \"gemini-1.5-pro\"  # @param {type:\"string\"}\n",
         "\n",
         "model = GenerativeModel(MODEL_ID)"
       ]

diff --git a/gemini/getting-started/intro_gemini_chat.ipynb b/gemini/getting-started/intro_gemini_chat.ipynb
@@ -74,7 +74,7 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This notebook demonstrates how to send chat prompts to the Gemini 1.5 Pro model (`gemini-1.5-pro-001`) by using the Vertex AI SDK for Python and LangChain. Gemini 1.5 Pro supports prompts with text-only input, including natural language tasks, multi-turn text and code chat, and code generation. It can output text and code.\n",
+        "This notebook demonstrates how to send chat prompts to the Gemini 1.5 Pro model (`gemini-1.5-pro`) by using the Vertex AI SDK for Python and LangChain. Gemini 1.5 Pro supports prompts with text-only input, including natural language tasks, multi-turn text and code chat, and code generation. It can output text and code.\n",
         "\n",
         "Learn more about [Sending chat prompt requests (Gemini)](https://cloud.google.com/vertex-ai/docs/generative-ai/multimodal/send-chat-prompts-gemini)."
       ]
@@ -87,7 +87,7 @@
       "source": [
         "### Objectives\n",
         "\n",
-        "In this tutorial, you learn how to send chat prompts to the Gemini 1.5 Pro model (`gemini-1.5-pro-001`) using the Vertex AI SDK for Python and LangChain.\n",
+        "In this tutorial, you learn how to send chat prompts to the Gemini 1.5 Pro model (`gemini-1.5-pro`) using the Vertex AI SDK for Python and LangChain.\n",
         "\n",
         "You will complete the following tasks:\n",
         "\n",
@@ -292,7 +292,7 @@
       },
       "outputs": [],
       "source": [
-        "model = GenerativeModel(\"gemini-1.5-pro-001\")"
+        "model = GenerativeModel(\"gemini-1.5-pro\")"
       ]
     },
     {
@@ -718,7 +718,7 @@
         "messages = [SystemMessage(content=system_message), HumanMessage(content=human_message)]\n",
         "\n",
         "chat = ChatVertexAI(\n",
-        "    model_name=\"gemini-1.5-pro-001\",\n",
+        "    model_name=\"gemini-1.5-pro\",\n",
         "    convert_system_message_to_human=True,\n",
         "    safety_settings={\n",
         "        HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE\n",
@@ -792,7 +792,7 @@
         "prompt = ChatPromptTemplate.from_messages(messages)\n",
         "\n",
         "chat = ChatVertexAI(\n",
-        "    model_name=\"gemini-1.5-pro-001\",\n",
+        "    model_name=\"gemini-1.5-pro\",\n",
         "    convert_system_message_to_human=True,\n",
         "    safety_settings={\n",
         "        HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE\n",
@@ -851,7 +851,7 @@
       ],
       "source": [
         "model = ChatVertexAI(\n",
-        "    model_name=\"gemini-1.5-pro-001\",\n",
+        "    model_name=\"gemini-1.5-pro\",\n",
         "    convert_system_message_to_human=True,\n",
         "    safety_settings={\n",
         "        HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE\n",

diff --git a/gemini/grounding/intro-grounding-gemini.ipynb b/gemini/grounding/intro-grounding-gemini.ipynb
@@ -324,7 +324,7 @@
       },
       "outputs": [],
       "source": [
-        "model = GenerativeModel(\"gemini-1.5-pro-001\")"
+        "model = GenerativeModel(\"gemini-1.5-pro\")"
       ]
     },
     {

diff --git a/gemini/long-context/intro_long_context.ipynb b/gemini/long-context/intro_long_context.ipynb
@@ -314,7 +314,7 @@
       },
       "outputs": [],
       "source": [
-        "MODEL_ID = \"gemini-1.5-flash-001\"  # @param {type:\"string\"}\n",
+        "MODEL_ID = \"gemini-1.5-flash\"  # @param {type:\"string\"}\n",
         "\n",
         "model = GenerativeModel(\n",
         "    MODEL_ID, generation_config=GenerationConfig(max_output_tokens=8192)\n",

diff --git a/gemini/orchestration/intro_langchain_gemini.ipynb b/gemini/orchestration/intro_langchain_gemini.ipynb
@@ -332,7 +332,7 @@
       "source": [
         "# LLM model\n",
         "llm = VertexAI(\n",
-        "    model_name=\"gemini-1.5-flash-001\",\n",
+        "    model_name=\"gemini-1.5-flash\",\n",
         "    verbose=True,\n",
         ")\n",
         "\n",

diff --git a/gemini/qa-ops/building_DIY_multimodal_qa_system_with_mRAG.ipynb b/gemini/qa-ops/building_DIY_multimodal_qa_system_with_mRAG.ipynb
@@ -384,12 +384,12 @@
         "\n",
         "# Multimodal models: Choose based on your performance/cost needs\n",
         "multimodal_model_15 = GenerativeModel(\n",
-        "    \"gemini-1.5-pro-001\"\n",
+        "    \"gemini-1.5-pro\"\n",
         ")  # works with text, code, images, video(with or without audio) and audio(mp3) with 1M input context - complex reasoning\n",
         "\n",
         "# Multimodal models: Choose based on your performance/cost needs\n",
         "multimodal_model_15_flash = GenerativeModel(\n",
-        "    \"gemini-1.5-flash-001\"\n",
+        "    \"gemini-1.5-flash\"\n",
         ")  # works with text, code, images, video(with or without audio) and audio(mp3) with 1M input context - faster inference\n",
         "\n",
         "multimodal_model_10 = GenerativeModel(\n",

diff --git a/gemini/responsible-ai/gemini_prompt_attacks_mitigation_examples.ipynb b/gemini/responsible-ai/gemini_prompt_attacks_mitigation_examples.ipynb
@@ -337,7 +337,7 @@
         ")\n",
         "\n",
         "model = GenerativeModel(\n",
-        "    model_name=\"gemini-1.5-flash-001\",\n",
+        "    model_name=\"gemini-1.5-flash\",\n",
         "    system_instruction=[\n",
         "        \"You are friendly travel expert and provide travel advice and company information.\",\n",
         "        \"The company name is: ABC GmbH\",\n",
@@ -1741,7 +1741,7 @@
         "            \"\"\"\n",
         "\n",
         "    model_guardrail = GenerativeModel(\n",
-        "        model_name=\"gemini-1.5-flash-001\",\n",
+        "        model_name=\"gemini-1.5-flash\",\n",
         "        system_instruction=[\n",
         "            \"You are a helpful, harmless, and unbiased AI assistant programmed to provide safe, ethical, and objective information.\",\n",
         "            \"Your mission is to analyze a user question and determine if it should be blocked based on the following safety and ethical guidelines.\",\n",

diff --git a/gemini/responsible-ai/react_rag_attacks_mitigations_examples.ipynb b/gemini/responsible-ai/react_rag_attacks_mitigations_examples.ipynb
@@ -345,7 +345,7 @@
       "outputs": [],
       "source": [
         "vertexai.init(project=project_id, location=\"us-central1\")\n",
-        "model = GenerativeModel(\"gemini-1.5-flash-001\")\n",
+        "model = GenerativeModel(\"gemini-1.5-flash\")\n",
         "\n",
         "# Generation Config with low temperature for reproducible results\n",
         "config = GenerationConfig(\n",

diff --git a/...ps/fixmycar/backend/src/main/java/com/cpet/fixmycarbackend/FixMyCarBackendController.java b/...ps/fixmycar/backend/src/main/java/com/cpet/fixmycarbackend/FixMyCarBackendController.java
@@ -123,7 +123,7 @@ public String geminiInference(String userPrompt, String vectorSearchResults) {
     logger.info("🔮 Gemini Prompt: " + geminiPrompt);
 
     String geminiLocation = "us-central1";
-    String modelName = "gemini-1.5-flash-001";
+    String modelName = "gemini-1.5-flash";
     try {
       VertexAI vertexAI = new VertexAI(projectId, geminiLocation);
       GenerateContentResponse response;

diff --git a/gemini/sample-apps/gemini-streamlit-cloudrun/app.py b/gemini/sample-apps/gemini-streamlit-cloudrun/app.py
@@ -25,9 +25,7 @@
 @st.cache_resource
 def load_models() -> Tuple[GenerativeModel, GenerativeModel]:
     """Load Gemini 1.5 Flash and Pro models."""
-    return GenerativeModel("gemini-1.5-flash-001"), GenerativeModel(
-        "gemini-1.5-pro-001"
-    )
+    return GenerativeModel("gemini-1.5-flash"), GenerativeModel("gemini-1.5-pro")
 
 
 def get_gemini_response(