Remove deprecated models and replace them with newer ones

examples/Animated_Story_Video_Generation_gemini.ipynb

@@ -280,7 +280,7 @@
         "# Create a client for text generation using Gemini.\n",
         "MODEL = \"models/gemini-2.5-flash-lite\"\n",
         "# Create a client for image generation using Imagen.\n",
-        "IMAGE_MODEL_ID = \"imagen-3.0-generate-002\"\n"
+        "IMAGE_MODEL_ID = \"imagen-4.0-generate-001\"\n"
       ]
     },
     {

examples/Anomaly_detection_with_embeddings.ipynb

@@ -1214,7 +1214,7 @@
       "source": [
         "### API changes to Embeddings with model embedding-001\n",
         "\n",
-        "For the embeddings model, `text-embedding-004`, there is a task type parameter and the optional title (only valid with task_type=`RETRIEVAL_DOCUMENT`).\n",
+        "For the embeddings model, `gemini-embedding-001`, there is a task type parameter and the optional title (only valid with task_type=`RETRIEVAL_DOCUMENT`).\n",
         "\n",
         "These parameters apply only to the embeddings models. The task types are:\n",
         "\n",
@@ -1276,7 +1276,7 @@
         "\n",
         "\n",
         "def create_embeddings(df):\n",
-        "    MODEL_ID = \"text-embedding-004\" # @param [\"embedding-001\",\"text-embedding-004\"] {allow-input: true}\n",
+        "    MODEL_ID = \"gemini-embedding-001\" # @param [\"embedding-001\",\"gemini-embedding-001\"] {allow-input: true}\n",
         "    model = f\"models/{MODEL_ID}\"\n",
         "    embed_fn = make_embed_text_fn(model)\n",
         "\n",

examples/Browser_as_a_tool.ipynb

@@ -155,7 +155,7 @@
         "\n",
         "client = genai.Client(api_key=GOOGLE_API_KEY)\n",
         "\n",
-        "LIVE_MODEL = 'gemini-2.5-flash-native-audio-preview-09-2025'  # @param ['gemini-2.0-flash-live-001', 'gemini-live-2.5-flash-preview', 'gemini-2.5-flash-native-audio-preview-09-2025'] {allow-input: true, isTemplate: true}\n",
+        "LIVE_MODEL = 'gemini-2.5-flash-native-audio-preview-09-2025'  # @param ['gemini-2.5-flash-native-audio-preview-12-2025', 'gemini-2.5-flash-native-audio-preview-12-2025', 'gemini-2.5-flash-native-audio-preview-09-2025'] {allow-input: true, isTemplate: true}\n",
         "MODEL = 'gemini-2.5-flash'  # @param ['gemini-2.5-flash'] {allow-input: true, isTemplate: true}"
       ]
     },

examples/Classify_text_with_embeddings.ipynb

@@ -163,7 +163,7 @@
           "output_type": "stream",
           "text": [
             "models/embedding-001\n",
-            "models/text-embedding-004\n",
+            "models/gemini-embedding-001\n",
             "models/gemini-embedding-exp-03-07\n",
             "models/gemini-embedding-exp\n",
             "models/gemini-embedding-001\n"
@@ -193,7 +193,7 @@
       },
       "outputs": [],
       "source": [
-        "MODEL_ID = \"gemini-embedding-001\" # @param [\"gemini-embedding-001\", \"text-embedding-004\"] {\"allow-input\":true, isTemplate: true}"
+        "MODEL_ID = \"gemini-embedding-001\" # @param [\"gemini-embedding-001\", \"gemini-embedding-001\"] {\"allow-input\":true, isTemplate: true}"
       ]
     },
     {

examples/Google_IO2025_Live_Coding.ipynb

@@ -192,7 +192,7 @@
       "source": [
         "### Select the Imagen3 model to be used\n",
         "\n",
-        "The `imagen-3.0-generate-002` model is specifically designed for high-quality image generation from textual prompts."
+        "The `imagen-4.0-generate-001` model is specifically designed for high-quality image generation from textual prompts."
       ]
     },
     {
@@ -203,7 +203,7 @@
       },
       "outputs": [],
       "source": [
-        "MODEL_ID = \"imagen-3.0-generate-002\" # @param {isTemplate: true}"
+        "MODEL_ID = \"imagen-4.0-generate-001\" # @param {isTemplate: true}"
       ]
     },
     {
@@ -301,7 +301,7 @@
         "## Generating images with Gemini 2.0 Flash image out model (experimental)\n",
         "\n",
         "\n",
-        "The `gemini-2.0-flash-preview-image-generation model` extends Gemini's multimodal capabilities to include conversational image generation and editing. This model can generate images along with text responses, making it highly versatile for mixed-media content creation."
+        "The `gemini-2.5-flash-image model` extends Gemini's multimodal capabilities to include conversational image generation and editing. This model can generate images along with text responses, making it highly versatile for mixed-media content creation."
       ]
     },
     {
@@ -323,7 +323,7 @@
       },
       "outputs": [],
       "source": [
-        "MODEL_ID = \"gemini-2.0-flash-preview-image-generation\""
+        "MODEL_ID = \"gemini-2.5-flash-image\""
       ]
     },
     {

examples/Pdf_structured_outputs_on_invoices_and_forms.ipynb

@@ -470,7 +470,7 @@
         "client = genai.Client()\n",
         "\n",
         "response = client.models.generate_content(\n",
-        "    model=\"gemini-2.0-flash\",\n",
+        "    model=\"gemini-2.5-flash\",\n",
         "    contents=\"Extract invoice details: Invoice #12345 dated 2024-01-15 from Acme Corp for $1,250.00\",\n",
         "    config=types.GenerateContentConfig(\n",
         "        response_mime_type=\"application/json\",\n",

examples/Search_Wikipedia_using_ReAct.ipynb

@@ -55,7 +55,7 @@
         "id": "sdkuZY1IdRal"
       },
       "source": [
-        "This notebook is a minimal implementation of [ReAct: Synergizing Reasoning and Acting in Language Models](https://arxiv.org/abs/2210.03629) with the Google `gemini-2.0-flash` model. You'll use ReAct prompting to configure a model to search Wikipedia to find the answer to a user's question.\n"
+        "This notebook is a minimal implementation of [ReAct: Synergizing Reasoning and Acting in Language Models](https://arxiv.org/abs/2210.03629) with the Google `gemini-2.5-flash` model. You'll use ReAct prompting to configure a model to search Wikipedia to find the answer to a user's question.\n"
       ]
     },
     {
@@ -854,7 +854,7 @@
         }
       ],
       "source": [
-        "gemini_ReAct_chat = ReAct(model='gemini-2.0-flash', ReAct_prompt='model_instructions.txt')\n",
+        "gemini_ReAct_chat = ReAct(model='gemini-2.5-flash', ReAct_prompt='model_instructions.txt')\n",
         "# Note: try different combinations of generational_config parameters for variational results\n",
         "gemini_ReAct_chat(\"What are the total of ages of the main trio from the new Percy Jackson and the Olympians TV series in real life?\", temperature=0.2)"
       ]
@@ -865,7 +865,7 @@
         "id": "ZIfeyyI6hoIE"
       },
       "source": [
-        "Now, try asking the same question to `gemini-2.0-flash` model without the ReAct prompt."
+        "Now, try asking the same question to `gemini-2.5-flash` model without the ReAct prompt."
       ]
     },
     {

examples/Search_reranking_using_embeddings.ipynb

@@ -313,7 +313,7 @@
         "      search_history.add(search_term) # add to search history\n",
         "\n",
         "      try:\n",
-        "        # extract the relevant data by using `gemini-2.0-flash` model\n",
+        "        # extract the relevant data by using `gemini-2.5-flash` model\n",
         "        page = wikipedia.page(search_term, auto_suggest=False)\n",
         "        url = page.url\n",
         "        print(f\"Information Source: {url}\")\n",
@@ -1190,7 +1190,7 @@
       },
       "outputs": [],
       "source": [
-        "EMBEDDINGS_MODEL_ID = \"gemini-embedding-001\"  # @param [\"gemini-embedding-001\", \"text-embedding-004\"] {\"allow-input\": true, \"isTemplate\": true}\n",
+        "EMBEDDINGS_MODEL_ID = \"gemini-embedding-001\"  # @param [\"gemini-embedding-001\", \"gemini-embedding-001\"] {\"allow-input\": true, \"isTemplate\": true}\n",
         "\n",
         "def get_embeddings(content: list[str]) -> np.ndarray:\n",
         "  embeddings = client.models.embed_content(\n",

examples/Story_Writing_with_Prompt_Chaining.ipynb

@@ -2133,7 +2133,7 @@
       "source": [
         "Language models like Gemini process text in units called tokens. For Gemini models, each token is equivalent to about 4 characters.\n",
         "\n",
-        "`gemini-2.0-flash` has an output limit of 8192 tokens per generation call. This means that each individual prompt response cannot exceed this limit. By using iterative generation, you can create a story that is much longer than 8192 tokens by building it piece by piece.\n",
+        "`gemini-2.5-flash` has an output limit of 8192 tokens per generation call. This means that each individual prompt response cannot exceed this limit. By using iterative generation, you can create a story that is much longer than 8192 tokens by building it piece by piece.\n",
         "\n",
         "Let's see how many tokens the final story is. Is it longer than 8192 tokens?"
       ]
@@ -2155,7 +2155,7 @@
       ],
       "source": [
         "# Check the number of tokens in the final story\n",
-        "# gemini-2.0-flash output token limit is 8192\n",
+        "# gemini-2.5-flash output token limit is 8192\n",
         "total_tokens=client.models.count_tokens(\n",
         "    model=MODEL_ID, \n",
         "    contents=final,\n",

examples/Tag_and_caption_images.ipynb

@@ -230,7 +230,7 @@
         "from PIL import Image as PILImage\n",
         "import time\n",
         "\n",
-        "MODEL_ID='gemini-2.0-flash' # @param [\"gemini-2.5-flash-lite\", \"gemini-2.5-flash\", \"gemini-2.5-pro\",\"gemini-3-pro-preview\"] {\"allow-input\":true, isTemplate: true}\n",
+        "MODEL_ID='gemini-2.5-flash' # @param [\"gemini-2.5-flash-lite\", \"gemini-2.5-flash\", \"gemini-2.5-pro\",\"gemini-3-pro-preview\"] {\"allow-input\":true, isTemplate: true}\n",
         "\n",
         "# a helper function for calling\n",
         "\n",
@@ -443,7 +443,7 @@
       "source": [
         "import pandas as pd\n",
         "\n",
-        "EMBEDDINGS_MODEL_ID = \"embedding-001\" # @param [\"embedding-001\", \"text-embedding-004\",\"gemini-embedding-exp-03-07\"] {\"allow-input\":true, isTemplate: true}\n",
+        "EMBEDDINGS_MODEL_ID = \"embedding-001\" # @param [\"embedding-001\", \"gemini-embedding-001\",\"gemini-embedding-exp-03-07\"] {\"allow-input\":true, isTemplate: true}\n",
         "\n",
         "def embed(text):\n",
         "    embedding = client.models.embed_content(\n",

examples/Talk_to_documents_with_embeddings.ipynb

@@ -176,7 +176,7 @@
         "    prototype with generative AI applications\n",
         "\"\"\"\n",
         "\n",
-        "EMBEDDING_MODEL_ID = MODEL_ID = \"gemini-embedding-001\"  # @param [\"gemini-embedding-001\", \"text-embedding-004\"] {\"allow-input\": true, \"isTemplate\": true}\n",
+        "EMBEDDING_MODEL_ID = MODEL_ID = \"gemini-embedding-001\"  # @param [\"gemini-embedding-001\", \"gemini-embedding-001\"] {\"allow-input\": true, \"isTemplate\": true}\n",
         "embedding = client.models.embed_content(\n",
         "        model=EMBEDDING_MODEL_ID,\n",
         "        contents=sample_text,\n",

examples/anomaly_detection.ipynb

@@ -183,7 +183,7 @@
           "output_type": "stream",
           "text": [
             "models/embedding-001\n",
-            "models/text-embedding-004\n",
+            "models/gemini-embedding-001\n",
             "models/gemini-embedding-exp-03-07\n",
             "models/gemini-embedding-exp\n",
             "models/gemini-embedding-001\n"
@@ -213,7 +213,7 @@
       },
       "outputs": [],
       "source": [
-        "MODEL_ID = \"gemini-embedding-001\" # @param [\"gemini-embedding-001\", \"text-embedding-004\"] {\"allow-input\":true, isTemplate: true}"
+        "MODEL_ID = \"gemini-embedding-001\" # @param [\"gemini-embedding-001\", \"gemini-embedding-001\"] {\"allow-input\":true, isTemplate: true}"
       ]
     },
     {

examples/chromadb/Vectordb_with_chroma.ipynb

@@ -195,7 +195,7 @@
           "output_type": "stream",
           "text": [
             "models/embedding-001\n",
-            "models/text-embedding-004\n",
+            "models/gemini-embedding-001\n",
             "models/gemini-embedding-exp-03-07\n",
             "models/gemini-embedding-exp\n",
             "models/gemini-embedding-001\n"
@@ -312,7 +312,7 @@
         "\n",
         "class GeminiEmbeddingFunction(EmbeddingFunction):\n",
         "  def __call__(self, input: Documents) -> Embeddings:\n",
-        "    EMBEDDING_MODEL_ID = \"gemini-embedding-001\"  # @param [\"gemini-embedding-001\", \"text-embedding-004\"] {\"allow-input\": true, \"isTemplate\": true}\n",
+        "    EMBEDDING_MODEL_ID = \"gemini-embedding-001\"  # @param [\"gemini-embedding-001\", \"gemini-embedding-001\"] {\"allow-input\": true, \"isTemplate\": true}\n",
         "    title = \"Custom query\"\n",
         "    response = client.models.embed_content(\n",
         "        model=EMBEDDING_MODEL_ID,\n",

examples/clustering_with_embeddings.ipynb

@@ -187,7 +187,7 @@
           "output_type": "stream",
           "text": [
             "models/embedding-001\n",
-            "models/text-embedding-004\n",
+            "models/gemini-embedding-001\n",
             "models/gemini-embedding-exp-03-07\n",
             "models/gemini-embedding-exp\n",
             "models/gemini-embedding-001\n"
@@ -217,7 +217,7 @@
       },
       "outputs": [],
       "source": [
-        "MODEL_ID = \"gemini-embedding-001\" # @param [\"gemini-embedding-001\", \"text-embedding-004\"] {\"allow-input\":true, isTemplate: true}"
+        "MODEL_ID = \"gemini-embedding-001\" # @param [\"gemini-embedding-001\", \"gemini-embedding-001\"] {\"allow-input\":true, isTemplate: true}"
       ]
     },
     {

examples/document_search.ipynb

@@ -179,7 +179,7 @@
           "output_type": "stream",
           "text": [
             "models/embedding-001\n",
-            "models/text-embedding-004\n",
+            "models/gemini-embedding-001\n",
             "models/gemini-embedding-exp-03-07\n",
             "models/gemini-embedding-exp\n",
             "models/gemini-embedding-001\n"
@@ -209,7 +209,7 @@
       },
       "outputs": [],
       "source": [
-        "MODEL_ID = \"gemini-embedding-001\" # @param [\"gemini-embedding-001\", \"text-embedding-004\"] {\"allow-input\":true, isTemplate: true}"
+        "MODEL_ID = \"gemini-embedding-001\" # @param [\"gemini-embedding-001\", \"gemini-embedding-001\"] {\"allow-input\":true, isTemplate: true}"
       ]
     },
     {

examples/langchain/Gemini_LangChain_QA_Chroma_WebLoad.ipynb

@@ -409,7 +409,7 @@
         "### Initialize Gemini\n",
         "\n",
         "You must import `ChatGoogleGenerativeAI` from LangChain to initialize your model.\n",
-        " In this example, you will use **gemini-2.0-flash**, as it supports text summarization. To know more about the text model, read Google AI's [language documentation](https://ai.google.dev/models/gemini).\n",
+        " In this example, you will use **gemini-2.5-flash**, as it supports text summarization. To know more about the text model, read Google AI's [language documentation](https://ai.google.dev/models/gemini).\n",
         "\n",
         "You can configure the model parameters such as ***temperature*** or ***top_p***,  by passing the appropriate values when initializing the `ChatGoogleGenerativeAI` LLM.  To learn more about the parameters and their uses, read Google AI's [concepts guide](https://ai.google.dev/docs/concepts#model_parameters)."
       ]
@@ -516,7 +516,7 @@
         "#    the chain.\n",
         "# 3. The `context` and `question` are then passed to the prompt where they\n",
         "#    are populated in the respective variables.\n",
-        "# 4. This prompt is then passed to the LLM (`gemini-2.0-flash`).\n",
+        "# 4. This prompt is then passed to the LLM (`gemini-2.5-flash`).\n",
         "# 5. Output from the LLM is passed through an output parser\n",
         "#    to structure the model's response.\n",
         "rag_chain = (\n",

examples/langchain/Gemini_LangChain_QA_Pinecone_WebLoad.ipynb

@@ -484,7 +484,7 @@
         "### Initialize Gemini\n",
         "\n",
         "You must import `ChatGoogleGenerativeAI` from LangChain to initialize your model.\n",
-        " In this example, you will use **gemini-2.0-flash**, as it supports text summarization. To know more about the text model, read Google AI's [language documentation](https://ai.google.dev/models/gemini).\n",
+        " In this example, you will use **gemini-2.5-flash**, as it supports text summarization. To know more about the text model, read Google AI's [language documentation](https://ai.google.dev/models/gemini).\n",
         "\n",
         "You can configure the model parameters such as ***temperature*** or ***top_p***,  by passing the appropriate values when initializing the `ChatGoogleGenerativeAI` LLM.  To learn more about the parameters and their uses, read Google AI's [concepts guide](https://ai.google.dev/docs/concepts#model_parameters)."
       ]
@@ -592,7 +592,7 @@
         "# 2. Use the `RunnablePassthrough` option to provide question during invoke.\n",
         "# 3. The `context` and `question` are then passed to the prompt and\n",
         "#    input variables in the prompt are populated.\n",
-        "# 4. The prompt is then passed to the LLM (`gemini-2.0-flash`).\n",
+        "# 4. The prompt is then passed to the LLM (`gemini-2.5-flash`).\n",
         "# 5. Output from the LLM is passed through an output parser\n",
         "#    to structure the model response.\n",
         "rag_chain = (\n",

examples/llamaindex/Gemini_LlamaIndex_QA_Chroma_WebPageReader.ipynb

@@ -322,7 +322,7 @@
         "### Initialize Gemini\n",
         "\n",
         "You must import `Gemini` from LlamaIndex to initialize your model.\n",
-        " In this example, you will use **gemini-2.0-flash**, as it supports text summarization. To know more about the text model, read Google AI's [model documentation](https://ai.google.dev/models/gemini).\n",
+        " In this example, you will use **gemini-2.5-flash**, as it supports text summarization. To know more about the text model, read Google AI's [model documentation](https://ai.google.dev/models/gemini).\n",
         "\n",
         "You can configure the model parameters such as ***temperature*** or ***top_p***,  using the  ***generation_config*** parameter when initializing the `Gemini` LLM.  To learn more about the model parameters and their uses, read Google AI's [concepts guide](https://ai.google.dev/docs/concepts#model_parameters)."
       ]

examples/prompting/Providing_base_cases.ipynb

@@ -149,7 +149,7 @@
       },
       "outputs": [],
       "source": [
-        "model = genai.GenerativeModel(model_name='gemini-2.0-flash', system_instruction=instructions)"
+        "model = genai.GenerativeModel(model_name='gemini-2.5-flash', system_instruction=instructions)"
       ]
     },
     {
@@ -213,7 +213,7 @@
       },
       "outputs": [],
       "source": [
-        "model = genai.GenerativeModel(model_name='gemini-2.0-flash', system_instruction=instructions)"
+        "model = genai.GenerativeModel(model_name='gemini-2.5-flash', system_instruction=instructions)"
       ]
     },
     {

examples/qdrant/Movie_Recommendation.ipynb

@@ -661,7 +661,7 @@
         "import time\n",
         "from google.api_core import exceptions, retry\n",
         "\n",
-        "MODEL_FOR_EMBEDDING = \"gemini-embedding-001\" # @param [\"gemini-embedding-001\", \"embedding-001\", \"text-embedding-004\"] {\"allow-input\":true, isTemplate: true}\n",
+        "MODEL_FOR_EMBEDDING = \"gemini-embedding-001\" # @param [\"gemini-embedding-001\", \"embedding-001\", \"gemini-embedding-001\"] {\"allow-input\":true, isTemplate: true}\n",
         "\n",
         "BATCH_SIZE = 25\n",
         "QDRANT_BATCH_SIZE = 3072\n",

examples/qdrant/Qdrant_similarity_search.ipynb

@@ -319,7 +319,7 @@
         "from google.genai import types\n",
         "\n",
         "# Select embedding model\n",
-        "MODEL_ID = \"gemini-embedding-001\"  # @param [\"gemini-embedding-001\", \"text-embedding-004\"] {\"allow-input\": true, \"isTemplate\": true}\n",
+        "MODEL_ID = \"gemini-embedding-001\"  # @param [\"gemini-embedding-001\", \"gemini-embedding-001\"] {\"allow-input\": true, \"isTemplate\": true}\n",
         "\n",
         "\n",
         "# Function to convert text to embeddings\n",