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diff --git a/site/en/gemini-api/docs/function-calling/python.ipynb b/site/en/gemini-api/docs/function-calling/python.ipynb
@@ -326,13 +326,13 @@
       "source": [
         "Examine the chat history to see the flow of the conversation and how function calls are integrated within it.\n",
         "\n",
-        "The `ChatSession.history` property stores a chronological record of the conversation between the user and the Gemini model. Each turn in the conversation is represented by a [`genai.protos.Content`](https://ai.google.dev/api/python/google/ai/generativelanguage/Content) object, which contains the following information:\n",
+        "The `ChatSession.history` property stores a chronological record of the conversation between the user and the Gemini model. Each turn in the conversation is represented by a [`genai.protos.Content`](https://ai.google.dev/api/python/google/generativeai/protos/Content) object, which contains the following information:\n",
         "\n",
         "*   **Role**: Identifies whether the content originated from the \"user\" or the \"model\".\n",
-        "*   **Parts**: A list of [`genai.protos.Part`](https://ai.google.dev/api/python/google/ai/generativelanguage/Part) objects that represent individual components of the message. With a text-only model, these parts can be:\n",
+        "*   **Parts**: A list of [`genai.protos.Part`](https://ai.google.dev/api/python/google/generativeai/protos/Part) objects that represent individual components of the message. With a text-only model, these parts can be:\n",
         "    *   **Text**: Plain text messages.\n",
-        "    *   **Function Call** ([`genai.protos.FunctionCall`](https://ai.google.dev/api/python/google/ai/generativelanguage/FunctionCall)): A request from the model to execute a specific function with provided arguments.\n",
-        "    *   **Function Response** ([`genai.protos.FunctionResponse`](https://ai.google.dev/api/python/google/ai/generativelanguage/FunctionResponse)): The result returned by the user after executing the requested function.\n",
+        "    *   **Function Call** ([`genai.protos.FunctionCall`](https://ai.google.dev/api/python/google/generativeai/protos/FunctionCall)): A request from the model to execute a specific function with provided arguments.\n",
+        "    *   **Function Response** ([`genai.protos.FunctionResponse`](https://ai.google.dev/api/python/google/generativeai/protos/FunctionResponse)): The result returned by the user after executing the requested function.\n",
         "\n",
         " In the previous example with the mittens calculation, the history shows the following sequence:\n",
         "\n",

diff --git a/site/en/gemini-api/docs/get-started/python.ipynb b/site/en/gemini-api/docs/get-started/python.ipynb
@@ -419,7 +419,7 @@
       "source": [
         "Gemini can generate multiple possible responses for a single prompt. These possible responses are called `candidates`, and you can review them to select the most suitable one as the response.\n",
         "\n",
-        "View the response candidates with <a href=\"https://ai.google.dev/api/python/google/ai/generativelanguage/GenerateContentResponse#candidates\"><code>GenerateContentResponse.candidates</code></a>:"
+        "View the response candidates with <a href=\"https://ai.google.dev/api/python/google/generativeai/protos/GenerateContentResponse#candidates\"><code>GenerateContentResponse.candidates</code></a>:"
       ]
     },
     {
@@ -1488,7 +1488,7 @@
         "id": "-fthdIItnqki"
       },
       "source": [
-        "Underlying the Python SDK is the <a href=\"https://ai.google.dev/api/python/google/ai/generativelanguage\"><code>google.ai.generativelanguage</code></a> client library:"
+        "Underlying the Python SDK is the <a href=\"https://ai.google.dev/api/python/google/generativeai/protos\"><code>google.ai.generativelanguage</code></a> client library:"
       ]
     },
     {

diff --git a/site/en/gemini-api/docs/semantic_retrieval.ipynb b/site/en/gemini-api/docs/semantic_retrieval.ipynb
@@ -70,7 +70,7 @@
         "\n",
         "A common approach used to overcome these constraints is called Retrieval Augmented Generation (RAG), which augments the prompt sent to an LLM with relevant data retrieved from an external knowledge base through an Information Retrieval (IR) mechanism. The knowledge base can be your own corpora of documents, databases, or APIs.\n",
         "\n",
-        "This notebook walks you through a workflow to improve an LLM's response by augmenting its knowledge with external text corpora and performing semantic information retrieval to answer questions using the Semantic Retriever and the Attributed Question & Answering (AQA) APIs of the [Generative Language API](https://ai.google.dev/api/python/google/ai/generativelanguage).\n",
+        "This notebook walks you through a workflow to improve an LLM's response by augmenting its knowledge with external text corpora and performing semantic information retrieval to answer questions using the Semantic Retriever and the Attributed Question & Answering (AQA) APIs of the Generative Language API.\n",
         "\n",
         "Note: This API is currently in [beta](https://ai.google.dev/gemini-api/docs/api-versions) and is [only available in certain regions](https://ai.google.dev/gemini-api/docs/available-regions).\n"
       ]
@@ -719,7 +719,7 @@
       "source": [
         "## Attributed Question-Answering\n",
         "\n",
-        "Use the [`GenerateAnswer`](https://ai.google.dev/api/python/google/ai/generativelanguage/GenerateAnswerRequest) method to perform Attributed Question-Answering over your document, corpus, or a set of passages.\n",
+        "Use the [`GenerateAnswer`](https://ai.google.dev/api/python/google/generativeai/protos/GenerateAnswerRequest) method to perform Attributed Question-Answering over your document, corpus, or a set of passages.\n",
         "\n",
         "Attributed Question-Answering (AQA) refers to answering questions grounded to a given context and providing attributions(s), while minimizing hallucination.\n",
         "\n",
@@ -769,7 +769,7 @@
       "source": [
         "### AQA Helpful Tips\n",
         "\n",
-        "For full API specifications, refer to the [`GenerateAnswerRequest` API Reference](https://ai.google.dev/api/python/google/ai/generativelanguage/GenerateAnswerRequest).\n",
+        "For full API specifications, refer to the [`GenerateAnswerRequest` API Reference](https://ai.google.dev/api/python/google/generativeai/protos/GenerateAnswerRequest).\n",
         "\n",
         "* *Passage length*: Up to 300 tokens per passage are recommended.\n",
         "* *Passage sorting*:\n",
@@ -870,7 +870,7 @@
       "source": [
         "## Share the corpus\n",
         "\n",
-        "You can choose to share the corpus with others using the [`CreatePermissionRequest`](https://ai.google.dev/api/python/google/ai/generativelanguage/CreatePermissionRequest) API.\n",
+        "You can choose to share the corpus with others using the [`CreatePermissionRequest`](https://ai.google.dev/api/python/google/generativeai/protos/CreatePermissionRequest) API.\n",
         "\n",
         "Constraints:\n",
         "\n",
@@ -912,7 +912,7 @@
       "source": [
         "## Delete the corpus\n",
         "\n",
-        "Use [`DeleteCorpusRequest`](https://ai.google.dev/api/python/google/ai/generativelanguage/DeleteCorpusRequest) to delete a user corpus and all associated `Document`s & `Chunk`s.\n",
+        "Use [`DeleteCorpusRequest`](https://ai.google.dev/api/python/google/generativeai/protos/DeleteCorpusRequest) to delete a user corpus and all associated `Document`s & `Chunk`s.\n",
         "\n",
         "Note that non-empty corpora will throw an error without specifying an `force=True` flag. If you set `force=True`, any `Chunk`s and objects related to this `Document` will also be deleted.\n",
         "\n",