diff --git a/docs/docs/integrations/chat/google_generative_ai.ipynb b/docs/docs/integrations/chat/google_generative_ai.ipynb
index 2b59d339982..148d839f40b 100644
--- a/docs/docs/integrations/chat/google_generative_ai.ipynb
+++ b/docs/docs/integrations/chat/google_generative_ai.ipynb
@@ -252,9 +252,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import base64\n",
+    "\n",
     "from langchain_core.messages import HumanMessage\n",
     "from langchain_google_genai import ChatGoogleGenerativeAI\n",
-    "import base64\n",
     "\n",
     "# Example using a public URL (remains the same)\n",
     "message_url = HumanMessage(\n",
@@ -273,18 +274,12 @@
     "image_file_path = \"/Users/philschmid/projects/google-gemini/langchain/docs/static/img/agents_vs_chains.png\"\n",
     "\n",
     "with open(image_file_path, \"rb\") as image_file:\n",
-    "    encoded_image = base64.b64encode(image_file.read()).decode('utf-8')\n",
+    "    encoded_image = base64.b64encode(image_file.read()).decode(\"utf-8\")\n",
     "\n",
     "message_local = HumanMessage(\n",
     "    content=[\n",
-    "        {\n",
-    "            \"type\": \"text\",\n",
-    "            \"text\": \"Describe the local image.\"\n",
-    "        },\n",
-    "        {\n",
-    "            \"type\": \"image_url\",\n",
-    "            \"image_url\": f\"data:image/png;base64,{encoded_image}\"\n",
-    "        }\n",
+    "        {\"type\": \"text\", \"text\": \"Describe the local image.\"},\n",
+    "        {\"type\": \"image_url\", \"image_url\": f\"data:image/png;base64,{encoded_image}\"},\n",
     "    ]\n",
     ")\n",
     "result_local = llm.invoke([message_local])\n",
@@ -312,32 +307,30 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.messages import HumanMessage\n",
     "import base64\n",
     "\n",
+    "from langchain_core.messages import HumanMessage\n",
+    "\n",
     "# Ensure you have an audio file named 'example_audio.mp3' or provide the correct path.\n",
     "audio_file_path = \"example_audio.mp3\"\n",
-    "audio_mime_type=\"audio/mpeg\"\n",
+    "audio_mime_type = \"audio/mpeg\"\n",
     "\n",
     "\n",
     "with open(audio_file_path, \"rb\") as audio_file:\n",
-    "    encoded_audio = base64.b64encode(audio_file.read()).decode('utf-8')\n",
+    "    encoded_audio = base64.b64encode(audio_file.read()).decode(\"utf-8\")\n",
     "\n",
     "message = HumanMessage(\n",
     "    content=[\n",
-    "        {\n",
-    "            \"type\": \"text\",\n",
-    "            \"text\": \"Transcribe the audio.\"\n",
-    "        },\n",
+    "        {\"type\": \"text\", \"text\": \"Transcribe the audio.\"},\n",
     "        {\n",
     "            \"type\": \"media\",\n",
-    "            \"data\": encoded_audio, # Use base64 string directly\n",
+    "            \"data\": encoded_audio,  # Use base64 string directly\n",
     "            \"mime_type\": audio_mime_type,\n",
-    "        }\n",
+    "        },\n",
     "    ]\n",
     ")\n",
-    "response = llm.invoke([message]) # Uncomment to run\n",
-    "print(f\"Response for audio: {response.content}\")\n"
+    "response = llm.invoke([message])  # Uncomment to run\n",
+    "print(f\"Response for audio: {response.content}\")"
    ]
   },
   {
@@ -357,34 +350,31 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import base64\n",
+    "\n",
     "from langchain_core.messages import HumanMessage\n",
     "from langchain_google_genai import ChatGoogleGenerativeAI\n",
-    "import base64\n",
     "\n",
     "# Ensure you have a video file named 'example_video.mp4' or provide the correct path.\n",
     "video_file_path = \"example_video.mp4\"\n",
-    "video_mime_type=\"video/mp4\"\n",
+    "video_mime_type = \"video/mp4\"\n",
     "\n",
     "\n",
     "with open(video_file_path, \"rb\") as video_file:\n",
-    "    encoded_video = base64.b64encode(video_file.read()).decode('utf-8')\n",
+    "    encoded_video = base64.b64encode(video_file.read()).decode(\"utf-8\")\n",
     "\n",
     "message = HumanMessage(\n",
     "    content=[\n",
-    "        {\n",
-    "            \"type\": \"text\",\n",
-    "            \"text\": \"Describe the first few frames of the video.\"\n",
-    "        },\n",
+    "        {\"type\": \"text\", \"text\": \"Describe the first few frames of the video.\"},\n",
     "        {\n",
     "            \"type\": \"media\",\n",
-    "            \"data\": encoded_video, # Use base64 string directly\n",
+    "            \"data\": encoded_video,  # Use base64 string directly\n",
     "            \"mime_type\": video_mime_type,\n",
-    "        }\n",
+    "        },\n",
     "    ]\n",
     ")\n",
-    "response = llm.invoke([message]) # Uncomment to run\n",
-    "print(f\"Response for video: {response.content}\")\n",
-    "    "
+    "response = llm.invoke([message])  # Uncomment to run\n",
+    "print(f\"Response for video: {response.content}\")"
    ]
   },
   {
@@ -575,14 +565,16 @@
     }
    ],
    "source": [
-    "from langchain_google_genai import ChatGoogleGenerativeAI\n",
     "from langchain_core.tools import tool\n",
+    "from langchain_google_genai import ChatGoogleGenerativeAI\n",
+    "\n",
     "\n",
     "# Define the tool\n",
     "@tool(description=\"Get the current weather in a given location\")\n",
     "def get_weather(location: str) -> str:\n",
     "    return \"It's sunny.\"\n",
     "\n",
+    "\n",
     "# Initialize the model and bind the tool\n",
     "llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash\")\n",
     "llm_with_tools = llm.bind_tools([get_weather])\n",
@@ -596,8 +588,12 @@
     "\n",
     "# Example tool call message would be needed here if you were actually running the tool\n",
     "from langchain_core.messages import ToolMessage\n",
-    "tool_message = ToolMessage(content=get_weather(*ai_msg.tool_calls[0]['args']), tool_call_id=ai_msg.tool_calls[0]['id'])\n",
-    "llm_with_tools.invoke([ai_msg, tool_message]) # Example of passing tool result back\n"
+    "\n",
+    "tool_message = ToolMessage(\n",
+    "    content=get_weather(*ai_msg.tool_calls[0][\"args\"]),\n",
+    "    tool_call_id=ai_msg.tool_calls[0][\"id\"],\n",
+    ")\n",
+    "llm_with_tools.invoke([ai_msg, tool_message])  # Example of passing tool result back"
    ]
   },
   {
@@ -628,19 +624,24 @@
     "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "from langchain_google_genai import ChatGoogleGenerativeAI\n",
     "\n",
+    "\n",
     "# Define the desired structure\n",
     "class Person(BaseModel):\n",
-    "    '''Information about a person.'''\n",
+    "    \"\"\"Information about a person.\"\"\"\n",
+    "\n",
     "    name: str = Field(..., description=\"The person's name\")\n",
     "    height_m: float = Field(..., description=\"The person's height in meters\")\n",
     "\n",
+    "\n",
     "# Initialize the model\n",
     "llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash\", temperature=0)\n",
     "structured_llm = llm.with_structured_output(Person)\n",
     "\n",
     "# Invoke the model with a query asking for structured information\n",
-    "result = structured_llm.invoke(\"Who was the 16th president of the USA, and how tall was he in meters?\")\n",
-    "print(result)\n"
+    "result = structured_llm.invoke(\n",
+    "    \"Who was the 16th president of the USA, and how tall was he in meters?\"\n",
+    ")\n",
+    "print(result)"
    ]
   },
   {
@@ -763,9 +764,9 @@
     "\n",
     "for c in resp.content:\n",
     "    if isinstance(c, dict):\n",
-    "        if c[\"type\"] == 'code_execution_result':\n",
+    "        if c[\"type\"] == \"code_execution_result\":\n",
     "            print(f\"Code execution result: {c['code_execution_result']}\")\n",
-    "        elif c[\"type\"] == 'executable_code':\n",
+    "        elif c[\"type\"] == \"executable_code\":\n",
     "            print(f\"Executable code: {c['executable_code']}\")\n",
     "    else:\n",
     "        print(c)"
@@ -813,6 +814,7 @@
     "\n",
     "llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash\")\n",
     "\n",
+    "\n",
     "async def run_async_calls():\n",
     "    # Async invoke\n",
     "    result_ainvoke = await llm.ainvoke(\"Why is the sky blue?\")\n",
@@ -820,7 +822,9 @@
     "\n",
     "    # Async stream\n",
     "    print(\"\\nAsync Stream Result:\")\n",
-    "    async for chunk in llm.astream(\"Write a short poem about asynchronous programming.\"):\n",
+    "    async for chunk in llm.astream(\n",
+    "        \"Write a short poem about asynchronous programming.\"\n",
+    "    ):\n",
     "        print(chunk.content, end=\"\", flush=True)\n",
     "    print(\"\\n\")\n",
     "\n",
@@ -828,6 +832,7 @@
     "    results_abatch = await llm.abatch([\"What is 1+1?\", \"What is 2+2?\"])\n",
     "    print(\"Async Batch Results:\", [res.content for res in results_abatch])\n",
     "\n",
+    "\n",
     "await run_async_calls()"
    ]
   },
diff --git a/docs/docs/integrations/text_embedding/google_generative_ai.ipynb b/docs/docs/integrations/text_embedding/google_generative_ai.ipynb
index 0fd52ed6ad6..98aba2ddb6a 100644
--- a/docs/docs/integrations/text_embedding/google_generative_ai.ipynb
+++ b/docs/docs/integrations/text_embedding/google_generative_ai.ipynb
@@ -274,12 +274,14 @@
     ")\n",
     "\n",
     "q_embed = query_embeddings.embed_query(\"What is the capital of France?\")\n",
-    "d_embed = doc_embeddings.embed_documents([\"The capital of France is Paris.\", \"Philipp is likes to eat pizza.\"])\n",
+    "d_embed = doc_embeddings.embed_documents(\n",
+    "    [\"The capital of France is Paris.\", \"Philipp is likes to eat pizza.\"]\n",
+    ")\n",
     "\n",
     "for i, d in enumerate(d_embed):\n",
     "    print(f\"Document {i+1}:\")\n",
     "    print(f\"Cosine similarity with query: {cosine_similarity([q_embed], [d])[0][0]}\")\n",
-    "    print(\"---\")\n"
+    "    print(\"---\")"
    ]
   },
   {