docs: add multimodal (image input) examples to chat generator docs

docs-website/docs/pipeline-components/generators/amazonbedrockchatgenerator.mdx

@@ -96,6 +96,21 @@ response = generator.run(messages)
 print(response)
 ```
 
+With multimodal inputs:
+
+```python
+from haystack.dataclasses import ChatMessage, ImageContent
+from haystack_integrations.components.generators.amazon_bedrock import AmazonBedrockChatGenerator
+
+generator = AmazonBedrockChatGenerator(model="anthropic.claude-3-5-sonnet-20240620-v1:0")
+
+image = ImageContent.from_file_path("apple.jpg")
+message = ChatMessage.from_user(content_parts=["Describe the image using 10 words at most.", image])
+
+response = generator.run(messages=[message])
+print(response)
+```
+
 ### In a pipeline
 
 In a RAG pipeline:

docs-website/docs/pipeline-components/generators/anthropicchatgenerator.mdx

@@ -148,6 +148,20 @@ message = ChatMessage.from_user("What's Natural Language Processing? Be brief.")
 print(generator.run([message]))
 ```
 
+With multimodal inputs:
+
+```python
+from haystack.dataclasses import ChatMessage, ImageContent
+from haystack_integrations.components.generators.anthropic import AnthropicChatGenerator
+
+image = ImageContent.from_file_path("path/to/image.jpg")
+messages = [ChatMessage.from_user(content_parts=["What's in this image?", image])]
+
+generator = AnthropicChatGenerator()
+result = generator.run(messages)
+print(result)
+```
+
 ### In a pipeline
 
 You can also use `AnthropicChatGenerator`with the Anthropic chat models in your pipeline.

docs-website/docs/pipeline-components/generators/azureopenaichatgenerator.mdx

@@ -149,6 +149,29 @@ response = client.run(
 print(response)
 ```
 
+With multimodal inputs:
+
+```python
+from haystack.dataclasses import ChatMessage, ImageContent
+from haystack.components.generators.chat import AzureOpenAIChatGenerator
+
+llm = AzureOpenAIChatGenerator(
+    azure_endpoint="<Your Azure endpoint>",
+    azure_deployment="gpt-4o-mini",
+)
+
+image = ImageContent.from_file_path("apple.jpg", detail="low")
+user_message = ChatMessage.from_user(content_parts=[
+	"What does the image show? Max 5 words.",
+	image
+	])
+
+response = llm.run([user_message])["replies"][0].text
+print(response)
+
+# Fresh red apple on straw.
+```
+
 ### In a pipeline
 
 ```python

docs-website/docs/pipeline-components/generators/coherechatgenerator.mdx

@@ -87,6 +87,24 @@ message = ChatMessage.from_user("What's Natural Language Processing? Be brief.")
 print(generator.run([message]))
 ```
 
+With multimodal inputs:
+
+```python
+from haystack.dataclasses import ChatMessage, ImageContent
+from haystack_integrations.components.generators.cohere import CohereChatGenerator
+
+# Create an image from file path or base64
+image = ImageContent.from_file_path("path/to/your/image.jpg")
+
+# Create a multimodal message with both text and image
+messages = [ChatMessage.from_user(content_parts=["What's in this image?", image])]
+
+# Use a multimodal model like Command A Vision
+generator = CohereChatGenerator(model="command-a-vision-07-2025")
+response = generator.run(messages)
+print(response)
+```
+
 #### In a Pipeline
 
 You can also use `CohereChatGenerator` to use cohere chat models in your pipeline.

docs-website/docs/pipeline-components/generators/googlegenaichatgenerator.mdx

@@ -128,6 +128,21 @@ response = chat_generator.run(messages=messages)
 print(response["replies"][0].text)
 ```
 
+With multimodal inputs:
+
+```python
+from haystack.dataclasses import ChatMessage, ImageContent
+from haystack_integrations.components.generators.google_genai import GoogleGenAIChatGenerator
+
+chat_generator = GoogleGenAIChatGenerator()
+
+image = ImageContent.from_file_path("apple.jpg")
+messages = [ChatMessage.from_user(content_parts=["What does the image show? Max 5 words.", image])]
+
+response = chat_generator.run(messages=messages)
+print(response["replies"][0].text)
+```
+
 You can also easily use function calls. First, define the function locally and convert into a [Tool](https://www.notion.so/docs/tool):
 
 ```python

docs-website/docs/pipeline-components/generators/llamacppchatgenerator.mdx

@@ -155,6 +155,31 @@ messages = [ChatMessage.from_user("Who is the best American actor?")]
 result = generator.run(messages)
 ```
 
+### With multimodal (image + text) inputs
+
+```python
+from haystack.dataclasses import ChatMessage, ImageContent
+from haystack_integrations.components.generators.llama_cpp import LlamaCppChatGenerator
+
+# Create an image from file path or base64
+image = ImageContent.from_file_path("path/to/your/image.jpg")
+
+# Create a multimodal message with both text and image
+messages = [ChatMessage.from_user(content_parts=["What's in this image?", image])]
+
+# Initialize with multimodal support
+generator = LlamaCppChatGenerator(
+    model="llava-v1.5-7b-q4_0.gguf",
+    chat_handler_name="Llava15ChatHandler",  # Use llava-1-5 handler
+    model_clip_path="mmproj-model-f16.gguf",  # CLIP model
+    n_ctx=4096  # Larger context for image processing
+)
+generator.warm_up()
+
+result = generator.run(messages)
+print(result)
+```
+
 The `generation_kwargs` can also be passed to the `run` method of the generator directly:
 
 ```python

docs-website/docs/pipeline-components/generators/metallamachatgenerator.mdx

@@ -117,6 +117,21 @@ response = llm.run(
 print("\n\n Model used: ", response["replies"][0].meta["model"])
 ```
 
+With multimodal inputs:
+
+```python
+from haystack.dataclasses import ChatMessage, ImageContent
+from haystack_integrations.components.generators.meta_llama import MetaLlamaChatGenerator
+
+llm = MetaLlamaChatGenerator(model="Llama-4-Scout-17B-16E-Instruct-FP8")
+
+image = ImageContent.from_file_path("apple.jpg")
+messages = [ChatMessage.from_user(content_parts=["What does the image show? Max 5 words.", image])]
+
+response = llm.run(messages)
+print(response["replies"][0].text)
+```
+
 ### In a pipeline
 
 ```python

docs-website/docs/pipeline-components/generators/mistralchatgenerator.mdx

@@ -97,6 +97,21 @@ message = ChatMessage.from_user("What's Natural Language Processing? Be brief.")
 print(generator.run([message]))
 ```
 
+With multimodal inputs:
+
+```python
+from haystack.dataclasses import ChatMessage, ImageContent
+from haystack_integrations.components.generators.mistral import MistralChatGenerator
+
+generator = MistralChatGenerator(model="pixtral-12b-2409")
+
+image = ImageContent.from_file_path("apple.jpg")
+messages = [ChatMessage.from_user(content_parts=["What does the image show? Max 5 words.", image])]
+
+response = generator.run(messages)
+print(response)
+```
+
 #### In a Pipeline
 
 Below is an example RAG Pipeline where we answer questions based on the URL contents. We add the contents of the URL into our `messages` in the `ChatPromptBuilder` and generate an answer with the `MistralChatGenerator`.

docs-website/docs/pipeline-components/generators/nvidiachatgenerator.mdx

@@ -92,6 +92,21 @@ print(result["replies"])
 print(result["meta"])
 ```
 
+With multimodal inputs:
+
+```python
+from haystack.dataclasses import ChatMessage, ImageContent
+from haystack_integrations.components.generators.nvidia import NvidiaChatGenerator
+
+generator = NvidiaChatGenerator(model="meta/llama-3.2-11b-vision-instruct")
+
+image = ImageContent.from_file_path("apple.jpg")
+messages = [ChatMessage.from_user(content_parts=["What does the image show? Max 5 words.", image])]
+
+result = generator.run(messages)
+print(result["replies"])
+```
+
 ### In a Pipeline
 
 ```python

docs-website/docs/pipeline-components/generators/ollamachatgenerator.mdx

@@ -167,6 +167,21 @@ print(generator.run(messages=messages))
 }
 ```
 
+With multimodal inputs:
+
+```python
+from haystack.dataclasses import ChatMessage, ImageContent
+from haystack_integrations.components.generators.ollama import OllamaChatGenerator
+
+generator = OllamaChatGenerator(model="llava", url="http://localhost:11434")
+
+image = ImageContent.from_file_path("apple.jpg")
+messages = [ChatMessage.from_user(content_parts=["What does the image show? Max 5 words.", image])]
+
+response = generator.run(messages=messages)
+print(response)
+```
+
 ### In a Pipeline
 
 ```python

docs-website/docs/pipeline-components/generators/openrouterchatgenerator.mdx

@@ -108,6 +108,21 @@ response = client.run(
 print("\n\n Model used: ", response["replies"][0].meta["model"])
 ```
 
+With multimodal inputs:
+
+```python
+from haystack.dataclasses import ChatMessage, ImageContent
+from haystack_integrations.components.generators.openrouter import OpenRouterChatGenerator
+
+client = OpenRouterChatGenerator(model="anthropic/claude-3-5-sonnet")
+
+image = ImageContent.from_file_path("apple.jpg")
+messages = [ChatMessage.from_user(content_parts=["What does the image show? Max 5 words.", image])]
+
+response = client.run(messages)
+print(response["replies"][0].text)
+```
+
 ### In a pipeline
 
 ```python

docs-website/docs/pipeline-components/generators/stackitchatgenerator.mdx

@@ -63,6 +63,21 @@ result = generator.run([ChatMessage.from_user("Tell me a joke.")])
 print(result)
 ```
 
+With multimodal inputs:
+
+```python
+from haystack.dataclasses import ChatMessage, ImageContent
+from haystack_integrations.components.generators.stackit import STACKITChatGenerator
+
+generator = STACKITChatGenerator(model="meta-llama/Llama-3.2-11B-Vision-Instruct")
+
+image = ImageContent.from_file_path("apple.jpg")
+messages = [ChatMessage.from_user(content_parts=["What does the image show? Max 5 words.", image])]
+
+result = generator.run(messages)
+print(result)
+```
+
 ### In a pipeline
 
 You can also use `STACKITChatGenerator` in your pipeline.

docs-website/docs/pipeline-components/generators/watsonxchatgenerator.mdx

@@ -64,6 +64,24 @@ message = ChatMessage.from_user("What's Natural Language Processing? Be brief.")
 print(generator.run([message]))
 ```
 
+With multimodal inputs:
+
+```python
+from haystack.dataclasses import ChatMessage, ImageContent
+from haystack_integrations.components.generators.watsonx.chat.chat_generator import WatsonxChatGenerator
+
+# Create an image from file path or base64
+image = ImageContent.from_file_path("path/to/your/image.jpg")
+
+# Create a multimodal message with both text and image
+messages = [ChatMessage.from_user(content_parts=["What's in this image?", image])]
+
+# Use a multimodal model
+generator = WatsonxChatGenerator(model="meta-llama/llama-3-2-11b-vision-instruct")
+response = generator.run(messages)
+print(response)
+```
+
 #### In a Pipeline
 
 You can also use `WatsonxChatGenerator` to use IBM watsonx.ai chat models in your pipeline.