docs: sync Haystack API reference on Docusaurus (#11585)

Co-authored-by: davidsbatista <7937824+davidsbatista@users.noreply.github.com>

Author: HaystackBot

docs-website/reference/haystack-api/audio_api.md

@@ -18,13 +18,15 @@ For the supported audio formats, languages, and other parameters, see the
 
 ### Usage example
 
-<!-- test-ignore -->
-
 ```python
 from haystack.components.audio import LocalWhisperTranscriber
 
 whisper = LocalWhisperTranscriber(model="small")
 transcription = whisper.run(sources=["test/test_files/audio/answer.wav"])
+print(transcription)
+
+# >> {'documents': [Document(id=dae7051417caaf19304a4ef2ec845e981abe1efd4c8e6ee7ffb25867f165c411,
+# >> content: ' Answer.', meta: {'audio_file': PosixPath('test/test_files/audio/answer.wav'), 'language': 'en'})]}
 ```
 
 #### __init__

docs-website/reference/haystack-api/converters_api.md

@@ -385,17 +385,13 @@ Converts files to FileContent objects to be included in ChatMessage objects.
 from haystack.components.converters import FileToFileContent
 
 converter = FileToFileContent()
-
-sources = ["document.pdf", "video.mp4"]
-
+sources = ["test/test_files/pdf/react_paper.pdf", "test/test_files/images/haystack-logo.png"]
 file_contents = converter.run(sources=sources)["file_contents"]
-print(file_contents)
 
-# [FileContent(base64_data='...',
-#              mime_type='application/pdf',
-#              filename='document.pdf',
-#              extra={}),
-#  ...]
+print(file_contents)
+# >> [FileContent(base64_data='...', mime_type='application/pdf', filename='react_paper.pdf', extra={}),
+# >>  FileContent(base64_data='...', mime_type='image/png', filename='haystack-logo.png', extra={})
+# >>]
 ```
 
 #### run
@@ -537,32 +533,33 @@ Documents are expected to have metadata containing:
 
 ### Usage example
 
-<!-- test-ignore -->
-
 ```python
 from haystack import Document
 from haystack.components.converters.image.document_to_image import DocumentToImageContent
 
 converter = DocumentToImageContent(
     file_path_meta_field="file_path",
-    root_path="/data/files",
+    root_path="test/test_files",
     detail="high",
     size=(800, 600)
 )
 
 documents = [
-    Document(content="Optional description of image.jpg", meta={"file_path": "image.jpg"}),
-    Document(content="Text content of page 1 of doc.pdf", meta={"file_path": "doc.pdf", "page_number": 1})
+    Document(content="Optional description of apple.jpg", meta={"file_path": "images/apple.jpg"}),
+    Document(
+        content="Optional description of sample_pdf_1.pdf",
+        meta={"file_path": "pdf/sample_pdf_1.pdf", "page_number": 1}
+    )
 ]
 
 result = converter.run(documents)
 image_contents = result["image_contents"]
 # [ImageContent(
-#    base64_image='/9j/4A...', mime_type='image/jpeg', detail='high', meta={'file_path': 'image.jpg'}
+#    base64_image='/9j/4A...', mime_type='image/jpeg', detail='high', meta={'file_path': 'images/apple.jpg'}
 #  ),
 #  ImageContent(
 #    base64_image='/9j/4A...', mime_type='image/jpeg', detail='high',
-#    meta={'page_number': 1, 'file_path': 'doc.pdf'}
+#    meta={'file_path': 'pdf/sample_pdf_1.pdf', 'page_number': 1})
 #  )]
 ```
 

docs-website/reference/haystack-api/extractors_api.md

@@ -469,8 +469,6 @@ in the documents.
 
 Usage example:
 
-<!-- test-ignore -->
-
 ```python
 from haystack import Document
 from haystack.components.extractors.named_entity_extractor import NamedEntityExtractor
@@ -483,6 +481,10 @@ extractor = NamedEntityExtractor(backend="hugging_face", model="dslim/bert-base-
 results = extractor.run(documents=documents)["documents"]
 annotations = [NamedEntityExtractor.get_stored_annotations(doc) for doc in results]
 print(annotations)
+# >> [[NamedEntityAnnotation(entity='PER', start=4, end=10, score=np.float32(0.99054915))],
+# >> [NamedEntityAnnotation(entity='PER', start=11, end=16, score=np.float32(0.99641764)),
+# >>  NamedEntityAnnotation(entity='LOC', start=31, end=39, score=np.float32(0.996198)),
+# >>  NamedEntityAnnotation(entity='LOC', start=41, end=51, score=np.float32(0.9990196))]]
 ```
 
 #### __init__

docs-website/reference/haystack-api/generators_api.md

@@ -32,15 +32,15 @@ For details on OpenAI API parameters, see
 ```python
 from haystack.components.generators import AzureOpenAIGenerator
 from haystack.utils import Secret
+
 client = AzureOpenAIGenerator(
-    azure_endpoint="<Your Azure endpoint e.g. `https://your-company.azure.openai.com/>",
-    api_key=Secret.from_token("<your-api-key>"),
-    azure_deployment="<this is a model name, e.g. gpt-4.1-mini>")
+    azure_endpoint=Secret.from_env_var("AZURE_OPENAI_ENDPOINT").resolve_value(),
+    api_key=Secret.from_env_var("AZURE_OPENAI_API_KEY"),
+    azure_deployment="gpt-4.1-mini")
+
 response = client.run("What's Natural Language Processing? Be brief.")
-print(response)
-```
 
-```
+print(response)
 # >> {'replies': ['Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on
 # >> the interaction between computers and human language. It involves enabling computers to understand, interpret,
 # >> and respond to natural human language in a way that is both meaningful and useful.'], 'meta': [{'model':
@@ -708,28 +708,32 @@ format for input and output. Use it to generate text with Hugging Face APIs:
 
 #### With the serverless inference API (Inference Providers) - free tier available
 
-<!-- test-ignore -->
-
 ```python
 from haystack.components.generators.chat import HuggingFaceAPIChatGenerator
 from haystack.dataclasses import ChatMessage
 from haystack.utils import Secret
 from haystack.utils.hf import HFGenerationAPIType
 
 messages = [ChatMessage.from_system("\nYou are a helpful, respectful and honest assistant"),
-            ChatMessage.from_user("What's Natural Language Processing?")]
+            ChatMessage.from_user("What's Natural Language Processing? Please be succinct")]
 
 # the api_type can be expressed using the HFGenerationAPIType enum or as a string
 api_type = HFGenerationAPIType.SERVERLESS_INFERENCE_API
 api_type = "serverless_inference_api" # this is equivalent to the above
 
-generator = HuggingFaceAPIChatGenerator(api_type=api_type,
-                                        api_params={"model": "Qwen/Qwen2.5-7B-Instruct",
-                                                    "provider": "together"},
-                                        token=Secret.from_token("<your-api-key>"))
+generator = HuggingFaceAPIChatGenerator(
+    api_type=api_type,
+    api_params={"model": "Qwen/Qwen2.5-7B-Instruct", "provider": "together"},
+    token=Secret.from_env_var("HF_API_TOKEN")
+)
 
 result = generator.run(messages)
 print(result)
+# >> {'replies': [ChatMessage(_role=<ChatRole.ASSISTANT: 'assistant'>,
+# >> _content=[TextContent(text='Natural Language Processing (NLP) is a field of AI that focuses on the interaction
+# >> between humans and computers using natural language. It enables machines to understand, interpret, and
+# >> generate human language.')], _name=None, _meta={'model': 'Qwen/Qwen2.5-7B-Instruct', 'finish_reason':
+# >> 'tool_calls', 'index': 0, 'usage': {'prompt_tokens': 33, 'completion_tokens': 39}})]}
 ```
 
 #### With the serverless inference API (Inference Providers) and text+image input
@@ -743,18 +747,17 @@ from haystack.utils import Secret
 from haystack.utils.hf import HFGenerationAPIType
 
 # Create an image from file path, URL, or base64
-image = ImageContent.from_file_path("path/to/your/image.jpg")
+image = ImageContent.from_file_path("test/test_files/images/apple.jpg")
 
 # Create a multimodal message with both text and image
 messages = [ChatMessage.from_user(content_parts=["Describe this image in detail", image])]
 
 generator = HuggingFaceAPIChatGenerator(
     api_type=HFGenerationAPIType.SERVERLESS_INFERENCE_API,
     api_params={
-        "model": "Qwen/Qwen2.5-VL-7B-Instruct",  # Vision Language Model
-        "provider": "hyperbolic"
+        "model": "Qwen/Qwen3.5-9B", "provider": "together"  # Vision Language Model
     },
-    token=Secret.from_token("<your-api-key>")
+    token=Secret.from_env_var("HF_API_TOKEN")
 )
 
 result = generator.run(messages)

docs-website/reference/haystack-api/image_converters_api.md

@@ -24,32 +24,33 @@ Documents are expected to have metadata containing:
 
 ### Usage example
 
-<!-- test-ignore -->
-
 ```python
 from haystack import Document
 from haystack.components.converters.image.document_to_image import DocumentToImageContent
 
 converter = DocumentToImageContent(
     file_path_meta_field="file_path",
-    root_path="/data/files",
+    root_path="test/test_files",
     detail="high",
     size=(800, 600)
 )
 
 documents = [
-    Document(content="Optional description of image.jpg", meta={"file_path": "image.jpg"}),
-    Document(content="Text content of page 1 of doc.pdf", meta={"file_path": "doc.pdf", "page_number": 1})
+    Document(content="Optional description of apple.jpg", meta={"file_path": "images/apple.jpg"}),
+    Document(
+        content="Optional description of sample_pdf_1.pdf",
+        meta={"file_path": "pdf/sample_pdf_1.pdf", "page_number": 1}
+    )
 ]
 
 result = converter.run(documents)
 image_contents = result["image_contents"]
 # [ImageContent(
-#    base64_image='/9j/4A...', mime_type='image/jpeg', detail='high', meta={'file_path': 'image.jpg'}
+#    base64_image='/9j/4A...', mime_type='image/jpeg', detail='high', meta={'file_path': 'images/apple.jpg'}
 #  ),
 #  ImageContent(
 #    base64_image='/9j/4A...', mime_type='image/jpeg', detail='high',
-#    meta={'page_number': 1, 'file_path': 'doc.pdf'}
+#    meta={'file_path': 'pdf/sample_pdf_1.pdf', 'page_number': 1})
 #  )]
 ```