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docs: sync Haystack API reference on Docusaurus #11585

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6 changes: 4 additions & 2 deletions docs-website/reference/haystack-api/audio_api.md
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Expand Up @@ -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__
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29 changes: 13 additions & 16 deletions docs-website/reference/haystack-api/converters_api.md
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Expand Up @@ -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
Expand Down Expand Up @@ -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})
# )]
```

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6 changes: 4 additions & 2 deletions docs-website/reference/haystack-api/extractors_api.md
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Expand Up @@ -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
Expand All @@ -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__
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37 changes: 20 additions & 17 deletions docs-website/reference/haystack-api/generators_api.md
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Expand Up @@ -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':
Expand Down Expand Up @@ -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
Expand All @@ -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)
Expand Down
15 changes: 8 additions & 7 deletions docs-website/reference/haystack-api/image_converters_api.md
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Expand Up @@ -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})
# )]
```

Expand Down