docs: sync Haystack API reference on Docusaurus

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

@@ -121,11 +121,11 @@ Sends an HTTP request as described by this path.
 - **data** (<code>Any | None</code>) – The request body to send.
 - **parameters** (<code>dict\[str, Any\] | None</code>) – The parameters used to create the path.
 - **raw_response** (<code>bool</code>) – If true, return the raw response instead of validating
-  and exterpolating it.
+  and extrapolating it.
 - **security** (<code>dict\[str, str\] | None</code>) – The security scheme to use, and the values it needs to
   process successfully.
 - **session** (<code>Any | None</code>) – A persistent request session.
-- **verify** (<code>bool | str</code>) – If we should do an ssl verification on the request or not.
+- **verify** (<code>bool | str</code>) – If we should do an SSL verification on the request or not.
   In case str was provided, will use that as the CA.
 
 **Returns:**

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

@@ -147,6 +147,7 @@ It can attach metadata to the resulting documents.
 
 ```python
 from haystack.components.converters.csv import CSVToDocument
+from datetime import datetime
 converter = CSVToDocument()
 results = converter.run(sources=["sample.csv"], meta={"date_added": datetime.now().isoformat()})
 documents = results["documents"]
@@ -1836,7 +1837,6 @@ Converts text files to documents.
 
 ### XLSXToDocument
 
-````
 Converts XLSX (Excel) files into Documents.
 
 Supports reading data from specific sheets or all sheets in the Excel file. If all sheets are read, a Document is
@@ -1846,18 +1846,14 @@ created for each sheet. The content of the Document is the table which can be sa
 
 ```python
 from haystack.components.converters.xlsx import XLSXToDocument
+from datetime import datetime
 
 converter = XLSXToDocument()
 results = converter.run(sources=["sample.xlsx"], meta={"date_added": datetime.now().isoformat()})
 documents = results["documents"]
 print(documents[0].content)
-# ",A,B
-````
-
-1,col_a,col_b
-2,1.5,test
-"
-\`\`\`
+# ",A,B\n1,col_a,col_b\n2,1.5,test\n"
+```
 
 #### __init__
 

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

@@ -483,6 +483,57 @@ Returns the number of unique values for each specified metadata field from docum
 - <code>dict\[str, int\]</code> – A dictionary mapping each metadata field name (without "meta." prefix)
   to the count of its unique values among the filtered documents.
 
+#### get_metadata_fields_info_async
+
+```python
+get_metadata_fields_info_async() -> dict[str, dict[str, str]]
+```
+
+Returns information about the metadata fields present in the stored documents.
+
+Types are inferred from the stored values (keyword, int, float, boolean).
+
+**Returns:**
+
+- <code>dict\[str, dict\[str, str\]\]</code> – A dictionary mapping each metadata field name to a dict with a "type" key.
+
+#### get_metadata_field_min_max_async
+
+```python
+get_metadata_field_min_max_async(metadata_field: str) -> dict[str, Any]
+```
+
+Returns the minimum and maximum values for the given metadata field across all documents.
+
+**Parameters:**
+
+- **metadata_field** (<code>str</code>) – The metadata field name. Can include or omit the "meta." prefix.
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – A dictionary with "min" and "max" keys. Returns `{"min": None, "max": None}`
+  if the field is missing or has no values.
+
+#### get_metadata_field_unique_values_async
+
+```python
+get_metadata_field_unique_values_async(
+    metadata_field: str, search_term: str | None = None
+) -> tuple[list[str], int]
+```
+
+Returns unique values for a metadata field, optionally filtered by a search term in content.
+
+**Parameters:**
+
+- **metadata_field** (<code>str</code>) – The metadata field name. Can include or omit the "meta." prefix.
+- **search_term** (<code>str | None</code>) – If set, only documents whose content contains this term (case-insensitive)
+  are considered.
+
+**Returns:**
+
+- <code>tuple\[list\[str\], int\]</code> – A tuple of (list of unique values, total count of unique values).
+
 #### delete_all_documents_async
 
 ```python

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

@@ -635,7 +635,7 @@ print(result["score"])
 # 0.5
 print(result["results"])
 # [{'statements': ['Python is a high-level general-purpose programming language.',
-'Python was created by George Lucas.'], 'statement_scores': [1, 0], 'score': 0.5}]
+# 'Python was created by George Lucas.'], 'statement_scores': [1, 0], 'score': 0.5}]
 ```
 
 #### __init__

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

@@ -746,7 +746,7 @@ print(result)
 
 #### With paid inference endpoints
 
-````python
+```python
 from haystack.components.generators.chat import HuggingFaceAPIChatGenerator
 from haystack.dataclasses import ChatMessage
 from haystack.utils import Secret
@@ -760,6 +760,7 @@ generator = HuggingFaceAPIChatGenerator(api_type="inference_endpoints",
 
 result = generator.run(messages)
 print(result)
+```
 
 #### With self-hosted text generation inference
 
@@ -775,7 +776,7 @@ generator = HuggingFaceAPIChatGenerator(api_type="text_generation_inference",
 
 result = generator.run(messages)
 print(result)
-````
+```
 
 #### __init__
 
@@ -2140,11 +2141,11 @@ client = OpenAIGenerator()
 response = client.run("What's Natural Language Processing? Be brief.")
 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':
->> 'gpt-5-mini', 'index': 0, 'finish_reason': 'stop', 'usage': {'prompt_tokens': 16,
->> 'completion_tokens': 49, 'total_tokens': 65}}]}
+# >> {'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':
+# >> 'gpt-5-mini', 'index': 0, 'finish_reason': 'stop', 'usage': {'prompt_tokens': 16,
+# >> 'completion_tokens': 49, 'total_tokens': 65}}]}
 ```
 
 #### __init__

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

@@ -199,8 +199,8 @@ result = pipe.run(
 print(json.loads(result["validator"]["validated"][0].text))
 
 
->> {'first_name': 'Peter', 'last_name': 'Parker', 'nationality': 'American', 'name': 'Spider-Man', 'occupation':
->> 'Superhero', 'age': 23, 'location': 'New York City'}
+# >> {'first_name': 'Peter', 'last_name': 'Parker', 'nationality': 'American', 'name': 'Spider-Man', 'occupation':
+# >> 'Superhero', 'age': 23, 'location': 'New York City'}
 ```
 
 Note that `BranchJoiner` can manage only one data type at a time. In this case, `BranchJoiner` is created for
@@ -545,7 +545,7 @@ pipeline.connect("prompt_builder_2.prompt", "string_joiner.strings")
 
 print(pipeline.run(data={"prompt_builder_1": {"query": string_1}, "prompt_builder_2": {"query": string_2}}))
 
->> {"string_joiner": {"strings": ["Builder 1: What's Natural Language Processing?", "Builder 2: What is life?"]}}
+# >> {"string_joiner": {"strings": ["Builder 1: What's Natural Language Processing?", "Builder 2: What is life?"]}}
 ```
 
 #### run

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

@@ -420,6 +420,7 @@ Answer:
 
 retriever = InMemoryBM25Retriever(document_store=document_store)
 prompt_builder = PromptBuilder(template=prompt_template)
+api_key = "your-openai-api-key"
 llm = OpenAIGenerator(api_key=Secret.from_token(api_key))
 
 rag_pipeline = Pipeline()

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

@@ -861,12 +861,12 @@ AI technology is widely used throughout industry, government, and science. Some
 doc = Document(content=text)
 doc_chunks = chunker.run([doc])
 print(doc_chunks["documents"])
->[
->Document(id=..., content: 'Artificial intelligence (AI) - Introduction\n\n', meta: {'original_id': '...', 'split_id': 0, 'split_idx_start': 0, '_split_overlap': []})
->Document(id=..., content: 'AI, in its broadest sense, is intelligence exhibited by machines, particularly computer systems.\n', meta: {'original_id': '...', 'split_id': 1, 'split_idx_start': 45, '_split_overlap': []})
->Document(id=..., content: 'AI technology is widely used throughout industry, government, and science.', meta: {'original_id': '...', 'split_id': 2, 'split_idx_start': 142, '_split_overlap': []})
->Document(id=..., content: ' Some high-profile applications include advanced web search engines; recommendation systems; interac...', meta: {'original_id': '...', 'split_id': 3, 'split_idx_start': 216, '_split_overlap': []})
->]
+# [
+# Document(id=..., content: 'Artificial intelligence (AI) - Introduction\n\n', meta: {'original_id': '...', 'split_id': 0, 'split_idx_start': 0, '_split_overlap': []})
+# Document(id=..., content: 'AI, in its broadest sense, is intelligence exhibited by machines, particularly computer systems.\n', meta: {'original_id': '...', 'split_id': 1, 'split_idx_start': 45, '_split_overlap': []})
+# Document(id=..., content: 'AI technology is widely used throughout industry, government, and science.', meta: {'original_id': '...', 'split_id': 2, 'split_idx_start': 142, '_split_overlap': []})
+# Document(id=..., content: ' Some high-profile applications include advanced web search engines; recommendation systems; interac...', meta: {'original_id': '...', 'split_id': 3, 'split_idx_start': 216, '_split_overlap': []})
+# ]
 ```
 
 #### __init__

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

@@ -516,12 +516,12 @@ Categorize files or byte streams according to their MIME types.
 
 ### LLMMessagesRouter
 
-````
 Routes Chat Messages to different connections using a generative Language Model to perform classification.
 
 This component can be used with general-purpose LLMs and with specialized LLMs for moderation like Llama Guard.
 
 ### Usage example
+
 ```python
 from haystack.components.generators.chat import HuggingFaceAPIChatGenerator
 from haystack.components.routers.llm_messages_router import LLMMessagesRouter
@@ -541,20 +541,17 @@ router = LLMMessagesRouter(chat_generator=chat_generator,
 print(router.run([ChatMessage.from_user("How to rob a bank?")]))
 
 # {
-#     'chat_generator_text': 'unsafe
-````
-
-S2',
-\# 'unsafe': \[
-\# ChatMessage(
-\# \_role=\<ChatRole.USER: 'user'>,
-\# \_content=[TextContent(text='How to rob a bank?')],
-\# \_name=None,
-\# \_meta={}
-\# )
-\# \]
-\# }
-\`\`\`
+#     'chat_generator_text': 'unsafe\nS2',
+#     'unsafe': [
+#         ChatMessage(
+#             _role=<ChatRole.USER: 'user'>,
+#             _content=[TextContent(text='How to rob a bank?')],
+#             _name=None,
+#             _meta={}
+#         )
+#     ]
+# }
+```
 
 #### __init__