docs: Fix double hashtag in code comments (#11301)

Author: sjrl

docs-website/docs/concepts/components/custom-components.mdx

@@ -159,7 +159,7 @@ class DerivedComponent(BaseComponent):
         super(DerivedComponent, self).__init__()
 
 
-## ...
+# ...
 
 dc = DerivedComponent()  # ok
 ```

docs-website/docs/concepts/components/supercomponents.mdx

@@ -20,7 +20,7 @@ With this decorator, the `to_dict` and `from_dict` serialization is optional, as
 The custom HybridRetriever example SuperComponent below turns your query into embeddings, then runs both a BM25 search and an embedding-based search at the same time. It finally merges those two result sets and returns the combined documents.
 
 ```python
-## pip install haystack-ai datasets "sentence-transformers>=3.0.0"
+# pip install haystack-ai datasets "sentence-transformers>=3.0.0"
 
 from haystack import Document, Pipeline, super_component
 from haystack.components.joiners import DocumentJoiner
@@ -145,7 +145,7 @@ pipeline.add_component("llm", OpenAIChatGenerator())
 pipeline.connect("retriever.documents", "prompt_builder.documents")
 pipeline.connect("prompt_builder.prompt", "llm.messages")
 
-## Create a super component with simplified input/output mapping
+# Create a super component with simplified input/output mapping
 wrapper = SuperComponent(
     pipeline=pipeline,
     input_mapping={
@@ -157,7 +157,7 @@ wrapper = SuperComponent(
     }
 )
 
-## Run the pipeline with simplified interface
+# Run the pipeline with simplified interface
 result = wrapper.run(query="What is the capital of France?")
 print(result)
 {'replies': [ChatMessage(_role=<ChatRole.ASSISTANT: 'assistant'>,

docs-website/docs/concepts/data-classes.mdx

@@ -193,14 +193,14 @@ class StreamingChunk:
 ```python
 from haystack.dataclasses import StreamingChunk, ToolCallDelta, ReasoningContent
 
-## Basic text chunk
+# Basic text chunk
 chunk = StreamingChunk(
     content="Hello world",
     start=True,
     meta={"model": "gpt-5-mini"},
 )
 
-## Tool call chunk
+# Tool call chunk
 tool_chunk = StreamingChunk(
     content="",
     tool_calls=[
@@ -215,7 +215,7 @@ tool_chunk = StreamingChunk(
     finish_reason="tool_calls",
 )
 
-## Reasoning chunk
+# Reasoning chunk
 reasoning_chunk = StreamingChunk(
     content="",
     reasoning=ReasoningContent(

docs-website/docs/concepts/data-classes/chatmessage.mdx

@@ -380,10 +380,10 @@ You can no longer directly initialize `ChatMessage` using `role`, `content`, and
 ```python
 from haystack.dataclasses import ChatMessage
 
-## LEGACY - DOES NOT WORK IN 2.9.0
+# LEGACY - DOES NOT WORK IN 2.9.0
 message = ChatMessage(role=ChatRole.USER, content="Hello!")
 
-## Use the class method instead
+# Use the class method instead
 message = ChatMessage.from_user("Hello!")
 ```
 
@@ -405,9 +405,9 @@ from haystack.dataclasses import ChatMessage
 
 message = ChatMessage.from_user("Hello!")
 
-## LEGACY - DOES NOT WORK IN 2.9.0
+# LEGACY - DOES NOT WORK IN 2.9.0
 print(message.content)
 
-## Use the appropriate property instead
+# Use the appropriate property instead
 print(message.text)
 ```

docs-website/docs/concepts/device-management.mdx

@@ -37,7 +37,7 @@ To use a single device for inference, use either the `ComponentDevice.from_singl
 from haystack.utils import ComponentDevice, Device
 
 device = ComponentDevice.from_single(Device.gpu(id=1))
-## Alternatively, use a PyTorch device string
+# Alternatively, use a PyTorch device string
 device = ComponentDevice.from_str("cuda:1")
 generator = HuggingFaceLocalGenerator(model="llama2", device=device)
 ```
@@ -98,16 +98,16 @@ class MyComponent(Component):
         init_params["device"] = ComponentDevice.from_dict(init_params["device"])
         return default_from_dict(cls, data)
 
-## Automatically selects a device.
+# Automatically selects a device.
 c = MyComponent(device=None)
 
-## Uses the first GPU available.
+# Uses the first GPU available.
 c = MyComponent(device=ComponentDevice.from_str("cuda:0"))
 
-## Uses the CPU.
+# Uses the CPU.
 c = MyComponent(device=ComponentDevice.from_single(Device.cpu()))
 
-## Allow the component to use multiple devices using a device map.
+# Allow the component to use multiple devices using a device map.
 c = MyComponent(device=ComponentDevice.from_multiple(DeviceMap({
     "layer1": Device.cpu(),
     "layer2": Device.gpu(1),

docs-website/docs/concepts/experimental-package.mdx

@@ -48,11 +48,11 @@ c.run([ChatMessage.from_user("What's an experiment? Be brief.")])
 Experiments can also override existing Haystack features. For example, you can opt into an experimental type of `Pipeline` by changing the usual import:
 
 ```python
-## from haystack import Pipeline
+# from haystack import Pipeline
 from haystack_experimental import Pipeline
 
 pipe = Pipeline()
-## ...
+# ...
 pipe.run(...)
 ```
 

docs-website/docs/concepts/jinja-templates.mdx

@@ -36,7 +36,7 @@ template = """
 Language: {{ language }}
 Question: {{ question }}
 """
-## pass both variables when rendering
+# pass both variables when rendering
 ```
 
 It you need to use an f‑string (escape braces):

docs-website/docs/concepts/pipelines/creating-pipelines.mdx

@@ -56,11 +56,11 @@ Add components to the pipeline one by one. The order in which you do this doesn'
 ```python
 query_pipeline.add_component("component_name", component_type)
 
-## Here is an example of how you'd add the components initialized in step 2 above:
+# Here is an example of how you'd add the components initialized in step 2 above:
 query_pipeline.add_component("text_embedder", text_embedder)
 query_pipeline.add_component("retriever", retriever)
 
-## You could also add components without initializing them before:
+# You could also add components without initializing them before:
 query_pipeline.add_component("text_embedder", SentenceTransformersTextEmbedder())
 query_pipeline.add_component(
     "retriever",
@@ -77,30 +77,30 @@ To understand what inputs are expected to run your pipeline, use an `.inputs()`
 Here's a more visual explanation within the code:
 
 ```python
-## This is the syntax to connect components. Here you're connecting output1 of component1 to input1 of component2:
+# This is the syntax to connect components. Here you're connecting output1 of component1 to input1 of component2:
 pipeline.connect("component1.output1", "component2.input1")
 
-## If both components have only one output and input, you can just pass their names:
+# If both components have only one output and input, you can just pass their names:
 pipeline.connect("component1", "component2")
 
-## If one of the components has only one output but the other has multiple inputs,
-## you can pass just the name of the component with a single output, but for the component with
-## multiple inputs, you must specify which input you want to connect
+# If one of the components has only one output but the other has multiple inputs,
+# you can pass just the name of the component with a single output, but for the component with
+# multiple inputs, you must specify which input you want to connect
 
-## Here, component1 has only one output, but component2 has multiple inputs:
+# Here, component1 has only one output, but component2 has multiple inputs:
 pipeline.connect("component1", "component2.input1")
 
-## And here's how it should look like for the semantic document search pipeline we're using as an example:
+# And here's how it should look like for the semantic document search pipeline we're using as an example:
 pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
-## Because the InMemoryEmbeddingRetriever only has one input, this is also correct:
+# Because the InMemoryEmbeddingRetriever only has one input, this is also correct:
 pipeline.connect("text_embedder.embedding", "retriever")
 ```
 
 You need to link all the components together, connecting them gradually in pairs. Here's an explicit example for the pipeline we're assembling:
 
 ```python
-## Imagine this pipeline has four components: text_embedder, retriever, prompt_builder and llm.
-## Here's how you would connect them into a pipeline:
+# Imagine this pipeline has four components: text_embedder, retriever, prompt_builder and llm.
+# Here's how you would connect them into a pipeline:
 
 query_pipeline.connect("text_embedder.embedding", "retriever")
 query_pipeline.connect("retriever", "prompt_builder.documents")
@@ -112,13 +112,13 @@ query_pipeline.connect("prompt_builder", "llm")
 Wait for the pipeline to validate the components and connections. If everything is OK, you can now run the pipeline. `Pipeline.run()` can be called in two ways, either passing a dictionary of the component names and their inputs, or by directly passing just the inputs. When passed directly, the pipeline resolves inputs to the correct components.
 
 ```python
-## Here's one way of calling the run() method
+# Here's one way of calling the run() method
 results = pipeline.run({"component1": {"input1_value": value1, "input2_value": value2}})
 
-## The inputs can also be passed directly without specifying component names
+# The inputs can also be passed directly without specifying component names
 results = pipeline.run({"input1_value": value1, "input2_value": value2})
 
-## This is how you'd run the semantic document search pipeline we're using as an example:
+# This is how you'd run the semantic document search pipeline we're using as an example:
 query = "Here comes the query text"
 results = query_pipeline.run({"text_embedder": {"text": query}})
 ```
@@ -130,7 +130,7 @@ If you need to understand what component inputs are expected to run your pipelin
 This is how it works:
 
 ```python
-## A short pipeline example that converts webpages into documents
+# A short pipeline example that converts webpages into documents
 from haystack import Pipeline
 from haystack.document_stores.in_memory import InMemoryDocumentStore
 from haystack.components.fetchers import LinkContentFetcher
@@ -150,19 +150,19 @@ pipeline.add_component(instance=writer, name="writer")
 pipeline.connect("fetcher.streams", "converter.sources")
 pipeline.connect("converter.documents", "writer.documents")
 
-## Requesting a list of required inputs
+# Requesting a list of required inputs
 pipeline.inputs()
 
-## {'fetcher': {'urls': {'type': typing.List[str], 'is_mandatory': True}},
-## 'converter': {'meta': {'type': typing.Union[typing.Dict[str, typing.Any], typing.List[typing.Dict[str, typing.Any]], NoneType],
-## 'is_mandatory': False,
-## 'default_value': None},
-## 'extraction_kwargs': {'type': typing.Optional[typing.Dict[str, typing.Any]],
-## 'is_mandatory': False,
-## 'default_value': None}},
-## 'writer': {'policy': {'type': typing.Optional[haystack.document_stores.types.policy.DuplicatePolicy],
-## 'is_mandatory': False,
-## 'default_value': None}}}
+# {'fetcher': {'urls': {'type': typing.List[str], 'is_mandatory': True}},
+# 'converter': {'meta': {'type': typing.Union[typing.Dict[str, typing.Any], typing.List[typing.Dict[str, typing.Any]], NoneType],
+# 'is_mandatory': False,
+# 'default_value': None},
+# 'extraction_kwargs': {'type': typing.Optional[typing.Dict[str, typing.Any]],
+# 'is_mandatory': False,
+# 'default_value': None}},
+# 'writer': {'policy': {'type': typing.Optional[haystack.document_stores.types.policy.DuplicatePolicy],
+# 'is_mandatory': False,
+# 'default_value': None}}}
 ```
 
 From the above response, you can see that the `urls` input is mandatory for `LinkContentFetcher`. This is how you would then run this pipeline:

docs-website/docs/concepts/pipelines/debugging-pipelines.mdx

@@ -31,13 +31,13 @@ from haystack.components.generators.chat import OpenAIChatGenerator
 from haystack.components.builders.chat_prompt_builder import ChatPromptBuilder
 from haystack.dataclasses import ChatMessage
 
-## Documents
+# Documents
 documents = [
     Document(content="Joe lives in Berlin"),
     Document(content="Joe is a software engineer"),
 ]
 
-## Define prompt template
+# Define prompt template
 prompt_template = [
     ChatMessage.from_system("You are a helpful assistant."),
     ChatMessage.from_user(
@@ -47,7 +47,7 @@ prompt_template = [
     ),
 ]
 
-## Define pipeline
+# Define pipeline
 p = Pipeline()
 p.add_component(
     instance=ChatPromptBuilder(
@@ -62,16 +62,16 @@ p.add_component(
 )
 p.connect("prompt_builder", "llm.messages")
 
-## Define question
+# Define question
 question = "Where does Joe live?"
 
-## Execute pipeline
+# Execute pipeline
 result = p.run(
     {"prompt_builder": {"documents": documents, "query": question}},
     include_outputs_from="prompt_builder",
 )
 
-## Print result
+# Print result
 print(result)
 ```
 

docs-website/docs/concepts/pipelines/pipeline-breakpoints.mdx

@@ -25,14 +25,14 @@ Create a `Breakpoint` by specifying the component name and the visit count at wh
 from haystack.dataclasses.breakpoints import Breakpoint
 from haystack.core.errors import BreakpointException
 
-## Create a breakpoint that triggers on the first visit to the "llm" component
+# Create a breakpoint that triggers on the first visit to the "llm" component
 break_point = Breakpoint(
     component_name="llm",
     visit_count=0,  # 0 = first visit, 1 = second visit, etc.
     snapshot_file_path="/path/to/snapshots",  # Optional: save snapshot to file
 )
 
-## Run pipeline with breakpoint
+# Run pipeline with breakpoint
 try:
     result = pipeline.run(data=input_data, break_point=break_point)
 except BreakpointException as e:
@@ -103,10 +103,10 @@ Use the `load_pipeline_snapshot()` to first load the JSON and then pass it to th
 ```python
 from haystack.core.pipeline.breakpoint import load_pipeline_snapshot
 
-## Load the snapshot
+# Load the snapshot
 snapshot = load_pipeline_snapshot("llm_2025_05_03_11_23_23.json")
 
-## Resume execution from the snapshot
+# Resume execution from the snapshot
 result = pipeline.run(data={}, pipeline_snapshot=snapshot)
 print(result["llm"]["replies"])
 ```
@@ -123,7 +123,7 @@ A `ChatGenerator` breakpoint is defined as shown below. You need to define a `Br
 ```python
 from haystack.dataclasses.breakpoints import AgentBreakpoint, Breakpoint, ToolBreakpoint
 
-## Break at chat generator (LLM calls)
+# Break at chat generator (LLM calls)
 chat_bp = Breakpoint(component_name="chat_generator", visit_count=0)
 agent_breakpoint = AgentBreakpoint(break_point=chat_bp, agent_name="my_agent")
 ```
@@ -137,7 +137,7 @@ Then, define an `AgentBreakpoint` passing the `ToolBreakpoint` defined before as
 ```python
 from haystack.dataclasses.breakpoints import AgentBreakpoint, Breakpoint, ToolBreakpoint
 
-## Break at tool invoker (tool calls)
+# Break at tool invoker (tool calls)
 tool_bp = ToolBreakpoint(
     component_name="tool_invoker",
     visit_count=0,
@@ -153,11 +153,11 @@ When an Agent breakpoint is triggered, you can resume execution using the sa
 ```python
 from haystack.core.pipeline.breakpoint import load_pipeline_snapshot
 
-## Load the snapshot
+# Load the snapshot
 snapshot_file = "./agent_debug/agent_chat_generator_2025_07_11_23_23.json"
 snapshot = load_pipeline_snapshot(snapshot_file)
 
-## Resume pipeline execution
+# Resume pipeline execution
 result = pipeline.run(data={}, pipeline_snapshot=snapshot)
 print("Pipeline resumed successfully")
 print(f"Final result: {result}")