docs: stop documenting components removed from v3 + other fixes

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

@@ -9,7 +9,7 @@ description: "This page discusses the concept of device management in the contex
 
 This page discusses the concept of device management in the context of Haystack.
 
-Many Haystack components, such as `HuggingFaceLocalGenerator` , `AzureOpenAIGenerator`, and others, allow users the ability to pick and choose which language model is to be queried and executed. For components that interface with cloud-based services, the service provider automatically takes care of the details of provisioning the requisite hardware (like GPUs). However, if you wish to use models on your local machine, you’ll need to figure out how to deploy them on your hardware. Further complicating things, different ML libraries have different APIs to launch models on specific devices.
+Many Haystack components, such as `TransformersChatGenerator`, `AzureOpenAIChatGenerator`, and others, allow users the ability to pick and choose which language model is to be queried and executed. For components that interface with cloud-based services, the service provider automatically takes care of the details of provisioning the requisite hardware (like GPUs). However, if you wish to use models on your local machine, you’ll need to figure out how to deploy them on your hardware. Further complicating things, different ML libraries have different APIs to launch models on specific devices.
 
 To make the process of running inference on local models as straightforward as possible, Haystack uses a framework-agnostic device management implementation. Exposing devices through this interface means you no longer need to worry about library-specific invocations and device representations.
 
@@ -31,21 +31,35 @@ Find the full code for the abstractions above in the Haystack GitHub [repo](http
 
 ## Usage
 
+:::info
+The examples below use the [`TransformersChatGenerator`](../pipeline-components/generators/transformerschatgenerator.mdx), which is part of the `transformers-haystack` integration. Install it with:
+
+```bash
+pip install transformers-haystack
+```
+:::
+
 To use a single device for inference, use either the `ComponentDevice.from_single` or `ComponentDevice.from_str` class method:
 
 ```python
 from haystack.utils import ComponentDevice, Device
+from haystack_integrations.components.generators.transformers import (
+    TransformersChatGenerator,
+)
 
 device = ComponentDevice.from_single(Device.gpu(id=1))
 # Alternatively, use a PyTorch device string
 device = ComponentDevice.from_str("cuda:1")
-generator = HuggingFaceLocalGenerator(model="llama2", device=device)
+generator = TransformersChatGenerator(model="Qwen/Qwen3-0.6B", device=device)
 ```
 
 To use multiple devices, use the `ComponentDevice.from_multiple` class method:
 
 ```python
 from haystack.utils import ComponentDevice, Device, DeviceMap
+from haystack_integrations.components.generators.transformers import (
+    TransformersChatGenerator,
+)
 
 device_map = DeviceMap(
     {
@@ -56,7 +70,7 @@ device_map = DeviceMap(
     },
 )
 device = ComponentDevice.from_multiple(device_map)
-generator = HuggingFaceLocalGenerator(model="llama2", device=device)
+generator = TransformersChatGenerator(model="Qwen/Qwen3-0.6B", device=device)
 ```
 
 ### Integrating Devices in Custom Components
@@ -115,13 +129,17 @@ c = MyComponent(device=ComponentDevice.from_multiple(DeviceMap({
 })))
 ```
 
-If the component’s backend provides a more specialized API to manage devices, it could add an additional init parameter that acts as a conduit. For instance, `HuggingFaceLocalGenerator` exposes a `huggingface_pipeline_kwargs` parameter through which Hugging Face-specific `device_map`  arguments can be passed:
+If the component’s backend provides a more specialized API to manage devices, it could add an additional init parameter that acts as a conduit. For instance, `TransformersChatGenerator` exposes a `huggingface_pipeline_kwargs` parameter through which Hugging Face-specific `device_map`  arguments can be passed:
 
 ```python
-generator = HuggingFaceLocalGenerator(
-    model="llama2",
+from haystack_integrations.components.generators.transformers import (
+    TransformersChatGenerator,
+)
+
+generator = TransformersChatGenerator(
+    model="Qwen/Qwen3-0.6B",
     huggingface_pipeline_kwargs={"device_map": "balanced"},
 )
 ```
 
-In such cases, ensure that the parameter precedence and selection behavior is clearly documented. In the case of `HuggingFaceLocalGenerator`, the device map passed through the `huggingface_pipeline_kwargs` parameter overrides the explicit `device` parameter and is documented as such.
+In such cases, ensure that the parameter precedence and selection behavior is clearly documented. In the case of `TransformersChatGenerator`, the device map passed through the `huggingface_pipeline_kwargs` parameter overrides the explicit `device` parameter and is documented as such.

docs-website/docs/development/enabling-gpu-acceleration.mdx

@@ -15,7 +15,7 @@ The Transformer models used in Haystack are designed to be run on GPU-accelerate
 
 Once you have GPU enabled on your machine, you can set the `device` on which a given model for a component is loaded.
 
-For example, to load a model for the `HuggingFaceLocalGenerator`, set `device="ComponentDevice.from_single(Device.gpu(id=0))` or `device = ComponentDevice.from_str("cuda:0")` when initializing.
+For example, to load a model for the `TransformersChatGenerator`, set `device=ComponentDevice.from_single(Device.gpu(id=0))` or `device = ComponentDevice.from_str("cuda:0")` when initializing.
 
 You can find more information on the [Device management](../concepts/device-management.mdx) page.
 

docs-website/docs/overview/migration.mdx

@@ -159,7 +159,7 @@ If you need help migrating a 1.x node without a 2.x counterpart, open an [issue]
 | Crawler                    | Scrapes text from websites. **Example usage:** To run searches on your website content.                                                                                                 | Not Available                                                                        |
 | DocumentClassifier         | Classifies documents by attaching metadata to them. **Example usage:** Labeling documents by their characteristic (for example, sentiment).                                             | [TransformersZeroShotDocumentClassifier](../pipeline-components/classifiers/transformerszeroshotdocumentclassifier.mdx) |
 | DocumentLanguageClassifier | Detects the language of the documents you pass to it and adds it to the document metadata.                                                                                              | [DocumentLanguageClassifier](../pipeline-components/classifiers/documentlanguageclassifier.mdx)                       |
-| EntityExtractor            | Extracts predefined entities out of a piece of text. **Example usage:** Named entity extraction (NER).                                                                                  | [NamedEntityExtractor](../pipeline-components/extractors/namedentityextractor.mdx)                                   |
+| EntityExtractor            | Extracts predefined entities out of a piece of text. **Example usage:** Named entity extraction (NER).                                                                                  | [NamedEntityExtractor](../pipeline-components/extractors/transformersnamedentityextractor.mdx)                                   |
 | FileClassifier             | Distinguishes between text, PDF, Markdown, Docx, and HTML files. **Example usage:** Routing files to appropriate converters (for example, it routes PDF files to `PDFToTextConverter`). | [FileTypeRouter](../pipeline-components/routers/filetyperouter.mdx)                                               |
 | FileConverter              | Cleans and splits documents in different formats. **Example usage:** In indexing pipelines, extracting text from a file and casting it into the Document class format.                  | [Converters](../pipeline-components/converters.mdx)                                                       |
 | PreProcessor               | Cleans and splits documents. **Example usage:** Normalizing white spaces, getting rid of headers and footers, splitting documents into smaller ones.                                    | [PreProcessors](../pipeline-components/preprocessors.mdx)                                                 |

docs-website/docs/pipeline-components/builders/promptbuilder.mdx

@@ -159,7 +159,7 @@ Below is an example of a RAG pipeline where we use a `PromptBuilder` to render a
 ```python
 from haystack import Pipeline, Document
 from haystack.utils import Secret
-from haystack.components.generators import OpenAIGenerator
+from haystack.components.generators.chat import OpenAIChatGenerator
 from haystack.components.builders.prompt_builder import PromptBuilder
 
 # in a real world use case documents could come from a retriever, web, or any other source
@@ -179,7 +179,7 @@ prompt_template = """
 p = Pipeline()
 p.add_component(instance=PromptBuilder(template=prompt_template), name="prompt_builder")
 p.add_component(
-    instance=OpenAIGenerator(api_key=Secret.from_env_var("OPENAI_API_KEY")),
+    instance=OpenAIChatGenerator(api_key=Secret.from_env_var("OPENAI_API_KEY")),
     name="llm",
 )
 p.connect("prompt_builder", "llm")
@@ -278,20 +278,21 @@ components:
       model: gpt-5-mini
       organization: null
       streaming_callback: null
-      system_prompt: null
       timeout: null
-    type: haystack.components.generators.openai.OpenAIGenerator
+      tools: null
+      tools_strict: false
+    type: haystack.components.generators.chat.openai.OpenAIChatGenerator
   prompt_builder:
     init_parameters:
-      required_variables: null
-      template: "\n    Given these documents, answer the question.\\nDocuments:\n\
-        \    {% for doc in documents %}\n        {{ doc.content }}\n    {% endfor\
-        \ %}\n\n    \\nQuestion: {{query}}\n    \\nAnswer:\n    "
+      required_variables: '*'
+      template: "\n    Given these documents, answer the question.\nDocuments:\n \
+        \   {% for doc in documents %}\n        {{ doc.content }}\n    {% endfor %}\n\
+        \n    \nQuestion: {{query}}\n    \nAnswer:\n    "
       variables: null
     type: haystack.components.builders.prompt_builder.PromptBuilder
 connection_type_validation: true
 connections:
-- receiver: llm.prompt
+- receiver: llm.messages
   sender: prompt_builder.prompt
 max_runs_per_component: 100
 metadata: {}

docs-website/docs/pipeline-components/extractors.mdx

@@ -10,7 +10,6 @@ slug: "/extractors"
 | --- | --- |
 | [LLMDocumentContentExtractor](extractors/llmdocumentcontentextractor.mdx) | Extracts textual content from image-based documents using a vision-enabled Large Language Model (LLM).                                     |
 | [LLMMetadataExtractor](extractors/llmmetadataextractor.mdx)               | Extracts metadata from documents using a Large Language Model. The metadata is extracted by providing a prompt to a LLM that generates it. |
-| [NamedEntityExtractor](extractors/namedentityextractor.mdx)               | Extracts predefined entities out of a piece of text and writes them into documents' meta field.                                            |
 | [PresidioEntityExtractor](extractors/presidioentityextractor.mdx)         | Detects PII in Documents and stores entities as structured metadata, without modifying the text. Powered by Microsoft Presidio.            |
 | [RegexTextExtractor](extractors/regextextextractor.mdx)                   | Extracts text from chat messages or strings using a regular expression pattern.                                                            |
 | [SpacyNamedEntityExtractor](extractors/spacynamedentityextractor.mdx)     | Extracts predefined entities out of a piece of text and writes them into documents' meta field. Uses a spaCy model.                        |