chore: deprecate HF API components + update docs

docs-website/docs/pipeline-components/embedders/huggingfaceapidocumentembedder.mdx

@@ -17,9 +17,9 @@ Use this component to compute document embeddings using various Hugging Face API
 | **Mandatory init variables** | `api_type`: The type of Hugging Face API to use  <br /> <br />`api_params`: A dictionary with one of the following keys:  <br /> <br />- `model`: Hugging Face model ID. Required when `api_type` is `SERVERLESS_INFERENCE_API`.**OR** - `url`: URL of the inference endpoint. Required when `api_type` is `INFERENCE_ENDPOINTS` or `TEXT_EMBEDDINGS_INFERENCE`.  <br /> <br />`token`: The Hugging Face API token. Can be set with `HF_API_TOKEN` or `HF_TOKEN` env var. |
 | **Mandatory run variables** | `documents`: A list of documents to be embedded |
 | **Output variables** | `documents`: A list of documents to be embedded (enriched with embeddings) |
-| **API reference** | [Embedders](/reference/embedders-api) |
-| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/embedders/hugging_face_api_document_embedder.py |
-| **Package name** | `haystack-ai` |
+| **API reference** | [Hugging Face API](/reference/integrations-huggingface-api) |
+| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/huggingface_api |
+| **Package name** | `huggingface-api-haystack` |
 
 </div>
 
@@ -43,8 +43,14 @@ The token is needed:
 
 ## Usage
 
+Install the `huggingface-api-haystack` package to use the `HuggingFaceAPIDocumentEmbedder`:
+
+```shell
+pip install huggingface-api-haystack
+```
+
 Similarly to other Document Embedders, this component allows adding prefixes (and postfixes) to include instruction and embedding metadata.
-For more fine-grained details, refer to the component’s [API reference](/reference/embedders-api#huggingfaceapidocumentembedder).
+For more fine-grained details, refer to the component’s [API reference](/reference/integrations-huggingface-api#huggingfaceapidocumentembedder).
 
 ### On its own
 
@@ -56,7 +62,9 @@ To use this API, you need a [free Hugging Face token](https://huggingface.co/set
 The Embedder expects the `model` in `api_params`.
 
 ```python
-from haystack.components.embedders import HuggingFaceAPIDocumentEmbedder
+from haystack_integrations.components.embedders.huggingface_api import (
+    HuggingFaceAPIDocumentEmbedder,
+)
 from haystack.utils import Secret
 from haystack.dataclasses import Document
 
@@ -84,7 +92,9 @@ Additionally, in this case, you need to provide your Hugging Face token.
 The Embedder expects the `url` of your endpoint in `api_params`.
 
 ```python
-from haystack.components.embedders import HuggingFaceAPIDocumentEmbedder
+from haystack_integrations.components.embedders.huggingface_api import (
+    HuggingFaceAPIDocumentEmbedder,
+)
 from haystack.utils import Secret
 from haystack.dataclasses import Document
 
@@ -123,7 +133,9 @@ For more information, refer to the [official TEI repository](https://github.com/
 The Embedder expects the `url` of your TEI instance in `api_params`.
 
 ```python
-from haystack.components.embedders import HuggingFaceAPIDocumentEmbedder
+from haystack_integrations.components.embedders.huggingface_api import (
+    HuggingFaceAPIDocumentEmbedder,
+)
 from haystack.dataclasses import Document
 
 doc = Document(content="I love pizza!")
@@ -145,38 +157,53 @@ print(result["documents"][0].embedding)
 from haystack import Document
 from haystack import Pipeline
 from haystack.document_stores.in_memory import InMemoryDocumentStore
-from haystack.components.embedders import HuggingFaceAPITextEmbedder, HuggingFaceAPIDocumentEmbedder
+from haystack_integrations.components.embedders.huggingface_api import (
+    HuggingFaceAPITextEmbedder,
+    HuggingFaceAPIDocumentEmbedder,
+)
 from haystack.components.writers import DocumentWriter
 from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
 
 document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")
 
-documents = [Document(content="My name is Wolfgang and I live in Berlin"),
-             Document(content="I saw a black horse running"),
-             Document(content="Germany has many big cities")]
+documents = [
+    Document(content="My name is Wolfgang and I live in Berlin"),
+    Document(content="I saw a black horse running"),
+    Document(content="Germany has many big cities"),
+]
 
-document_embedder = HuggingFaceAPIDocumentEmbedder(api_type="serverless_inference_api",
-				                                           api_params={"model": "BAAI/bge-small-en-v1.5"})
+document_embedder = HuggingFaceAPIDocumentEmbedder(
+    api_type="serverless_inference_api",
+    api_params={"model": "BAAI/bge-small-en-v1.5"},
+)
 
 indexing_pipeline = Pipeline()
 indexing_pipeline.add_component("document_embedder", document_embedder)
-indexing_pipeline.add_component("doc_writer", DocumentWriter(document_store=document_store)
+indexing_pipeline.add_component(
+    "doc_writer",
+    DocumentWriter(document_store=document_store),
+)
 indexing_pipeline.connect("document_embedder", "doc_writer")
 indexing_pipeline.run({"document_embedder": {"documents": documents}})
 
-text_embedder = HuggingFaceAPITextEmbedder(api_type="serverless_inference_api",
-                                           api_params={"model": "BAAI/bge-small-en-v1.5"})
+text_embedder = HuggingFaceAPITextEmbedder(
+    api_type="serverless_inference_api",
+    api_params={"model": "BAAI/bge-small-en-v1.5"},
+)
 
 query_pipeline = Pipeline()
 query_pipeline.add_component("text_embedder", text_embedder)
-query_pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store))
+query_pipeline.add_component(
+    "retriever",
+    InMemoryEmbeddingRetriever(document_store=document_store),
+)
 query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
 
 query = "Who lives in Berlin?"
 
-result = query_pipeline.run({"text_embedder":{"text": query}})
+result = query_pipeline.run({"text_embedder": {"text": query}})
 
-print(result['retriever']['documents'][0])
+print(result["retriever"]["documents"][0])
 
 # Document(id=..., content: 'My name is Wolfgang and I live in Berlin', ...)
 ```

docs-website/docs/pipeline-components/embedders/huggingfaceapitextembedder.mdx

@@ -17,9 +17,9 @@ Use this component to embed strings using various Hugging Face APIs.
 | **Mandatory init variables** | `api_type`: The type of Hugging Face API to use  <br /> <br />`api_params`: A dictionary with one of the following keys:  <br /> <br />- `model`: Hugging Face model ID. Required when `api_type` is `SERVERLESS_INFERENCE_API`.**OR** - `url`: URL of the inference endpoint. Required when `api_type` is `INFERENCE_ENDPOINTS` or `TEXT_EMBEDDINGS_INFERENCE`.  <br /> <br />`token`: The Hugging Face API token. Can be set with `HF_API_TOKEN` or `HF_TOKEN` env var. |
 | **Mandatory run variables** | `text`: A string |
 | **Output variables** | `embedding`: A list of float numbers |
-| **API reference** | [Embedders](/reference/embedders-api) |
-| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/embedders/hugging_face_api_text_embedder.py |
-| **Package name** | `haystack-ai` |
+| **API reference** | [Hugging Face API](/reference/integrations-huggingface-api) |
+| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/huggingface_api |
+| **Package name** | `huggingface-api-haystack` |
 
 </div>
 
@@ -43,8 +43,14 @@ The token is needed:
 
 ## Usage
 
+Install the `huggingface-api-haystack` package to use the `HuggingFaceAPITextEmbedder`:
+
+```shell
+pip install huggingface-api-haystack
+```
+
 Similarly to other text Embedders, this component allows adding prefixes (and postfixes) to include instructions.
-For more fine-grained details, refer to the component’s [API reference](/reference/embedders-api#huggingfaceapitextembedder).
+For more fine-grained details, refer to the component’s [API reference](/reference/integrations-huggingface-api#huggingfaceapitextembedder).
 
 ### On its own
 
@@ -56,7 +62,9 @@ To use this API, you need a [free Hugging Face token](https://huggingface.co/set
 The Embedder expects the `model` in `api_params`.
 
 ```python
-from haystack.components.embedders import HuggingFaceAPITextEmbedder
+from haystack_integrations.components.embedders.huggingface_api import (
+    HuggingFaceAPITextEmbedder,
+)
 from haystack.utils import Secret
 
 text_embedder = HuggingFaceAPITextEmbedder(
@@ -80,7 +88,9 @@ Additionally, in this case, you need to provide your Hugging Face token.
 The Embedder expects the `url` of your endpoint in `api_params`.
 
 ```python
-from haystack.components.embedders import HuggingFaceAPITextEmbedder
+from haystack_integrations.components.embedders.huggingface_api import (
+    HuggingFaceAPITextEmbedder,
+)
 from haystack.utils import Secret
 
 text_embedder = HuggingFaceAPITextEmbedder(
@@ -115,7 +125,9 @@ For more information, refer to the [official TEI repository](https://github.com/
 The Embedder expects the `url` of your TEI instance in `api_params`.
 
 ```python
-from haystack.components.embedders import HuggingFaceAPITextEmbedder
+from haystack_integrations.components.embedders.huggingface_api import (
+    HuggingFaceAPITextEmbedder,
+)
 from haystack.utils import Secret
 
 text_embedder = HuggingFaceAPITextEmbedder(
@@ -134,7 +146,7 @@ print(text_embedder.run("I love pizza!"))
 from haystack import Document
 from haystack import Pipeline
 from haystack.document_stores.in_memory import InMemoryDocumentStore
-from haystack.components.embedders import (
+from haystack_integrations.components.embedders.huggingface_api import (
     HuggingFaceAPITextEmbedder,
     HuggingFaceAPIDocumentEmbedder,
 )

docs-website/docs/pipeline-components/generators/huggingfaceapichatgenerator.mdx

@@ -17,9 +17,9 @@ This generator enables chat completion using various Hugging Face APIs.
 | **Mandatory init variables** | `api_type`: The type of Hugging Face API to use  <br /> <br />`api_params`: A dictionary with one of the following keys:  <br /> <br />- `model`: Hugging Face model ID. Required when `api_type` is `SERVERLESS_INFERENCE_API`.**OR** - `url`: URL of the inference endpoint. Required when `api_type` is `INFERENCE_ENDPOINTS` or `TEXT_EMBEDDINGS_INFERENCE`.`token`: The Hugging Face API token. Can be set with `HF_API_TOKEN` or `HF_TOKEN` env var. |
 | **Mandatory run variables** | `messages`: A list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx) objects representing the chat or a plain string |
 | **Output variables** | `replies`: A list of replies of the LLM to the input chat |
-| **API reference** | [Generators](/reference/generators-api) |
-| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/generators/chat/hugging_face_api.py |
-| **Package name** | `haystack-ai` |
+| **API reference** | [Hugging Face API](/reference/integrations-huggingface-api) |
+| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/huggingface_api |
+| **Package name** | `huggingface-api-haystack` |
 
 </div>
 
@@ -49,6 +49,12 @@ This Generator supports [streaming](guides-to-generators/choosing-the-right-gene
 
 ## Usage
 
+Install the `huggingface-api-haystack` package to use the `HuggingFaceAPIChatGenerator`:
+
+```shell
+pip install huggingface-api-haystack
+```
+
 ### On its own
 
 #### Using Serverless Inference API (Inference Providers) - Free Tier Available
@@ -59,10 +65,14 @@ To use this API, you need a [free Hugging Face token](https://huggingface.co/set
 The Generator expects the `model` in `api_params`. It's also recommended to specify a `provider` for better performance and reliability.
 
 ```python
-from haystack.components.generators.chat import HuggingFaceAPIChatGenerator
+from haystack_integrations.components.generators.huggingface_api import (
+    HuggingFaceAPIChatGenerator,
+)
 from haystack.dataclasses import ChatMessage
 from haystack.utils import Secret
-from haystack.utils.hf import HFGenerationAPIType
+from haystack_integrations.components.common.huggingface_api.utils import (
+    HFGenerationAPIType,
+)
 
 messages = [
     ChatMessage.from_system("\\nYou are a helpful, respectful and honest assistant"),
@@ -93,7 +103,9 @@ Additionally, in this case, you need to provide your Hugging Face token.
 The Generator expects the `url` of your endpoint in `api_params`.
 
 ```python
-from haystack.components.generators.chat import HuggingFaceAPIChatGenerator
+from haystack_integrations.components.generators.huggingface_api import (
+    HuggingFaceAPIChatGenerator,
+)
 from haystack.dataclasses import ChatMessage
 from haystack.utils import Secret
 
@@ -117,10 +129,14 @@ print(result)
 You can also use this component with multimodal models that support both text and image input:
 
 ```python
-from haystack.components.generators.chat import HuggingFaceAPIChatGenerator
+from haystack_integrations.components.generators.huggingface_api import (
+    HuggingFaceAPIChatGenerator,
+)
 from haystack.dataclasses import ChatMessage, ImageContent
 from haystack.utils import Secret
-from haystack.utils.hf import HFGenerationAPIType
+from haystack_integrations.components.common.huggingface_api.utils import (
+    HFGenerationAPIType,
+)
 
 # Create an image from file path, URL, or base64
 image = ImageContent.from_file_path("path/to/your/image.jpg")
@@ -163,7 +179,9 @@ For more information, refer to the [official TGI repository](https://github.com/
 The Generator expects the `url` of your TGI instance in `api_params`.
 
 ```python
-from haystack.components.generators.chat import HuggingFaceAPIChatGenerator
+from haystack_integrations.components.generators.huggingface_api import (
+    HuggingFaceAPIChatGenerator,
+)
 from haystack.dataclasses import ChatMessage
 
 messages = [
@@ -184,11 +202,15 @@ print(result)
 
 ```python
 from haystack.components.builders import ChatPromptBuilder
-from haystack.components.generators.chat import HuggingFaceAPIChatGenerator
+from haystack_integrations.components.generators.huggingface_api import (
+    HuggingFaceAPIChatGenerator,
+)
 from haystack.dataclasses import ChatMessage
 from haystack import Pipeline
 from haystack.utils import Secret
-from haystack.utils.hf import HFGenerationAPIType
+from haystack_integrations.components.common.huggingface_api.utils import (
+    HFGenerationAPIType,
+)
 
 # no parameter init, we don't use any runtime template variables
 prompt_builder = ChatPromptBuilder()

docs-website/docs/pipeline-components/rankers/huggingfaceteiranker.mdx

@@ -17,9 +17,9 @@ Use this component to rank documents based on their similarity to the query usin
 | **Mandatory init variables** | `url`: Base URL of the TEI reranking service (for example, "https://api.example.com"). |
 | **Mandatory run variables** | `query`: A query string  <br /> <br />`documents`: A list of document objects |
 | **Output variables** | `documents`: A grouped list of documents |
-| **API reference** | [Rankers](/reference/rankers-api) |
-| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/rankers/hugging_face_tei.py |
-| **Package name** | `haystack-ai` |
+| **API reference** | [Hugging Face API](/reference/integrations-huggingface-api) |
+| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/huggingface_api |
+| **Package name** | `huggingface-api-haystack` |
 
 </div>
 
@@ -38,6 +38,12 @@ Depending on your TEI server configuration, you may also require a Hugging Face
 
 ## Usage
 
+Install the `huggingface-api-haystack` package to use the `HuggingFaceTEIRanker`:
+
+```shell
+pip install huggingface-api-haystack
+```
+
 ### On its own
 
 You can use `HuggingFaceTEIRanker` outside of a pipeline to order documents based on your query.
@@ -46,7 +52,7 @@ This example uses the `HuggingFaceTEIRanker` to rank two simple documents. To ru
 
 ```python
 from haystack import Document
-from haystack.components.rankers import HuggingFaceTEIRanker
+from haystack_integrations.components.rankers.huggingface_api import HuggingFaceTEIRanker
 from haystack.utils import Secret
 
 reranker = HuggingFaceTEIRanker(
@@ -76,7 +82,9 @@ Below is an example of a pipeline that retrieves documents from an `InMemoryDocu
 from haystack import Document, Pipeline
 from haystack.document_stores.in_memory import InMemoryDocumentStore
 from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
-from haystack.components.rankers import HuggingFaceTEIRanker
+from haystack_integrations.components.rankers.huggingface_api import (
+    HuggingFaceTEIRanker,
+)
 
 docs = [
     Document(content="Paris is in France"),