docs: replace migrated component imports with haystack_integrations paths (#11860)

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>

Author: julian-risch

docs-website/docs/concepts/agents.mdx

@@ -76,11 +76,17 @@ export OPENAI_API_KEY=<your-openai-key>
 export SERPERDEV_API_KEY=<your-serperdev-key>
 ```
 
+The examples on this page use SerperDev web search component that have moved to the `serperdev-haystack` package. Install it to run the examples:
+
+```shell
+pip install serperdev-haystack
+```
+
 ```python
 from haystack.components.agents import Agent
 from haystack.components.generators.chat import OpenAIChatGenerator
 from haystack.components.generators.utils import print_streaming_chunk
-from haystack.components.websearch import SerperDevWebSearch
+from haystack_integrations.components.websearch.serperdev import SerperDevWebSearch
 from haystack.dataclasses import ChatMessage
 from haystack.tools import ComponentTool
 

docs-website/docs/concepts/agents/multi-agent-systems.mdx

@@ -31,14 +31,20 @@ Wrapping an agent inside a `@tool` function gives you full control over what the
 This approach works better with smaller LLMs because the tool has a clean, minimal signature.
 The coordinator only needs to provide a query string - all the `ChatMessage` construction and result unpacking is hidden inside the function.
 
+The examples on this page use SerperDev web search component that have moved to the `serperdev-haystack` package. Install it to run the examples:
+
+```shell
+pip install serperdev-haystack
+```
+
 ```python
 from typing import Annotated
 from haystack.components.agents import Agent
 from haystack.components.generators.chat import OpenAIChatGenerator
 from haystack.components.generators.utils import print_streaming_chunk
 from haystack.dataclasses import ChatMessage
 from haystack.tools import ComponentTool, tool
-from haystack.components.websearch import SerperDevWebSearch
+from haystack_integrations.components.websearch.serperdev import SerperDevWebSearch
 from haystack.utils import Secret
 
 
@@ -198,7 +204,7 @@ components:
                       exclude_subdomains: false
                       search_params: {}
                       top_k: 3
-                    type: haystack.components.websearch.serper_dev.SerperDevWebSearch
+                    type: haystack_integrations.components.websearch.serperdev.websearch.SerperDevWebSearch
                   description: Search the web for current information on any topic
                   inputs_from_state: null
                   name: web_search
@@ -254,7 +260,7 @@ from haystack.components.converters import HTMLToDocument
 from haystack.components.fetchers.link_content import LinkContentFetcher
 from haystack.components.generators.chat import OpenAIChatGenerator
 from haystack.components.generators.utils import print_streaming_chunk
-from haystack.components.websearch import SerperDevWebSearch
+from haystack_integrations.components.websearch.serperdev import SerperDevWebSearch
 from haystack.dataclasses import ChatMessage
 from haystack.tools import ComponentTool, tool
 from haystack.utils import Secret

docs-website/docs/concepts/components.mdx

@@ -45,9 +45,17 @@ Returns a list of Documents ranked by their similarity to the given query.
 
 Components that use heavy resources, like LLMs or embedding models, have a `warm_up()` method that loads the necessary resources (such as models) into memory. This method is automatically called the first time the component runs, so you can use components directly without explicitly calling `warm_up()`:
 
+The examples on this page use Sentence Transformers embedders that have moved to the `sentence-transformers-haystack` package. Install it to run the examples:
+
+```shell
+pip install sentence-transformers-haystack
+```
+
 ```python
 from haystack import Document
-from haystack.components.embedders import SentenceTransformersDocumentEmbedder
+from haystack_integrations.components.embedders.sentence_transformers import (
+    SentenceTransformersDocumentEmbedder,
+)
 
 doc = Document(content="I love pizza!")
 doc_embedder = SentenceTransformersDocumentEmbedder()

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

@@ -19,12 +19,20 @@ 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.
 
+The examples on this page use Sentence Transformers embedders that have moved to the `sentence-transformers-haystack` package. Install it to run the examples:
+
+```shell
+pip install sentence-transformers-haystack
+```
+
 ```python
-# pip install haystack-ai datasets "sentence-transformers>=3.0.0"
+# pip install haystack-ai datasets sentence-transformers-haystack
 
 from haystack import Document, Pipeline, super_component
 from haystack.components.joiners import DocumentJoiner
-from haystack.components.embedders import SentenceTransformersTextEmbedder
+from haystack_integrations.components.embedders.sentence_transformers import (
+    SentenceTransformersTextEmbedder,
+)
 from haystack.components.retrievers import (
     InMemoryBM25Retriever,
     InMemoryEmbeddingRetriever,

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

@@ -50,12 +50,18 @@ You can find more details in our [API Reference](/reference/pipeline-api#asyncpi
 
 ## Example
 
+The examples on this page use Sentence Transformers embedders that have moved to the `sentence-transformers-haystack` package. Install it to run the examples:
+
+```shell
+pip install sentence-transformers-haystack
+```
+
 ```python
 import asyncio
 
 from haystack import AsyncPipeline, Document
 from haystack.components.builders import ChatPromptBuilder
-from haystack.components.embedders import (
+from haystack_integrations.components.embedders.sentence_transformers import (
     SentenceTransformersDocumentEmbedder,
     SentenceTransformersTextEmbedder,
 )
@@ -75,7 +81,6 @@ documents = [
 ]
 
 docs_embedder = SentenceTransformersDocumentEmbedder()
-docs_embedder.warm_up()
 
 document_store = InMemoryDocumentStore()
 document_store.write_documents(docs_embedder.run(documents=documents)["documents"])

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

@@ -26,10 +26,18 @@ For each component you want to use in your pipeline, you must know the names of
 Import all the dependencies, like pipeline, documents, Document Store, and all the components you want to use in your pipeline.
 For example, to create a semantic document search pipelines, you need the `Document` object, the pipeline, the Document Store, Embedders, and a Retriever:
 
+The examples on this page use Sentence Transformers embedders that have moved to the `sentence-transformers-haystack` package. Install it to run the examples:
+
+```shell
+pip install sentence-transformers-haystack
+```
+
 ```python
 from haystack import Document, Pipeline
 from haystack.document_stores.in_memory import InMemoryDocumentStore
-from haystack.components.embedders import SentenceTransformersTextEmbedder
+from haystack_integrations.components.embedders.sentence_transformers import (
+    SentenceTransformersTextEmbedder,
+)
 from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
 ```
 

docs-website/docs/document-stores/oracledocumentstore.mdx

@@ -25,6 +25,12 @@ It stores documents alongside dense vector embeddings in a native `VECTOR` colum
 pip install oracle-haystack
 ```
 
+The examples on this page use Sentence Transformers embedders that have moved to the `sentence-transformers-haystack` package. Install it to run the examples:
+
+```shell
+pip install sentence-transformers-haystack
+```
+
 ## Connection
 
 `OracleDocumentStore` connects to Oracle using the `OracleConnectionConfig` dataclass, which supports two connection modes:
@@ -108,7 +114,7 @@ document_store = OracleDocumentStore(
 ```python
 from haystack import Document, Pipeline
 from haystack.document_stores.types import DuplicatePolicy
-from haystack.components.embedders import (
+from haystack_integrations.components.embedders.sentence_transformers import (
     SentenceTransformersDocumentEmbedder,
     SentenceTransformersTextEmbedder,
 )
@@ -146,7 +152,6 @@ documents = [
 doc_embedder = SentenceTransformersDocumentEmbedder(
     model="sentence-transformers/all-MiniLM-L6-v2",
 )
-doc_embedder.warm_up()
 embedded_docs = doc_embedder.run(documents)["documents"]
 document_store.write_documents(embedded_docs, policy=DuplicatePolicy.OVERWRITE)
 

docs-website/docs/document-stores/supabasedocumentstore.mdx

@@ -27,6 +27,12 @@ description: "Use Supabase as a document store in Haystack, with vector search (
 pip install supabase-haystack
 ```
 
+The examples on this page use Sentence Transformers embedders that have moved to the `sentence-transformers-haystack` package. Install it to run the examples:
+
+```shell
+pip install sentence-transformers-haystack
+```
+
 ## SupabasePgvectorDocumentStore
 
 `SupabasePgvectorDocumentStore` is a thin wrapper around [`PgvectorDocumentStore`](./pgvectordocumentstore.mdx) with Supabase-specific defaults:
@@ -70,7 +76,7 @@ To learn more about the initialization parameters, see the [API docs](/reference
 ```python
 from haystack import Document, Pipeline
 from haystack.document_stores.types.policy import DuplicatePolicy
-from haystack.components.embedders import (
+from haystack_integrations.components.embedders.sentence_transformers import (
     SentenceTransformersTextEmbedder,
     SentenceTransformersDocumentEmbedder,
 )

docs-website/docs/document-stores/valkeydocumentstore.mdx

@@ -28,6 +28,12 @@ You can install the Valkey Haystack integration with:
 pip install valkey-haystack
 ```
 
+The examples on this page use Sentence Transformers embedders that have moved to the `sentence-transformers-haystack` package. Install it to run the examples:
+
+```shell
+pip install sentence-transformers-haystack
+```
+
 ## Initialization
 
 To use Valkey as your data storage for Haystack pipelines, you need a Valkey server with the search module running. Initialize a `ValkeyDocumentStore` like this:
@@ -65,7 +71,9 @@ To write documents to your `ValkeyDocumentStore`, create an indexing pipeline or
 from haystack import Pipeline
 from haystack.components.converters import MarkdownToDocument
 from haystack.components.writers import DocumentWriter
-from haystack.components.embedders import SentenceTransformersDocumentEmbedder
+from haystack_integrations.components.embedders.sentence_transformers import (
+    SentenceTransformersDocumentEmbedder,
+)
 from haystack.components.preprocessors import DocumentSplitter
 from haystack_integrations.document_stores.valkey import ValkeyDocumentStore
 
@@ -99,7 +107,9 @@ Once documents are in your `ValkeyDocumentStore`, you can use [`ValkeyEmbeddingR
 from haystack import Pipeline
 from haystack.utils import Secret
 from haystack.dataclasses import ChatMessage
-from haystack.components.embedders import SentenceTransformersTextEmbedder
+from haystack_integrations.components.embedders.sentence_transformers import (
+    SentenceTransformersTextEmbedder,
+)
 from haystack.components.builders import ChatPromptBuilder
 from haystack.components.generators.chat import OpenAIChatGenerator
 from haystack_integrations.document_stores.valkey import ValkeyDocumentStore

docs-website/docs/optimization/advanced-rag-techniques/hypothetical-document-embeddings-hyde.mdx

@@ -28,6 +28,12 @@ Many embedding retrievers generalize poorly to new, unseen domains. This approac
 
 First, prepare all the components that you would need:
 
+The examples on this page use Sentence Transformers embedders that have moved to the `sentence-transformers-haystack` package. Install it to run the examples:
+
+```shell
+pip install sentence-transformers-haystack
+```
+
 ```python
 import os
 from numpy import array, mean
@@ -37,7 +43,9 @@ from haystack.components.generators.chat import OpenAIChatGenerator
 from haystack.components.builders import ChatPromptBuilder
 from haystack import component, Document
 from haystack.components.converters import OutputAdapter
-from haystack.components.embedders import SentenceTransformersDocumentEmbedder
+from haystack_integrations.components.embedders.sentence_transformers import (
+    SentenceTransformersDocumentEmbedder,
+)
 from haystack.dataclasses import ChatMessage
 
 # We need to ensure we have the OpenAI API key in our environment variables
@@ -69,7 +77,6 @@ adapter = OutputAdapter(
 embedder = SentenceTransformersDocumentEmbedder(
     model="sentence-transformers/all-MiniLM-L6-v2",
 )
-embedder.warm_up()
 
 
 # Adding one custom component that returns one, "average" embedding from multiple (hypothetical) document embeddings