docs: Add YAML representation of pipeline for SerperDevWebSearch

docs-website/docs/pipeline-components/websearch/serperdevwebsearch.mdx

@@ -104,6 +104,97 @@ query = "What is the most famous landmark in Berlin?"
 pipe.run(data={"search": {"query": query}, "prompt_builder": {"query": query}})
 ```
 
+### In YAML
+This is the YAML representation of the RAG pipeline shown above. It searches the web, fetches the resulting pages, converts them to text, builds a prompt with the content, and generates an answer using a chat model.
+
+```yaml
+components:
+  converter:
+    init_parameters:
+      extraction_kwargs: {}
+      store_full_path: false
+    type: haystack.components.converters.html.HTMLToDocument
+  fetcher:
+    init_parameters:
+      client_kwargs:
+        follow_redirects: true
+        timeout: 3
+      http2: false
+      raise_on_failure: true
+      request_headers: {}
+      retry_attempts: 2
+      timeout: 3
+      user_agents:
+      - haystack/LinkContentFetcher/2.27.0rc0
+    type: haystack.components.fetchers.link_content.LinkContentFetcher
+  llm:
+    init_parameters:
+      api_base_url: null
+      api_key:
+        env_vars:
+        - OPENAI_API_KEY
+        strict: true
+        type: env_var
+      generation_kwargs: {}
+      http_client_kwargs: null
+      max_retries: null
+      model: gpt-4o-mini
+      organization: null
+      streaming_callback: null
+      timeout: null
+      tools: null
+      tools_strict: false
+    type: haystack.components.generators.chat.openai.OpenAIChatGenerator
+  prompt_builder:
+    init_parameters:
+      required_variables:
+      - documents
+      - query
+      template:
+      - content:
+        - text: You are a helpful assistant.
+        meta: {}
+        name: null
+        role: system
+      - content:
+        - text: 'Given the information below:
+
+            {% for document in documents %}{{ document.content }}{% endfor %}
+
+            Answer question: {{ query }}.
+
+            Answer:'
+        meta: {}
+        name: null
+        role: user
+      variables: null
+    type: haystack.components.builders.chat_prompt_builder.ChatPromptBuilder
+  search:
+    init_parameters:
+      allowed_domains: null
+      api_key:
+        env_vars:
+        - SERPERDEV_API_KEY
+        strict: true
+        type: env_var
+      exclude_subdomains: false
+      search_params: {}
+      top_k: 2
+    type: haystack.components.websearch.serper_dev.SerperDevWebSearch
+connection_type_validation: true
+connections:
+- receiver: fetcher.urls
+  sender: search.links
+- receiver: converter.sources
+  sender: fetcher.streams
+- receiver: prompt_builder.documents
+  sender: converter.documents
+- receiver: llm.messages
+  sender: prompt_builder.prompt
+max_runs_per_component: 100
+metadata: {}
+```
+
 ## Additional References
 
 :notebook: Tutorial: [Building Fallbacks to Websearch with Conditional Routing](https://haystack.deepset.ai/tutorials/36_building_fallbacks_with_conditional_routing)

docs-website/versioned_docs/version-2.27/pipeline-components/websearch/serperdevwebsearch.mdx

@@ -104,6 +104,97 @@ query = "What is the most famous landmark in Berlin?"
 pipe.run(data={"search": {"query": query}, "prompt_builder": {"query": query}})
 ```
 
+### In YAML
+This is the YAML representation of the RAG pipeline shown above. It searches the web, fetches the resulting pages, converts them to text, builds a prompt with the content, and generates an answer using a chat model.
+
+```yaml
+components:
+  converter:
+    init_parameters:
+      extraction_kwargs: {}
+      store_full_path: false
+    type: haystack.components.converters.html.HTMLToDocument
+  fetcher:
+    init_parameters:
+      client_kwargs:
+        follow_redirects: true
+        timeout: 3
+      http2: false
+      raise_on_failure: true
+      request_headers: {}
+      retry_attempts: 2
+      timeout: 3
+      user_agents:
+      - haystack/LinkContentFetcher/2.27.0rc0
+    type: haystack.components.fetchers.link_content.LinkContentFetcher
+  llm:
+    init_parameters:
+      api_base_url: null
+      api_key:
+        env_vars:
+        - OPENAI_API_KEY
+        strict: true
+        type: env_var
+      generation_kwargs: {}
+      http_client_kwargs: null
+      max_retries: null
+      model: gpt-4o-mini
+      organization: null
+      streaming_callback: null
+      timeout: null
+      tools: null
+      tools_strict: false
+    type: haystack.components.generators.chat.openai.OpenAIChatGenerator
+  prompt_builder:
+    init_parameters:
+      required_variables:
+      - documents
+      - query
+      template:
+      - content:
+        - text: You are a helpful assistant.
+        meta: {}
+        name: null
+        role: system
+      - content:
+        - text: 'Given the information below:
+
+            {% for document in documents %}{{ document.content }}{% endfor %}
+
+            Answer question: {{ query }}.
+
+            Answer:'
+        meta: {}
+        name: null
+        role: user
+      variables: null
+    type: haystack.components.builders.chat_prompt_builder.ChatPromptBuilder
+  search:
+    init_parameters:
+      allowed_domains: null
+      api_key:
+        env_vars:
+        - SERPERDEV_API_KEY
+        strict: true
+        type: env_var
+      exclude_subdomains: false
+      search_params: {}
+      top_k: 2
+    type: haystack.components.websearch.serper_dev.SerperDevWebSearch
+connection_type_validation: true
+connections:
+- receiver: fetcher.urls
+  sender: search.links
+- receiver: converter.sources
+  sender: fetcher.streams
+- receiver: prompt_builder.documents
+  sender: converter.documents
+- receiver: llm.messages
+  sender: prompt_builder.prompt
+max_runs_per_component: 100
+metadata: {}
+```
+
 ## Additional References
 
 :notebook: Tutorial: [Building Fallbacks to Websearch with Conditional Routing](https://haystack.deepset.ai/tutorials/36_building_fallbacks_with_conditional_routing)