fix: add backward compatibility for legacy RAG tool config format

Fixes #4028 - WebsiteSearchTool always requires OpenAI API key even when
Ollama or other providers are specified.

The issue was that the documentation showed the old config format with
'llm' and 'embedder' keys, but the actual RagToolConfig type expects
'embedding_model' and 'vectordb' keys. When the old format was passed,
the embedder config was not recognized, causing the tool to fall back
to the default OpenAI embedding function which requires OPENAI_API_KEY.

Changes:
- Add _normalize_legacy_config method to RagTool that maps legacy
  'embedder' key to 'embedding_model'
- Emit deprecation warnings for legacy config keys
- Ignore 'llm' key with warning (not used in RAG tools)
- Add tests for backward compatibility
- Update documentation to show new config format with examples

Co-Authored-By: João <joao@crewai.com>

docs/en/tools/search-research/websitesearchtool.mdx

@@ -50,29 +50,72 @@ tool = WebsiteSearchTool(website='https://example.com')
 
 ## Customization Options
 
-By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
-
+By default, the tool uses OpenAI for embeddings. To customize the embedding model, you can use a config dictionary as follows:
 
 ```python Code
 tool = WebsiteSearchTool(
     config=dict(
-        llm=dict(
-            provider="ollama", # or google, openai, anthropic, llama2, ...
+        embedding_model=dict(
+            provider="ollama",  # or openai, google-generativeai, azure, etc.
             config=dict(
-                model="llama2",
-                # temperature=0.5,
-                # top_p=1,
-                # stream=true,
-            ),
-        ),
-        embedder=dict(
-            provider="google-generativeai", # or openai, ollama, ...
-            config=dict(
-                model_name="gemini-embedding-001",
-                task_type="RETRIEVAL_DOCUMENT",
-                # title="Embeddings",
+                model_name="nomic-embed-text",
+                url="http://localhost:11434/api/embeddings",
             ),
         ),
     )
 )
-```
+```
+
+### Available Embedding Providers
+
+The following embedding providers are supported:
+
+- `openai` - OpenAI embeddings (default)
+- `ollama` - Ollama local embeddings
+- `google-generativeai` - Google Generative AI embeddings
+- `azure` - Azure OpenAI embeddings
+- `huggingface` - HuggingFace embeddings
+- `cohere` - Cohere embeddings
+- `voyageai` - Voyage AI embeddings
+- And more...
+
+### Example with Google Generative AI
+
+```python Code
+tool = WebsiteSearchTool(
+    config=dict(
+        embedding_model=dict(
+            provider="google-generativeai",
+            config=dict(
+                model_name="models/embedding-001",
+                task_type="RETRIEVAL_DOCUMENT",
+            ),
+        ),
+    )
+)
+```
+
+### Example with Azure OpenAI
+
+```python Code
+tool = WebsiteSearchTool(
+    config=dict(
+        embedding_model=dict(
+            provider="azure",
+            config=dict(
+                model="text-embedding-3-small",
+                api_key="your-api-key",
+                api_base="https://your-resource.openai.azure.com/",
+                api_version="2024-02-01",
+                deployment_id="your-deployment-id",
+            ),
+        ),
+    )
+)
+```
+
+<Note>
+The `llm` and `embedder` config keys from older documentation are deprecated. 
+Please use `embedding_model` instead. The `llm` key is not used by RAG tools - 
+the LLM for generation is controlled by the agent's LLM configuration.
+</Note>