Add pt-BR docs translation (#3039)

* docs: add pt-br translations

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* Update mcp/overview.mdx brazilian docs

Its en-US counterpart was updated after I did a pass,
so now it includes the new section about @CrewBase

docs/en/tools/file-document/pdfsearchtool.mdx

@@ -0,0 +1,71 @@
+---
+title: PDF RAG Search
+description: The `PDFSearchTool` is designed to search PDF files and return the most relevant results.
+icon: file-pdf
+---
+
+# `PDFSearchTool`
+
+<Note>
+    We are still working on improving tools, so there might be unexpected behavior or changes in the future.
+</Note>
+
+## Description
+
+The PDFSearchTool is a RAG tool designed for semantic searches within PDF content. It allows for inputting a search query and a PDF document, leveraging advanced search techniques to find relevant content efficiently. 
+This capability makes it especially useful for extracting specific information from large PDF files quickly.
+
+## Installation
+
+To get started with the PDFSearchTool, first, ensure the crewai_tools package is installed with the following command:
+
+```shell
+pip install 'crewai[tools]'
+```
+
+## Example
+Here's how to use the PDFSearchTool to search within a PDF document:
+
+```python Code
+from crewai_tools import PDFSearchTool
+
+# Initialize the tool allowing for any PDF content search if the path is provided during execution
+tool = PDFSearchTool()
+
+# OR
+
+# Initialize the tool with a specific PDF path for exclusive search within that document
+tool = PDFSearchTool(pdf='path/to/your/document.pdf')
+```
+
+## Arguments
+
+- `pdf`: **Optional** The PDF path for the search. Can be provided at initialization or within the `run` method's arguments. If provided at initialization, the tool confines its search to the specified document.
+
+## Custom model and embeddings
+
+By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
+
+```python Code
+tool = PDFSearchTool(
+    config=dict(
+        llm=dict(
+            provider="ollama", # or google, openai, anthropic, llama2, ...
+            config=dict(
+                model="llama2",
+                # temperature=0.5,
+                # top_p=1,
+                # stream=true,
+            ),
+        ),
+        embedder=dict(
+            provider="google", # or openai, ollama, ...
+            config=dict(
+                model="models/embedding-001",
+                task_type="retrieval_document",
+                # title="Embeddings",
+            ),
+        ),
+    )
+)
+```