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docs/tools/directorysearchtool.mdx

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+---
+title: Directory RAG Search
+description: The `DirectorySearchTool` is a powerful RAG (Retrieval-Augmented Generation) tool designed for semantic searches within a directory's content.
+icon: address-book
+---
+
+# `DirectorySearchTool`
+
+<Note>
+    **Experimental**: The DirectorySearchTool is under continuous development. Features and functionalities might evolve, and unexpected behavior may occur as we refine the tool.
+</Note>
+
+## Description
+
+The DirectorySearchTool enables semantic search within the content of specified directories, leveraging the Retrieval-Augmented Generation (RAG) methodology for efficient navigation through files. Designed for flexibility, it allows users to dynamically specify search directories at runtime or set a fixed directory during initial setup.
+
+## Installation
+
+To use the DirectorySearchTool, begin by installing the crewai_tools package. Execute the following command in your terminal:
+
+```shell
+pip install 'crewai[tools]'
+```
+
+## Initialization and Usage
+
+Import the DirectorySearchTool from the `crewai_tools` package to start. You can initialize the tool without specifying a directory, enabling the setting of the search directory at runtime. Alternatively, the tool can be initialized with a predefined directory.
+
+```python Code
+from crewai_tools import DirectorySearchTool
+
+# For dynamic directory specification at runtime
+tool = DirectorySearchTool()
+
+# For fixed directory searches
+tool = DirectorySearchTool(directory='/path/to/directory')
+```
+
+## Arguments
+
+- `directory`: A string argument that specifies the search directory. This is optional during initialization but required for searches if not set initially.
+
+## Custom Model and Embeddings
+
+The DirectorySearchTool uses OpenAI for embeddings and summarization by default. Customization options for these settings include changing the model provider and configuration, enhancing flexibility for advanced users.
+
+```python Code
+tool = DirectorySearchTool(
+    config=dict(
+        llm=dict(
+            provider="ollama", # Options include ollama, google, anthropic, llama2, and more
+            config=dict(
+                model="llama2",
+                # Additional configurations here
+            ),
+        ),
+        embedder=dict(
+            provider="google", # or openai, ollama, ...
+            config=dict(
+                model="models/embedding-001",
+                task_type="retrieval_document",
+                # title="Embeddings",
+            ),
+        ),
+    )
+)
+```