Documentation Index#

Fetch the complete documentation index at: https://docs.crewai.com/llms.txt Use this file to discover all available pages before exploring further.

The DirectorySearchTool is a powerful RAG (Retrieval-Augmented Generation) tool designed for semantic searches within a directory’s content.

DirectorySearchTool#

Experimental: The DirectorySearchTool is under continuous development. Features and functionalities might evolve, and unexpected behavior may occur as we refine the tool.

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:

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.

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.

from chromadb.config import Settings

tool = DirectorySearchTool(
    config={
        "embedding_model": {
            "provider": "openai",
            "config": {
                "model": "text-embedding-3-small",
                # "api_key": "sk-...",
            },
        },
        "vectordb": {
            "provider": "chromadb",  # or "qdrant"
            "config": {
                # "settings": Settings(persist_directory="/content/chroma", allow_reset=True, is_persistent=True),
                # from qdrant_client.models import VectorParams, Distance
                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
            }
        },
    }
)