c960f26601
0b3f00e6 chore: update project version to 0.73.0 and revise uv.lock dependencies (#455) ad19b074 feat: replace embedchain with native crewai adapter (#451) git-subtree-dir: packages/tools git-subtree-split: 0b3f00e67c0dae24d188c292dc99759fd1c841f7
DirectorySearchTool
Description
This tool is designed to perform a semantic search for queries within the content of a specified directory. Utilizing the RAG (Retrieval-Augmented Generation) methodology, it offers a powerful means to semantically navigate through the files of a given directory. The tool can be dynamically set to search any directory specified at runtime or can be pre-configured to search within a specific directory upon initialization.
Installation
To start using the DirectorySearchTool, you need to install the crewai_tools package. Execute the following command in your terminal:
pip install 'crewai[tools]'
Example
The following examples demonstrate how to initialize the DirectorySearchTool for different use cases and how to perform a search:
from crewai_tools import DirectorySearchTool
# To enable searching within any specified directory at runtime
tool = DirectorySearchTool()
# Alternatively, to restrict searches to a specific directory
tool = DirectorySearchTool(directory='/path/to/directory')
Arguments
directory: This string argument specifies the directory within which to search. It is mandatory if the tool has not been initialized with a directory; otherwise, the tool will only search within the initialized directory.
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:
tool = DirectorySearchTool(
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",
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
)