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59 lines
2.1 KiB
Markdown
59 lines
2.1 KiB
Markdown
# DirectorySearchTool
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!!! note "Experimental"
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We are still working on improving tools, so there might be unexpected behavior or changes in the future.
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## Description
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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.
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## Installation
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To start using the DirectorySearchTool, you need to install the crewai_tools package. Execute the following command in your terminal:
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```shell
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pip install 'crewai[tools]'
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```
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## Example
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The following examples demonstrate how to initialize the DirectorySearchTool for different use cases and how to perform a search:
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```python
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from crewai_tools import DirectorySearchTool
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# To enable searching within any specified directory at runtime
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tool = DirectorySearchTool()
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# Alternatively, to restrict searches to a specific directory
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tool = DirectorySearchTool(directory='/path/to/directory')
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```
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## Arguments
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- `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.
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## Custom model and embeddings
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By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
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```python
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tool = DirectorySearchTool(
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config=dict(
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llm=dict(
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provider="ollama", # or google, openai, anthropic, llama2, ...
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config=dict(
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model="llama2",
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# temperature=0.5,
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# top_p=1,
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# stream=true,
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),
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),
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embedder=dict(
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provider="google",
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config=dict(
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model="models/embedding-001",
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task_type="retrieval_document",
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# title="Embeddings",
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),
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),
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)
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)
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```
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