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* docs(cli): document device-code login and config reset guidance; renumber sections * docs(cli): fix duplicate numbering (renumber Login/API Keys/Configuration sections) * docs: Fix webhook documentation to include meta dict in all webhook payloads - Add note explaining that meta objects from kickoff requests are included in all webhook payloads - Update webhook examples to show proper payload structure including meta field - Fix webhook examples to match actual API implementation - Apply changes to English, Korean, and Portuguese documentation Resolves the documentation gap where meta dict passing to webhooks was not documented despite being implemented in the API. * WIP: CrewAI docs theme, changelog, GEO, localization * docs(cli): fix merge markers; ensure mode: "wide"; convert ASCII tables to Markdown (en/pt-BR/ko) * docs: add group icons across locales; split Automation/Integrations; update tools overviews and links
68 lines
2.5 KiB
Plaintext
68 lines
2.5 KiB
Plaintext
---
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title: Directory RAG Search
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description: The `DirectorySearchTool` is a powerful RAG (Retrieval-Augmented Generation) tool designed for semantic searches within a directory's content.
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icon: address-book
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mode: "wide"
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---
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# `DirectorySearchTool`
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<Note>
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**Experimental**: The DirectorySearchTool is under continuous development. Features and functionalities might evolve, and unexpected behavior may occur as we refine the tool.
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</Note>
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## Description
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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.
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## Installation
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To use the DirectorySearchTool, begin by installing 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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## Initialization and Usage
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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.
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```python Code
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from crewai_tools import DirectorySearchTool
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# For dynamic directory specification at runtime
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tool = DirectorySearchTool()
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# For fixed directory searches
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tool = DirectorySearchTool(directory='/path/to/directory')
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```
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## Arguments
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- `directory`: A string argument that specifies the search directory. This is optional during initialization but required for searches if not set initially.
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## Custom Model and Embeddings
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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.
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```python Code
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tool = DirectorySearchTool(
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config=dict(
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llm=dict(
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provider="ollama", # Options include ollama, google, anthropic, llama2, and more
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config=dict(
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model="llama2",
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# Additional configurations here
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),
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),
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embedder=dict(
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provider="google", # or openai, ollama, ...
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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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``` |