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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
85 lines
3.0 KiB
Plaintext
85 lines
3.0 KiB
Plaintext
---
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title: Code Docs RAG Search
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description: The `CodeDocsSearchTool` is a powerful RAG (Retrieval-Augmented Generation) tool designed for semantic searches within code documentation.
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icon: code
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mode: "wide"
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---
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# `CodeDocsSearchTool`
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<Note>
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**Experimental**: We are still working on improving tools, so there might be unexpected behavior or changes in the future.
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</Note>
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## Description
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The CodeDocsSearchTool is a powerful RAG (Retrieval-Augmented Generation) tool designed for semantic searches within code documentation.
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It enables users to efficiently find specific information or topics within code documentation. By providing a `docs_url` during initialization,
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the tool narrows down the search to that particular documentation site. Alternatively, without a specific `docs_url`,
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it searches across a wide array of code documentation known or discovered throughout its execution, making it versatile for various documentation search needs.
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## Installation
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To start using the CodeDocsSearchTool, first, install the crewai_tools package via pip:
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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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Utilize the CodeDocsSearchTool as follows to conduct searches within code documentation:
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```python Code
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from crewai_tools import CodeDocsSearchTool
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# To search any code documentation content
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# if the URL is known or discovered during its execution:
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tool = CodeDocsSearchTool()
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# OR
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# To specifically focus your search on a given documentation site
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# by providing its URL:
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tool = CodeDocsSearchTool(docs_url='https://docs.example.com/reference')
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```
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<Note>
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Substitute 'https://docs.example.com/reference' with your target documentation URL
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and 'How to use search tool' with the search query relevant to your needs.
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</Note>
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## Arguments
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The following parameters can be used to customize the `CodeDocsSearchTool`'s behavior:
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| Argument | Type | Description |
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|:---------------|:---------|:-------------------------------------------------------------------------------------------------------------------------------------|
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| **docs_url** | `string` | _Optional_. Specifies the URL of the code documentation to be searched. |
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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 Code
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tool = CodeDocsSearchTool(
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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", # 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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``` |