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* mentioning ollama on the docs as embedder * lowering barrier to match tool with simialr name * Fixing agent tools to support co_worker * Adding new tests * Fixing type" * updating tests * fixing conflict
66 lines
2.4 KiB
Markdown
66 lines
2.4 KiB
Markdown
# CodeDocsSearchTool
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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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The CodeDocsSearchTool is a powerful RAG (Retrieval-Augmented Generation) tool designed for semantic searches within code documentation. It enables users to efficiently find specific information or topics within code documentation. By providing a `docs_url` during initialization, the tool narrows down the search to that particular documentation site. Alternatively, without a specific `docs_url`, 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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```
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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
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from crewai_tools import CodeDocsSearchTool
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# To search any code documentation content 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 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: Substitute 'https://docs.example.com/reference' with your target documentation URL and 'How to use search tool' with the search query relevant to your needs.
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## Arguments
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- `docs_url`: Optional. Specifies the URL of the code documentation to be searched. Providing this during the tool's initialization focuses the search on the specified documentation content.
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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 = 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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```
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