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docs: major docs updates (#2897)
This commit is contained in:
Tony Kipkemboi
96
docs/tools/search-research/bravesearchtool.mdx
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96
docs/tools/search-research/bravesearchtool.mdx
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---
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title: Brave Search
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description: The `BraveSearchTool` is designed to search the internet using the Brave Search API.
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icon: searchengin
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---
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# `BraveSearchTool`
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## Description
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This tool is designed to perform web searches using the Brave Search API. It allows you to search the internet with a specified query and retrieve relevant results. The tool supports customizable result counts and country-specific searches.
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## Installation
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To incorporate this tool into your project, follow the installation instructions below:
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```shell
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pip install 'crewai[tools]'
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```
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||||
|
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## Steps to Get Started
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|
||||
To effectively use the `BraveSearchTool`, follow these steps:
|
||||
|
||||
1. **Package Installation**: Confirm that the `crewai[tools]` package is installed in your Python environment.
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2. **API Key Acquisition**: Acquire a Brave Search API key by registering at [Brave Search API](https://api.search.brave.com/app/keys).
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3. **Environment Configuration**: Store your obtained API key in an environment variable named `BRAVE_API_KEY` to facilitate its use by the tool.
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## Example
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The following example demonstrates how to initialize the tool and execute a search with a given query:
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```python Code
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from crewai_tools import BraveSearchTool
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# Initialize the tool for internet searching capabilities
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tool = BraveSearchTool()
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# Execute a search
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results = tool.run(search_query="CrewAI agent framework")
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print(results)
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```
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## Parameters
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The `BraveSearchTool` accepts the following parameters:
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- **search_query**: Mandatory. The search query you want to use to search the internet.
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- **country**: Optional. Specify the country for the search results. Default is empty string.
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- **n_results**: Optional. Number of search results to return. Default is `10`.
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- **save_file**: Optional. Whether to save the search results to a file. Default is `False`.
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## Example with Parameters
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Here is an example demonstrating how to use the tool with additional parameters:
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```python Code
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from crewai_tools import BraveSearchTool
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# Initialize the tool with custom parameters
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tool = BraveSearchTool(
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country="US",
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n_results=5,
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save_file=True
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)
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# Execute a search
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results = tool.run(search_query="Latest AI developments")
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print(results)
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```
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## Agent Integration Example
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Here's how to integrate the `BraveSearchTool` with a CrewAI agent:
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```python Code
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from crewai import Agent
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from crewai.project import agent
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from crewai_tools import BraveSearchTool
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# Initialize the tool
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brave_search_tool = BraveSearchTool()
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# Define an agent with the BraveSearchTool
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@agent
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def researcher(self) -> Agent:
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return Agent(
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config=self.agents_config["researcher"],
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allow_delegation=False,
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tools=[brave_search_tool]
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)
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```
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## Conclusion
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By integrating the `BraveSearchTool` into Python projects, users gain the ability to conduct real-time, relevant searches across the internet directly from their applications. The tool provides a simple interface to the powerful Brave Search API, making it easy to retrieve and process search results programmatically. By adhering to the setup and usage guidelines provided, incorporating this tool into projects is streamlined and straightforward.
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84
docs/tools/search-research/codedocssearchtool.mdx
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84
docs/tools/search-research/codedocssearchtool.mdx
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---
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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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---
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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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|
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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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```
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52
docs/tools/search-research/exasearchtool.mdx
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52
docs/tools/search-research/exasearchtool.mdx
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---
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title: EXA Search Web Loader
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description: The `EXASearchTool` is designed to perform a semantic search for a specified query from a text's content across the internet.
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icon: globe-pointer
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---
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# `EXASearchTool`
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## Description
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The EXASearchTool is designed to perform a semantic search for a specified query from a text's content across the internet.
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It utilizes the [exa.ai](https://exa.ai/) API to fetch and display the most relevant search results based on the query provided by the user.
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## Installation
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|
||||
To incorporate this tool into your project, follow the installation instructions below:
|
||||
|
||||
```shell
|
||||
pip install 'crewai[tools]'
|
||||
```
|
||||
|
||||
## Example
|
||||
|
||||
The following example demonstrates how to initialize the tool and execute a search with a given query:
|
||||
|
||||
```python Code
|
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from crewai_tools import EXASearchTool
|
||||
|
||||
# Initialize the tool for internet searching capabilities
|
||||
tool = EXASearchTool()
|
||||
```
|
||||
|
||||
## Steps to Get Started
|
||||
|
||||
To effectively use the EXASearchTool, follow these steps:
|
||||
|
||||
<Steps>
|
||||
<Step title="Package Installation">
|
||||
Confirm that the `crewai[tools]` package is installed in your Python environment.
|
||||
</Step>
|
||||
<Step title="API Key Acquisition">
|
||||
Acquire a [exa.ai](https://exa.ai/) API key by registering for a free account at [exa.ai](https://exa.ai/).
|
||||
</Step>
|
||||
<Step title="Environment Configuration">
|
||||
Store your obtained API key in an environment variable named `EXA_API_KEY` to facilitate its use by the tool.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
## Conclusion
|
||||
|
||||
By integrating the `EXASearchTool` into Python projects, users gain the ability to conduct real-time, relevant searches across the internet directly from their applications.
|
||||
By adhering to the setup and usage guidelines provided, incorporating this tool into projects is streamlined and straightforward.
|
||||
83
docs/tools/search-research/githubsearchtool.mdx
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83
docs/tools/search-research/githubsearchtool.mdx
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---
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title: Github Search
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description: The `GithubSearchTool` is designed to search websites and convert them into clean markdown or structured data.
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icon: github
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---
|
||||
|
||||
# `GithubSearchTool`
|
||||
|
||||
<Note>
|
||||
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
|
||||
</Note>
|
||||
|
||||
## Description
|
||||
|
||||
The GithubSearchTool is a Retrieval-Augmented Generation (RAG) tool specifically designed for conducting semantic searches within GitHub repositories. Utilizing advanced semantic search capabilities, it sifts through code, pull requests, issues, and repositories, making it an essential tool for developers, researchers, or anyone in need of precise information from GitHub.
|
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|
||||
## Installation
|
||||
|
||||
To use the GithubSearchTool, first ensure the crewai_tools package is installed in your Python environment:
|
||||
|
||||
```shell
|
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pip install 'crewai[tools]'
|
||||
```
|
||||
|
||||
This command installs the necessary package to run the GithubSearchTool along with any other tools included in the crewai_tools package.
|
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|
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## Example
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|
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Here’s how you can use the GithubSearchTool to perform semantic searches within a GitHub repository:
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||||
|
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```python Code
|
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from crewai_tools import GithubSearchTool
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|
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# Initialize the tool for semantic searches within a specific GitHub repository
|
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tool = GithubSearchTool(
|
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github_repo='https://github.com/example/repo',
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gh_token='your_github_personal_access_token',
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content_types=['code', 'issue'] # Options: code, repo, pr, issue
|
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)
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# OR
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|
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# Initialize the tool for semantic searches within a specific GitHub repository, so the agent can search any repository if it learns about during its execution
|
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tool = GithubSearchTool(
|
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gh_token='your_github_personal_access_token',
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content_types=['code', 'issue'] # Options: code, repo, pr, issue
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||||
)
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```
|
||||
|
||||
## Arguments
|
||||
|
||||
- `github_repo` : The URL of the GitHub repository where the search will be conducted. This is a mandatory field and specifies the target repository for your search.
|
||||
- `gh_token` : Your GitHub Personal Access Token (PAT) required for authentication. You can create one in your GitHub account settings under Developer Settings > Personal Access Tokens.
|
||||
- `content_types` : Specifies the types of content to include in your search. You must provide a list of content types from the following options: `code` for searching within the code,
|
||||
`repo` for searching within the repository's general information, `pr` for searching within pull requests, and `issue` for searching within issues.
|
||||
This field is mandatory and allows tailoring the search to specific content types within the GitHub repository.
|
||||
|
||||
## 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:
|
||||
|
||||
```python Code
|
||||
tool = GithubSearchTool(
|
||||
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", # or openai, ollama, ...
|
||||
config=dict(
|
||||
model="models/embedding-001",
|
||||
task_type="retrieval_document",
|
||||
# title="Embeddings",
|
||||
),
|
||||
),
|
||||
)
|
||||
)
|
||||
112
docs/tools/search-research/linkupsearchtool.mdx
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112
docs/tools/search-research/linkupsearchtool.mdx
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|
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---
|
||||
title: Linkup Search Tool
|
||||
description: The `LinkupSearchTool` enables querying the Linkup API for contextual information.
|
||||
icon: link
|
||||
---
|
||||
|
||||
# `LinkupSearchTool`
|
||||
|
||||
## Description
|
||||
|
||||
The `LinkupSearchTool` provides the ability to query the Linkup API for contextual information and retrieve structured results. This tool is ideal for enriching workflows with up-to-date and reliable information from Linkup, allowing agents to access relevant data during their tasks.
|
||||
|
||||
## Installation
|
||||
|
||||
To use this tool, you need to install the Linkup SDK:
|
||||
|
||||
```shell
|
||||
uv add linkup-sdk
|
||||
```
|
||||
|
||||
## Steps to Get Started
|
||||
|
||||
To effectively use the `LinkupSearchTool`, follow these steps:
|
||||
|
||||
1. **API Key**: Obtain a Linkup API key.
|
||||
2. **Environment Setup**: Set up your environment with the API key.
|
||||
3. **Install SDK**: Install the Linkup SDK using the command above.
|
||||
|
||||
## Example
|
||||
|
||||
The following example demonstrates how to initialize the tool and use it in an agent:
|
||||
|
||||
```python Code
|
||||
from crewai_tools import LinkupSearchTool
|
||||
from crewai import Agent
|
||||
import os
|
||||
|
||||
# Initialize the tool with your API key
|
||||
linkup_tool = LinkupSearchTool(api_key=os.getenv("LINKUP_API_KEY"))
|
||||
|
||||
# Define an agent that uses the tool
|
||||
@agent
|
||||
def researcher(self) -> Agent:
|
||||
'''
|
||||
This agent uses the LinkupSearchTool to retrieve contextual information
|
||||
from the Linkup API.
|
||||
'''
|
||||
return Agent(
|
||||
config=self.agents_config["researcher"],
|
||||
tools=[linkup_tool]
|
||||
)
|
||||
```
|
||||
|
||||
## Parameters
|
||||
|
||||
The `LinkupSearchTool` accepts the following parameters:
|
||||
|
||||
### Constructor Parameters
|
||||
- **api_key**: Required. Your Linkup API key.
|
||||
|
||||
### Run Parameters
|
||||
- **query**: Required. The search term or phrase.
|
||||
- **depth**: Optional. The search depth. Default is "standard".
|
||||
- **output_type**: Optional. The type of output. Default is "searchResults".
|
||||
|
||||
## Advanced Usage
|
||||
|
||||
You can customize the search parameters for more specific results:
|
||||
|
||||
```python Code
|
||||
# Perform a search with custom parameters
|
||||
results = linkup_tool.run(
|
||||
query="Women Nobel Prize Physics",
|
||||
depth="deep",
|
||||
output_type="searchResults"
|
||||
)
|
||||
```
|
||||
|
||||
## Return Format
|
||||
|
||||
The tool returns results in the following format:
|
||||
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"results": [
|
||||
{
|
||||
"name": "Result Title",
|
||||
"url": "https://example.com/result",
|
||||
"content": "Content of the result..."
|
||||
},
|
||||
// Additional results...
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
If an error occurs, the response will be:
|
||||
|
||||
```json
|
||||
{
|
||||
"success": false,
|
||||
"error": "Error message"
|
||||
}
|
||||
```
|
||||
|
||||
## Error Handling
|
||||
|
||||
The tool gracefully handles API errors and provides structured feedback. If the API request fails, the tool will return a dictionary with `success: false` and an error message.
|
||||
|
||||
## Conclusion
|
||||
|
||||
The `LinkupSearchTool` provides a seamless way to integrate Linkup's contextual information retrieval capabilities into your CrewAI agents. By leveraging this tool, agents can access relevant and up-to-date information to enhance their decision-making and task execution.
|
||||
71
docs/tools/search-research/overview.mdx
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71
docs/tools/search-research/overview.mdx
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@@ -0,0 +1,71 @@
|
||||
---
|
||||
title: "Overview"
|
||||
description: "Perform web searches, find repositories, and research information across the internet"
|
||||
icon: "face-smile"
|
||||
---
|
||||
|
||||
These tools enable your agents to search the web, research topics, and find information across various platforms including search engines, GitHub, and YouTube.
|
||||
|
||||
## **Available Tools**
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Serper Dev Tool" icon="google" href="/tools/search-research/serperdevtool">
|
||||
Google search API integration for comprehensive web search capabilities.
|
||||
</Card>
|
||||
|
||||
<Card title="Brave Search Tool" icon="shield" href="/tools/search-research/bravesearchtool">
|
||||
Privacy-focused search with Brave's independent search index.
|
||||
</Card>
|
||||
|
||||
<Card title="Exa Search Tool" icon="magnifying-glass" href="/tools/search-research/exasearchtool">
|
||||
AI-powered search for finding specific and relevant content.
|
||||
</Card>
|
||||
|
||||
<Card title="LinkUp Search Tool" icon="link" href="/tools/search-research/linkupsearchtool">
|
||||
Real-time web search with fresh content indexing.
|
||||
</Card>
|
||||
|
||||
<Card title="GitHub Search Tool" icon="github" href="/tools/search-research/githubsearchtool">
|
||||
Search GitHub repositories, code, issues, and documentation.
|
||||
</Card>
|
||||
|
||||
<Card title="Website Search Tool" icon="globe" href="/tools/search-research/websitesearchtool">
|
||||
Search within specific websites and domains.
|
||||
</Card>
|
||||
|
||||
<Card title="Code Docs Search Tool" icon="code" href="/tools/search-research/codedocssearchtool">
|
||||
Search through code documentation and technical resources.
|
||||
</Card>
|
||||
|
||||
<Card title="YouTube Channel Search" icon="youtube" href="/tools/search-research/youtubechannelsearchtool">
|
||||
Search YouTube channels for specific content and creators.
|
||||
</Card>
|
||||
|
||||
<Card title="YouTube Video Search" icon="play" href="/tools/search-research/youtubevideosearchtool">
|
||||
Find and analyze YouTube videos by topic, keyword, or criteria.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
## **Common Use Cases**
|
||||
|
||||
- **Market Research**: Search for industry trends and competitor analysis
|
||||
- **Content Discovery**: Find relevant articles, videos, and resources
|
||||
- **Code Research**: Search repositories and documentation for solutions
|
||||
- **Lead Generation**: Research companies and individuals
|
||||
- **Academic Research**: Find scholarly articles and technical papers
|
||||
|
||||
```python
|
||||
from crewai_tools import SerperDevTool, GitHubSearchTool, YoutubeVideoSearchTool
|
||||
|
||||
# Create research tools
|
||||
web_search = SerperDevTool()
|
||||
code_search = GitHubSearchTool()
|
||||
video_research = YoutubeVideoSearchTool()
|
||||
|
||||
# Add to your agent
|
||||
agent = Agent(
|
||||
role="Research Analyst",
|
||||
tools=[web_search, code_search, video_research],
|
||||
goal="Gather comprehensive information on any topic"
|
||||
)
|
||||
```
|
||||
112
docs/tools/search-research/serperdevtool.mdx
Normal file
112
docs/tools/search-research/serperdevtool.mdx
Normal file
@@ -0,0 +1,112 @@
|
||||
---
|
||||
title: Google Serper Search
|
||||
description: The `SerperDevTool` is designed to search the internet and return the most relevant results.
|
||||
icon: google
|
||||
---
|
||||
|
||||
# `SerperDevTool`
|
||||
|
||||
<Note>
|
||||
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
|
||||
</Note>
|
||||
|
||||
## Description
|
||||
|
||||
This tool is designed to perform a semantic search for a specified query from a text's content across the internet. It utilizes the [serper.dev](https://serper.dev) API
|
||||
to fetch and display the most relevant search results based on the query provided by the user.
|
||||
|
||||
## Installation
|
||||
|
||||
To incorporate this tool into your project, follow the installation instructions below:
|
||||
|
||||
```shell
|
||||
pip install 'crewai[tools]'
|
||||
```
|
||||
|
||||
## Example
|
||||
|
||||
The following example demonstrates how to initialize the tool and execute a search with a given query:
|
||||
|
||||
```python Code
|
||||
from crewai_tools import SerperDevTool
|
||||
|
||||
# Initialize the tool for internet searching capabilities
|
||||
tool = SerperDevTool()
|
||||
```
|
||||
|
||||
## Steps to Get Started
|
||||
|
||||
To effectively use the `SerperDevTool`, follow these steps:
|
||||
|
||||
1. **Package Installation**: Confirm that the `crewai[tools]` package is installed in your Python environment.
|
||||
2. **API Key Acquisition**: Acquire a `serper.dev` API key by registering for a free account at `serper.dev`.
|
||||
3. **Environment Configuration**: Store your obtained API key in an environment variable named `SERPER_API_KEY` to facilitate its use by the tool.
|
||||
|
||||
## Parameters
|
||||
|
||||
The `SerperDevTool` comes with several parameters that will be passed to the API :
|
||||
|
||||
- **search_url**: The URL endpoint for the search API. (Default is `https://google.serper.dev/search`)
|
||||
|
||||
- **country**: Optional. Specify the country for the search results.
|
||||
- **location**: Optional. Specify the location for the search results.
|
||||
- **locale**: Optional. Specify the locale for the search results.
|
||||
- **n_results**: Number of search results to return. Default is `10`.
|
||||
|
||||
The values for `country`, `location`, `locale` and `search_url` can be found on the [Serper Playground](https://serper.dev/playground).
|
||||
|
||||
## Example with Parameters
|
||||
|
||||
Here is an example demonstrating how to use the tool with additional parameters:
|
||||
|
||||
```python Code
|
||||
from crewai_tools import SerperDevTool
|
||||
|
||||
tool = SerperDevTool(
|
||||
search_url="https://google.serper.dev/scholar",
|
||||
n_results=2,
|
||||
)
|
||||
|
||||
print(tool.run(search_query="ChatGPT"))
|
||||
|
||||
# Using Tool: Search the internet
|
||||
|
||||
# Search results: Title: Role of chat gpt in public health
|
||||
# Link: https://link.springer.com/article/10.1007/s10439-023-03172-7
|
||||
# Snippet: … ChatGPT in public health. In this overview, we will examine the potential uses of ChatGPT in
|
||||
# ---
|
||||
# Title: Potential use of chat gpt in global warming
|
||||
# Link: https://link.springer.com/article/10.1007/s10439-023-03171-8
|
||||
# Snippet: … as ChatGPT, have the potential to play a critical role in advancing our understanding of climate
|
||||
# ---
|
||||
|
||||
```
|
||||
|
||||
```python Code
|
||||
from crewai_tools import SerperDevTool
|
||||
|
||||
tool = SerperDevTool(
|
||||
country="fr",
|
||||
locale="fr",
|
||||
location="Paris, Paris, Ile-de-France, France",
|
||||
n_results=2,
|
||||
)
|
||||
|
||||
print(tool.run(search_query="Jeux Olympiques"))
|
||||
|
||||
# Using Tool: Search the internet
|
||||
|
||||
# Search results: Title: Jeux Olympiques de Paris 2024 - Actualités, calendriers, résultats
|
||||
# Link: https://olympics.com/fr/paris-2024
|
||||
# Snippet: Quels sont les sports présents aux Jeux Olympiques de Paris 2024 ? · Athlétisme · Aviron · Badminton · Basketball · Basketball 3x3 · Boxe · Breaking · Canoë ...
|
||||
# ---
|
||||
# Title: Billetterie Officielle de Paris 2024 - Jeux Olympiques et Paralympiques
|
||||
# Link: https://tickets.paris2024.org/
|
||||
# Snippet: Achetez vos billets exclusivement sur le site officiel de la billetterie de Paris 2024 pour participer au plus grand événement sportif au monde.
|
||||
# ---
|
||||
```
|
||||
|
||||
## Conclusion
|
||||
|
||||
By integrating the `SerperDevTool` into Python projects, users gain the ability to conduct real-time, relevant searches across the internet directly from their applications.
|
||||
The updated parameters allow for more customized and localized search results. By adhering to the setup and usage guidelines provided, incorporating this tool into projects is streamlined and straightforward.
|
||||
77
docs/tools/search-research/websitesearchtool.mdx
Normal file
77
docs/tools/search-research/websitesearchtool.mdx
Normal file
@@ -0,0 +1,77 @@
|
||||
---
|
||||
title: Website RAG Search
|
||||
description: The `WebsiteSearchTool` is designed to perform a RAG (Retrieval-Augmented Generation) search within the content of a website.
|
||||
icon: globe-stand
|
||||
---
|
||||
|
||||
# `WebsiteSearchTool`
|
||||
|
||||
<Note>
|
||||
The WebsiteSearchTool is currently in an experimental phase. We are actively working on incorporating this tool into our suite of offerings and will update the documentation accordingly.
|
||||
</Note>
|
||||
|
||||
## Description
|
||||
|
||||
The WebsiteSearchTool is designed as a concept for conducting semantic searches within the content of websites.
|
||||
It aims to leverage advanced machine learning models like Retrieval-Augmented Generation (RAG) to navigate and extract information from specified URLs efficiently.
|
||||
This tool intends to offer flexibility, allowing users to perform searches across any website or focus on specific websites of interest.
|
||||
Please note, the current implementation details of the WebsiteSearchTool are under development, and its functionalities as described may not yet be accessible.
|
||||
|
||||
## Installation
|
||||
|
||||
To prepare your environment for when the WebsiteSearchTool becomes available, you can install the foundational package with:
|
||||
|
||||
```shell
|
||||
pip install 'crewai[tools]'
|
||||
```
|
||||
|
||||
This command installs the necessary dependencies to ensure that once the tool is fully integrated, users can start using it immediately.
|
||||
|
||||
## Example Usage
|
||||
|
||||
Below are examples of how the WebsiteSearchTool could be utilized in different scenarios. Please note, these examples are illustrative and represent planned functionality:
|
||||
|
||||
```python Code
|
||||
from crewai_tools import WebsiteSearchTool
|
||||
|
||||
# Example of initiating tool that agents can use
|
||||
# to search across any discovered websites
|
||||
tool = WebsiteSearchTool()
|
||||
|
||||
# Example of limiting the search to the content of a specific website,
|
||||
# so now agents can only search within that website
|
||||
tool = WebsiteSearchTool(website='https://example.com')
|
||||
```
|
||||
|
||||
## Arguments
|
||||
|
||||
- `website`: An optional argument intended to specify the website URL for focused searches. This argument is designed to enhance the tool's flexibility by allowing targeted searches when necessary.
|
||||
|
||||
## Customization Options
|
||||
|
||||
By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
|
||||
|
||||
|
||||
```python Code
|
||||
tool = WebsiteSearchTool(
|
||||
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", # or openai, ollama, ...
|
||||
config=dict(
|
||||
model="models/embedding-001",
|
||||
task_type="retrieval_document",
|
||||
# title="Embeddings",
|
||||
),
|
||||
),
|
||||
)
|
||||
)
|
||||
```
|
||||
194
docs/tools/search-research/youtubechannelsearchtool.mdx
Normal file
194
docs/tools/search-research/youtubechannelsearchtool.mdx
Normal file
@@ -0,0 +1,194 @@
|
||||
---
|
||||
title: YouTube Channel RAG Search
|
||||
description: The `YoutubeChannelSearchTool` is designed to perform a RAG (Retrieval-Augmented Generation) search within the content of a Youtube channel.
|
||||
icon: youtube
|
||||
---
|
||||
|
||||
# `YoutubeChannelSearchTool`
|
||||
|
||||
<Note>
|
||||
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
|
||||
</Note>
|
||||
|
||||
## Description
|
||||
|
||||
This tool is designed to perform semantic searches within a specific Youtube channel's content.
|
||||
Leveraging the RAG (Retrieval-Augmented Generation) methodology, it provides relevant search results,
|
||||
making it invaluable for extracting information or finding specific content without the need to manually sift through videos.
|
||||
It streamlines the search process within Youtube channels, catering to researchers, content creators, and viewers seeking specific information or topics.
|
||||
|
||||
## Installation
|
||||
|
||||
To utilize the YoutubeChannelSearchTool, the `crewai_tools` package must be installed. Execute the following command in your shell to install:
|
||||
|
||||
```shell
|
||||
pip install 'crewai[tools]'
|
||||
```
|
||||
|
||||
## Example
|
||||
|
||||
The following example demonstrates how to use the `YoutubeChannelSearchTool` with a CrewAI agent:
|
||||
|
||||
```python Code
|
||||
from crewai import Agent, Task, Crew
|
||||
from crewai_tools import YoutubeChannelSearchTool
|
||||
|
||||
# Initialize the tool for general YouTube channel searches
|
||||
youtube_channel_tool = YoutubeChannelSearchTool()
|
||||
|
||||
# Define an agent that uses the tool
|
||||
channel_researcher = Agent(
|
||||
role="Channel Researcher",
|
||||
goal="Extract relevant information from YouTube channels",
|
||||
backstory="An expert researcher who specializes in analyzing YouTube channel content.",
|
||||
tools=[youtube_channel_tool],
|
||||
verbose=True,
|
||||
)
|
||||
|
||||
# Example task to search for information in a specific channel
|
||||
research_task = Task(
|
||||
description="Search for information about machine learning tutorials in the YouTube channel {youtube_channel_handle}",
|
||||
expected_output="A summary of the key machine learning tutorials available on the channel.",
|
||||
agent=channel_researcher,
|
||||
)
|
||||
|
||||
# Create and run the crew
|
||||
crew = Crew(agents=[channel_researcher], tasks=[research_task])
|
||||
result = crew.kickoff(inputs={"youtube_channel_handle": "@exampleChannel"})
|
||||
```
|
||||
|
||||
You can also initialize the tool with a specific YouTube channel handle:
|
||||
|
||||
```python Code
|
||||
# Initialize the tool with a specific YouTube channel handle
|
||||
youtube_channel_tool = YoutubeChannelSearchTool(
|
||||
youtube_channel_handle='@exampleChannel'
|
||||
)
|
||||
|
||||
# Define an agent that uses the tool
|
||||
channel_researcher = Agent(
|
||||
role="Channel Researcher",
|
||||
goal="Extract relevant information from a specific YouTube channel",
|
||||
backstory="An expert researcher who specializes in analyzing YouTube channel content.",
|
||||
tools=[youtube_channel_tool],
|
||||
verbose=True,
|
||||
)
|
||||
```
|
||||
|
||||
## Parameters
|
||||
|
||||
The `YoutubeChannelSearchTool` accepts the following parameters:
|
||||
|
||||
- **youtube_channel_handle**: Optional. The handle of the YouTube channel to search within. If provided during initialization, the agent won't need to specify it when using the tool. If the handle doesn't start with '@', it will be automatically added.
|
||||
- **config**: Optional. Configuration for the underlying RAG system, including LLM and embedder settings.
|
||||
- **summarize**: Optional. Whether to summarize the retrieved content. Default is `False`.
|
||||
|
||||
When using the tool with an agent, the agent will need to provide:
|
||||
|
||||
- **search_query**: Required. The search query to find relevant information in the channel content.
|
||||
- **youtube_channel_handle**: Required only if not provided during initialization. The handle of the YouTube channel to search within.
|
||||
|
||||
## 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:
|
||||
|
||||
```python Code
|
||||
youtube_channel_tool = YoutubeChannelSearchTool(
|
||||
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", # or openai, ollama, ...
|
||||
config=dict(
|
||||
model="models/embedding-001",
|
||||
task_type="retrieval_document",
|
||||
# title="Embeddings",
|
||||
),
|
||||
),
|
||||
)
|
||||
)
|
||||
```
|
||||
|
||||
## Agent Integration Example
|
||||
|
||||
Here's a more detailed example of how to integrate the `YoutubeChannelSearchTool` with a CrewAI agent:
|
||||
|
||||
```python Code
|
||||
from crewai import Agent, Task, Crew
|
||||
from crewai_tools import YoutubeChannelSearchTool
|
||||
|
||||
# Initialize the tool
|
||||
youtube_channel_tool = YoutubeChannelSearchTool()
|
||||
|
||||
# Define an agent that uses the tool
|
||||
channel_researcher = Agent(
|
||||
role="Channel Researcher",
|
||||
goal="Extract and analyze information from YouTube channels",
|
||||
backstory="""You are an expert channel researcher who specializes in extracting
|
||||
and analyzing information from YouTube channels. You have a keen eye for detail
|
||||
and can quickly identify key points and insights from video content across an entire channel.""",
|
||||
tools=[youtube_channel_tool],
|
||||
verbose=True,
|
||||
)
|
||||
|
||||
# Create a task for the agent
|
||||
research_task = Task(
|
||||
description="""
|
||||
Search for information about data science projects and tutorials
|
||||
in the YouTube channel {youtube_channel_handle}.
|
||||
|
||||
Focus on:
|
||||
1. Key data science techniques covered
|
||||
2. Popular tutorial series
|
||||
3. Most viewed or recommended videos
|
||||
|
||||
Provide a comprehensive summary of these points.
|
||||
""",
|
||||
expected_output="A detailed summary of data science content available on the channel.",
|
||||
agent=channel_researcher,
|
||||
)
|
||||
|
||||
# Run the task
|
||||
crew = Crew(agents=[channel_researcher], tasks=[research_task])
|
||||
result = crew.kickoff(inputs={"youtube_channel_handle": "@exampleDataScienceChannel"})
|
||||
```
|
||||
|
||||
## Implementation Details
|
||||
|
||||
The `YoutubeChannelSearchTool` is implemented as a subclass of `RagTool`, which provides the base functionality for Retrieval-Augmented Generation:
|
||||
|
||||
```python Code
|
||||
class YoutubeChannelSearchTool(RagTool):
|
||||
name: str = "Search a Youtube Channels content"
|
||||
description: str = "A tool that can be used to semantic search a query from a Youtube Channels content."
|
||||
args_schema: Type[BaseModel] = YoutubeChannelSearchToolSchema
|
||||
|
||||
def __init__(self, youtube_channel_handle: Optional[str] = None, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
if youtube_channel_handle is not None:
|
||||
kwargs["data_type"] = DataType.YOUTUBE_CHANNEL
|
||||
self.add(youtube_channel_handle)
|
||||
self.description = f"A tool that can be used to semantic search a query the {youtube_channel_handle} Youtube Channels content."
|
||||
self.args_schema = FixedYoutubeChannelSearchToolSchema
|
||||
self._generate_description()
|
||||
|
||||
def add(
|
||||
self,
|
||||
youtube_channel_handle: str,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
if not youtube_channel_handle.startswith("@"):
|
||||
youtube_channel_handle = f"@{youtube_channel_handle}"
|
||||
super().add(youtube_channel_handle, **kwargs)
|
||||
```
|
||||
|
||||
## Conclusion
|
||||
|
||||
The `YoutubeChannelSearchTool` provides a powerful way to search and extract information from YouTube channel content using RAG techniques. By enabling agents to search across an entire channel's videos, it facilitates information extraction and analysis tasks that would otherwise be difficult to perform. This tool is particularly useful for research, content analysis, and knowledge extraction from YouTube channels.
|
||||
187
docs/tools/search-research/youtubevideosearchtool.mdx
Normal file
187
docs/tools/search-research/youtubevideosearchtool.mdx
Normal file
@@ -0,0 +1,187 @@
|
||||
---
|
||||
title: YouTube Video RAG Search
|
||||
description: The `YoutubeVideoSearchTool` is designed to perform a RAG (Retrieval-Augmented Generation) search within the content of a Youtube video.
|
||||
icon: youtube
|
||||
---
|
||||
|
||||
# `YoutubeVideoSearchTool`
|
||||
|
||||
<Note>
|
||||
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
|
||||
</Note>
|
||||
|
||||
## Description
|
||||
|
||||
This tool is part of the `crewai_tools` package and is designed to perform semantic searches within Youtube video content, utilizing Retrieval-Augmented Generation (RAG) techniques.
|
||||
It is one of several "Search" tools in the package that leverage RAG for different sources.
|
||||
The YoutubeVideoSearchTool allows for flexibility in searches; users can search across any Youtube video content without specifying a video URL,
|
||||
or they can target their search to a specific Youtube video by providing its URL.
|
||||
|
||||
## Installation
|
||||
|
||||
To utilize the `YoutubeVideoSearchTool`, you must first install the `crewai_tools` package.
|
||||
This package contains the `YoutubeVideoSearchTool` among other utilities designed to enhance your data analysis and processing tasks.
|
||||
Install the package by executing the following command in your terminal:
|
||||
|
||||
```shell
|
||||
pip install 'crewai[tools]'
|
||||
```
|
||||
|
||||
## Example
|
||||
|
||||
The following example demonstrates how to use the `YoutubeVideoSearchTool` with a CrewAI agent:
|
||||
|
||||
```python Code
|
||||
from crewai import Agent, Task, Crew
|
||||
from crewai_tools import YoutubeVideoSearchTool
|
||||
|
||||
# Initialize the tool for general YouTube video searches
|
||||
youtube_search_tool = YoutubeVideoSearchTool()
|
||||
|
||||
# Define an agent that uses the tool
|
||||
video_researcher = Agent(
|
||||
role="Video Researcher",
|
||||
goal="Extract relevant information from YouTube videos",
|
||||
backstory="An expert researcher who specializes in analyzing video content.",
|
||||
tools=[youtube_search_tool],
|
||||
verbose=True,
|
||||
)
|
||||
|
||||
# Example task to search for information in a specific video
|
||||
research_task = Task(
|
||||
description="Search for information about machine learning frameworks in the YouTube video at {youtube_video_url}",
|
||||
expected_output="A summary of the key machine learning frameworks mentioned in the video.",
|
||||
agent=video_researcher,
|
||||
)
|
||||
|
||||
# Create and run the crew
|
||||
crew = Crew(agents=[video_researcher], tasks=[research_task])
|
||||
result = crew.kickoff(inputs={"youtube_video_url": "https://youtube.com/watch?v=example"})
|
||||
```
|
||||
|
||||
You can also initialize the tool with a specific YouTube video URL:
|
||||
|
||||
```python Code
|
||||
# Initialize the tool with a specific YouTube video URL
|
||||
youtube_search_tool = YoutubeVideoSearchTool(
|
||||
youtube_video_url='https://youtube.com/watch?v=example'
|
||||
)
|
||||
|
||||
# Define an agent that uses the tool
|
||||
video_researcher = Agent(
|
||||
role="Video Researcher",
|
||||
goal="Extract relevant information from a specific YouTube video",
|
||||
backstory="An expert researcher who specializes in analyzing video content.",
|
||||
tools=[youtube_search_tool],
|
||||
verbose=True,
|
||||
)
|
||||
```
|
||||
|
||||
## Parameters
|
||||
|
||||
The `YoutubeVideoSearchTool` accepts the following parameters:
|
||||
|
||||
- **youtube_video_url**: Optional. The URL of the YouTube video to search within. If provided during initialization, the agent won't need to specify it when using the tool.
|
||||
- **config**: Optional. Configuration for the underlying RAG system, including LLM and embedder settings.
|
||||
- **summarize**: Optional. Whether to summarize the retrieved content. Default is `False`.
|
||||
|
||||
When using the tool with an agent, the agent will need to provide:
|
||||
|
||||
- **search_query**: Required. The search query to find relevant information in the video content.
|
||||
- **youtube_video_url**: Required only if not provided during initialization. The URL of the YouTube video to search within.
|
||||
|
||||
## 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:
|
||||
|
||||
```python Code
|
||||
youtube_search_tool = YoutubeVideoSearchTool(
|
||||
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", # or openai, ollama, ...
|
||||
config=dict(
|
||||
model="models/embedding-001",
|
||||
task_type="retrieval_document",
|
||||
# title="Embeddings",
|
||||
),
|
||||
),
|
||||
)
|
||||
)
|
||||
```
|
||||
|
||||
## Agent Integration Example
|
||||
|
||||
Here's a more detailed example of how to integrate the `YoutubeVideoSearchTool` with a CrewAI agent:
|
||||
|
||||
```python Code
|
||||
from crewai import Agent, Task, Crew
|
||||
from crewai_tools import YoutubeVideoSearchTool
|
||||
|
||||
# Initialize the tool
|
||||
youtube_search_tool = YoutubeVideoSearchTool()
|
||||
|
||||
# Define an agent that uses the tool
|
||||
video_researcher = Agent(
|
||||
role="Video Researcher",
|
||||
goal="Extract and analyze information from YouTube videos",
|
||||
backstory="""You are an expert video researcher who specializes in extracting
|
||||
and analyzing information from YouTube videos. You have a keen eye for detail
|
||||
and can quickly identify key points and insights from video content.""",
|
||||
tools=[youtube_search_tool],
|
||||
verbose=True,
|
||||
)
|
||||
|
||||
# Create a task for the agent
|
||||
research_task = Task(
|
||||
description="""
|
||||
Search for information about recent advancements in artificial intelligence
|
||||
in the YouTube video at {youtube_video_url}.
|
||||
|
||||
Focus on:
|
||||
1. Key AI technologies mentioned
|
||||
2. Real-world applications discussed
|
||||
3. Future predictions made by the speaker
|
||||
|
||||
Provide a comprehensive summary of these points.
|
||||
""",
|
||||
expected_output="A detailed summary of AI advancements, applications, and future predictions from the video.",
|
||||
agent=video_researcher,
|
||||
)
|
||||
|
||||
# Run the task
|
||||
crew = Crew(agents=[video_researcher], tasks=[research_task])
|
||||
result = crew.kickoff(inputs={"youtube_video_url": "https://youtube.com/watch?v=example"})
|
||||
```
|
||||
|
||||
## Implementation Details
|
||||
|
||||
The `YoutubeVideoSearchTool` is implemented as a subclass of `RagTool`, which provides the base functionality for Retrieval-Augmented Generation:
|
||||
|
||||
```python Code
|
||||
class YoutubeVideoSearchTool(RagTool):
|
||||
name: str = "Search a Youtube Video content"
|
||||
description: str = "A tool that can be used to semantic search a query from a Youtube Video content."
|
||||
args_schema: Type[BaseModel] = YoutubeVideoSearchToolSchema
|
||||
|
||||
def __init__(self, youtube_video_url: Optional[str] = None, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
if youtube_video_url is not None:
|
||||
kwargs["data_type"] = DataType.YOUTUBE_VIDEO
|
||||
self.add(youtube_video_url)
|
||||
self.description = f"A tool that can be used to semantic search a query the {youtube_video_url} Youtube Video content."
|
||||
self.args_schema = FixedYoutubeVideoSearchToolSchema
|
||||
self._generate_description()
|
||||
```
|
||||
|
||||
## Conclusion
|
||||
|
||||
The `YoutubeVideoSearchTool` provides a powerful way to search and extract information from YouTube video content using RAG techniques. By enabling agents to search within video content, it facilitates information extraction and analysis tasks that would otherwise be difficult to perform. This tool is particularly useful for research, content analysis, and knowledge extraction from video sources.
|
||||
Reference in New Issue
Block a user