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Custom model docs (#368)

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Gui Vieira
2024-03-22 03:01:34 -03:00
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31
docs/tools/CSVSearchTool.md
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@@ -1,8 +1,5 @@
# CSVSearchTool
!!! note "Depend on OpenAI"
All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
!!! note "Experimental"
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
@@ -35,3 +32,31 @@ tool = CSVSearchTool()
## Arguments
- `csv` : The path to the CSV file you want to search. This is a mandatory argument if the tool was initialized without a specific CSV file; otherwise, it is optional.
## 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
tool = CSVSearchTool(
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",
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
)
```

65
docs/tools/CodeDocsSearchTool.md
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@@ -0,0 +1,65 @@
# CodeDocsSearchTool
!!! note "Experimental"
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
## Description
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.
## Installation
To start using the CodeDocsSearchTool, first, install the crewai_tools package via pip:
```
pip install 'crewai[tools]'
```
## Example
Utilize the CodeDocsSearchTool as follows to conduct searches within code documentation:
```python
from crewai_tools import CodeDocsSearchTool
# To search any code documentation content if the URL is known or discovered during its execution:
tool = CodeDocsSearchTool()
# OR
# To specifically focus your search on a given documentation site by providing its URL:
tool = CodeDocsSearchTool(docs_url='https://docs.example.com/reference')
```
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.
## Arguments
- `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.
## 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
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",
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
)
```

31
docs/tools/DOCXSearchTool.md
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@@ -1,8 +1,5 @@
# DOCXSearchTool
!!! note "Depend on OpenAI"
All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
!!! note "Experimental"
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
@@ -33,3 +30,31 @@ tool = DOCXSearchTool(docx='path/to/your/document.docx')
## Arguments
- `docx`: An optional file path to a specific DOCX document you wish to search. If not provided during initialization, the tool allows for later specification of any DOCX file's content path for searching.
## 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
tool = DOCXSearchTool(
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",
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
)
```

31
docs/tools/DirectorySearchTool.md
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@@ -1,8 +1,5 @@
# DirectorySearchTool
!!! note "Depend on OpenAI"
All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
!!! note "Experimental"
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
@@ -31,3 +28,31 @@ tool = DirectorySearchTool(directory='/path/to/directory')
## Arguments
- `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.
## 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
tool = DirectorySearchTool(
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",
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
)
```

31
docs/tools/GitHubSearchTool.md
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@@ -1,8 +1,5 @@
# GitHubSearchTool
!!! note "Depend on OpenAI"
All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
!!! note "Experimental"
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
@@ -40,3 +37,31 @@ tool = GitHubSearchTool(
## 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.
- `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
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",
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
)
```

31
docs/tools/JSONSearchTool.md
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@@ -1,8 +1,5 @@
# JSONSearchTool
!!! note "Depend on OpenAI"
All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
!!! note "Experimental"
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
@@ -31,3 +28,31 @@ tool = JSONSearchTool(json_path='./path/to/your/file.json')
## Arguments
- `json_path` (str): An optional argument that defines the path to the JSON file to be searched. This parameter is only necessary if the tool is initialized without a specific JSON path. Providing this argument restricts the search to the specified JSON file.
## 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
tool = JSONSearchTool(
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",
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
)
```

31
docs/tools/MDXSearchTool.md
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@@ -1,8 +1,5 @@
# MDXSearchTool
!!! note "Depend on OpenAI"
All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
!!! note "Experimental"
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
@@ -33,3 +30,31 @@ tool = MDXSearchTool(mdx='path/to/your/document.mdx')
## Arguments
- mdx: **Optional** The MDX path for the search. Can be provided at initialization
## 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
tool = MDXSearchTool(
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",
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
)
```

31
docs/tools/PDFSearchTool.md
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@@ -1,8 +1,5 @@
# PDFSearchTool
!!! note "Depend on OpenAI"
All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
!!! note "Experimental"
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
@@ -33,3 +30,31 @@ tool = PDFSearchTool(pdf='path/to/your/document.pdf')
## Arguments
- `pdf`: **Optinal** The PDF path for the search. Can be provided at initialization or within the `run` method's arguments. If provided at initialization, the tool confines its search to the specified document.
## 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
tool = PDFSearchTool(
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",
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
)
```

32
docs/tools/PGSearchTool.md
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@@ -1,8 +1,5 @@
# PGSearchTool
!!! note "Depend on OpenAI"
All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
!!! note "Experimental"
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
@@ -24,7 +21,6 @@ from crewai_tools import PGSearchTool
# Initialize the tool with the database URI and the target table name
tool = PGSearchTool(db_uri='postgresql://user:password@localhost:5432/mydatabase', table_name='employees')
```
## Arguments
@@ -32,3 +28,31 @@ The PGSearchTool requires the following arguments for its operation:
- `db_uri`: A string representing the URI of the PostgreSQL database to be queried. This argument is mandatory and must include the necessary authentication details and the location of the database.
- `table_name`: A string specifying the name of the table within the database on which the semantic search will be performed. This argument is mandatory.
## 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
tool = PGSearchTool(
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",
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
)
```

3
docs/tools/ScrapeWebsiteTool.md
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@@ -1,8 +1,5 @@
# ScrapeWebsiteTool
!!! note "Depend on OpenAI"
All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
!!! note "Experimental"
We are still working on improving tools, so there might be unexpected behavior or changes in the future.

31
docs/tools/TXTSearchTool.md
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@@ -1,8 +1,5 @@
# TXTSearchTool
!!! note "Depend on OpenAI"
All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
!!! note "Experimental"
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
@@ -35,3 +32,31 @@ tool = TXTSearchTool(txt='path/to/text/file.txt')
## Arguments
- `txt` (str): **Optinal**. The path to the text file you want to search. This argument is only required if the tool was not initialized with a specific text file; otherwise, the search will be conducted within the initially provided text file.
## 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
tool = TXTSearchTool(
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",
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
)
```

31
docs/tools/WebsiteSearchTool.md
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@@ -1,8 +1,5 @@
# WebsiteSearchTool
!!! note "Depend on OpenAI"
All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
!!! note "Experimental"
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
@@ -33,3 +30,31 @@ tool = WebsiteSearchTool(website='https://example.com')
## Arguments
- `website` : An optional argument that specifies the valid website URL to perform the search on. This becomes necessary if the tool is initialized without a specific website. In the `WebsiteSearchToolSchema`, this argument is mandatory. However, in the `FixedWebsiteSearchToolSchema`, it becomes optional if a website is provided during the tool's initialization, as it will then only search within the predefined website's content.
## 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
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",
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
)
```

35
docs/tools/XMLSearchTool copy.md
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@@ -1,35 +0,0 @@
# XMLSearchTool
!!! note "Depend on OpenAI"
All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
!!! note "Experimental"
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
## Description
The XMLSearchTool is a cutting-edge RAG tool engineered for conducting semantic searches within XML files. Ideal for users needing to parse and extract information from XML content efficiently, this tool supports inputting a search query and an optional XML file path. By specifying an XML path, users can target their search more precisely to the content of that file, thereby obtaining more relevant search outcomes.
## Installation
To start using the XMLSearchTool, you must first install the crewai_tools package. This can be easily done with the following command:
```shell
pip install 'crewai[tools]'
```
## Example
Here are two examples demonstrating how to use the XMLSearchTool. The first example shows searching within a specific XML file, while the second example illustrates initiating a search without predefining an XML path, providing flexibility in search scope.
```python
from crewai_tools.tools.xml_search_tool import XMLSearchTool
# Allow agents to search within any XML file's content as it learns about their paths during execution
tool = XMLSearchTool()
# OR
# Initialize the tool with a specific XML file path for exclusive search within that document
tool = XMLSearchTool(xml='path/to/your/xmlfile.xml')
```
## Arguments
- `xml`: This is the path to the XML file you wish to search. It is an optional parameter during the tool's initialization but must be provided either at initialization or as part of the `run` method's arguments to execute a search.

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docs/tools/XMLSearchTool.md
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@@ -1,8 +1,5 @@
# XMLSearchTool
!!! note "Depend on OpenAI"
All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
!!! note "Experimental"
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
@@ -33,3 +30,31 @@ tool = XMLSearchTool(xml='path/to/your/xmlfile.xml')
## Arguments
- `xml`: This is the path to the XML file you wish to search. It is an optional parameter during the tool's initialization but must be provided either at initialization or as part of the `run` method's arguments to execute a search.
## 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
tool = XMLSearchTool(
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",
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
)
```

31
docs/tools/YoutubeChannelSearchTool.md
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@@ -1,8 +1,5 @@
# YoutubeChannelSearchTool
!!! note "Depend on OpenAI"
All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
!!! note "Experimental"
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
@@ -33,3 +30,31 @@ tool = YoutubeChannelSearchTool(youtube_channel_handle='@exampleChannel')
## Arguments
- `youtube_channel_handle` : A mandatory string representing the Youtube channel handle. This parameter is crucial for initializing the tool to specify the channel you want to search within. The tool is designed to only search within the content of the provided channel handle.
## 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
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",
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
)
```

32
docs/tools/YoutubeVideoSearchTool.md
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@@ -1,8 +1,5 @@
# YoutubeVideoSearchTool
!!! note "Depend on OpenAI"
All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
!!! note "Experimental"
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
@@ -31,8 +28,37 @@ tool = YoutubeVideoSearchTool()
# Targeted search within a specific Youtube video's content
tool = YoutubeVideoSearchTool(youtube_video_url='https://youtube.com/watch?v=example')
```
## Arguments
The YoutubeVideoSearchTool accepts the following initialization arguments:
- `youtube_video_url`: An optional argument at initialization but required if targeting a specific Youtube video. It specifies the Youtube video URL path you want 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
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",
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
)
```

2
mkdocs.yml
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@@ -153,7 +153,7 @@ nav:
- Github RAG Search: 'tools/GitHubSearchTool.md'
- Code Docs RAG Search: 'tools/CodeDocsSearchTool.md'
- Youtube Video RAG Search: 'tools/YoutubeVideoSearchTool.md'
- Youtube Chanel RAG Search: 'tools/YoutubeChannelSearchTool.md'
- Youtube Channel RAG Search: 'tools/YoutubeChannelSearchTool.md'
- Examples:
- Trip Planner Crew: https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner"
- Create Instagram Post: https://github.com/joaomdmoura/crewAI-examples/tree/main/instagram_post"