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63 lines
2.4 KiB
Markdown
63 lines
2.4 KiB
Markdown
# TXTSearchTool
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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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This tool is used to perform a RAG (Retrieval-Augmented Generation) search within the content of a text file. It allows for semantic searching of a query within a specified text file's content, making it an invaluable resource for quickly extracting information or finding specific sections of text based on the query provided.
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## Installation
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To use the TXTSearchTool, you first need to install the crewai_tools package. This can be done using pip, a package manager for Python. Open your terminal or command prompt and enter the following command:
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```shell
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pip install 'crewai[tools]'
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```
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This command will download and install the TXTSearchTool along with any necessary dependencies.
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## Example
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The following example demonstrates how to use the TXTSearchTool to search within a text file. This example shows both the initialization of the tool with a specific text file and the subsequent search within that file's content.
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```python
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from crewai_tools import TXTSearchTool
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# Initialize the tool to search within any text file's content the agent learns about during its execution
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tool = TXTSearchTool()
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# OR
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# Initialize the tool with a specific text file, so the agent can search within the given text file's content
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tool = TXTSearchTool(txt='path/to/text/file.txt')
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```
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## Arguments
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- `txt` (str): **Optional**. 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.
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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 = TXTSearchTool(
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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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