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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
61 lines
2.1 KiB
Markdown
61 lines
2.1 KiB
Markdown
# DOCXSearchTool
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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 DOCXSearchTool is a RAG tool designed for semantic searching within DOCX documents. It enables users to effectively search and extract relevant information from DOCX files using query-based searches. This tool is invaluable for data analysis, information management, and research tasks, streamlining the process of finding specific information within large document collections.
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## Installation
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Install the crewai_tools package by running the following command in your terminal:
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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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The following example demonstrates initializing the DOCXSearchTool to search within any DOCX file's content or with a specific DOCX file path.
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```python
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from crewai_tools import DOCXSearchTool
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# Initialize the tool to search within any DOCX file's content
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tool = DOCXSearchTool()
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# OR
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# Initialize the tool with a specific DOCX file, so the agent can only search the content of the specified DOCX file
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tool = DOCXSearchTool(docx='path/to/your/document.docx')
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```
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## Arguments
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- `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.
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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 = DOCXSearchTool(
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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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