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71 lines
2.2 KiB
Plaintext
71 lines
2.2 KiB
Plaintext
---
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title: PDF RAG Search
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description: The `PDFSearchTool` is designed to search PDF files and return the most relevant results.
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icon: file-pdf
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---
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# `PDFSearchTool`
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<Note>
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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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</Note>
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## Description
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The PDFSearchTool is a RAG tool designed for semantic searches within PDF content. It allows for inputting a search query and a PDF document, leveraging advanced search techniques to find relevant content efficiently.
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This capability makes it especially useful for extracting specific information from large PDF files quickly.
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## Installation
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To get started with the PDFSearchTool, first, ensure the crewai_tools package is installed with the following command:
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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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Here's how to use the PDFSearchTool to search within a PDF document:
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```python Code
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from crewai_tools import PDFSearchTool
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# Initialize the tool allowing for any PDF content search if the path is provided during execution
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tool = PDFSearchTool()
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# OR
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# Initialize the tool with a specific PDF path for exclusive search within that document
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tool = PDFSearchTool(pdf='path/to/your/document.pdf')
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```
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## Arguments
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- `pdf`: **Optional** 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.
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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 = PDFSearchTool(
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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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``` |