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crewAI/docs/tools/PDFSearchTool.md
Bruno Tanabe 9e61b8325b fix: Fix grammar error in documentation in PDF Search Tool (#819)
Correction of grammar error in the CrewAI documentation, on the page 'https://docs.crewai.com/tools/PDFSearchTool/' it says 'Optinal' instead of 'Optional'.
2024-06-27 00:41:22 -03:00

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# PDFSearchTool
!!! note "Experimental"
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
## Description
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. This capability makes it especially useful for extracting specific information from large PDF files quickly.
## Installation
To get started with the PDFSearchTool, first, ensure the crewai_tools package is installed with the following command:
```shell
pip install 'crewai[tools]'
```
## Example
Here's how to use the PDFSearchTool to search within a PDF document:
```python
from crewai_tools import PDFSearchTool
# Initialize the tool allowing for any PDF content search if the path is provided during execution
tool = PDFSearchTool()
# OR
# Initialize the tool with a specific PDF path for exclusive search within that document
tool = PDFSearchTool(pdf='path/to/your/document.pdf')
```
## Arguments
- `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.
## 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", # or openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
)
```