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81 lines
2.3 KiB
Markdown
81 lines
2.3 KiB
Markdown
### Example 1: Fetching Research Papers from arXiv with CrewAI
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This example demonstrates how to build a simple CrewAI workflow that automatically searches for and downloads academic papers from [arXiv.org](https://arxiv.org). The setup uses:
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* A custom `ArxivPaperTool` to fetch metadata and download PDFs
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* A single `Agent` tasked with locating relevant papers based on a given research topic
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* A `Task` to define the data retrieval and download process
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* A sequential `Crew` to orchestrate execution
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The downloaded PDFs are saved to a local directory (`./DOWNLOADS`). Filenames are optionally based on sanitized paper titles, ensuring compatibility with your operating system.
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> The saved PDFs can be further used in **downstream tasks**, such as:
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>
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> * **RAG (Retrieval-Augmented Generation)**
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> * **Summarization**
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> * **Citation extraction**
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> * **Embedding-based search or analysis**
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---
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```
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from crewai import Agent, Task, Crew, Process, LLM
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from crewai_tools import ArxivPaperTool
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llm = LLM(
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model="ollama/llama3.1",
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base_url="http://localhost:11434",
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temperature=0.1
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)
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topic = "Crew AI"
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max_results = 3
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save_dir = "./DOWNLOADS"
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use_title_as_filename = True
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tool = ArxivPaperTool(
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download_pdfs=True,
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save_dir=save_dir,
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use_title_as_filename=True
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)
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tool.result_as_answer = True #Required,otherwise
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arxiv_paper_fetch = Agent(
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role="Arxiv Data Fetcher",
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goal=f"Retrieve relevant papers from arXiv based on a research topic {topic} and maximum number of papers to be downloaded is{max_results},try to use title as filename {use_title_as_filename} and download PDFs to {save_dir},",
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backstory="An expert in scientific data retrieval, skilled in extracting academic content from arXiv.",
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# tools=[ArxivPaperTool()],
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llm=llm,
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verbose=True,
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allow_delegation=False
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)
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fetch_task = Task(
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description=(
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f"Search arXiv for the topic '{topic}' and fetch up to {max_results} papers. "
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f"Download PDFs for analysis and store them at {save_dir}."
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),
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expected_output="PDFs saved to disk for downstream agents.",
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agent=arxiv_paper_fetch,
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tools=[tool], # Use the actual tool instance here
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)
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pdf_qa_crew = Crew(
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agents=[arxiv_paper_fetch],
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tasks=[fetch_task],
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process=Process.sequential,
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verbose=True,
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)
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result = pdf_qa_crew.kickoff()
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print(f"\n🤖 Answer:\n\n{result.raw}\n")
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```
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