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Building an Advanced PaperQA2 Research Agent with Google Gemini for Scientific Literature Analysis

This tutorial guides you through the process of creating an advanced PaperQA2 AI Agent powered by Google’s Gemini model, specifically designed for scientific literature analysis. You’ll set up your environment in Google Colab/Notebook, configure the Gemini API, and integrate it seamlessly with PaperQA2 for processing and querying multiple research papers. By the end of this tutorial, you will have an intelligent agent capable of answering complex questions, performing multi-question analyses, and conducting comparative research across papers, all while providing clear answers backed by evidence from source documents.

Understanding Your Audience

The target audience for this tutorial primarily consists of:

• Researchers and scientists looking for efficient methods to analyze vast amounts of scientific literature.
• Business analysts in tech companies interested in leveraging AI for insights from academic papers.
• Data scientists and AI practitioners who want to explore advanced machine learning models like Gemini.
• Academic professionals and graduate students conducting literature reviews for their projects.

Identifying Pain Points

The audience faces several challenges, including:

• Difficulty in manually sifting through large volumes of academic papers.
• Lack of efficient tools for conducting comparative analyses of related studies.
• Time constraints in extracting key findings and evidence from research articles.
• Need for accurate and concise answers from myriad sources in a quick turnaround.

Goals and Interests

The primary goals of the target audience include:

• Finding reliable methodologies for literature reviews.
• Streamlining the research process by automating information extraction.
• Enhancing the depth of their analyses through AI-driven insights.

Communication Preferences

The preferred communication styles among the audience are:

• Clear, concise explanations of technical processes.
• Step-by-step guides that include code snippets and practical examples.
• Interactive demonstrations for better understanding of functionality.

Setting Up the Environment

We’ll begin by installing the required libraries, including PaperQA2 and Google’s Generative AI SDK:

!pip install paper-qa>=5 google-generativeai requests pypdf2 -q

Next, configure your API key:

import os
import google.generativeai as genai

GEMINI_API_KEY = "Use Your Own API Key Here"
os.environ["GEMINI_API_KEY"] = GEMINI_API_KEY
genai.configure(api_key=GEMINI_API_KEY)
print("Gemini API key configured successfully!")

Downloading Sample Papers

We’ll download a set of well-known AI/ML research papers for analysis:

def download_sample_papers():
   papers = {
       "attention_is_all_you_need.pdf": "https://arxiv.org/pdf/1706.03762.pdf",
       "bert_paper.pdf": "https://arxiv.org/pdf/1810.04805.pdf",
       "gpt3_paper.pdf": "https://arxiv.org/pdf/2005.14165.pdf"
   }
   papers_dir = Path("sample_papers")
   papers_dir.mkdir(exist_ok=True)

   for filename, url in papers.items():
       filepath = papers_dir / filename
       if not filepath.exists():
           response = requests.get(url, stream=True, timeout=30)
           response.raise_for_status()
           with open(filepath, 'wb') as f:
               for chunk in response.iter_content(chunk_size=8192):
                   f.write(chunk)
               print(f"Downloaded: {filename}")
       else:
           print(f"Already exists: {filename}")

   return str(papers_dir)

papers_directory = download_sample_papers()

Creating Optimized Settings for PaperQA2

def create_gemini_settings(paper_dir: str, temperature: float = 0.1):
   return Settings(
       llm="gemini/gemini-1.5-flash",
       agent=AgentSettings(
           agent_llm="gemini/gemini-1.5-flash",
           search_count=6,
           timeout=300.0,
       ),
       embedding="gemini/text-embedding-004",
       temperature=temperature,
       paper_directory=paper_dir,
       answer=dict(
           evidence_k=8,
           answer_max_sources=4,
           evidence_summary_length="about 80 words",
           answer_length="about 150 words, but can be longer",
           max_concurrent_requests=2,
       ),
       parsing=dict(
           chunk_size=4000,
           overlap=200,
       ),
       verbosity=1,
   )

Building the PaperQA Agent

Define a PaperQAAgent class to utilize the PaperQA2 settings:

class PaperQAAgent:
   def __init__(self, papers_directory: str, temperature: float = 0.1):
       self.settings = create_gemini_settings(papers_directory, temperature)
       self.papers_dir = papers_directory
       print(f"PaperQA Agent initialized with papers from: {papers_directory}")

Running Basic and Advanced Demonstrations

We demonstrate the basic functionality of PaperQA and explore advanced multi-question analysis, as well as comparative research analysis:

async def basic_demo():
   agent = PaperQAAgent(papers_directory)
   question = "What is the transformer architecture and why is it important?"
   response = await agent.ask_question(question)
   agent.display_answer(response)

async def advanced_demo():
   agent = PaperQAAgent(papers_directory, temperature=0.2)
   questions = [
       "How do attention mechanisms work in transformers?",
       "What are the computational challenges of large language models?"
   ]
   results = await agent.multi_question_analysis(questions)
   for question, response in results.items():
       print(f"Q: {question}, A: {response.answer if response else 'No answer available'}")

Creating an Interactive Agent

An interactive query helper allows users to ask custom questions:

def create_interactive_agent():
   agent = PaperQAAgent(papers_directory)

   async def query(question: str):
       response = await agent.ask_question(question)
       return response

   return query

interactive_query = create_interactive_agent()
print("Interactive agent ready! You can now ask custom questions.") 

Saving Analysis Results

Finally, you can save all analysis results to a file:

def save_analysis_results(results: dict, filename: str = "paperqa_analysis.txt"):
   with open(filename, 'w', encoding='utf-8') as f:
       f.write("PaperQA2 Analysis Results\n")
       for question, response in results.items():
           f.write(f"Question: {question}\n")
           f.write(f"Answer: {response.answer if response else 'No response available'}\n")
   print(f"Results saved to: {filename}") 

Conclusion

You now have a fully functional AI research assistant leveraging the speed and versatility of Gemini. This setup improves your ability to digest complex research, streamlining the literature review process and helping you focus on critical insights over manual searching.

Further Reading and Resources

Feel free to explore our resources for tutorials and codes, including access to additional documentation on platforms such as GitHub and relevant forums.

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