Building a Structured Research Automation System Using Pydantic

In the dynamic field of academic research, efficient information gathering, synthesis, and presentation are paramount. The manual process of literature review is time-consuming, hindering deeper analysis. A multi-agent research assistant system built with Pydantic offers a sophisticated solution: specialized agents collaborate to tackle complex tasks modularly and scalably. However, managing multiple agents requires careful consideration of data consistency, validation, and communication. This Pydantic-based system addresses these challenges by enforcing robust data schemas, improving data handling, and simplifying system complexity.

This article details the construction of a structured multi-agent research assistant using Pydantic, integrating tools like Pydantic-ai and arXiv. We'll provide step-by-step code explanations and expected results.

Key Learning Objectives

• Grasp the importance of structured data modeling in a Pydantic-powered multi-agent research assistant for reliable inter-agent communication.
• Define and implement structured data schemas using Pydantic for seamless integration, modular agent orchestration, and efficient automated research workflows.
• Design and orchestrate modular agents for specific tasks: query refinement, data retrieval, keyword extraction, and summarization.
• Integrate external APIs (like arXiv) into automated workflows via structured agent interactions.
• Generate high-quality outputs (e.g., PDF reports) directly from structured agent outputs, enhancing the practical utility of automated research workflows.

This article is part of the Data Science Blogathon.

Table of Contents

• Defining Data Models with Pydantic
• Structuring the Multi-Agent Framework
• Refining Queries with the Prompt Processor Agent
• Efficient Paper Retrieval with the Paper Retrieval Agent
• Keyword Extraction with the Keyword Extraction Agent
• Concise Summarization with the Summarization Agent
• Orchestrating Agents
• Generating Professional Outputs
• Practical Examples
• Conclusion
• Frequently Asked Questions

Defining Data Models with Pydantic

Well-defined data models are crucial in multi-agent systems. Consistent, predictable data exchange between agents is essential. Pydantic elegantly addresses this by providing a straightforward method for defining data schemas in Python, ensuring data consistency, reducing runtime errors, and enabling seamless validation.

Here's an example of structured data models using Pydantic:

from pydantic import BaseModel, Field

class PaperMetadata(BaseModel):
    title: str = Field(..., description="Paper title")
    abstract: str = Field(..., description="Paper abstract")
    authors: list[str] = Field(..., description="List of authors")
    publication_date: str = Field(..., description="Publication date")

Field Explanations

• title: Paper title for easy reference and organization.
• abstract: Concise summary for keyword extraction and summarization.
• authors: Author list for further queries or citation tracking.
• publication_date: Publication date for sorting and filtering.

Our system includes five agents:

• Prompt Processor Agent
• Paper Retrieval Agent
• Keyword Extraction Agent
• Summarization Agent
• Router (Orchestrator) Agent

These agents communicate using the Pydantic-defined models, ensuring predictable and validated data, minimizing errors, and enhancing system robustness.

We'll delve into each agent's implementation, role, and expected outputs.

Structuring the Multi-Agent Framework

Building on the Pydantic data models, we now examine the multi-agent framework. Each agent has a specific role and interacts seamlessly with others.

Agent Roles

• Prompt Processor Agent: Refines user queries for improved search relevance.
• Paper Retrieval Agent: Retrieves relevant papers from external databases (like arXiv).
• Keyword Extraction Agent: Extracts key terms from paper abstracts.
• Summarization Agent: Generates concise summaries of paper abstracts.
• Router Agent (Orchestrator): Coordinates the workflow, managing communication and data flow.

Agent Interactions

The agents interact sequentially:

1. The Prompt Processor refines the user query.
2. The refined query is sent to the Paper Retrieval Agent.
3. The Router sends abstracts to the Keyword Extraction and Summarization agents.
4. The Router compiles the results into a final report.

This modular design ensures maintainability and scalability. Agents can be improved or replaced independently. We'll explore each agent's implementation.

Refining Research Queries with the Prompt Processor Agent

Precise queries are vital for effective searching. The Prompt Processor Agent refines user queries to improve the relevance of results from academic databases.

Here's the Prompt Processor Agent implementation:

@prompt_processor_agent.tool
async def process_prompt(ctx: RunContext[ResearchContext], topic: str) -> str:
    topic = topic.strip().lower()
    if ' in ' in topic:
        subtopics = topic.split(' in ')
        main_topic = subtopics[0].strip()
        context = subtopics[1].strip()
        refined_query = f"all:{main_topic} AND cat:{context.replace(' ', '_')}"
    else:
        refined_query = f"ti:\"{topic}\" OR abs:\"{topic}\""
    return refined_query

This improved implementation normalizes input, parses contextual cues ("in"), builds structured queries, and includes fallback handling for broader topics. This leads to more precise searches.

Fetching Research Papers Efficiently with the Paper Retrieval Agent

The Paper Retrieval Agent interacts with external APIs (like arXiv) to retrieve relevant papers based on the refined query. It uses Pydantic models for consistent data handling.

@paper_retrieval_agent.tool
async def fetch_papers(ctx: RunContext[ResearchContext]) -> list[PaperMetadata]:
    search = arxiv.Search(query=ctx.deps.query, max_results=5, sort_by=arxiv.SortCriterion.SubmittedDate)
    results = list(search.results())
    papers = []
    for result in results:
        published_str = result.published.strftime("%Y-%m-%d") if hasattr(result, "published") and result.published is not None else "Unknown"
        paper = PaperMetadata(title=result.title, abstract=result.summary, authors=[author.name for author in result.authors], publication_date=published_str)
        papers.append(paper)
    return papers

Pydantic ensures data validation and consistency, simplifying downstream processing.

Extracting Valuable Keywords with the Keyword Extraction Agent

The Keyword Extraction Agent identifies key terms from abstracts to help researchers quickly assess paper relevance.

@keyword_extraction_agent.tool
async def extract_keywords(ctx: RunContext[ResearchContext], abstract: str) -> KeywordResult:
    words = abstract.split()
    seen = set()
    unique_words = []
    for word in words:
        normalized = word.strip('.,;:"()').lower()
        if normalized and normalized not in seen:
            seen.add(normalized)
            unique_words.append(normalized)
        if len(unique_words) >= 5:
            break
    return KeywordResult(keywords=unique_words)

This is a simplified example; production systems would use more advanced NLP techniques. Pydantic maintains structured, consistent outputs.

Summarizing Papers Concisely with the Summarization Agent

The Summarization Agent generates concise summaries of abstracts.

@summary_agent.tool
async def summarize_paper(ctx: RunContext[ResearchContext], abstract: str) -> PaperSummary:
    summary_text = abstract[:150]   "..." if len(abstract) > 150 else abstract
    return PaperSummary(summary=summary_text)

This is a basic example; advanced summarization models could significantly improve the quality of summaries.

Bringing it all Together: Agentic Orchestration

The Router Agent orchestrates the entire workflow.

@router_agent.tool
async def orchestrate_workflow(ctx: RunContext[ResearchContext]) -> str:
    refined_query = await prompt_processor_agent.run(ctx.deps.query, deps=ctx.deps)
    papers = await paper_retrieval_agent.run(refined_query.data, deps=ctx.deps)
    response = "Final Report:\n"
    for paper in papers.data:
        keywords = await keyword_extraction_agent.run(paper.abstract, deps=ctx.deps)
        summary = await summary_agent.run(paper.abstract, deps=ctx.deps)
        response  = (
            f"\nTitle: {paper.title}\n"
            f"Keywords: {keywords.data.keywords}\n"
            f"Summary: {summary.data.summary}\n"
        )
    return response

This uses asynchronous operations for efficiency. Structured logging aids debugging.

Generating Professional Outputs with Structured Data

The structured data is converted into a professional PDF report.

def generate_pdf_report(report_text: str, output_filename: str = "Final_Report.pdf"):
    import markdown2
    from xhtml2pdf import pisa
    html_text = markdown2.markdown(report_text)
    with open(output_filename, "w b") as result_file:
        pisa.CreatePDF(html_text, dest=result_file)

This leverages the structured data for easy conversion to a readable PDF.

Multi-Agent System in Action: Practical Examples

The system's effectiveness is demonstrated through examples. (Examples would be included here, showing the system's output for different research topics.)

Conclusion

This multi-agent research assistant system, built with Pydantic, automates research workflows efficiently and generates professional reports. Pydantic's structured data handling is key to the system's reliability and scalability.

Frequently Asked Questions

(FAQs would be included here, addressing common questions about the system.)

(Note: The image and code snippets are placeholders. The complete code would need to be provided separately.)

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