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How to Build a Multi-Round Deep Research Agent with Gemini, DuckDuckGo API, and Automated Reporting

In this tutorial, we will design a modular deep research system that operates directly on Google Colab. The system will utilize Gemini as the core reasoning engine, integrate DuckDuckGo’s Instant Answer API for lightweight web searches, and orchestrate multi-round querying with deduplication and delay handling. Our focus will be on efficiency by limiting API calls, parsing concise snippets, and using structured prompts to extract key points, themes, and insights.

Understanding the Target Audience

The target audience for this tutorial includes:

• Data scientists and AI researchers looking to streamline their research processes.
• Business analysts who require efficient data gathering and reporting tools.
• Developers interested in integrating AI with web search capabilities.

Common pain points include:

• Time-consuming manual research processes.
• Difficulty in synthesizing large volumes of information.
• Challenges in ensuring the relevance and accuracy of sourced data.

Goals of the audience include:

• Automating research workflows to save time.
• Improving the quality of insights derived from data.
• Creating comprehensive reports that are easy to interpret.

Interests may include:

• Advancements in AI and machine learning.
• Tools and frameworks for data analysis.
• Best practices in research methodologies.

Preferred communication methods are likely to be:

• Technical documentation and tutorials.
• Webinars and online courses.
• Community forums and discussion groups.

Building the Research System

We begin by importing essential Python libraries that handle system operations, JSON processing, web requests, and data structures. We also incorporate Google’s Generative AI SDK and utilities, such as URL encoding, to ensure our research system operates smoothly.

import os
import json
import time
import requests
from typing import List, Dict, Any
from dataclasses import dataclass
import google.generativeai as genai
from urllib.parse import quote_plus
import re

Defining Research Configuration

We define a ResearchConfig dataclass to manage parameters like API keys, source limits, and delays. Then, we build a DeepResearchSystem class that integrates Gemini with DuckDuckGo search.

@dataclass
class ResearchConfig:
   gemini_api_key: str
   max_sources: int = 10
   max_content_length: int = 5000
   search_delay: float = 1.0

Implementing the Deep Research System

The DeepResearchSystem class includes methods for web search, key point extraction, source analysis, and report generation, allowing us to orchestrate multi-round research and produce structured insights in a streamlined workflow.

class DeepResearchSystem:
   def __init__(self, config: ResearchConfig):
       self.config = config
       genai.configure(api_key=config.gemini_api_key)
       self.model = genai.GenerativeModel('gemini-1.5-flash')

   def search_web(self, query: str, num_results: int = 5) -> List[Dict[str, str]]:
       ...

Conducting Research

The conduct_research method orchestrates the entire research process, allowing for multiple rounds of querying and analysis.

def conduct_research(self, query: str, depth: str = "standard") -> Dict[str, Any]:
   ...

Generating Reports

Finally, we generate a comprehensive report based on the analysis of the sources collected during the research process.

def generate_comprehensive_report(self, query: str, sources: List[Dict[str, str]],
                                   analysis: Dict[str, Any]) -> str:
   ...

Setting Up the Research System

We create a setup_research_system function that simplifies initialization in Google Colab by wrapping our configuration in ResearchConfig and returning a ready-to-use DeepResearchSystem instance with custom limits and delays.

def setup_research_system(api_key: str) -> DeepResearchSystem:
   ...

Conclusion

In conclusion, we have demonstrated how to build a multi-round deep research agent using Gemini and DuckDuckGo API. This system allows for efficient data collection, analysis, and reporting, providing a solid foundation for more advanced research applications.

For further exploration, feel free to check out our GitHub Page for tutorials, codes, and notebooks. Also, follow us on Twitter and join our community for discussions and updates.

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