Research Orchestration Service

A powerful, AI-driven research orchestration service that gathers, analyzes, and synthesizes information from multiple sources to provide comprehensive answers to complex queries. Built on Cloudflare Workers, this service leverages multiple specialized tools and AI capabilities to deliver well-structured, properly cited research results.

Overview

This service transforms natural language queries into structured research tasks, intelligently selecting and orchestrating various specialized tools to gather relevant information. It then synthesizes the collected data into a coherent, well-cited response with confidence indicators.

Key Features

Intelligent Query Analysis:
- Advanced analysis of user queries to understand intent, entities, and constraints
- Query optimization for each specialized research tool
- Automatic URL and context extraction
Multi-tool Orchestration:
- Dynamic tool selection based on query context and relevance scoring
- Parallel execution with smart retry logic
- Automatic query adaptation for each tool
- Intelligent tool reuse across research iterations
Iterative Research Process:
- Multiple research iterations with targeted gap analysis
- Highly focused follow-up queries for missing information
- Effective handling of follow-up iterations
- Smart termination when sufficient information is gathered
AI-powered Synthesis:
- Combines information from diverse sources into comprehensive answers
- Proper citation and source attribution
- Structured formatting with sections and highlights
Quality Assurance:
- Confidence scoring for individual results and overall synthesis
- Source credibility assessment
- Relevance filtering of results
- Batch processing for efficient analysis
Performance Optimization:
- Built-in caching system using Cloudflare KV
- Parallel execution of tools
- Smart retry logic for API failures
- Efficient batching for result assessment

Architecture

Core Components

Orchestrator (src/orchestrator.ts):
- Coordinates the entire research process
- Manages tool selection and execution with intelligent reuse
- Handles result aggregation and synthesis
- Supports metadata-enriched tool execution for better context
Query Optimizer (src/queryOptimizer.ts):
- Analyzes queries for intent and context
- Optimizes queries for different tools
- Extracts entities, URLs, and YouTube video IDs
Tool Manager (src/toolManager.ts):
- Handles tool selection based on relevance
- Manages tool execution with retry logic and caching
- Provides unified error handling
- Uses a fallback to score-based selection when needed
Result Processor (src/resultProcessor.ts):
- Assesses result relevance with batch processing
- Analyzes information gaps and creates targeted follow-up queries
- Synthesizes final results
- Implements parallel processing for better performance
Formatter (src/formatter.ts):
- Structures output with proper formatting
- Handles citation management
- Provides confidence indicators

Research Process

The research process follows these steps:

Query Analysis: The query is analyzed to understand intent, extract entities, identify URLs, and determine query types.
Tool Selection:
- Tools are selected based on relevance to the query
- On initial iterations, previously used tools are filtered out
- For follow-up queries, tools can be reused to explore new aspects
Query Optimization: The query is optimized for each selected tool to maximize relevance.
Tool Execution:
- Tools are executed in parallel with metadata context
- Automatic extraction of URLs and YouTube video IDs when relevant
- Results are cached for performance
Relevance Assessment:
- Results are assessed for relevance against the original query
- Processing occurs in batches to handle large result sets efficiently
- Diversity is ensured through batch processing
Gap Analysis:
- Information gaps are identified
- Targeted follow-up queries focus on missing aspects
- Analysis provides specific missing aspects and explains the gaps
Iteration:
- Process repeats with follow-up queries until gaps are filled
- Tools can be reused between iterations for different aspects
- Each iteration builds on previous knowledge
Synthesis:
- All relevant results are synthesized into a comprehensive answer
- Sources are properly cited and organized
- Confidence score is calculated based on source quality and content

Research Tools

The service integrates with multiple specialized research tools:

Web Search:
- Brave Search: Privacy-focused web search with broad coverage
- Tavily Search: AI-powered search with enhanced relevance
Technical Information:
- GitHub Repository Search: Find relevant repositories
- GitHub Code Search: Search for code examples and implementations
- Stack Exchange: Technical Q&A from Stack Overflow and related sites
Academic Research:
- arXiv: Academic papers and preprints
- Patent Search: Intellectual property and innovation tracking
Current Events:
- News API: Recent news and developments
- Media Monitoring: Current events tracking
Content Extraction:
- Fire Crawl: Web content extraction and analysis
- YouTube Transcript: Video content transcription
Reference Information:
- Wikipedia Search: General knowledge and reference
- Book Search: Literature and bibliographic information

Usage

Basic Example

const worker = new ResearchWorker();

const result = await worker.research(
  "What are the latest developments in quantum computing?",
  3  // Research depth (1-5)
);

console.log(result.content[0].text);

Response Format

interface ResearchResult {
  answer: string;          // Synthesized research answer
  sources: Source[];       // List of sources used
  confidence: number;      // Overall confidence score (0-1)
  metadata: {
    executionTime: number;
    iterations: number;
    totalResults: number;
    queryTypes: string[];
    toolsUsed: string[];
    toolResults: ToolResult[];
  }
}

Setup and Configuration

Prerequisites

Cloudflare Workers account
Node.js 16+
Wrangler CLI

Required API Keys

# Core APIs
BRAVE_API_KEY=your_brave_api_key
TAVILY_API_KEY=your_tavily_api_key
GITHUB_TOKEN=your_github_token
FIRE_CRAWL_API_KEY=your_fire_crawl_api_key
NEWS_API_KEY=your_news_api_key
PATENTSVIEW_API_KEY=your_patentsview_api_key

# LLM APIs
OPENAI_API_KEY=your_openai_api_key
GROQ_API_KEY=your_groq_api_key

# Cloudflare Resources
SHARED_SECRET=your_shared_secret     # For API authentication
RESEARCH_CACHE=your_kv_namespace     # For result caching

Installation

Clone the repository:

git clone https://github.com/yourusername/research-orchestration-service.git
cd research-orchestration-service

Install dependencies:
```
npm install
```

Configure environment variables:

cp .env.example .env
# Edit .env with your API keys

Deploy to Cloudflare Workers:
```
wrangler publish
```

Configuration Options

Research Depth

The depth parameter (1-5) controls:

Number of research iterations
Number of tools used (depth * 1.5)
Result synthesis complexity

Tool Selection

Tool selection is managed through:

Relevance scoring in src/tools.ts
Tool compatibility metadata for query types
Intelligent reuse between iterations
Context-aware query optimization

Batch Processing

The system uses batch processing for:

Relevance assessment (batch size: 5, parallel batches: 3)
Gap analysis (batch size: 8)
Final diversity pass for large result sets

Caching

The system implements caching at multiple levels:

Full research results (TTL: 3 days)
Individual tool executions
Contextual metadata to improve cache hits

Advanced Features

Targeted Follow-up Queries

The system generates highly targeted follow-up queries that:

Focus specifically on missing information
Use precise terms related to identified gaps
Avoid repeating already gathered information
Include explanations of the missing aspects

Intelligent Tool Reuse

Unlike traditional systems that avoid tool repetition:

Initial queries filter out previously used tools
Follow-up queries can reuse tools for new aspects
Prevents repeating the exact same tool set consecutively
Ensures comprehensive coverage of topics

Metadata-Enriched Execution

Tool execution includes rich context:

Iteration number
Original and current queries
Follow-up query indicators
Extracted URLs and media IDs

Error Handling

The service implements comprehensive error handling:

Automatic retries for transient failures
Fallback strategies for tool failures
Detailed error reporting
Request validation

Future Improvements

Add support for more specialized research tools
Implement advanced caching strategies
Enhance query understanding with better NLP
Add support for domain-specific research workflows
Improve source verification and fact-checking
Add support for real-time updates and streaming results
Implement rate limiting and quota management
Add support for custom tool configurations

Contributing

Fork the repository
Create your feature branch (git checkout -b feature/AmazingFeature)
Commit your changes (git commit -m 'Add some AmazingFeature')
Push to the branch (git push origin feature/AmazingFeature)
Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

This project leverages multiple APIs and services:

Brave Search API for web search
Tavily AI for enhanced search
GitHub API for code search
arXiv API for academic research
News API for current events
Open Library API for book information
PatentsView API for patent information
Cloudflare Workers for hosting and execution
OpenAI and Groq APIs for AI processing

Special thanks to:

The Octotools team for inspiring the orchestration component architecture
All the API providers and the open-source community for making this project possible.