Thunk - Deep Research Agent

A sophisticated AI-powered research assistant that autonomously performs multi-step web research and synthesizes findings into comprehensive reports using Google's Vertex AI and Gemini models.

🎯 Overview

Thunk replicates advanced research capabilities similar to Google's Gemini Deep Research, providing:

• Autonomous Research Planning - Breaks down complex queries into structured research steps
• Multiple Search Providers - Support for web search (SerpAPI) and academic papers (arXiv)
• Intelligent Content Filtering - AI-powered quality filtering and relevance evaluation
• Content Analysis - Fetches and processes web pages, PDFs, and academic papers
• Vertex AI RAG Integration - Stores and manages research findings using Google's RAG engine
• Interactive CLI - User-friendly command-line interface with clarification support
• Report Synthesis - Generates comprehensive reports with proper citations

🏗️ Architecture & Research Flow

graph TD
    A[User Query] --> B[Clarification Questions]
    B --> C[Research Planning]
    C --> D[Generate Search Queries]
    D --> E[Parallel Web Search]
    E --> F[Source Evaluation & Filtering]
    F --> G[Content Fetching]
    G --> H[Content Summarization]
    H --> I[Document Storage]
    I --> J{Research Complete?}
    J -->|No| K[Focused Research]
    K --> D
    J -->|Yes| L[Vertex AI RAG Synthesis]
    L --> M[Final Report]
    
    N[Existing Corpus] --> L
    
    subgraph "Storage Layer"
        O[Vertex AI RAG Corpus]
        P[Local Backup]
    end
    
    I --> O
    I --> P
    N --> O

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The research flow follows these phases:

1. Query Analysis - Analyzes complexity and generates clarifying questions
2. Research Planning - Creates structured multi-step research plan
3. Parallel Execution - Concurrent search queries with rate limiting
4. Quality Filtering - AI-powered source relevance evaluation
5. Content Processing - Fetch, summarize, and store documents
6. Completeness Assessment - Determines if additional focused research is needed
7. Synthesis - Uses Vertex AI RAG for enhanced report generation

🚀 Quick Start

Prerequisites

1. Python 3.9+
2. uv - Fast Python package manager (install uv)
3. Google Cloud Project with Vertex AI API enabled
4. API Keys for SerpAPI and Google Cloud authentication

Installation

# Clone the repository
git clone git@github.com:wbeard/thunk.git
cd thunk

# Install dependencies with uv
uv sync

# Set up Google Cloud authentication
gcloud auth application-default login

Configuration

Create a .env file with your API keys:

# Required for web search (not needed for arXiv-only research)
SERPAPI_KEY=your_serpapi_key_here

# Always required
GOOGLE_CLOUD_PROJECT=your_gcp_project_id

# Optional (with defaults)
VERTEX_AI_LOCATION=us-central1
RAG_CORPUS_NAME=research_corpus_6
RAG_MODEL_NAME=gemini-2.5-flash-preview-05-20
MODEL_NAME=gemini-2.5-flash-preview-05-20

API Keys Setup

SerpAPI Key (for web search):

1. Sign up at SerpAPI
2. Get your API key from the dashboard
3. Add to .env file as SERPAPI_KEY
4. Note: Not required if using --search-provider arxiv exclusively

Google Cloud Setup:

1. Create a Google Cloud Project
2. Enable Vertex AI API in the console
3. Set up authentication: gcloud auth application-default login
4. Set project ID in .env as GOOGLE_CLOUD_PROJECT

Verify Configuration

# Check configuration
uv run thunk --check-config

🖥️ CLI Usage

The CLI provides multiple ways to interact with the research agent:

Basic Research Query

# Simple web research query
uv run thunk "Latest developments in quantum computing 2024"

# Academic paper research using arXiv
uv run thunk "quantum computing" --search-provider arxiv

# With specific corpus and output options
uv run thunk "AI safety research trends" --corpus my_research --output report.md --full

# Quiet mode (minimal output)
uv run thunk "research query" --quiet

Interactive Mode

# Start interactive session with web search
uv run thunk --interactive

# Start interactive session with arXiv search
uv run thunk --interactive --search-provider arxiv

# Available commands in interactive mode:
# <query>                     - Run research with clarification
# regenerate <query>          - Regenerate from existing corpus
# regenerate-no-rag <query>   - Regenerate without Vertex AI RAG
# corpus-info                 - Show corpus information
# quit                        - Exit interactive mode

Regeneration Mode

# Regenerate report from existing corpus (uses Vertex AI RAG)
uv run thunk --regenerate "quantum computing trends"

# Regenerate without using Vertex AI RAG
uv run thunk --regenerate "AI developments" --no-rag

# Regenerate with specific corpus
uv run thunk --regenerate "research topic" --corpus specific_corpus

Debug and Configuration

# Check configuration and API keys
uv run thunk --check-config

# Enable debug mode with verbose output
uv run thunk "research query" --debug

# Use specific RAG corpus
uv run thunk "query" --corpus my_research_corpus

CLI Options

Option	Short	Description
--interactive	-i	Run in interactive mode
--query	-q	Research query (alternative to positional)
--regenerate	-r	Regenerate from existing corpus
--search-provider	-s	Search provider: web (default) or arxiv
--output	-o	Output file for report
--corpus	-c	Name of Vertex AI RAG corpus to use
--no-save		Don't save report to file
--full		Display full report (don't truncate)
--quiet	-k	Minimal output mode
--debug	-d	Enable debug mode with verbose output
--no-rag		Don't use Vertex AI RAG for regeneration
--check-config		Check configuration and exit

Example Workflows

# First-time setup
uv run thunk --check-config

# Interactive research session with web search
uv run thunk --interactive

# Academic research session with arXiv
uv run thunk --interactive --search-provider arxiv

# Quick web research with custom corpus
uv run thunk "AI trends 2024" --corpus ai_research --debug

# Academic paper research
uv run thunk "machine learning quantum computing" --search-provider arxiv

# Regenerate previous research
uv run thunk --regenerate "previous query" --corpus ai_research

⚙️ Configuration Options

Environment Variables

The following environment variables can be configured:

Variable	Required	Default	Description
SERPAPI_KEY	Only for web search	-	SerpAPI key for web search
GOOGLE_CLOUD_PROJECT	✅ Yes	-	Google Cloud project ID
VERTEX_AI_LOCATION	No	us-central1	Vertex AI region
RAG_CORPUS_NAME	No	research_corpus_6	Name for RAG corpus
RAG_MODEL_NAME	No	gemini-2.5-flash-preview-05-20	Model for RAG synthesis
MODEL_NAME	No	gemini-2.5-flash-preview-05-20	Primary Gemini model

ResearchConfig Class

The ResearchConfig class automatically loads and validates configuration:

from src.thunk.types import ResearchConfig

# Automatically loads from environment variables
config = ResearchConfig(corpus_display_name="my_corpus", search_provider="web")

# For arXiv-only research (no SERPAPI_KEY needed)
config_arxiv = ResearchConfig(corpus_display_name="my_corpus", search_provider="arxiv")

# Validates required configuration
try:
    config._validate_config()
    print("Configuration is valid")
except ValueError as e:
    print(f"Configuration error: {e}")

Troubleshooting Configuration

Common configuration issues:

1. Missing API Keys:
   • For web search: Ensure SERPAPI_KEY and GOOGLE_CLOUD_PROJECT are set
   • For arXiv search: Only GOOGLE_CLOUD_PROJECT is required
2. Authentication: Run gcloud auth application-default login
3. Corpus Name: Provide via --corpus argument or RAG_CORPUS_NAME environment variable

# Debug configuration issues
uv run thunk --check-config

# Check configuration for arXiv search (no SerpAPI needed)
uv run thunk --check-config --search-provider arxiv

# Example error messages:
# "Missing required configuration: SERPAPI_KEY" (for web search)
# "Missing required configuration: GOOGLE_CLOUD_PROJECT"
# "Missing required configuration: --corpus argument or RAG_CORPUS_NAME environment variable"

🔍 Search Providers

Thunk supports multiple search providers through a pluggable architecture:

Web Search (Default)

• Provider: SerpAPI integration
• Content: Web pages, news articles, blog posts
• Requirements: SERPAPI_KEY environment variable
• Usage: --search-provider web (default)

Academic Papers (arXiv)

• Provider: arXiv.org academic repository
• Content: Research papers, preprints, academic publications
• Requirements: No additional API keys needed
• Usage: --search-provider arxiv

Example Usage Patterns

# General web research
uv run thunk "latest AI developments 2024" --search-provider web

# Academic research
uv run thunk "transformer architectures" --search-provider arxiv

# Mixed research (use different providers in separate sessions)
uv run thunk "quantum computing applications" --search-provider web --corpus quantum_web
uv run thunk "quantum computing theory" --search-provider arxiv --corpus quantum_papers

🤝 Contributing

The system is designed to be extensible:

• Add new search providers - Implement the SearchProvider interface
• Add new content fetchers - Support different file types
• Implement custom RAG engines - Beyond Vertex AI
• Create specialized event subscribers - For different use cases
• Extend the CLI - With additional features

📄 License

[Add your license information here]

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Thunk provides a powerful foundation for automated research tasks while maintaining flexibility for customization and extension.