Seeker Ai

A local research assistant that performs iterative, deep-dive investigations using search engines, web scraping, and LLMs. Combines breadth-first exploration with depth-first analysis for comprehensive topic coverage.

Clariciation of Search Intent

Research Process

End of Task

Features

• Iterative Research - Self-refining research direction based on initial findings
• Follow-up Question System - Clarifies research goals through interactive dialogue
• Configurable Parameters - Control research breadth (3-10) and depth (1-5)
• Multi-format Output - Generates detailed reports or concise answers
• LLM Agnostic - Supports OpenAI, Fireworks.ai, and local LLMs via custom endpoints
• Concurrent Processing - Parallelizes research tasks for faster results

Quick Start 🚀

Prerequisites

• Node.js v18+
• API keys for:
  • Firecrawl (web scraping)
  • OpenAI or Fireworks.ai

```bash

Clone repository

git clone https://github.com/iBz-04/Seeker.git cd Seeker

Install dependencies

npm install

Configure environment

cp .env.example .env.local

Edit .env.local with your API keys

```

Basic Usage

```bash npm start

? What would you like to research? Effects of climate change on coffee production ? Research breadth (3-10): 5 ? Research depth (1-5): 2 ```

Advanced Configuration ⚙️

Environment Variables

```ini

Required

FIRECRAWLAPIKEY="yourfirecrawlkey" OPENAIAPIKEY="youropenaikey" # OR FIREWORKSAPIKEY="yourfireworkskey"

Optional

RESEARCHCONCURRENCY=5 # Parallel requests MAXTOKENS=4096 # LLM context window ```

Docker Deployment

bash docker compose up -d --build docker exec -it deep-research npm run docker

Research Process Flow 🔄

1. Initial Query Analysis

   • Parse user's research question
   • Generate clarifying follow-up questions
   • Collect additional context through interactive Q&A

2. Search Strategy Development

   • Generate multiple search queries based on research goals
   • Prioritize queries using relevance scoring
   • Execute parallel web searches using Firecrawl

3. Content Analysis

   • Extract key insights from web content
   • Maintain context-aware knowledge graph
   • Identify promising research directions for deep-dive

4. Recursive Exploration

   • Repeat search/analysis process at specified depth
   • Prune irrelevant branches
   • Merge findings across iterations

5. Report Generation

   • Synthesize findings into structured markdown
   • Include sources and confidence ratings
   • Generate both technical and executive summaries

Customization Options 🛠️

Adjust Research Parameters

typescript // deep-research.ts const config = { maxConcurrency: 5, // Parallel requests minRelevanceScore: 0.7, // Content filtering threshold maxPagesPerQuery: 10, // Resource limits timeout: 30000 // Per-request timeout };

Supported LLM Providers

```ini

Local AI

OPENAIBASEURL="http://localhost:1234/v1" OPENAI_MODEL="your-local-model"

OpenRouter

OPENAIBASEURL="https://openrouter.ai/api/v1" OPENAI_MODEL="anthropic/claude-3-haiku" ```

Example Output 📄

```markdown

Research Report: Climate Change Impact on Coffee Production

Key Findings

• Yield Reductions: 50% projected decrease in suitable growing areas by 2050
• Quality Impacts: Increased temperatures correlate with flavor profile degradation
• Economic Costs: $2.5B annual losses anticipated in major producing regions

Recommended Actions

1. Develop heat-resistant coffee cultivars
2. Implement agroforestry practices
3. Diversify geographic production areas

Sources

1. Journal of Agricultural Science
2. FAO Climate Report ```

Troubleshooting ⚠️

Common Issues: - Rate Limiting: Reduce RESEARCH_CONCURRENCY in free tier - Timeouts: Increase TIMEOUT duration for complex queries - Relevance Filtering: Adjust MIN_RELEVANCE_SCORE for sensitive content