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SKILL.md
Khoj Research Guide
Overview
Khoj is an open-source AI personal research assistant with over 33,000 GitHub stars that acts as a second brain for researchers, students, and knowledge workers. It can search and chat with your personal notes, documents, and the web to help you find information, synthesize knowledge, and conduct deep research. Khoj combines personal knowledge management with AI-powered research capabilities, making it a unique tool for academic researchers who need to work with large collections of papers, notes, and data.
Unlike general-purpose AI assistants, Khoj is designed to work with your own data. It indexes your documents -- including PDFs, markdown files, org-mode notes, plaintext, and images -- and provides an AI interface that can reason over this personal knowledge base alongside web search results. This means you can ask questions that require combining information from your personal research notes with the latest findings from the web.
Khoj supports both cloud-hosted and fully self-hosted deployments. The self-hosted option is particularly attractive for researchers working with sensitive data, unpublished manuscripts, or proprietary datasets that cannot be sent to third-party services. It supports multiple LLM backends including OpenAI, Anthropic, and local models via Ollama.
Installation and Setup
Self-Hosted Deployment (Recommended for Researchers)
# Using Docker (recommended)
docker run -d \
--name khoj \
-p 42110:42110 \
-v ~/.khoj:/root/.khoj \
ghcr.io/khoj-ai/khoj:latest
# Or using pip
pip install khoj
# Start the server
khoj --host 0.0.0.0 --port 42110
Configuration
After starting Khoj, configure it through the web interface at http://localhost:42110/config:
Content sources: Point Khoj to your document directories
LLM configuration: Set up your preferred language model backend
Search settings: Configure web search and document search parameters
# Set environment variables for LLM access
export OPENAI_API_KEY=$OPENAI_API_KEY
# Or for local models
export OLLAMA_HOST=http://localhost:11434
Client Integrations
Khoj provides clients for multiple platforms to integrate into your existing workflow:
Web interface: Full-featured browser UI at http://localhost:42110
Obsidian plugin: Search and chat from within Obsidian
Emacs package: Native integration for Emacs/org-mode users
Desktop app: Cross-platform Electron app
WhatsApp/Telegram: Chat with Khoj via messaging apps
# Install the Obsidian plugin
# In Obsidian: Settings > Community Plugins > Search "Khoj"
# Configure server URL: http://localhost:42110
Core Research Features
Document Indexing and Search
Khoj indexes your research documents and makes them searchable using semantic search:
Khoj includes a dedicated research mode that goes beyond simple question-answering. It iteratively searches, reads, and synthesizes information from both your personal knowledge base and the web:
# Trigger deep research via the API
response = requests.post("http://localhost:42110/api/chat", json={
"q": "Synthesize the key findings from my notes on transformer "
"efficiency and relate them to recent papers on sparse attention",
"research_mode": True,
"max_iterations": 8,
})
# The response includes:
# - Synthesized answer drawing from personal notes and web sources
# - Citations to specific documents and web pages
# - Follow-up questions for further exploration
Conversational Research
Chat with Khoj about your research, and it will draw on your indexed documents:
User: What are the main arguments in the papers I've saved about
few-shot learning?
Khoj: Based on your indexed papers, I found 7 documents related to
few-shot learning. The main arguments include:
1. [From paper_x.pdf] Meta-learning approaches...
2. [From notes/ml-review.md] Prototypical networks...
...
Automated Research Agents
Khoj supports automated agents that can perform scheduled research tasks:
# Create a research agent that monitors new papers
requests.post("http://localhost:42110/api/agents", json={
"name": "paper-monitor",
"schedule": "daily",
"task": "Search for new papers on 'graph neural networks for "
"molecular property prediction' published in the last "
"24 hours and summarize the key findings",
"notify": True,
})
Advanced Research Workflows
Literature Review Pipeline
Use Khoj to build a structured literature review:
Collect: Index your downloaded papers and notes in Khoj
Explore: Ask broad questions to understand the landscape
Synthesize: Request comparative analyses across papers
Identify gaps: Ask Khoj to find areas where your collection lacks coverage
Expand: Use web search to find additional papers on identified gaps
Knowledge Graph Building
Khoj maintains connections between your documents, allowing you to discover relationships:
User: What connections exist between my notes on attention mechanisms
and my notes on computational efficiency?
Khoj: I found several connections:
- 3 papers discuss efficient attention variants
- Your notes from 2024-03 mention linear attention
- The survey paper you saved covers both topics in Section 4
Multi-Modal Research
Khoj can index and reason about images alongside text, which is useful for researchers working with figures, diagrams, and visual data:
Index experiment result plots and ask questions about trends
Upload architecture diagrams and ask for explanations
Search for specific visual patterns across your document collection
Privacy and Data Sovereignty
For researchers handling sensitive or unpublished data, Khoj offers strong privacy guarantees in self-hosted mode:
All data stays on your local machine or institutional server
No telemetry or data sharing with third parties
Compatible with fully local LLMs via Ollama (no external API calls)
Encrypted storage for indexed documents
Fine-grained access controls for multi-user deployments
