# local-deep-research
**Repository Path**: cool_c0de/local-deep-research
## Basic Information
- **Project Name**: local-deep-research
- **Description**: from https://github.com/LearningCircuit/local-deep-research 20251026
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-10-26
- **Last Updated**: 2025-10-26
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# Local Deep Research
[](https://github.com/LearningCircuit/local-deep-research/stargazers)
[](https://hub.docker.com/r/localdeepresearch/local-deep-research)
[](https://pypi.org/project/local-deep-research/)
[](https://trendshift.io/repositories/14116)
[](https://github.com/LearningCircuit/local-deep-research/commits/main)
[](https://github.com/LearningCircuit/local-deep-research/commits/main)
[](https://github.com/LearningCircuit/local-deep-research/tree/main/community_benchmark_results)
[](docs/SQLCIPHER_INSTALL.md)
[](https://github.com/LearningCircuit/local-deep-research/security/code-scanning)
[](https://discord.gg/ttcqQeFcJ3)
[](https://www.reddit.com/r/LocalDeepResearch/)
**AI-powered research assistant for deep, iterative research**
*Performs deep, iterative research using multiple LLMs and search engines with proper citations*
## 🚀 What is Local Deep Research?
LDR is an AI research assistant that performs systematic research by:
- **Breaking down complex questions** into focused sub-queries
- **Searching multiple sources** in parallel (web, academic papers, local documents)
- **Verifying information** across sources for accuracy
- **Creating comprehensive reports** with proper citations
It aims to help researchers, students, and professionals find accurate information quickly while maintaining transparency about sources.
## 🎯 Why Choose LDR?
- **Privacy-Focused**: Run entirely locally with Ollama + SearXNG
- **Flexible**: Use any LLM, any search engine, any vector store
- **Comprehensive**: Multiple research modes from quick summaries to detailed reports
- **Transparent**: Track costs and performance with built-in analytics
- **Open Source**: MIT licensed with an active community
## 🔒 Security & Privacy
[](docs/SQLCIPHER_INSTALL.md)
[](docs/MIGRATION_GUIDE_v1.md)
[](docs/SQLCIPHER_INSTALL.md)
- **Signal-level encryption**: SQLCipher with AES-256 (same technology used by Signal messenger) protects all user data at rest
- **Per-user isolated databases**: Each user has their own encrypted database - complete data isolation
- **Zero-knowledge architecture**: No password recovery mechanism ensures true privacy
- **Advanced key derivation**: PBKDF2-SHA512 with 256,000 iterations prevents brute-force attacks
- **Data integrity**: HMAC-SHA512 verification prevents tampering
## 📊 Performance
**~95% accuracy on SimpleQA benchmark** (preliminary results)
- Tested with GPT-4.1-mini + SearXNG + focused-iteration strategy
- Comparable to state-of-the-art AI research systems
- Local models can achieve similar performance with proper configuration
- [Join our community benchmarking effort →](https://github.com/LearningCircuit/local-deep-research/tree/main/community_benchmark_results)
## ✨ Key Features
### 🔍 Research Modes
- **Quick Summary** - Get answers in 30 seconds to 3 minutes with citations
- **Detailed Research** - Comprehensive analysis with structured findings
- **Report Generation** - Professional reports with sections and table of contents
- **Document Analysis** - Search your private documents with AI
### 🛠️ Advanced Capabilities
- **[LangChain Integration](docs/LANGCHAIN_RETRIEVER_INTEGRATION.md)** - Use any vector store as a search engine
- **[REST API](docs/api-quickstart.md)** - Authenticated HTTP access with per-user databases
- **[Benchmarking](docs/BENCHMARKING.md)** - Test and optimize your configuration
- **[Analytics Dashboard](docs/analytics-dashboard.md)** - Track costs, performance, and usage metrics
- **Real-time Updates** - WebSocket support for live research progress
- **Export Options** - Download results as PDF or Markdown
- **Research History** - Save, search, and revisit past research
- **Adaptive Rate Limiting** - Intelligent retry system that learns optimal wait times
- **Keyboard Shortcuts** - Navigate efficiently (ESC, Ctrl+Shift+1-5)
- **Per-User Encrypted Databases** - Secure, isolated data storage for each user
### 📰 News & Research Subscriptions
- **Automated Research Digests** - Subscribe to topics and receive AI-powered research summaries
- **Customizable Frequency** - Daily, weekly, or custom schedules for research updates
- **Smart Filtering** - AI filters and summarizes only the most relevant developments
- **Multi-format Delivery** - Get updates as markdown reports or structured summaries
- **Topic & Query Support** - Track specific searches or broad research areas
### 🌐 Search Sources
#### Free Search Engines
- **Academic**: arXiv, PubMed, Semantic Scholar
- **General**: Wikipedia, SearXNG
- **Technical**: GitHub, Elasticsearch
- **Historical**: Wayback Machine
- **News**: The Guardian
#### Premium Search Engines
- **Tavily** - AI-powered search
- **Google** - Via SerpAPI or Programmable Search Engine
- **Brave Search** - Privacy-focused web search
#### Custom Sources
- **Local Documents** - Search your files with AI
- **LangChain Retrievers** - Any vector store or database
- **Meta Search** - Combine multiple engines intelligently
[Full Search Engines Guide →](docs/search-engines.md)
## ⚡ Quick Start
### Option 1: Docker
```bash
# Step 1: Pull and run SearXNG for optimal search results
docker run -d -p 8080:8080 --name searxng searxng/searxng
# Step 2: Pull and run Local Deep Research (Please build your own docker on ARM)
docker run -d -p 5000:5000 --network host --name local-deep-research --volume 'deep-research:/data' -e LDR_DATA_DIR=/data localdeepresearch/local-deep-research
```
### Option 2: Docker Compose (Recommended)
LDR uses Docker compose to bundle the web app and all it's dependencies so
you can get up and running quickly.
#### Option 2a: Quick Start (One Command)
Linux:
```bash
curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.yml && docker compose up -d
```
Windows:
```bash
curl.exe -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.yml; docker compose up -d
```
Use with a different model:
```bash
MODEL=gemma:1b curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.yml && docker compose up -d
```
Open http://localhost:5000 after ~30 seconds. This starts LDR with SearXNG and all dependencies.
#### Option 2b: DIY docker-compose
See [docker-compose.yml](./docker-compose.yml) for a docker-compose file with reasonable defaults to get up and running with ollama, searxng, and local deep research all running locally.
Things you may want/need to configure:
* Ollama GPU driver
* Ollama context length (depends on available VRAM)
* Ollama keep alive (duration model will stay loaded into VRAM and idle before getting unloaded automatically)
* Deep Research model (depends on available VRAM and preference)
#### Option 2c: Use Cookie Cutter to tailor a docker-compose to your needs:
##### Prerequisites
- [Docker](https://docs.docker.com/engine/install/)
- [Docker Compose](https://docs.docker.com/compose/install/)
- `cookiecutter`: Run `pip install --user cookiecutter`
Clone the repository:
```bash
git clone https://github.com/LearningCircuit/local-deep-research.git
cd local-deep-research
```
### Configuring with Docker Compose
Cookiecutter will interactively guide you through the process of creating a
`docker-compose` configuration that meets your specific needs. This is the
recommended approach if you are not very familiar with Docker.
In the LDR repository, run the following command
to generate the compose file:
```bash
cookiecutter cookiecutter-docker/
docker compose -f docker-compose.default.yml up
```
[Docker Compose Guide →](docs/docker-compose-guide.md)
### Option 3: Python Package
```bash
# Step 1: Install the package
pip install local-deep-research
# Step 2: Setup SearXNG for best results
docker pull searxng/searxng
docker run -d -p 8080:8080 --name searxng searxng/searxng
# Step 3: Install Ollama from https://ollama.ai
# Step 4: Download a model
ollama pull gemma3:12b
# Step 5: Build frontend assets (required for Web UI)
# Note: If installed via pip and using the Web UI, you need to build assets
# Navigate to the installation directory first (find with: pip show local-deep-research)
npm install
npm run build
# Step 6: Start the web interface
python -m local_deep_research.web.app
```
**Important for pip users:** If you installed via pip and want to use the web UI, you must run `npm install` and `npm run build` in the package installation directory to generate frontend assets (icons, styles). Without this, the UI will have missing icons and styling issues. For programmatic API usage only, these steps can be skipped.
[Full Installation Guide →](https://github.com/LearningCircuit/local-deep-research/wiki/Installation)
## 💻 Usage Examples
### Python API
```python
from local_deep_research.api import LDRClient, quick_query
# Option 1: Simplest - one line research
summary = quick_query("username", "password", "What is quantum computing?")
print(summary)
# Option 2: Client for multiple operations
client = LDRClient()
client.login("username", "password")
result = client.quick_research("What are the latest advances in quantum computing?")
print(result["summary"])
```
### HTTP API
```python
import requests
# Create session and authenticate
session = requests.Session()
# First, get the login page to retrieve CSRF token for login
login_page = session.get("http://localhost:5000/auth/login")
# Extract CSRF token from HTML form (use BeautifulSoup or regex)
# For simplicity, assuming you have the token
# Login with form data (not JSON) including CSRF token
session.post("http://localhost:5000/auth/login",
data={"username": "user", "password": "pass", "csrf_token": "..."})
# Get CSRF token for API requests
csrf = session.get("http://localhost:5000/auth/csrf-token").json()["csrf_token"]
# Make API request with CSRF header
response = session.post(
"http://localhost:5000/research/api/start",
json={"query": "Explain CRISPR gene editing"},
headers={"X-CSRF-Token": csrf}
)
```
[More Examples →](examples/api_usage/)
### Command Line Tools
```bash
# Run benchmarks from CLI
python -m local_deep_research.benchmarks --dataset simpleqa --examples 50
# Manage rate limiting
python -m local_deep_research.web_search_engines.rate_limiting status
python -m local_deep_research.web_search_engines.rate_limiting reset
```
## 🔗 Enterprise Integration
Connect LDR to your existing knowledge base:
```python
from local_deep_research.api import quick_summary
# Use your existing LangChain retriever
result = quick_summary(
query="What are our deployment procedures?",
retrievers={"company_kb": your_retriever},
search_tool="company_kb"
)
```
Works with: FAISS, Chroma, Pinecone, Weaviate, Elasticsearch, and any LangChain-compatible retriever.
[Integration Guide →](docs/LANGCHAIN_RETRIEVER_INTEGRATION.md)
## 📊 Performance & Analytics
### Benchmark Results
Early experiments on small SimpleQA dataset samples:
| Configuration | Accuracy | Notes |
|--------------|----------|--------|
| gpt-4.1-mini + SearXNG + focused_iteration | 90-95% | Limited sample size |
| gpt-4.1-mini + Tavily + focused_iteration | 90-95% | Limited sample size |
| gemini-2.0-flash-001 + SearXNG | 82% | Single test run |
Note: These are preliminary results from initial testing. Performance varies significantly based on query types, model versions, and configurations. [Run your own benchmarks →](docs/BENCHMARKING.md)
### Built-in Analytics Dashboard
Track costs, performance, and usage with detailed metrics. [Learn more →](docs/analytics-dashboard.md)
## 🤖 Supported LLMs
### Local Models (via Ollama)
- Llama 3, Mistral, Gemma, DeepSeek
- LLM processing stays local (search queries still go to web)
- No API costs
### Cloud Models
- OpenAI (GPT-4, GPT-3.5)
- Anthropic (Claude 3)
- Google (Gemini)
- 100+ models via OpenRouter
[Model Setup →](docs/env_configuration.md)
## 📚 Documentation
### Getting Started
- [Installation Guide](https://github.com/LearningCircuit/local-deep-research/wiki/Installation)
- [Frequently Asked Questions](docs/faq.md)
- [API Quickstart](docs/api-quickstart.md)
- [Configuration Guide](docs/env_configuration.md)
### Core Features
- [All Features Guide](docs/features.md)
- [Search Engines Guide](docs/search-engines.md)
- [Analytics Dashboard](docs/analytics-dashboard.md)
### Advanced Features
- [LangChain Integration](docs/LANGCHAIN_RETRIEVER_INTEGRATION.md)
- [Benchmarking System](docs/BENCHMARKING.md)
- [Elasticsearch Setup](docs/elasticsearch_search_engine.md)
- [SearXNG Setup](docs/SearXNG-Setup.md)
### Development
- [Docker Compose Guide](docs/docker-compose-guide.md)
- [Development Guide](docs/developing.md)
- [Security Guide](docs/security/CODEQL_GUIDE.md)
- [Release Guide](docs/RELEASE_GUIDE.md)
### Examples & Tutorials
- [API Examples](examples/api_usage/)
- [Benchmark Examples](examples/benchmarks/)
- [Optimization Examples](examples/optimization/)
## 🤝 Community & Support
- [Discord](https://discord.gg/ttcqQeFcJ3) - Get help and share research techniques
- [Reddit](https://www.reddit.com/r/LocalDeepResearch/) - Updates and showcases
- [GitHub Issues](https://github.com/LearningCircuit/local-deep-research/issues) - Bug reports
## 🚀 Contributing
We welcome contributions! See our [Contributing Guide](CONTRIBUTING.md) to get started.
## 📄 License
MIT License - see [LICENSE](LICENSE) file.
Built with: [LangChain](https://github.com/hwchase17/langchain), [Ollama](https://ollama.ai), [SearXNG](https://searxng.org/), [FAISS](https://github.com/facebookresearch/faiss)
> **Support Free Knowledge:** Consider donating to [Wikipedia](https://donate.wikimedia.org), [arXiv](https://arxiv.org/about/give), or [PubMed](https://www.nlm.nih.gov/pubs/donations/donations.html).