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SearchMuse Roadmap

This roadmap outlines SearchMuse's evolution from MVP to mature product. Timelines and features are subject to change based on community needs and contributions.

Version 0.1.0 - MVP (Core Functionality)

Status: In Development Target: Q2 2024 Focus: Basic automated research capability with local LLM

Features

Core Search Loop

  • Single-pass search execution via DuckDuckGo
  • HTTP-based content scraping (httpx)
  • Trafilatura-based content extraction
  • Basic relevance assessment (simple keyword matching)

LLM Integration

  • Ollama integration (Mistral model)
  • Simple search strategy generation
  • Initial relevance scoring
  • Basic synthesis

Output

  • Markdown output format (default)
  • Inline citations with reference list
  • Simple heading structure
  • No advanced formatting

Storage

  • SQLite for search history
  • Local result caching
  • Basic metadata storage

Example Usage

searchmuse "What is machine learning?"
# Returns markdown file with citations in 30-60 seconds

Not Included

  • ❌ Iterative refinement
  • ❌ Coverage assessment
  • ❌ JavaScript rendering
  • ❌ JSON output
  • ❌ API server
  • ❌ Configuration options

Version 0.2.0 - Iterative Refinement

Status: Planned Target: Q3 2024 Focus: Multi-iteration search with quality assessment

Major Features

Iterative Search Algorithm

  • Multi-iteration search loop (up to 5 iterations)
  • Gap analysis between iterations
  • Automatic strategy refinement
  • Coverage assessment (quality scoring)
  • Convergence detection

Quality Assessment

  • LLM-based relevance scoring (0.0-1.0)
  • Coverage score calculation
  • Source diversity assessment
  • Quality metrics per source

Configuration

  • YAML configuration file support
  • Adjustable iteration limits
  • Coverage thresholds
  • Source count minimums
  • Temperature settings per task

Logging and Monitoring

  • Detailed iteration logs
  • Progress output during search
  • Performance metrics
  • Error tracking

Example Usage

searchmuse --config config.yaml "machine learning interpretability"
# Iteration 1: Retrieved 8 sources, coverage 0.65
# Refining search based on gaps...
# Iteration 2: Retrieved 6 new sources, coverage 0.78
# COMPLETE: 14 sources, coverage 0.78

Not Included

  • ❌ JavaScript rendering
  • ❌ Multiple output formats (JSON, HTML)
  • ❌ API server
  • ❌ Web UI

Version 0.3.0 - Polish and Expansion

Status: Planned Target: Q4 2024 Focus: Multiple scraping strategies, output formats, and performance

Major Features

Multi-Strategy Scraping

  • Playwright support for JavaScript-heavy sites
  • Automatic strategy selection (httpx vs Playwright)
  • robots.txt compliance verification
  • Rate limiting implementation
  • Fallback extraction strategies

Output Formats

  • JSON structured output with full metadata
  • HTML format for web publishing
  • APA-style citations
  • Plain text output
  • Multiple citation formats per output

Advanced CLI

  • Rich terminal output with colors
  • Progress bars during search
  • Real-time iteration display
  • Result preview without file writing
  • Export to multiple formats

Caching and Performance

  • Result caching layer
  • Prompt response caching
  • Search result deduplication
  • Memory optimization
  • Concurrent request handling

Documentation

  • Comprehensive API documentation
  • User guide
  • Configuration reference
  • Troubleshooting guide
  • Example scripts

Example Usage

# Rich CLI with progress
searchmuse research "neural networks" --format json --output result.json

# Stream progress
searchmuse stream "blockchain technology" --iterations 4

# Quick preview
searchmuse preview "React vs Vue"

# Multiple output formats
searchmuse export "machine learning" --formats markdown,json,html,apa

New Dependencies

  • playwright for browser automation
  • rich for CLI formatting
  • tqdm for progress bars

Version 1.0.0 - Release Candidate

Status: Planned Target: Q1 2025 Focus: Production-ready stability, APIs, and extensibility

Major Features

Plugin System

  • Abstract adapter base classes
  • Custom scraper plugins
  • LLM provider plugins
  • Output format plugins
  • Search strategy plugins

API Server

  • FastAPI REST server
  • Async request handling
  • Request queuing
  • WebSocket streaming support
  • Comprehensive endpoint documentation

Docker Support

  • Official Docker image
  • Docker Compose setup
  • Pre-configured Ollama integration
  • Volume mounting for persistence

Testing and Quality

  • Unit test suite (>80% coverage)
  • Integration tests
  • E2E test scenarios
  • Performance benchmarks
  • Security audit

Configuration Management

  • Environment variable support
  • Config file validation
  • Secrets management
  • Multiple config profiles
  • Config migration tools

Logging

  • Structured logging (JSON)
  • Log level controls
  • Rotating file handlers
  • Search audit trail

Example Usage

from searchmuse import SearchMuse, Config

config = Config.from_file("config.yaml")
muse = SearchMuse(config=config)

results = muse.search("quantum computing")
print(results.to_markdown())

# Or via API
# curl http://localhost:8000/search -d '{"query": "..."}'

Docker Example

docker-compose up -d
curl http://localhost:8000/search \
  -d '{"query": "machine learning"}' \
  | jq .

Not Included Yet

  • ❌ Web UI
  • ❌ Multi-language support
  • ❌ PDF export
  • ❌ Collaborative features

Version 1.1.0 - Advanced Features

Status: Planned Target: Q2-Q3 2025 Focus: Advanced research capabilities

Features

Advanced Search Strategies

  • Domain-aware strategy selection
  • Multi-language query support
  • Semantic search capabilities
  • Query expansion and synonym detection
  • Cross-domain synthesis

Content Analysis

  • Claim extraction and verification
  • Automatic fact-checking
  • Citation analysis
  • Author credibility scoring
  • Source bias detection

Result Enhancement

  • Auto-generated summaries per section
  • Key insights extraction
  • Timeline generation (for historical topics)
  • Relationship mapping between concepts
  • Knowledge graph generation

Integrations

  • Zotero export
  • Notion integration
  • Obsidian plugin
  • Email results export
  • Slack bot

Version 2.0.0 - Major Expansion

Status: Planned Target: Q4 2025+ Focus: UI, multi-language, collaborative features

Major Features

Web User Interface

  • React-based frontend
  • Real-time search visualization
  • Result browsing and filtering
  • Citation management
  • Search history
  • Saved research collections

Multi-Language Support

  • 10+ languages (EN, ES, FR, DE, IT, PT, JA, ZH, RU, KO)
  • Language-specific extractors
  • Multilingual LLM support
  • Cross-language research

Collaborative Features

  • User accounts (optional)
  • Shared research collections
  • Collaborative editing
  • Comments and annotations
  • Version history

Advanced Exports

  • PDF generation with citations
  • DOCX export
  • LaTeX export
  • BibTeX citation lists
  • Custom export templates

Performance

  • GPU acceleration for LLM
  • Distributed search (multiple workers)
  • Caching layer optimization
  • Database optimization

Architecture Diagram

┌─────────────────┐
│   Web Frontend  │ (React)
└────────┬────────┘
         │
    ┌────▼─────────┐
    │  API Server  │ (FastAPI)
    └────┬─────────┘
         │
    ┌────▼────────────────┐
    │ Search Engine Layer │
    │ ┌──────────────────┐│
    │ │ Strategy Gen     ││ (LLM)
    │ │ Relevance Score  ││
    │ │ Coverage Assess  ││
    │ │ Synthesis        ││
    │ └──────────────────┘│
    └────┬────────────────┘
         │
    ┌────▼─────────────────────────┐
    │ Scraper Adapters             │
    │ ┌──────────┬──────────┐      │
    │ │ httpx    │Playwright│      │
    │ └──────────┴──────────┘      │
    └────┬─────────────────────────┘
         │
    ┌────▼────────────────────┐
    │ Content Extractors      │
    │ ┌──────┬────────────┐   │
    │ │Trafia│Readability │   │
    │ └──────┴────────────┘   │
    └────┬────────────────────┘
         │
    ┌────▼──────────────────┐
    │ Storage Layer         │
    │ (SQLite/PostgreSQL)   │
    └───────────────────────┘

Feature Priority Matrix

High Priority (Core)

  • ✅ Basic search loop (v0.1)
  • ✅ LLM integration (v0.1)
  • ⏳ Iterative refinement (v0.2)
  • ⏳ Coverage assessment (v0.2)
  • ⏳ Multiple output formats (v0.3)
  • ⏳ Multi-strategy scraping (v0.3)

Medium Priority (Value-Add)

  • ⏳ API server (v1.0)
  • ⏳ Plugin system (v1.0)
  • ⏳ Advanced search strategies (v1.1)
  • ⏳ Integrations (v1.1)

Low Priority (Nice-to-Have)

  • ⏳ Web UI (v2.0)
  • ⏳ Multi-language (v2.0)
  • ⏳ Collaborative features (v2.0)
  • ⏳ Advanced analytics

Community Contribution Areas

Easy (Good for First-Time Contributors)

  • Documentation improvements
  • Bug fixes
  • Additional supported websites
  • Custom extractors for specific sites
  • Example scripts and tutorials

Medium

  • New output formats
  • Configuration enhancements
  • Logging improvements
  • Testing coverage

Hard (Expertise Required)

  • LLM optimization
  • Performance improvements
  • Distributed search
  • Plugin architecture
  • Advanced NLP features

Known Technical Debt

Items for future cleanup:

Item Severity Target Version
Improve error handling High v1.0
Add comprehensive logging High v1.0
Optimize memory usage Medium v1.0
Refactor LLM adapters Medium v1.0
Add async/await throughout Low v1.1
Type hints coverage Low v1.0

Success Metrics

By Version

v0.1.0: - Single search query working - Output markdown with 3+ sources - Faster than manual research

v0.2.0: - Iterative refinement working - Coverage assessment accurate - 2-5 iterations typical for complex queries

v0.3.0: - Multiple output formats working - Multi-strategy scraping effective - 80%+ success rate on supported sites

v1.0.0: - API server stable - Test coverage >80% - Docker deployment working - Zero security issues found

v2.0.0: - Web UI functional - 1000+ active users - 100+ open-source contributors - Industry recognition

How to Stay Updated

  • Star the GitHub repository
  • Subscribe to releases
  • Join Discord community
  • Follow project updates

How to Contribute

  • Check CONTRIBUTING.md
  • Review open issues
  • Submit feature requests
  • Share feedback and bugs

Backward Compatibility

  • v0.x versions: No compatibility guarantees
  • v1.0.0+: Semantic versioning
  • Configuration format stability from v1.0
  • API stability from v1.0

Dependencies and Constraints

External Services

  • DuckDuckGo search (no API key required)
  • Ollama (self-hosted)
  • No other external dependencies

Hardware Evolution

  • v0.1-v0.3: 8GB RAM sufficient
  • v1.0: 16GB RAM recommended
  • v2.0: GPU acceleration important

Python Versions

  • v0.1-v0.3: Python 3.9+
  • v1.0+: Python 3.10+

Get Involved

Report Issues

  • GitHub Issues with reproducible examples
  • Include version, Python version, OS
  • Detailed error messages and logs

Suggest Features

  • GitHub Discussions
  • Explain use case
  • Describe desired behavior
  • Provide examples

Contribute Code

  • Fork repository
  • Create feature branch
  • Submit pull request
  • Ensure tests pass

Improve Documentation

  • Open pull requests for docs
  • Fix typos and clarifications
  • Add examples and tutorials

The future of SearchMuse depends on community input and contributions!