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Production-ready in 3 minutes.\n\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)\n[![Version](https://img.shields.io/badge/version-1.6.1-blue.svg)](CHANGELOG.md)\n[![Python](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/)\n[![Docker](https://img.shields.io/badge/docker-ready-brightgreen.svg)](https://hub.docker.com/r/flamehaven/filesearch)\n\n[Quick Start](#-quick-start) • [Features](#-features) • [Documentation](#-documentation) • [API Reference](http://localhost:8000/docs) • [Contributing](#-contributing)\n\n\u003c/div\u003e\n\n---\n\n## 🎯 Why FLAMEHAVEN FileSearch?\n\nStop sending your sensitive documents to third-party services. FLAMEHAVEN FileSearch is a production-grade RAG search engine — BM25+hybrid retrieval, 34 file formats, multi-LLM (Gemini, OpenAI, Claude, Ollama) — running self-hosted in minutes, not days.\n\n```bash\n# One command. Three minutes. Done.\ndocker run -d -p 8000:8000 -e GEMINI_API_KEY=\"your_key\" flamehaven-filesearch:1.6.1\n```\n\n\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd width=\"33%\" align=\"center\"\u003e\n\u003ch3\u003e🚀 Fast\u003c/h3\u003e\n\u003cp\u003eProduction deployment in 3 minutes\u003cbr/\u003e\nVector generation in \u0026lt;1ms\u003cbr/\u003e\nZero ML dependencies\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"33%\" align=\"center\"\u003e\n\u003ch3\u003e🔒 Private\u003c/h3\u003e\n\u003cp\u003e100% self-hosted\u003cbr/\u003e\nYour data never leaves your infrastructure\u003cbr/\u003e\nEnterprise-grade security\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"33%\" align=\"center\"\u003e\n\u003ch3\u003e💰 Cost-Effective\u003c/h3\u003e\n\u003cp\u003eFree tier: 1,500 queries/month\u003cbr/\u003e\nNo infrastructure costs\u003cbr/\u003e\nOpen source \u0026 MIT licensed\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/table\u003e\n\n---\n\n## Features ✨\n\n### Core Capabilities\n\n| Capability | Detail |\n|---|---|\n| **Search Modes** | Keyword, semantic, and hybrid (BM25+RRF) with automatic typo correction |\n| **34 File Formats** | PDF, DOCX/DOC, XLSX, PPTX, RTF, HTML, CSV, LaTeX, WebVTT, images + plain text — see [Document Parsing](docs/wiki/Document_Parsing.md) |\n| **RAG Pipeline** | Structure-aware chunking, KnowledgeAtom 2-level indexing, sliding-window context enrichment, mtime parse cache |\n| **Ultra-Fast Vectors** | DSP v2.0 generates embeddings in \u003c1ms — no ML frameworks required |\n| **Source Attribution** | Every answer links back to the originating document and chunk |\n| **Framework SDKs** | LangChain, LlamaIndex, Haystack, CrewAI adapters out of the box |\n| **Enterprise Auth** | API key hashing (SHA256+salt), OAuth2/OIDC, fine-grained permissions |\n| **Admin Dashboard** | Real-time metrics, quota management, batch processing (1–100 queries) |\n| **Flexible Storage** | SQLite (default) · PostgreSQL + pgvector · Redis cache (optional) |\n\n\u003e **What changed in each release?** See [CHANGELOG.md](CHANGELOG.md) for the full version history.\n\n---\n\n## Quick Start 🚀\n\n### Option 1: Docker (Recommended)\n\nThe fastest path to production:\n\n```bash\ndocker run -d \\\n  -p 8000:8000 \\\n  -e GEMINI_API_KEY=\"your_gemini_api_key\" \\\n  -e FLAMEHAVEN_ADMIN_KEY=\"secure_admin_password\" \\\n  -v $(pwd)/data:/app/data \\\n  flamehaven-filesearch:1.6.1\n```\n\n✅ Server running at `http://localhost:8000`\n\n### Option 2: Python SDK\n\nPerfect for integrating into existing applications:\n\n```python\nfrom flamehaven_filesearch import FlamehavenFileSearch, FileSearchConfig\n\n# Initialize\nconfig = FileSearchConfig(google_api_key=\"your_gemini_key\")\nfs = FlamehavenFileSearch(config)\n\n# Upload and search\nfs.upload_file(\"company_handbook.pdf\", store=\"docs\")\nresult = fs.search(\"What is our remote work policy?\", store=\"docs\")\n\nprint(result['answer'])\n# Output: \"Employees can work remotely up to 3 days per week...\"\n```\n\n### Option 3: REST API\n\nFor language-agnostic integration:\n\n```bash\n# 1. Generate API key\ncurl -X POST http://localhost:8000/api/admin/keys \\\n  -H \"X-Admin-Key: your_admin_key\" \\\n  -d '{\"name\":\"production\",\"permissions\":[\"upload\",\"search\"]}'\n\n# 2. Upload document\ncurl -X POST http://localhost:8000/api/upload/single \\\n  -H \"Authorization: Bearer sk_live_abc123...\" \\\n  -F \"file=@document.pdf\" \\\n  -F \"store=my_docs\"\n\n# 3. Search\ncurl -X POST http://localhost:8000/api/search \\\n  -H \"Authorization: Bearer sk_live_abc123...\" \\\n  -H \"Content-Type: application/json\" \\\n  -d \n  '{ \n    \"query\": \"What are the main findings?\",\n    \"store\": \"my_docs\",\n    \"search_mode\": \"hybrid\"\n  }'\n```\n\n---\n\n## 📦 Installation\n\n```bash\n# Core package (HTML, CSV, LaTeX, WebVTT, plain-text parsing included — zero extra deps)\npip install flamehaven-filesearch\n\n# + Document parsers: PDF (pymupdf/pypdf), DOCX, XLSX, PPTX, RTF\npip install flamehaven-filesearch[parsers]\n\n# + Image OCR (Pillow + pytesseract; requires Tesseract system binary)\npip install flamehaven-filesearch[vision]\n\n# + Google Gemini API\npip install flamehaven-filesearch[google]\n\n# + REST API server (FastAPI + uvicorn)\npip install flamehaven-filesearch[api]\n\n# + HNSW vector index\npip install flamehaven-filesearch[vector]\n\n# + PostgreSQL backend\npip install flamehaven-filesearch[postgres]\n\n# Everything\npip install flamehaven-filesearch[all]\n\n# Build from source\ngit clone https://github.com/flamehaven01/Flamehaven-Filesearch.git\ncd Flamehaven-Filesearch\ndocker build -t flamehaven-filesearch:1.6.1 .\n```\n\n### Framework Integrations\n\nFramework SDKs (LangChain, LlamaIndex, etc.) are imported lazily — install only\nwhat you need:\n\n```python\n# LangChain  (pip install langchain-core)\nfrom flamehaven_filesearch.integrations import FlamehavenLangChainLoader\ndocs = FlamehavenLangChainLoader(\"report.pdf\", chunk=True).load()\n\n# LlamaIndex  (pip install llama-index-core)\nfrom flamehaven_filesearch.integrations import FlamehavenLlamaIndexReader\nnodes = FlamehavenLlamaIndexReader(chunk=True).load_data([\"report.pdf\", \"slides.pptx\"])\n\n# Haystack  (pip install haystack-ai)\nfrom flamehaven_filesearch.integrations import FlamehavenHaystackConverter\nresult = FlamehavenHaystackConverter().run(sources=[\"report.pdf\"])\n\n# CrewAI  (pip install crewai)\nfrom flamehaven_filesearch.integrations import FlamehavenCrewAITool\ntool = FlamehavenCrewAITool()           # pass to your agent's tools list\n```\n\n---\n\n## Configuration ⚙️\n\n### Required Environment Variables\n\n```bash\nexport GEMINI_API_KEY=\"your_google_gemini_api_key\"\nexport FLAMEHAVEN_ADMIN_KEY=\"your_secure_admin_password\"\n```\n\n### Optional Configuration\n\n```bash\nexport HOST=\"0.0.0.0\"              # Bind address\nexport PORT=\"8000\"                  # Server port\nexport REDIS_HOST=\"localhost\"       # Distributed caching\nexport REDIS_PORT=\"6379\"            # Redis port\n```\n\n### Advanced Configuration\n\nCreate a `config.yaml` for fine-tuned control:\n\n```yaml\nvector_store:\n  quantization: int8\n  compression: gravitas_pack\n  \nsearch:\n  default_mode: hybrid\n  typo_correction: true\n  max_results: 10\n  \nsecurity:\n  rate_limit: 100  # requests per minute\n  max_file_size: 52428800  # 50MB\n```\n\n---\n\n## 📊 Performance\n\n\u003ctable\u003e\n\u003ctr\u003e\n\u003cth\u003eMetric\u003c/th\u003e\n\u003cth\u003eValue\u003c/th\u003e\n\u003cth\u003eNotes\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eVector Generation\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e\u0026lt;1ms\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eDSP v2.0, zero ML dependencies\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Footprint\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e75% reduced\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eInt8 quantization vs float32\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMetadata Size\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e90% smaller\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eGravitas-Pack compression\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTest Suite\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e476 tests\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eAll passing (pytest)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCold Start\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e3 seconds\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eDocker container ready\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/table\u003e\n\n### Real-World Benchmarks\n\n```\nEnvironment: Docker on Apple M1 Mac, 16GB RAM\nDocument Set: 500 PDFs, ~2GB total\n\nHealth Check:           8ms\nSearch (cache hit):     9ms\nSearch (cache miss):    1,250ms  (includes Gemini API call)\nBatch Search (10):      2,500ms  (parallel processing)\nUpload (50MB file):     3,200ms  (with indexing)\n```\n\n---\n\n## Architecture 🏗️\n\n```mermaid\nflowchart TD\n    Client([\"Client\\n(HTTP / SDK)\"])\n\n    subgraph API[\"REST API Layer (FastAPI)\"]\n        Upload[\"/api/upload\"]\n        Search[\"/api/search\"]\n        Admin[\"/api/admin\"]\n    end\n\n    subgraph Engine[\"Engine Layer\"]\n        FP[\"FileParser\\n+ BackendRegistry\\n(34 formats)\"]\n        Cache[\"ParseCache\\n(mtime-based)\"]\n        Chunker[\"TextChunker\\n+ KnowledgeAtom\\n(chunk atoms)\"]\n        DSP[\"DSP v2.0\\nEmbedding Generator\\n(\u0026lt;1ms, zero-ML)\"]\n        BM25[\"BM25 + RRF\\nHybrid Search\\n(v1.6.0)\"]\n        Scorer[\"SemanticScorer\\n+ TypoCorrector\"]\n    end\n\n    subgraph Storage[\"Storage Layer\"]\n        SQLite[(\"SQLite\\nMetadata Store\")]\n        Vec[(\"Vector Store\\n(local / pgvector)\")]\n        Redis[(\"Redis Cache\\n(optional)\")]\n    end\n\n    Gemini[\"Google Gemini API\\n(reasoning)\"]\n    Metrics[\"Metrics Logger\"]\n\n    Client --\u003e Upload \u0026 Search \u0026 Admin\n    Upload --\u003e FP\n    FP \u003c--\u003e|\"cache hit/miss\"| Cache\n    FP --\u003e Chunker\n    Chunker --\u003e DSP\n    DSP --\u003e Vec\n    FP --\u003e SQLite\n\n    Search --\u003e Scorer\n    Scorer --\u003e DSP\n    DSP --\u003e Vec\n    Scorer --\u003e Gemini\n    Gemini --\u003e Client\n\n    Admin --\u003e Metrics\n    Admin --\u003e SQLite\n    Storage \u003c--\u003e|\"read / write\"| Redis\n```\n\n\u003e Full layer detail: [Architecture.md](docs/wiki/Architecture.md)\n\n---\n\n## Security 🔒\n\nFLAMEHAVEN takes security seriously:\n\n- ✅ **API Key Hashing** - SHA256 with salt\n- ✅ **Rate Limiting** - Per-key quotas (default: 100/min)\n- ✅ **Permission System** - Granular access control\n- ✅ **Audit Logging** - Complete request history\n- ✅ **OWASP Headers** - Security headers enabled by default\n- ✅ **Input Validation** - Strict file type and size checks\n\n### Security Best Practices\n\n```bash\n# Use strong admin keys\nexport FLAMEHAVEN_ADMIN_KEY=$(openssl rand -base64 32)\n\n# Enable HTTPS in production\n# (use nginx/traefik as reverse proxy)\n\n# Rotate API keys regularly\ncurl -X DELETE http://localhost:8000/api/admin/keys/old_key_id \\\n  -H \"X-Admin-Key: $FLAMEHAVEN_ADMIN_KEY\"\n```\n\n---\n\n## Roadmap 🗺️\n\nFull roadmap: [ROADMAP.md](ROADMAP.md)\n\n### v1.4.x (Completed)\n- [x] Multimodal search (image + text)\n- [x] HNSW vector indexing for faster search\n- [x] OAuth2/OIDC integration\n- [x] PostgreSQL backend (metadata + pgvector)\n- [x] Usage-budget controls and reporting\n- [x] pgvector tuning and reliability hardening\n- [x] CI/CD — ruff replaces flake8; pipelines fully green\n\n### v1.5.x (Completed)\n- [x] Universal Document Parser — 34 formats, zero doc-AI dependency (v1.5.0)\n- [x] Internal text chunker — structure-aware + token-aware, zero ML deps (v1.5.0)\n- [x] Framework integrations — LangChain, LlamaIndex, Haystack, CrewAI (v1.5.0)\n- [x] Backend Plugin Architecture — `AbstractFormatBackend` + `BackendRegistry` (v1.5.2)\n- [x] Parse cache — mtime-based, `extract_text(use_cache=True)` (v1.5.2)\n- [x] ContextExtractor — sliding-window RAG chunk enrichment (v1.5.2)\n- [x] Multi-provider LLM support — OpenAI, Claude, Ollama, Gemini (v1.5.3)\n\n### v1.6.0 (Completed)\n- [x] BM25 + RRF hybrid search — Korean+English tokenizer, lazy per-store index\n- [x] KnowledgeAtom 2-level indexing — chunk atoms with fragment URIs\n- [x] Stable URI scheme — `local://\u003cstore\u003e/\u003cquote(abs_path)\u003e`, collision-free\n- [x] core.py mixin segmentation — 1258 → 221 lines, 3 focused modules\n- [x] Fix: `search_stream` double intent-refine bug\n\n### v2.0.0 (Q3 2026)\n- [ ] Multi-language support (15+ languages) — multilingual stopwords + jieba\n- [ ] Kubernetes Helm charts\n- [ ] Distributed indexing\n\n---\n\n## Troubleshooting 🐛\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003e❌ 401 Unauthorized Error\u003c/b\u003e\u003c/summary\u003e\n\n**Problem:** API returns 401 when making requests.\n\n**Solutions:**\n1. Verify `FLAMEHAVEN_ADMIN_KEY` environment variable is set\n2. Check `Authorization: Bearer sk_live_...` header format\n3. Ensure API key hasn't expired (check admin dashboard)\n\n```bash\n# Debug: Check if admin key is set\necho $FLAMEHAVEN_ADMIN_KEY\n\n# Regenerate API key\ncurl -X POST http://localhost:8000/api/admin/keys \\\n  -H \"X-Admin-Key: $FLAMEHAVEN_ADMIN_KEY\" \\\n  -d '{\"name\":\"debug\",\"permissions\":[\"search\"]}'\n```\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003e🐌 Slow Search Performance\u003c/b\u003e\u003c/summary\u003e\n\n**Problem:** Searches taking \u003e5 seconds.\n\n**Solutions:**\n1. Check cache hit rate: `FLAMEHAVEN_METRICS_ENABLED=1 curl http://localhost:8000/metrics`\n2. Enable Redis for distributed caching\n3. Verify Gemini API latency (should be \u003c1.5s)\n\n```bash\n# Enable Redis caching\ndocker run -d --name redis redis:7-alpine\nexport REDIS_HOST=localhost\n```\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003e💾 High Memory Usage\u003c/b\u003e\u003c/summary\u003e\n\n**Problem:** Container using \u003e2GB RAM.\n\n**Solutions:**\n1. Enable Redis with LRU eviction policy\n2. Reduce max file size in config\n3. Monitor with Prometheus endpoint\n\n```bash\n# Configure Redis memory limit\ndocker run -d \\\n  -p 6379:6379 \\\n  redis:7-alpine \\\n  --maxmemory 512mb \\\n  --maxmemory-policy allkeys-lru\n```\n\u003c/details\u003e\n\nMore solutions in our [Wiki Troubleshooting Guide](docs/wiki/Troubleshooting.md).\n\n---\n\n## Documentation 📚\n\n### Documentation Hub\n\nUse the links below to jump to the most relevant guide.\n\n| Topic | Description |\n|-------|-------------|\n| [Document Parsing](docs/wiki/Document_Parsing.md) | Supported formats, internal parsers, RAG chunking |\n| [Hybrid Search](docs/wiki/Hybrid_Search.md) | BM25+RRF, KnowledgeAtom indexing, stable URI scheme (v1.6.0) |\n| [Framework Integrations](docs/wiki/Framework_Integrations.md) | LangChain, LlamaIndex, Haystack, CrewAI adapters |\n| [API Reference](docs/wiki/API_Reference.md) | REST endpoints, payloads, rate limits |\n| [Architecture](docs/wiki/Architecture.md) | How all layers fit together (v1.6.0) |\n| [Configuration Reference](docs/wiki/Configuration.md) | Full list of environment variables and config fields |\n| [Production Deployment](docs/wiki/Production_Deployment.md) | Docker, systemd, reverse proxy, scaling tips |\n| [Troubleshooting](docs/wiki/Troubleshooting.md) | Step-by-step debugging playbook |\n| [Benchmarks](docs/wiki/Benchmarks.md) | Performance measurements and methodology |\n\nThese Markdown files live inside the repository so they stay versioned alongside the code. Feel free to contribute improvements via pull requests.\n\n### Additional Resources\n\n- **[Interactive API Docs](http://localhost:8000/docs)** - OpenAPI/Swagger interface (when server is running)\n- **[CHANGELOG](CHANGELOG.md)** - Version history and breaking changes\n- **[CONTRIBUTING](CONTRIBUTING.md)** - How to contribute code\n- **[Examples](examples/)** - Sample integrations and use cases\n\n---\n\n## Contributing 🤝\n\nWe love contributions! FLAMEHAVEN is better because of developers like you.\n\n### Good First Issues\n\n- 🟢 **[Easy]** Add dark mode to admin dashboard (1-2 hours)\n- 🟡 **[Medium]** PostgreSQL backend for usage tracker (multi-instance deployments)\n- 🔴 **[Advanced]** Kubernetes Helm charts for production deployment\n\nSee [CONTRIBUTING.md](CONTRIBUTING.md) for development setup and guidelines.\n\n### Contributors\n\n\u003ca href=\"https://github.com/flamehaven01/Flamehaven-Filesearch/graphs/contributors\"\u003e\n  \u003cimg src=\"https://contrib.rocks/image?repo=flamehaven01/Flamehaven-Filesearch\" /\u003e\n\u003c/a\u003e\n\n---\n\n## Community \u0026 Support 💬\n\n- **💬 Discussions:** [GitHub Discussions](https://github.com/flamehaven01/Flamehaven-Filesearch/discussions)\n- **🐛 Bug Reports:** [GitHub Issues](https://github.com/flamehaven01/Flamehaven-Filesearch/issues)\n- **🔒 Security:** security@flamehaven.space\n- **📧 General:** info@flamehaven.space\n\n---\n\n## License 📄\n\nDistributed under the MIT License. See [LICENSE](LICENSE) for more information.\n\n---\n\n## 🙏 Acknowledgments\n\nBuilt with amazing open source tools:\n\n- [FastAPI](https://fastapi.tiangolo.com/) - Modern Python web framework\n- [Google Gemini](https://ai.google.dev/) - Semantic understanding and reasoning\n- [SQLite](https://www.sqlite.org/) - Lightweight, embedded database\n- [Redis](https://redis.io/) - In-memory caching (optional)\n\n---\n\n\u003cdiv align=\"center\"\u003e\n\n**[⭐ Star us on GitHub](https://github.com/flamehaven01/Flamehaven-Filesearch)** • **[📖 Read the Docs](docs/wiki/README.md)** • **[🚀 Deploy Now](#-quick-start)**\n\nBuilt with 🔥 by the Flamehaven Core Team\n\n*Last updated: April 19, 2026 • Version 1.6.1*\n\n\u003c/div\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fflamehaven01%2Fflamehaven-filesearch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fflamehaven01%2Fflamehaven-filesearch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fflamehaven01%2Fflamehaven-filesearch/lists"}