Qdrant

# MISSION: Scaffold "Qdrant" Production Architecture
You are an expert Senior Staff Software Architect and Full-Stack Engineer. Your mission is to scaffold and implement a production-grade, highly reliable, and modular codebase following the proven architecture of **Qdrant**.
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## 1. PROJECT SPECIFICATIONS & BENCHMARK
- **Reference Architecture**: Qdrant
- **What It Does**: Qdrant is an open-source, high-performance vector database and similarity search engine written in Rust for production AI applications.
- **Domain & Category**: Vector Similarity Search Engine
- **Production Scale**: 20+ People
- **Development Mode**: CLASSIC
- **Architectural Rationale**: Authoring the engine from scratch in Rust provides zero-overhead memory safety, hardware SIMD vectorization, and lock-free concurrency for large-scale similarity search.
- **Live Website Reference**: https://qdrant.tech
- **Source Repository**: https://github.com/qdrant/qdrant
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## 2. PRODUCTION TECH STACK
- **Full Stack Array**: Rust, Tokio, Docker, gRPC, RocksDB
- **Primary Language(s)**: Rust, Python, Shell
- **License of the reference repo**: Apache-2.0
- **Backend & APIs**: Rust — Core systems language delivering zero-cost abstractions, memory safety, and SIMD vector math.; Tokio — Asynchronous runtime orchestrating non-blocking network I/O and concurrent segment indexing.; gRPC — High-performance binary transport interface implemented via Tonic in src/tonic.
- **Data & persistence**: RocksDB — Embedded key-value storage engine persisting point metadata, payload indexes, and WAL entries.
- **Infrastructure & deploy**: Docker — Multi-stage Dockerfile packaging the optimized Qdrant binary for bare-metal and cloud deployment.
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## 3. KEY ARCHITECTURAL DECISIONS (audited from https://github.com/qdrant/qdrant @ 6ab21ca)
1. **Modular segment architecture with background index merging**: Qdrant splits collections into immutable and appendable segments (lib/segment). New points write to appendable segments with a WAL, while background vacuum routines optimize and compile immutable HNSW graphs.
2. **Dual-protocol serving through Actix Web and Tonic gRPC**: The engine exposes identical vector search and point management APIs across REST (src/actix) and high-throughput gRPC (src/tonic), allowing web frontends and microservices to integrate flexibly.
3. **Hardware-accelerated SIMD vector distance calculation**: Distance calculations (Dot product, Cosine, Euclidean, Manhattan) leverage CPU vectorization instructions (AVX-512, AVX2, NEON) compiled conditionally in Rust to maximize query throughput.
4. **Raft consensus for distributed horizontal cluster replication**: Distributed clustering and shard state machines are coordinated through Raft consensus in src/consensus.rs, guaranteeing partition tolerance and continuous consistency across nodes.
5. **Payload-based pre-filtering integrated directly into HNSW traversal**: Unlike traditional systems that post-filter search results, Qdrant traverses HNSW graphs while applying payload attribute filters during the beam search, avoiding recall degradation.
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## 4. NON-NEGOTIABLE ARCHITECTURAL GUARDRAILS
1. **Monorepo & Modular Separation**:
   - Structure as a Turborepo monorepo with strict package boundaries:
     - `apps/web`: Application UI, routing, layouts, and server endpoints.
     - `packages/ui`: Shared design tokens, CSS variables, and Radix UI primitive components.
     - `packages/db`: Database schemas, client singleton, declarative migrations, and seed scripts.
     - `packages/config`: Shared TypeScript, ESLint, and build configurations.
2. **Strict Type Safety & Zero `any` Policy**:
   - Enable `strict: true`, `noImplicitAny: true`, and `strictNullChecks: true`.
   - Validate ALL external inputs, API request bodies, and query parameters with **Zod** schemas before execution.
3. **Frontend & Rendering Guidelines**:
   - Isolate interactive UI state to leaf components. Keep core pages lightweight and performant.
4. **Design System & Aesthetics**:
   - Keep every color, radius, shadow and font in a single token file (CSS variables) and consume tokens everywhere; never hardcode hex values in components.
   - Prefer crisp 1px borders and one subtle shadow scale over blurry default shadows. Pair one sans-serif for body/headings with one monospace for tags, badges, metrics, and code.
5. **Data Layer & Reliability**:
   - Write declarative schema definitions with foreign keys, composite indexes on queried filters, and automated timestamp triggers.
   - Use connection pooling and prepared statements for serverless database execution.
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## 5. STEP-BY-STEP SCAFFOLDING ROADMAP
- **Phase 1: Workspace & Root Config**: Initialize package manager, monorepo configuration (`turbo.json`, `tsconfig.base.json`, `package.json`).
- **Phase 2: Database Schema & Client**: Set up the data layer (RocksDB): client, connection pool, models, and migration scripts.
- **Phase 3: Design Tokens & UI Primitives**: Build accessible `Button`, `Input`, `Card`, `Badge`, and layout wrappers inside `packages/ui`.
- **Phase 4: Core Application Routes & Handlers**: Implement primary authentication, user session handling, and application routes.
- **Phase 5: Quality Assurance & Build Verification**: Run `tsc --noEmit`, ESLint, Prettier, and smoke test suites to ensure zero compilation or runtime errors.
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## 6. EXECUTION INSTRUCTIONS
1. Review all specifications, architectural guardrails, and stack choices above.
2. Present the full monorepo directory tree structure.
3. Systematically generate the complete, production-ready codebase according to the 5-phase roadmap above — starting with the root workspace setup, followed by the database schema, UI design system package, and full-stack application routes until the repository is fully scaffolded and ready to run.