vector-search-csharp/plan.md

Vector Search Application Implementation Plan

Overview

I've completed the planning phase for implementing a vector embedding database for USA postal addresses using the "sentence-transformers/all-MiniLM-L6-v2" model. The solution will be a console application that accepts address input, generates embeddings, and stores them in a dockerizable vector database.

Key Components and Decisions

1. Vector Database Solution

• Selected: Qdrant (free to self-host and dockerizable)
• Rationale:
  • Fully open-source and free for self-hosting
  • Excellent Docker support with official Docker images
  • Good performance for vector similarity search
  • Supports various vector similarity metrics (cosine, euclidean, etc.)
  • Active community and good documentation

2. Embedding Model Implementation

• Model: "sentence-transformers/all-MiniLM-L6-v2"
• Approach: Using Hugging Face's .NET library (HuggingFace.NET)
• Rationale:
  • Optimized for .NET environments
  • Good performance and accuracy
  • Active maintenance and community support
  • Compatible with the existing .NET 8 project structure

3. System Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Console App   │    │  Embedding API   │    │   Vector DB     │
│                 │    │ (Hugging Face)   │    │  (Qdrant)       │
│  Address Input  │───▶│  Generate Embed  │───▶│  Store/Query    │
│                 │    │  (all-MiniLM)    │    │                 │
└─────────────────┘    └──────────────────┘    └─────────────────┘

4. Console Application Flow

1. User enters postal address via console input
2. Address is processed through the embedding model
3. Generated embedding is stored in Qdrant vector database
4. Console displays the generated embedding as confirmation
5. Application continues to accept new addresses

5. Implementation Details

• Project Structure: Will extend the existing VectorSearchApp project
• Database Integration: Qdrant client library for .NET
• Embedding Generation: Hugging Face .NET library for sentence transformers
• Data Model: Address (text) → Embedding (vector) mapping
• Dockerization: Qdrant container with persistent storage

6. Technical Requirements

• .NET 8 runtime
• Docker for containerization
• Qdrant vector database (containerized)
• Hugging Face .NET libraries
• Vector search capabilities for similarity queries

This plan provides a solid foundation for implementing the vector search application with all specified requirements met.