Created a plan to get started.

plan.md

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+# 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.