👉 Try the demo: https://github.com/LM-Kit/lm-kit-net-samples/tree/main/console_net/rag-and-knowledge/vector-database/single_turn_rag_pgvector

Single-Turn RAG with PostgreSQL (pgvector) for C# .NET Applications

🎯 Purpose of the Demo

The Single-Turn RAG with PostgreSQL (pgvector) demo illustrates how to integrate LM-Kit.NET with a PostgreSQL (pgvector) vector store to build a single-turn Q&A system that uses Retrieval-Augmented Generation (RAG). This solution demonstrates how semantic search and large language models (LLMs) can be combined in a .NET application to provide accurate, context-aware responses from stored vector embeddings.

👥 Industry Target Audience

This sample benefits developers and organizations in areas like:

• 🛎️ Customer Support: Query large knowledge bases with fast, relevant results.
• 📚 Education: Build tutoring systems based on textbooks and training content.
• 🏥 Healthcare: Provide real-time answers from medical guidelines or literature.
• 📦 Logistics & Industry: Search across technical documentation and manuals.
• 🛍️ E-commerce: Enable chat-based access to product catalogs and FAQs.

🚀 Problem Solved

Managing large corpora of data and generating precise answers is a complex task. This demo solves the problem by:

• Persisting embeddings in a vector database (PostgreSQL with pgvector) for faster future access.
• Combining semantic search with generative AI for accurate, context-aware responses.
• Allowing reuse of processed data sources across multiple sessions.

💻 Sample Application Description

This is a console-based chatbot demo that loads a series of eBooks, stores their embeddings in a local PostgreSQL (pgvector) instance, and uses RAG to answer queries.

✨ Key Features

• 🔌 PostgreSQL (pgvector) Integration: Uses LM-Kit.NET.Data.Connectors.PgVector to persist embeddings locally.
• 📦 Model Loading: Load chat and embedding models from predefined URLs or custom paths.
• 🧠 Persistent Vector Store: Avoid redundant embedding computation across sessions.
• 📚 Semantic Search: Retrieve relevant content chunks before generation.
• 📈 Performance Tracking: Measure data source loading time.
• ♻️ Cached Data Handling: Reuse indexed vectors when available.

🧠 Supported Models

• Chat Model: Google Gemma 4 E4B (~6 GB VRAM)
• Embeddings Model: EmbeddingGemma 300M (~300 MB VRAM)

🧾 Data Sources

Three classic eBooks are loaded as semantic knowledge bases:

• Romeo and Juliet by William Shakespeare
• Moby Dick by Herman Melville
• Pride and Prejudice by Jane Austen

🛠️ Getting Started

📋 Prerequisites

• .NET 8.0+ SDK
• PostgreSQL with the pgvector extension
• Docker (for running PostgreSQL + pgvector locally)

🧰 PostgreSQL (pgvector) Setup

Run PostgreSQL with the pgvector extension locally using Docker:

docker run --name lmkit-pgvector -e POSTGRES_PASSWORD=postgres -p 5433:5432 -d pgvector/pgvector:pg17

The demo connects with a PostgreSQL connection string, for example:

Host=localhost;Port=5433;Database=vector_test;Username=postgres;Password=postgres

📥 Download the Project

• .NET Console Demo

▶️ Run the Application

1. 📂 Clone the repository:

git clone https://github.com/LM-Kit/lm-kit-net-samples

2. 📁 Navigate to the demo directory:

cd lm-kit-net-samples/console_net/rag-and-knowledge/vector-database/single_turn_rag_pgvector

3. 🔨 Build and run:

dotnet build
dotnet run

💬 Example Usage

1. Load both models (chat and embedding) from URLs or specify local paths.
2. The demo will load the eBooks, embedding them via LM-Kit and storing them in PostgreSQL (pgvector).
3. Enter your query, e.g.:
   • "Who are the main characters in Romeo and Juliet?"
   • "What is Captain Ahab’s goal in Moby Dick?"
4. The chatbot retrieves matching content and generates a response.
5. The app skips re-embedding if the data is already present in PostgreSQL (pgvector).

🛠️ Special Behavior

• ✅ Reuse of stored collections: Embeddings are reused if already indexed.
• 🔄 Fresh vector indexing: If a collection is not present, it’s created and persisted.
• 📉 Dynamic top-k: GPU-optimized systems select more partitions for improved accuracy.

🔓 Licensing

To run the application, you can use a free community license available at:

👉 https://lm-kit.com/products/community-edition/

📚 Related Repositories

• Demo source code (.NET 8.0): single_turn_rag_pgvector
• PostgreSQL (pgvector) Connector Assembly (Open Source): LM-Kit.NET.Data.Connectors.PgVector

By combining the power of semantic search and LLMs, this demo offers a robust foundation for building production-ready, intelligent chatbots in C# with persistent, scalable vector storage.

📚 Related Content

• How-To: Build a RAG Pipeline: Step-by-step guide to setting up retrieval-augmented generation with LM-Kit.NET.
• Glossary: Vector Database: Explains vector databases like PostgreSQL (pgvector) used for persistent embedding storage.
• Glossary: RAG: Covers Retrieval-Augmented Generation, the core technique behind this demo.
• Glossary: Embeddings: Explains vector embeddings that power semantic search in PostgreSQL (pgvector).
• Single-Turn RAG Demo: Simpler RAG demo using local file storage instead of a vector database.
• Single-Turn RAG with Qdrant: The same RAG pipeline backed by the Qdrant vector database.