Real RAG examples: .NET + Postgres vector store
What happened
A production‑oriented RAG implementation surfaced in.NET using Postgres vectors and Ollama Mistral embeddings, complete with retrieval, augmentation and query handling—source code available for engineers to study shared. Procurement Sciences likewise demoed a RAG+semantic search flow for document Q&A focused on trust, traceability and reduced hallucinations shared.
Why it matters
A public repository named "-Dotnet-RAG-PgVector" demonstrates a.NET 9 Web API that integrates local Ollama model calls with PostgreSQL + pgvector for embedding storage and retrieval. github.com The project's Program.cs and README reveal explicit wiring with Microsoft.Extensions.AI, pgvector-dotnet and ASP.NET Core to handle document ingestion, embedding generation, nearest‑neighbor retrieval and query‑time augmentation. github.com Deployment artifacts and companion how‑tos include Docker configurations to run a pgvector‑enabled Postgres instance alongside an Ollama container (used in community examples to host Mistral/Phi family models locally). jimsowers.com Procurement Sciences’ Awarded.AI materials and blog posts show a production RAG + semantic‑search flow that prioritizes provenance, human‑in‑the‑loop verification and compliance checks to reduce hallucinations in proposal/document Q&A. procurementsciences.com Third‑party writeups and company collateral state Awarded.AI has contributed to over $2B in guided awards and offers isolated, CUI‑compliant deployments for GovCon customers. orangeslices.ai
Key numbers
- A public repository named "-Dotnet-RAG-PgVector" demonstrates a.NET 9 Web API that integrates local Ollama model calls with PostgreSQL + pgvector for embedding storage and retrieval.
- procurementsciences.com Third‑party writeups and company collateral state Awarded.AI has contributed to over $2B in guided awards and offers isolated, CUI‑compliant deployments for GovCon customers.
Quick answers
What happened in Real RAG examples: .NET + Postgres vector store?
A production‑oriented RAG implementation surfaced in.NET using Postgres vectors and Ollama Mistral embeddings, complete with retrieval, augmentation and query handling—source code available for engineers to study shared. Procurement Sciences likewise demoed a RAG+semantic search flow for document Q&A focused on trust, traceability and reduced hallucinations shared.
Why does Real RAG examples: .NET + Postgres vector store matter?
A public repository named "-Dotnet-RAG-PgVector" demonstrates a.NET 9 Web API that integrates local Ollama model calls with PostgreSQL + pgvector for embedding storage and retrieval. github.com The project's Program.cs and README reveal explicit wiring with Microsoft.Extensions.AI, pgvector-dotnet and ASP.NET Core to handle document ingestion, embedding generation, nearest‑neighbor retrieval and query‑time augmentation. github.com Deployment artifacts and companion how‑tos include Docker configurations to run a pgvector‑enabled Postgres instance alongside an Ollama container (used in community examples to host Mistral/Phi family models locally). jimsowers.com Procurement Sciences’ Awarded.AI materials and blog posts show a production RAG + semantic‑search flow that prioritizes provenance, human‑in‑the‑loop verification and compliance checks to reduce hallucinations in proposal/document Q&A. procurementsciences.com Third‑party writeups and company collateral state Awarded.AI has contributed to over $2B in guided awards and offers isolated, CUI‑compliant deployments for GovCon customers. orangeslices.ai
