Ai-Engineering

Turn a question into an embedding, apply payload filters, query Qdrant from .NET, and inspect the ranked results without treating a similarity score as proof of relevance.

A small ASP.NET Core foundation for model access, validated configuration, tracing, buffered and streaming HTTP contracts, and future AI capabilities.

Index documents in Qdrant from .NET with Azure OpenAI or local Ollama embeddings, stable point IDs, useful payloads, and a read-back check.

List a feature’s required model capabilities and check them in one request flow before comparing candidate deployments.

Define allowed fields, redaction, access, and retention before capturing AI inputs, outputs, or tool content for debugging.

Choose AI evaluation criteria from the feature contract: answer quality, retrieval, tool effects, safety, reliability, latency, and cost.

Register .NET AI clients, tools, prompts, and retrieval services with explicit lifetimes, safe request state, and clear disposal ownership.

Check why generation stopped before accepting model output, and give incomplete responses an explicit application outcome.

Track progress per consumer and source ordering boundary, and advance each checkpoint only after the corresponding ingestion work succeeds.

Let one throttling response reduce pressure from every caller sharing the affected capacity pool.