Evaluating Retrieval Quality in .NET
Evaluate real Qdrant results in .NET with a custom IEvaluator, labeled chunks, Precision@K, Recall@K, MRR, nDCG, and persisted HTML reports.
Vector-Search
Assembling Context for the Model
Assemble model context from Qdrant results in .NET: deduplicate exact text, check a local token allowance, and trace citations to admitted evidence.
Payload Modeling and Metadata Filters in Qdrant with .NET
Extend the .NET Qdrant chunking sample with payload metadata, keyword indexes, and filters that restrict retrieval to published runbooks.
Chunking for Qdrant in .NET Before It Becomes a Quality Problem
Extend .NET and Qdrant indexing with section boundaries, paragraph overlap, deterministic chunk IDs, and queries that expose incomplete evidence.
Querying Qdrant from .NET
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.
Indexing Your First Documents
Index documents in Qdrant from .NET with Azure OpenAI or local Ollama embeddings, stable point IDs, useful payloads, and a read-back check.
Your First Qdrant Collection
Run Qdrant locally, connect with the official .NET client, and create a collection whose vector size and distance match the embedding model.
Embeddings in .NET
How to generate text embeddings with Microsoft.Extensions.AI and keep the model, dimensions, comparison metric, and indexing behavior compatible.
Why Retrieval Exists
Why application knowledge does not belong in model weights, what retrieval contributes, and when a prompt, database query, or deterministic tool is simpler.
RAG with EF Core and pgvector
How to build RAG retrieval in .NET by storing embeddings in PostgreSQL with pgvector and querying them through EF Core.