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.
Retrieval
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.
Persist ingestion checkpoints at source boundaries
Track progress per consumer and source ordering boundary, and advance each checkpoint only after the corresponding ingestion work succeeds.
Store a source checksum to skip unchanged documents
Compare a document’s source checksum, metadata, and ingestion profile before repeating extraction, chunking, embedding, and index writes.
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.
Separate ingestion health from retrieval readiness
A failed ingestion run should not stop retrieval while an accepted index remains queryable and eligible to serve.