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.
Rag
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.
Separate ingestion health from retrieval readiness
A failed ingestion run should not stop retrieval while an accepted index remains queryable and eligible to serve.
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.
Measure retrieval misses separately from model failures
Give retrieval its own outcome and telemetry so missing or irrelevant context is not diagnosed as a model problem.
Keep RAG citations tied to retrieved chunks
Citations should point back to the retrieved evidence, not to sources the model guessed after answering.
Keep vector search filters separate from semantic ranking
Use filters for eligibility and ranking for relevance instead of blending both concerns.
Agent Framework RAG for Agents: Giving Your Agent the Right Context
How to connect Microsoft Agent Framework agents to private application knowledge without stuffing full documents into the prompt.
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.
Controlling Token Growth with Chat Reducers
How to control token growth in Microsoft Agent Framework with message-count and summarizing chat reducers, including setup, tradeoffs, and when each approach fits.