Local LLM / Local RAG and private knowledge
Local LLM: Local reranking deployment
Run a bounded reranking stage on the same privacy boundary as the corpus.
Environment and inputs: Private document retrieval with local embedding and generation endpoints; network and storage privacy require verification.
When to use it
A local generator does not by itself guarantee a private retrieval pipeline. Trace document extraction, embedding, indexing and answer generation as separate stages, retaining source locations and access metadata throughout the pipeline.
Check the result
Measure ranking quality and resource contention with the chat model.
Save and compare results
Save the test input, configuration and result. Fix the failing boundary, repeat the same test and compare it with the saved working configuration.
Scope and limits
These exercises have not been tested on your systems. Check feature support and configuration names against the installed release and linked documentation.