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Standalone RAG Service

2026Data Pipelines & AutomationCapability

What it is

Retrieval-augmented generation as a service another application calls.

Client work is shown without identifying imagery

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Why this existed

Once a second product needs document Q&A, reimplementing retrieval is how you end up with four subtly different systems and four sets of bugs.

[ WHAT_WE_BUILT ]

What we built

A Python service with its own job store handling ingestion, indexing and query, deployed independently of the applications that consume it — so several products share one retrieval layer instead of each re-implementing it.

Standalone service handling ingestion, indexing and query
Own job store for processing state
Deployed independently of consuming applications

[ HOW_IT_IS_USED ]

How a company uses it

When more than one product needs document Q&A, building it once as a service is the difference between one thing to maintain and four.

Built with

PythonSQLiteVector search

[ COMMON_QUESTIONS ]

Questions clients ask

When is retrieval worth extracting into a service?

When the second consumer appears. Before that it is premature; after that, every additional consumer makes the duplication more expensive to unwind.

What are the downsides?

An extra deployment, network latency, and a versioning contract between the service and its consumers. Those are real costs — worth paying for several consumers, not for one.

How do you handle different document types across consumers?

Namespacing and per-consumer configuration. The retrieval mechanics are shared; chunking strategy and metadata often need to differ by document type.

[ RELATED_WORK ]