How do I set up a continuous evaluation pipeline using Ragas to measure the retrieval accuracy and faithfulness of our enterprise vector search
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How do I set up a continuous evaluation pipeline using Ragas to measure the retrieval accuracy and faithfulness of our enterprise vector search
2 real searches · 1 real-search pass
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
Classifying 2 queries…
Searches and sources
ChatGPT2 real searches · gpt-6
Ragas documentation metrics context precision context recall faithfulness evaluate testset latest API 2026 Google ↗
Ragas GitHub continuous evaluation CI/CD examples dataset schema SingleTurnSample Google ↗
- List of available metrics · docs.ragas.io
- ragas/docs/concepts/metrics/available_metrics/faithfulness.md at main · vibrantlabsai/ragas · github.com
- ragas/docs/concepts/metrics/available_metrics/context_recall.md at main · vibrantlabsai/ragas · github.com
- ragas/docs/concepts/metrics/available_metrics/context_precision.md at main · vibrantlabsai/ragas · github.com
- ragas/docs/concepts/components/eval_dataset.md at main · vibrantlabsai/ragas · github.com
- ragas/docs/getstarted/evals.md at main · vibrantlabsai/ragas · github.com
- Ragas official documentation · docs.ragas.io
- Langfuse observability documentation · langfuse.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
Ragas documentation metrics context precision context recall faithfulness evaluate testset latest API 2026 OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
Ragas GitHub continuous evaluation CI/CD examples dataset schema SingleTurnSample OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 |