diff --git a/qdrant-landing/content/documentation/headless/content/tutorials/search-engineering.md b/qdrant-landing/content/documentation/headless/content/tutorials/search-engineering.md index 1ecdd6033..da6d5abda 100644 --- a/qdrant-landing/content/documentation/headless/content/tutorials/search-engineering.md +++ b/qdrant-landing/content/documentation/headless/content/tutorials/search-engineering.md @@ -6,7 +6,7 @@ | [Collaborative Filtering](/documentation/tutorials-search-engineering/collaborative-filtering/) | Collaborative filtering using sparse embeddings. | Python | 45m | Intermediate | | [Multivector Document Retrieval](/documentation/tutorials-search-engineering/pdf-retrieval-at-scale/) | PDF RAG using ColPali and embedding pooling. | Python | 30m | Intermediate | | [Measuring ANN Precision](/documentation/tutorials-search-engineering/retrieval-quality/) | Measure ANN precision with the Web UI and tune HNSW parameters. | Web UI | 15m | Beginner | -| [Building a Golden Query Set](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/) | Generate ground truth data at scale and avoid data leakage. | Python | 40m | Intermediate | +| [Measuring Retrieval Relevance](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/) | Build a labeled golden set and score retrieval relevance with ranx. | Python | 40m | Intermediate | | [Evaluating Pipeline Output Quality](/documentation/tutorials-search-engineering/retrieval-quality-pipeline-output/) | Score a RAG pipeline with Ragas and isolate retrieval vs generation failures. | Python | 45m | Intermediate | | [Hybrid Search with Reranking](/documentation/tutorials-search-engineering/reranking-hybrid-search/) | Implement late interaction and sparse reranking. | Python | 40m | Intermediate | | [Semantic Search for Code](/documentation/tutorials-search-engineering/code-search/) | Navigate codebases using vector similarity. | Python | 45m | Intermediate | diff --git a/qdrant-landing/content/documentation/tutorials-lp-overview.md b/qdrant-landing/content/documentation/tutorials-lp-overview.md index 52b50dd23..8a746ee7f 100644 --- a/qdrant-landing/content/documentation/tutorials-lp-overview.md +++ b/qdrant-landing/content/documentation/tutorials-lp-overview.md @@ -69,7 +69,7 @@ partition: develop | [Collaborative Filtering](/documentation/tutorials-search-engineering/collaborative-filtering/) | Collaborative filtering using sparse embeddings. | Python | 45m | Intermediate | | [Multivector Document Retrieval](/documentation/tutorials-search-engineering/pdf-retrieval-at-scale/) | PDF RAG using ColPali and embedding pooling. | Python | 30m | Intermediate | | [Measuring ANN Precision](/documentation/tutorials-search-engineering/retrieval-quality/) | Measure ANN precision with the Web UI and tune HNSW parameters. | Web UI | 15m | Beginner | -| [Building a Golden Query Set](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/) | Generate ground truth data at scale and avoid data leakage. | Python | 40m | Intermediate | +| [Measuring Retrieval Relevance](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/) | Build a labeled golden set and score retrieval relevance with ranx. | Python | 40m | Intermediate | | [Evaluating Pipeline Output Quality](/documentation/tutorials-search-engineering/retrieval-quality-pipeline-output/) | Score a RAG pipeline with Ragas and isolate retrieval vs generation failures. | Python | 45m | Intermediate | | [Reranking for Better Search](/documentation/search-precision/reranking-semantic-search/) | Use multivector representations for better ranking. | Python | 30m | Intermediate | | [Hybrid Search with Reranking](/documentation/tutorials-search-engineering/reranking-hybrid-search/) | Implement late interaction and sparse reranking. | Python | 40m | Intermediate | diff --git a/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality-golden-set.md b/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality-golden-set.md index ec2f1895a..d564da2f9 100644 --- a/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality-golden-set.md +++ b/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality-golden-set.md @@ -1,11 +1,11 @@ --- -title: Building a Golden Query Set +title: Measuring Retrieval Relevance weight: 6 aliases: - /documentation/tutorials/retrieval-quality-golden-set/ --- -# Building a Golden Query Set +# Measuring Retrieval Relevance | Time: 40 min | Level: Intermediate | | | |--------------|---------------------|--|----| diff --git a/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality-pipeline-output.md b/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality-pipeline-output.md index 742db6f02..14d704faa 100644 --- a/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality-pipeline-output.md +++ b/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality-pipeline-output.md @@ -15,7 +15,7 @@ To measure pipeline output quality, you run your golden query set through the fu For orientation on the four layers of retrieval evaluation and where this tutorial fits, see [Measuring ANN Precision](/documentation/tutorials-search-engineering/retrieval-quality/#the-four-layers-of-retrieval-evaluation). -**Prerequisites.** A Qdrant collection with your corpus indexed (chunk text in a `text` payload field), a labeled golden set (see [Building a Golden Query Set](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/)), LLM access for generation and judging, and Python with `ragas` installed. The Wiring section shows the exact entry shape this tutorial expects. +**Prerequisites.** A Qdrant collection with your corpus indexed (chunk text in a `text` payload field), a labeled golden set (see [Measuring Retrieval Relevance](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/)), LLM access for generation and judging, and Python with `ragas` installed. The Wiring section shows the exact entry shape this tutorial expects. ## Wiring the RAG Pipeline diff --git a/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality.md b/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality.md index 785d24ac7..489ba79f1 100644 --- a/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality.md +++ b/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality.md @@ -19,7 +19,7 @@ To measure ANN precision, you compare Qdrant's approximate top-k against the exa Retrieval quality operates at four layers. Each catches different failure modes at a different cadence and cost. This tutorial covers layer 1. - **Layer 1: ANN precision** (this tutorial). How closely approximate nearest-neighbor search matches exact kNN. Run on every index or embedding change. -- **Layer 2: Retrieval relevance** ([Building a Golden Query Set](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/)). How well the results match query intent against a labeled dataset. Run weekly, or on retrieval-stack changes. +- **Layer 2: Retrieval relevance** ([Measuring Retrieval Relevance](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/)). How well the results match query intent against a labeled dataset. Run weekly, or on retrieval-stack changes. - **Layer 3: Pipeline output quality** ([Evaluating Pipeline Output Quality](/documentation/tutorials-search-engineering/retrieval-quality-pipeline-output/)). Whether the full pipeline (retrieval plus an LLM generator, a ranker, or a UI) produces the right output. Run weekly, or on retrieval or generator changes. - **Layer 4: Business impact**. Whether better retrieval moves the KPIs the business cares about. Measured per release once the offline layers pass. @@ -93,4 +93,4 @@ Wire it into CI and fail the job when precision falls below your target threshol Measuring ANN precision keeps HNSW tuning honest. The Search Quality tab gives you a quick interactive read; the Python helper plugs into CI to catch regressions after embedding model changes or index config updates. -Once ANN precision is on target, the next layer is whether the retrieved results are relevant to users. See [Building a Golden Query Set](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/). \ No newline at end of file +Once ANN precision is on target, the next layer is whether the retrieved results are relevant to users. See [Measuring Retrieval Relevance](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/). \ No newline at end of file