Add multi-representation search tutorial notebook#103
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Companion notebook to the multi-representation search tutorial in qdrant/landing_page#2334. Builds the recommended retrieval pipeline (three named-vector prefetches, RRF fusion, document-level grouping) step by step against a 2,000-paper ML/CS arXiv slice, with qualitative top-K results at each step. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
…ries as filter Brings the notebook to 1:1 parity with the refactored tutorial: - Switches to Qdrant Cloud Inference for dense + core BM25 for sparse (drops FastEmbed from the notebook dependencies) - Renames dense_summary to dense_abstract and sparse_keywords to sparse_title - Moves categories from BM25 input to a filterable payload field with a keyword index; sparse_title now indexes only the title (avg_len=10) - Adds dense_abstract as a fourth prefetch with its own Step 4, so the build-up now reads: chunk -> +sparse -> +title -> +abstract -> group -> formula - Adds an optional tags filter to retrieve_grouped - Updates probe_queries.py to match the new schema and step structure
Wraps every $score term in MultExpression (with weight 1.0 on chunk) so the formula reads uniformly. Adds a comment above the QdrantClient init pointing readers to https://cloud.qdrant.io for their own url and api_key.
Adds the missing keyword index on document_id so the grouping step (query_points_groups with group_by="document_id") works under strict mode. Tweaks the upload_points call to batch_size=256, parallel=2 for faster ingestion against Cloud Inference. Adds an expected-results summary to the wrap-up so readers running the same query against the same dataset can compare their output to the reference.
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May 12, 2026
Adds the missing keyword index on document_id so grouping works under strict mode (Cloud default), and tunes the upload_points call to batch_size=256, parallel=2 for faster ingestion. Mirrors the notebook in qdrant/examples#103.
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Summary
qdrant/landing_page#2334.
Scope
The tutorial covers why of the design; this notebook covers the how end to end:
gfissore/arxiv-abstracts-2021(cs.LG / cs.CV / cs.CL / cs.AI / stat.ML) so the demo queries have natural matches.dense_chunk,dense_title,dense_summary) and one named sparse vector (sparse_keywords) using FastEmbed (BAAI/bge-small-en-v1.5+Qdrant/bm25).dense_titleprefetchquery_points_groupsfor document-level groupingFormulaQueryboostFile
multi-representation-search/multi-representation-search.ipynb— single notebook, 22 cells.Requirements
Notebook is tested on Python 3.11 and 3.12. Some FastEmbed dependencies don't yet ship wheels for Python 3.14.