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Combine vector and keyword search for improved retrieval.
Passed 18 of 25 test cases with the skill, and 16 of 25 without it.
and 1 more in the full report
3 fixed15 passed either way6 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
In a custom Python hybrid RAG pipeline returning SearchResult dataclass objects, what metadata attribute should be included to identify whether a result came from vector search, keyword search, or fusion?
| Checked | Without skill | With skill |
|---|---|---|
| SearchResult dataclass includes a source string field. | Missed | Passed |
The instructions your model reads when the skill loads.
Patterns for combining vector similarity and keyword-based search.
Query → ┬─► Vector Search ──► Candidates ─┐
│ │
└─► Keyword Search ─► Candidates ─┴─► Fusion ─► Results
| Method | Description | Best For |
|---|---|---|
| RRF | Reciprocal Rank Fusion | General purpose |
| Linear | Weighted sum of scores | Tunable balance |
| Cross-encoder | Rerank with neural model | Highest quality |
| Cascade | Filter then rerank | Efficiency |
from typing import List, Dict, Tuple
from collections import defaultdict
def reciprocal_rank_fusion(
test cases passed, vs 16 / 25 without
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