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## What problem does this PR solve?
Implements `FetchChunkVectors` — the infrastructure needed to hydrate
chunk embedding vectors on demand. This is a prerequisite for
`insert_citations` (citation insertion in the `searchbots/ask`
endpoint), matching the Python `Dealer.fetch_chunk_vectors` pattern.
Without this, citation insertion cannot compute answer-vs-chunk vector
similarity.
## Type of change
- [x] New Feature (non-breaking change which adds functionality)
## Changes
### New Function
- `FetchChunkVectors(engine, chunkIDs, tenantIDs, kbIDs, dim)` — fetches
embedding vectors for a set of chunk IDs
- Consumer-side `vectorFetcher` interface with only `Search` + `GetType`
methods
- Both `*elasticsearchEngine` and `*infinityEngine` implicitly satisfy
the interface
### Engine Behavior
- **ES**: queries by chunk ID list via `Search` with filter `{"id":
chunkIDs}`, parses tab-separated `q_N_vec` string format
- **Infinity / OceanBase**: skips the round-trip (vectors already
shipped with chunks)
- **Degrades gracefully**: engine errors return zero vectors — citation
insertion will use placeholders instead of failing
### Vector Parsing
- Handles ES tab-separated string format (`"0.1\t0.2\t0.3"`)
- Handles `[]float64` and `[]interface{}` formats
- Returns zero vector for wrong-dimension or unparseable input
### Bug Fix
- `metadata_filter_test.go`: add missing `"sort"` import (pre-existing
build break)
### Tests
- 12 unit tests: empty input, Infinity/OceanBase skip, ES string vector,
ES float slice, ES interface slice, search error degradation, missing
chunk → zero, wrong dimension → zero, parse edge cases
## Files Changed
| File | Change |
|------|--------|
| `internal/service/chunk_vector.go` | New — FetchChunkVectors + parse
helpers |
| `internal/service/chunk_vector_test.go` | New — 12 tests |
| `internal/service/metadata_filter_test.go` | Fix missing `"sort"`
import |
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>