perf: optimize FTS and HNSW engines + real PostgreSQL benchmarks

FTS Engine (src/barabadb/fts/engine.nim):
- Fix bm25Score doing O(n) linear scan per document
- Cache IDF per token instead of recomputing for each doc
- Use entry.termFreq directly instead of searching postings again
- Result: FTS search +438% (249 -> 1360 queries/s)

HNSW Vector Engine (src/barabadb/vector/engine.nim):
- Optimize distance functions with float32 + 4x loop unrolling
- Rewrite searchLayer: swap+pop instead of O(n) del, track worst-nearest
  instead of sorting nearest on every iteration
- Result: HNSW insert +117% (245 -> 543 ops/s), search 2.2x faster

Benchmarks:
- Add real PostgreSQL comparison script (benchmarks/pg_bench.py)
- Add report generator (benchmarks/generate_report.py)
- Fix compare.nim cpuTime() bug (was dividing by 1M incorrectly)
- Add nimble tasks: bench_pg, bench_report

Docs:
- Update README.md and docs/en/performance.md with real measured numbers
- Add benchmarks/REAL_COMPARISON.md

Version bump: 1.1.7 -> 1.1.8

baradadb.nimble

@@ -1,5 +1,5 @@
 # Package
-version       = "1.1.7"
+version       = "1.1.8"
 author        = "BaraDB Team"
 description   = "BaraDB — Multimodal database written in Nim"
 license       = "Apache-2.0"
@@ -27,3 +27,9 @@ task test, "Run all tests":
 
 task bench, "Run benchmarks":
   exec "nim c -d:release -r benchmarks/bench_all.nim"
+
+task bench_pg, "Run PostgreSQL comparison benchmarks":
+  exec "python3 benchmarks/pg_bench.py"
+
+task bench_report, "Generate benchmark comparison report":
+  exec "python3 benchmarks/generate_report.py"