[Feature] Add efficient filtering (knn.filter) support for vectorSearch()

[mengweieric]

Summary

Adds filter_type=post|efficient option to vectorSearch() so WHERE clauses can be placed inside the knn clause (knn.filter) for efficient pre-filtering during ANN search, or outside as bool.filter for post-filtering (default). Also adds mandatory LIMIT enforcement for radial search.

What this PR adds

FilterType enum and option parsing

• New FilterType enum (POST, EFFICIENT) with fromString() validation
• filter_type added to allowed option keys in VectorSearchTableFunctionImplementation
• filter_type is stripped from options before knn JSON generation — it's a SQL-layer directive, not a knn parameter

Efficient filter pushdown

• VectorSearchQueryBuilder.pushDownFilter() branches on filter type:
  • POST (default): knn in bool.must + WHERE in bool.filter (post-filtering)
  • EFFICIENT: rebuilds knn query with WHERE embedded in knn.filter via callback
• Function<QueryBuilder, QueryBuilder> callback keeps JSON serialization in VectorSearchIndex
• buildKnnQueryJson() collapsed to accept optional filter JSON parameter — no duplication

Build-time validation

• build() override rejects explicit filter_type without a pushdownable WHERE clause
• Applies to both post and efficient — specifying the directive without a filter is always an error

Radial search LIMIT requirement

• Radial search (max_distance or min_score) without an explicit LIMIT clause is rejected at build time with a clear error message
• Prevents unbounded result sets from radial queries that could silently return up to maxResultWindow rows

Engine support

• knn.filter is supported for lucene and faiss engines (HNSW, IVF). Engine compatibility is not validated by the SQL plugin — unsupported engines reject at execution time.

SQL syntax

-- Post-filtering (default, same as omitting filter_type)
SELECT v._id, v._score
FROM vectorSearch(table='my-index', field='embedding',
     vector='[1.0, 2.0, 3.0]', option='k=10,filter_type=post') AS v
WHERE v.city = 'Seattle'
LIMIT 10

-- Efficient pre-filtering (WHERE inside knn.filter)
SELECT v._id, v._score
FROM vectorSearch(table='my-index', field='embedding',
     vector='[1.0, 2.0, 3.0]', option='k=10,filter_type=efficient') AS v
WHERE v.city = 'Seattle'
LIMIT 10

-- Radial search requires LIMIT
SELECT v._id, v._score
FROM vectorSearch(table='my-index', field='embedding',
     vector='[1.0, 2.0, 3.0]', option='max_distance=10.5') AS v
LIMIT 100

Test plan

• ./gradlew spotlessCheck — PASS
• ./gradlew :opensearch:test — PASS
• ./gradlew :integ-test:integTest -Dtests.class="*VectorSearchIT" — PASS (25 tests)
• Sole authorship verified — Eric Wei only, no Co-Authored-By