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fix: hybrid search explain plan analyze plan (#2360)

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Fix hybrid search explain plan analyze plan API

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

- **New Features**
- Added options to view the execution plan and analyze the runtime
performance of hybrid queries.
- **Refactor**
- Improved internal handling of query setup for better modularity and
maintainability.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->
This commit is contained in:
LuQQiu
2025-04-27 18:39:43 -07:00
committed by GitHub
parent b9be092cb1
commit 178bcf9c90
1 changed files with 107 additions and 45 deletions
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152
python/python/lancedb/query.py
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@@ -1636,51 +1636,7 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
raise NotImplementedError("to_query_object not yet supported on a hybrid query")
def to_arrow(self, *, timeout: Optional[timedelta] = None) -> pa.Table:
vector_query, fts_query = self._validate_query(
self._query, self._vector, self._text
)
self._fts_query = LanceFtsQueryBuilder(
self._table, fts_query, fts_columns=self._fts_columns
)
vector_query = self._query_to_vector(
self._table, vector_query, self._vector_column
)
self._vector_query = LanceVectorQueryBuilder(
self._table, vector_query, self._vector_column
)
if self._limit:
self._vector_query.limit(self._limit)
self._fts_query.limit(self._limit)
if self._columns:
self._vector_query.select(self._columns)
self._fts_query.select(self._columns)
if self._where:
self._vector_query.where(self._where, self._postfilter)
self._fts_query.where(self._where, self._postfilter)
if self._with_row_id:
self._vector_query.with_row_id(True)
self._fts_query.with_row_id(True)
if self._phrase_query:
self._fts_query.phrase_query(True)
if self._distance_type:
self._vector_query.metric(self._distance_type)
if self._nprobes:
self._vector_query.nprobes(self._nprobes)
if self._refine_factor:
self._vector_query.refine_factor(self._refine_factor)
if self._ef:
self._vector_query.ef(self._ef)
if self._bypass_vector_index:
self._vector_query.bypass_vector_index()
if self._lower_bound or self._upper_bound:
self._vector_query.distance_range(
lower_bound=self._lower_bound, upper_bound=self._upper_bound
)
if self._reranker is None:
self._reranker = RRFReranker()
self._create_query_builders()
with ThreadPoolExecutor() as executor:
fts_future = executor.submit(
self._fts_query.with_row_id(True).to_arrow, timeout=timeout
@@ -2003,6 +1959,112 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
self._bypass_vector_index = True
return self
def explain_plan(self, verbose: Optional[bool] = False) -> str:
"""Return the execution plan for this query.
Examples
--------
>>> import lancedb
>>> db = lancedb.connect("./.lancedb")
>>> table = db.create_table("my_table", [{"vector": [99.0, 99]}])
>>> query = [100, 100]
>>> plan = table.search(query).explain_plan(True)
>>> print(plan) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
ProjectionExec: expr=[vector@0 as vector, _distance@2 as _distance]
GlobalLimitExec: skip=0, fetch=10
FilterExec: _distance@2 IS NOT NULL
SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], preserve_partitioning=[false]
KNNVectorDistance: metric=l2
LanceScan: uri=..., projection=[vector], row_id=true, row_addr=false, ordered=false
Parameters
----------
verbose : bool, default False
Use a verbose output format.
Returns
-------
plan : str
""" # noqa: E501
self._create_query_builders()
results = ["Vector Search Plan:"]
results.append(
self._table._explain_plan(
self._vector_query.to_query_object(), verbose=verbose
)
)
results.append("FTS Search Plan:")
results.append(
self._table._explain_plan(
self._fts_query.to_query_object(), verbose=verbose
)
)
return "\n".join(results)
def analyze_plan(self):
"""Execute the query and display with runtime metrics.
Returns
-------
plan : str
"""
self._create_query_builders()
results = ["Vector Search Plan:"]
results.append(self._table._analyze_plan(self._vector_query.to_query_object()))
results.append("FTS Search Plan:")
results.append(self._table._analyze_plan(self._fts_query.to_query_object()))
return "\n".join(results)
def _create_query_builders(self):
"""Set up and configure the vector and FTS query builders."""
vector_query, fts_query = self._validate_query(
self._query, self._vector, self._text
)
self._fts_query = LanceFtsQueryBuilder(
self._table, fts_query, fts_columns=self._fts_columns
)
vector_query = self._query_to_vector(
self._table, vector_query, self._vector_column
)
self._vector_query = LanceVectorQueryBuilder(
self._table, vector_query, self._vector_column
)
# Apply common configurations
if self._limit:
self._vector_query.limit(self._limit)
self._fts_query.limit(self._limit)
if self._columns:
self._vector_query.select(self._columns)
self._fts_query.select(self._columns)
if self._where:
self._vector_query.where(self._where, self._postfilter)
self._fts_query.where(self._where, self._postfilter)
if self._with_row_id:
self._vector_query.with_row_id(True)
self._fts_query.with_row_id(True)
if self._phrase_query:
self._fts_query.phrase_query(True)
if self._distance_type:
self._vector_query.metric(self._distance_type)
if self._nprobes:
self._vector_query.nprobes(self._nprobes)
if self._refine_factor:
self._vector_query.refine_factor(self._refine_factor)
if self._ef:
self._vector_query.ef(self._ef)
if self._bypass_vector_index:
self._vector_query.bypass_vector_index()
if self._lower_bound or self._upper_bound:
self._vector_query.distance_range(
lower_bound=self._lower_bound, upper_bound=self._upper_bound
)
if self._reranker is None:
self._reranker = RRFReranker()
class AsyncQueryBase(object):
def __init__(self, inner: Union[LanceQuery, LanceVectorQuery]):