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AIS-324 minor updates #29

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Merged
merged 2 commits into from
Sep 3, 2025
Merged

AIS-324 minor updates #29

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a39746e
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aMahanna Sep 2, 2025
b3e3918
fix: version check order
aMahanna Sep 2, 2025
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128 changes: 54 additions & 74 deletions libs/arangodb/langchain_arangodb/vectorstores/arangodb_vector.py
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Original file line number Original file line Diff line number Diff line change
Expand Up @@ -142,10 +142,7 @@ def __init__(
DistanceStrategy.DOT_PRODUCT, DistanceStrategy.DOT_PRODUCT,
DistanceStrategy.MAX_INNER_PRODUCT, DistanceStrategy.MAX_INNER_PRODUCT,
]: ]:
m = ( m = "distance_strategy must be one of: 'COSINE', 'EUCLIDEAN_DISTANCE', 'JACCARD', 'DOT_PRODUCT', 'MAX_INNER_PRODUCT'" # noqa: E501
"distance_strategy must be one of: 'COSINE', 'EUCLIDEAN_DISTANCE', "
"'JACCARD', 'DOT_PRODUCT', 'MAX_INNER_PRODUCT'"
)
raise ValueError(m) raise ValueError(m)


self.embedding = embedding self.embedding = embedding
Expand Down Expand Up @@ -1223,15 +1220,15 @@ def _process_search_query(self, cursor: Cursor) -> List[tuple[Document, float]]:


return results return results


def _build_vector_search_query( def _get_score_query_and_sort_order(self, use_approx: bool) -> Tuple[str, str]:
self, """Get the score query and sort order for the given distance strategy.
embedding: List[float],
k: int, :param use_approx: Whether to use approximate nearest neighbor search.
return_fields: set[str], :type use_approx: bool
use_approx: bool, :return: A tuple containing the score query and sort order.
filter_clause: str, :rtype: Tuple[str, str]
metadata_clause: str, """
) -> Tuple[str, dict[str, Any]]:
if self._distance_strategy == DistanceStrategy.COSINE: if self._distance_strategy == DistanceStrategy.COSINE:
score_func = "APPROX_NEAR_COSINE" if use_approx else "COSINE_SIMILARITY" score_func = "APPROX_NEAR_COSINE" if use_approx else "COSINE_SIMILARITY"
scoring_query = f"{score_func}(doc.{self.embedding_field}, @embedding)" scoring_query = f"{score_func}(doc.{self.embedding_field}, @embedding)"
Expand All @@ -1240,38 +1237,57 @@ def _build_vector_search_query(
score_func = "APPROX_NEAR_L2" if use_approx else "L2_DISTANCE" score_func = "APPROX_NEAR_L2" if use_approx else "L2_DISTANCE"
scoring_query = f"{score_func}(doc.{self.embedding_field}, @embedding)" scoring_query = f"{score_func}(doc.{self.embedding_field}, @embedding)"
sort_order = "ASC" sort_order = "ASC"
elif self._distance_strategy == DistanceStrategy.JACCARD:
use_approx = False
score_func = "JACCARD"
scoring_query = f"{score_func}(doc.{self.embedding_field}, @embedding)"
sort_order = "DESC"
elif self._distance_strategy in [ elif self._distance_strategy in [
DistanceStrategy.JACCARD,
DistanceStrategy.MAX_INNER_PRODUCT, DistanceStrategy.MAX_INNER_PRODUCT,
DistanceStrategy.DOT_PRODUCT, DistanceStrategy.DOT_PRODUCT,
]: ]:
if use_approx: scoring_query = """
raise ValueError( SUM(
f"Unsupported metric: {self._distance_strategy} is not supported " FOR i IN 0..LENGTH(doc.embedding)-1
"for approximate search" RETURN doc.embedding[i] * @embedding[i]
)
if self._distance_strategy == DistanceStrategy.JACCARD:
score_func = "JACCARD"
scoring_query = f"{score_func}(doc.{self.embedding_field}, @embedding)"
elif self._distance_strategy in [
DistanceStrategy.DOT_PRODUCT,
DistanceStrategy.MAX_INNER_PRODUCT,
]:
scoring_query = (
"SUM(FOR i IN 0..LENGTH(doc.embedding)-1 "
"RETURN doc.embedding[i] * @embedding[i])"
) )
"""
sort_order = "DESC" sort_order = "DESC"
else: else:
raise ValueError(f"Unsupported metric: {self._distance_strategy}") raise ValueError(f"Unsupported metric: {self._distance_strategy}")


if use_approx: return scoring_query, sort_order
if version.parse(self.db.version()) < version.parse("3.12.4"): # type: ignore
m = "Approximate Nearest Neighbor search requires ArangoDB >= 3.12.4." def _ensure_vector_index(self) -> None:
raise ValueError(m) """Ensure the vector index exists."""
if self._distance_strategy in [
DistanceStrategy.JACCARD,
DistanceStrategy.DOT_PRODUCT,
DistanceStrategy.MAX_INNER_PRODUCT,
]:
m = f"Unsupported metric: {self._distance_strategy} is not supported for approximate search" # noqa: E501
raise ValueError(m)


if not self.retrieve_vector_index(): if version.parse(self.db.version()) < version.parse("3.12.4"): # type: ignore
self.create_vector_index() m = "Approximate Nearest Neighbor search requires ArangoDB >= 3.12.4."
raise ValueError(m)

if not self.retrieve_vector_index():
self.create_vector_index()

def _build_vector_search_query(
self,
embedding: List[float],
k: int,
return_fields: set[str],
use_approx: bool,
filter_clause: str,
metadata_clause: str,
) -> Tuple[str, dict[str, Any]]:
scoring_query, sort_order = self._get_score_query_and_sort_order(use_approx)

if use_approx:
self._ensure_vector_index()


return_fields.update({"_key", self.text_field}) return_fields.update({"_key", self.text_field})
return_fields_list = list(return_fields) return_fields_list = list(return_fields)
Expand Down Expand Up @@ -1336,49 +1352,13 @@ def _build_hybrid_search_query(
) -> Tuple[str, dict[str, Any]]: ) -> Tuple[str, dict[str, Any]]:
"""Build the hybrid search query using RRF.""" """Build the hybrid search query using RRF."""


scoring_query, sort_order = self._get_score_query_and_sort_order(use_approx)

if not self.retrieve_keyword_index(): if not self.retrieve_keyword_index():
self.create_keyword_index() self.create_keyword_index()


if self._distance_strategy == DistanceStrategy.COSINE:
score_func = "APPROX_NEAR_COSINE" if use_approx else "COSINE_SIMILARITY"
scoring_query = f"{score_func}(doc.{self.embedding_field}, @embedding)"
sort_order = "DESC"
elif self._distance_strategy == DistanceStrategy.EUCLIDEAN_DISTANCE:
score_func = "APPROX_NEAR_L2" if use_approx else "L2_DISTANCE"
scoring_query = f"{score_func}(doc.{self.embedding_field}, @embedding)"
sort_order = "ASC"
elif self._distance_strategy in [
DistanceStrategy.JACCARD,
DistanceStrategy.MAX_INNER_PRODUCT,
DistanceStrategy.DOT_PRODUCT,
]:
if use_approx:
raise ValueError(
f"Unsupported metric: {self._distance_strategy} is not supported "
"for approximate search"
)
if self._distance_strategy == DistanceStrategy.JACCARD:
score_func = "JACCARD"
scoring_query = f"{score_func}(doc.{self.embedding_field}, @embedding)"
elif self._distance_strategy in [
DistanceStrategy.DOT_PRODUCT,
DistanceStrategy.MAX_INNER_PRODUCT,
]:
scoring_query = (
"SUM(FOR i IN 0..LENGTH(doc.embedding)-1 "
"RETURN doc.embedding[i] * @embedding[i])"
)
sort_order = "DESC"
else:
raise ValueError(f"Unsupported metric: {self._distance_strategy}")

if use_approx: if use_approx:
if version.parse(self.db.version()) < version.parse("3.12.4"): # type: ignore self._ensure_vector_index()
m = "Approximate Nearest Neighbor search requires ArangoDB >= 3.12.4."
raise ValueError(m)

if not self.retrieve_vector_index():
self.create_vector_index()


return_fields.update({"_key", self.text_field}) return_fields.update({"_key", self.text_field})
return_fields_list = list(return_fields) return_fields_list = list(return_fields)
Expand Down