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remove .value (#26633)

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Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
This commit is contained in:
Asuka Minato
2025-10-11 10:08:29 +09:00
committed by GitHub
parent bb6a331490
commit 1bd621f819
138 changed files with 613 additions and 633 deletions
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30
api/core/rag/datasource/vdb/vikingdb/vikingdb_vector.py
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@@ -76,11 +76,11 @@ class VikingDBVector(BaseVector):
if not self._has_collection():
fields = [
Field(field_name=vdb_Field.PRIMARY_KEY.value, field_type=FieldType.String, is_primary_key=True),
Field(field_name=vdb_Field.METADATA_KEY.value, field_type=FieldType.String),
Field(field_name=vdb_Field.GROUP_KEY.value, field_type=FieldType.String),
Field(field_name=vdb_Field.CONTENT_KEY.value, field_type=FieldType.Text),
Field(field_name=vdb_Field.VECTOR.value, field_type=FieldType.Vector, dim=dimension),
Field(field_name=vdb_Field.PRIMARY_KEY, field_type=FieldType.String, is_primary_key=True),
Field(field_name=vdb_Field.METADATA_KEY, field_type=FieldType.String),
Field(field_name=vdb_Field.GROUP_KEY, field_type=FieldType.String),
Field(field_name=vdb_Field.CONTENT_KEY, field_type=FieldType.Text),
Field(field_name=vdb_Field.VECTOR, field_type=FieldType.Vector, dim=dimension),
]
self._client.create_collection(
@@ -100,7 +100,7 @@ class VikingDBVector(BaseVector):
collection_name=self._collection_name,
index_name=self._index_name,
vector_index=vector_index,
partition_by=vdb_Field.GROUP_KEY.value,
partition_by=vdb_Field.GROUP_KEY,
description="Index For Dify",
)
redis_client.set(collection_exist_cache_key, 1, ex=3600)
@@ -126,11 +126,11 @@ class VikingDBVector(BaseVector):
# FIXME: fix the type of metadata later
doc = Data(
{
vdb_Field.PRIMARY_KEY.value: metadatas[i]["doc_id"], # type: ignore
vdb_Field.VECTOR.value: embeddings[i] if embeddings else None,
vdb_Field.CONTENT_KEY.value: page_content,
vdb_Field.METADATA_KEY.value: json.dumps(metadata),
vdb_Field.GROUP_KEY.value: self._group_id,
vdb_Field.PRIMARY_KEY: metadatas[i]["doc_id"], # type: ignore
vdb_Field.VECTOR: embeddings[i] if embeddings else None,
vdb_Field.CONTENT_KEY: page_content,
vdb_Field.METADATA_KEY: json.dumps(metadata),
vdb_Field.GROUP_KEY: self._group_id,
}
)
docs.append(doc)
@@ -151,7 +151,7 @@ class VikingDBVector(BaseVector):
# Note: Metadata field value is an dict, but vikingdb field
# not support json type
results = self._client.get_index(self._collection_name, self._index_name).search(
filter={"op": "must", "field": vdb_Field.GROUP_KEY.value, "conds": [self._group_id]},
filter={"op": "must", "field": vdb_Field.GROUP_KEY, "conds": [self._group_id]},
# max value is 5000
limit=5000,
)
@@ -161,7 +161,7 @@ class VikingDBVector(BaseVector):
ids = []
for result in results:
metadata = result.fields.get(vdb_Field.METADATA_KEY.value)
metadata = result.fields.get(vdb_Field.METADATA_KEY)
if metadata is not None:
metadata = json.loads(metadata)
if metadata.get(key) == value:
@@ -189,12 +189,12 @@ class VikingDBVector(BaseVector):
docs = []
for result in results:
metadata = result.fields.get(vdb_Field.METADATA_KEY.value)
metadata = result.fields.get(vdb_Field.METADATA_KEY)
if metadata is not None:
metadata = json.loads(metadata)
if result.score >= score_threshold:
metadata["score"] = result.score
doc = Document(page_content=result.fields.get(vdb_Field.CONTENT_KEY.value), metadata=metadata)
doc = Document(page_content=result.fields.get(vdb_Field.CONTENT_KEY), metadata=metadata)
docs.append(doc)
docs = sorted(docs, key=lambda x: x.metadata.get("score", 0) if x.metadata else 0, reverse=True)
return docs