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fix: Compatibility issues with the summary index feature when using the weaviate vector database.

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FFXN 2026-04-13 18:44:53 +08:00
parent e24b6c27b0
commit 7fd549fd39
1 changed files with 34 additions and 18 deletions
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52
api/core/rag/datasource/vdb/weaviate/weaviate_vector.py
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@ -20,7 +20,7 @@ from pydantic import BaseModel, model_validator
from weaviate.classes.data import DataObject
from weaviate.classes.init import Auth
from weaviate.classes.query import Filter, MetadataQuery
from weaviate.exceptions import UnexpectedStatusCodeError
from weaviate.exceptions import UnexpectedStatusCodeError, WeaviateQueryError
from configs import dify_config
from core.rag.datasource.vdb.field import Field
@ -230,6 +230,8 @@ class WeaviateVector(BaseVector):
wc.Property(name="doc_id", data_type=wc.DataType.TEXT),
wc.Property(name="doc_type", data_type=wc.DataType.TEXT),
wc.Property(name="chunk_index", data_type=wc.DataType.INT),
wc.Property(name="is_summary", data_type=wc.DataType.BOOL),
wc.Property(name="original_chunk_id", data_type=wc.DataType.TEXT),
],
vector_config=wc.Configure.Vectors.self_provided(),
)
@ -262,6 +264,10 @@ class WeaviateVector(BaseVector):
to_add.append(wc.Property(name="doc_type", data_type=wc.DataType.TEXT))
if "chunk_index" not in existing:
to_add.append(wc.Property(name="chunk_index", data_type=wc.DataType.INT))
if "is_summary" not in existing:
to_add.append(wc.Property(name="is_summary", data_type=wc.DataType.BOOL))
if "original_chunk_id" not in existing:
to_add.append(wc.Property(name="original_chunk_id", data_type=wc.DataType.TEXT))
for prop in to_add:
try:
@ -400,15 +406,20 @@ class WeaviateVector(BaseVector):
top_k = int(kwargs.get("top_k", 4))
score_threshold = float(kwargs.get("score_threshold") or 0.0)
res = col.query.near_vector(
near_vector=query_vector,
limit=top_k,
return_properties=props,
return_metadata=MetadataQuery(distance=True),
include_vector=False,
filters=where,
target_vector="default",
)
query_kwargs = {
"near_vector": query_vector,
"limit": top_k,
"return_properties": props,
"return_metadata": MetadataQuery(distance=True),
"include_vector": False,
"filters": where,
"target_vector": "default",
}
try:
res = col.query.near_vector(**query_kwargs)
except WeaviateQueryError:
self._ensure_properties()
res = col.query.near_vector(**query_kwargs)
docs: list[Document] = []
for obj in res.objects:
@ -446,14 +457,19 @@ class WeaviateVector(BaseVector):
top_k = int(kwargs.get("top_k", 4))
res = col.query.bm25(
query=query,
query_properties=[Field.TEXT_KEY.value],
limit=top_k,
return_properties=props,
include_vector=True,
filters=where,
)
query_kwargs = {
"query": query,
"query_properties": [Field.TEXT_KEY.value],
"limit": top_k,
"return_properties": props,
"include_vector": True,
"filters": where,
}
try:
res = col.query.bm25(**query_kwargs)
except WeaviateQueryError:
self._ensure_properties()
res = col.query.bm25(**query_kwargs)
docs: list[Document] = []
for obj in res.objects: