fix:score threshold

api/core/rag/datasource/vdb/analyticdb/analyticdb_vector_openapi.py

@@ -256,7 +256,7 @@ class AnalyticdbVectorOpenAPI:
         response = self._client.query_collection_data(request)
         documents = []
         for match in response.body.matches.match:
-            if match.score > score_threshold:
+            if match.score >= score_threshold:
                 metadata = json.loads(match.metadata.get("metadata_"))
                 metadata["score"] = match.score
                 doc = Document(
@@ -293,7 +293,7 @@ class AnalyticdbVectorOpenAPI:
         response = self._client.query_collection_data(request)
         documents = []
         for match in response.body.matches.match:
-            if match.score > score_threshold:
+            if match.score >= score_threshold:
                 metadata = json.loads(match.metadata.get("metadata_"))
                 metadata["score"] = match.score
                 doc = Document(

api/core/rag/datasource/vdb/analyticdb/analyticdb_vector_sql.py

@@ -229,7 +229,7 @@ class AnalyticdbVectorBySql:
             documents = []
             for record in cur:
                 id, vector, score, page_content, metadata = record
-                if score > score_threshold:
+                if score >= score_threshold:
                     metadata["score"] = score
                     doc = Document(
                         page_content=page_content,

api/core/rag/datasource/vdb/baidu/baidu_vector.py

@@ -157,7 +157,7 @@ class BaiduVector(BaseVector):
             if meta is not None:
                 meta = json.loads(meta)
             score = row.get("score", 0.0)
-            if score > score_threshold:
+            if score >= score_threshold:
                 meta["score"] = score
                 doc = Document(page_content=row_data.get(self.field_text), metadata=meta)
                 docs.append(doc)

api/core/rag/datasource/vdb/chroma/chroma_vector.py

@@ -120,7 +120,7 @@ class ChromaVector(BaseVector):
             distance = distances[index]
             metadata = dict(metadatas[index])
             score = 1 - distance
-            if score > score_threshold:
+            if score >= score_threshold:
                 metadata["score"] = score
                 doc = Document(
                     page_content=documents[index],

api/core/rag/datasource/vdb/elasticsearch/elasticsearch_vector.py

@@ -216,7 +216,7 @@ class ElasticSearchVector(BaseVector):
         docs = []
         for doc, score in docs_and_scores:
             score_threshold = float(kwargs.get("score_threshold") or 0.0)
-            if score > score_threshold:
+            if score >= score_threshold:
                 if doc.metadata is not None:
                     doc.metadata["score"] = score
                     docs.append(doc)

api/core/rag/datasource/vdb/huawei/huawei_cloud_vector.py

@@ -127,7 +127,7 @@ class HuaweiCloudVector(BaseVector):
         docs = []
         for doc, score in docs_and_scores:
             score_threshold = float(kwargs.get("score_threshold") or 0.0)
-            if score > score_threshold:
+            if score >= score_threshold:
                 if doc.metadata is not None:
                     doc.metadata["score"] = score
             docs.append(doc)

api/core/rag/datasource/vdb/lindorm/lindorm_vector.py

@@ -275,7 +275,7 @@ class LindormVectorStore(BaseVector):
         docs = []
         for doc, score in docs_and_scores:
             score_threshold = kwargs.get("score_threshold", 0.0) or 0.0
-            if score > score_threshold:
+            if score >= score_threshold:
                 if doc.metadata is not None:
                     doc.metadata["score"] = score
                 docs.append(doc)

api/core/rag/datasource/vdb/opengauss/opengauss.py

@@ -194,7 +194,7 @@ class OpenGauss(BaseVector):
                 metadata, text, distance = record
                 score = 1 - distance
                 metadata["score"] = score
-                if score > score_threshold:
+                if score >= score_threshold:
                     docs.append(Document(page_content=text, metadata=metadata))
         return docs
 

api/core/rag/datasource/vdb/opensearch/opensearch_vector.py

@@ -211,7 +211,7 @@ class OpenSearchVector(BaseVector):
 
             metadata["score"] = hit["_score"]
             score_threshold = float(kwargs.get("score_threshold") or 0.0)
-            if hit["_score"] > score_threshold:
+            if hit["_score"] >= score_threshold:
                 doc = Document(page_content=hit["_source"].get(Field.CONTENT_KEY.value), metadata=metadata)
                 docs.append(doc)
 

api/core/rag/datasource/vdb/oracle/oraclevector.py

@@ -261,7 +261,7 @@ class OracleVector(BaseVector):
                     metadata, text, distance = record
                     score = 1 - distance
                     metadata["score"] = score
-                    if score > score_threshold:
+                    if score >= score_threshold:
                         docs.append(Document(page_content=text, metadata=metadata))
             conn.close()
         return docs

api/core/rag/datasource/vdb/pgvecto_rs/pgvecto_rs.py

@@ -202,7 +202,7 @@ class PGVectoRS(BaseVector):
             score = 1 - dis
             metadata["score"] = score
             score_threshold = float(kwargs.get("score_threshold") or 0.0)
-            if score > score_threshold:
+            if score >= score_threshold:
                 doc = Document(page_content=record.text, metadata=metadata)
                 docs.append(doc)
         return docs

api/core/rag/datasource/vdb/pgvector/pgvector.py

@@ -195,7 +195,7 @@ class PGVector(BaseVector):
                 metadata, text, distance = record
                 score = 1 - distance
                 metadata["score"] = score
-                if score > score_threshold:
+                if score >= score_threshold:
                     docs.append(Document(page_content=text, metadata=metadata))
         return docs
 

api/core/rag/datasource/vdb/pyvastbase/vastbase_vector.py

@@ -170,7 +170,7 @@ class VastbaseVector(BaseVector):
                 metadata, text, distance = record
                 score = 1 - distance
                 metadata["score"] = score
-                if score > score_threshold:
+                if score >= score_threshold:
                     docs.append(Document(page_content=text, metadata=metadata))
         return docs
 

api/core/rag/datasource/vdb/qdrant/qdrant_vector.py

@@ -369,7 +369,7 @@ class QdrantVector(BaseVector):
                 continue
             metadata = result.payload.get(Field.METADATA_KEY.value) or {}
             # duplicate check score threshold
-            if result.score > score_threshold:
+            if result.score >= score_threshold:
                 metadata["score"] = result.score
                 doc = Document(
                     page_content=result.payload.get(Field.CONTENT_KEY.value, ""),

api/core/rag/datasource/vdb/relyt/relyt_vector.py

@@ -233,7 +233,7 @@ class RelytVector(BaseVector):
         docs = []
         for document, score in results:
             score_threshold = float(kwargs.get("score_threshold") or 0.0)
-            if 1 - score > score_threshold:
+            if 1 - score >= score_threshold:
                 docs.append(document)
         return docs
 

api/core/rag/datasource/vdb/tablestore/tablestore_vector.py

@@ -300,7 +300,7 @@ class TableStoreVector(BaseVector):
         )
         documents = []
         for search_hit in search_response.search_hits:
-            if search_hit.score > score_threshold:
+            if search_hit.score >= score_threshold:
                 ots_column_map = {}
                 for col in search_hit.row[1]:
                     ots_column_map[col[0]] = col[1]

api/core/rag/datasource/vdb/tencent/tencent_vector.py

@@ -291,7 +291,7 @@ class TencentVector(BaseVector):
                 score = 1 - result.get("score", 0.0)
             else:
                 score = result.get("score", 0.0)
-            if score > score_threshold:
+            if score >= score_threshold:
                 meta["score"] = score
                 doc = Document(page_content=result.get(self.field_text), metadata=meta)
                 docs.append(doc)

api/core/rag/datasource/vdb/tidb_on_qdrant/tidb_on_qdrant_vector.py

@@ -351,7 +351,7 @@ class TidbOnQdrantVector(BaseVector):
             metadata = result.payload.get(Field.METADATA_KEY.value) or {}
             # duplicate check score threshold
             score_threshold = kwargs.get("score_threshold") or 0.0
-            if result.score > score_threshold:
+            if result.score >= score_threshold:
                 metadata["score"] = result.score
                 doc = Document(
                     page_content=result.payload.get(Field.CONTENT_KEY.value, ""),

api/core/rag/datasource/vdb/upstash/upstash_vector.py

@@ -110,7 +110,7 @@ class UpstashVector(BaseVector):
             score = record.score
             if metadata is not None and text is not None:
                 metadata["score"] = score
-                if score > score_threshold:
+                if score >= score_threshold:
                     docs.append(Document(page_content=text, metadata=metadata))
         return docs
 

api/core/rag/datasource/vdb/vikingdb/vikingdb_vector.py

@@ -192,7 +192,7 @@ class VikingDBVector(BaseVector):
             metadata = result.fields.get(vdb_Field.METADATA_KEY.value)
             if metadata is not None:
                 metadata = json.loads(metadata)
-            if result.score > score_threshold:
+            if result.score >= score_threshold:
                 metadata["score"] = result.score
                 doc = Document(page_content=result.fields.get(vdb_Field.CONTENT_KEY.value), metadata=metadata)
                 docs.append(doc)

api/core/rag/datasource/vdb/weaviate/weaviate_vector.py

@@ -220,7 +220,7 @@ class WeaviateVector(BaseVector):
         for doc, score in docs_and_scores:
             score_threshold = float(kwargs.get("score_threshold") or 0.0)
             # check score threshold
-            if score > score_threshold:
+            if score >= score_threshold:
                 if doc.metadata is not None:
                     doc.metadata["score"] = score
                     docs.append(doc)

api/core/rag/index_processor/processor/paragraph_index_processor.py

@@ -123,7 +123,7 @@ class ParagraphIndexProcessor(BaseIndexProcessor):
         for result in results:
             metadata = result.metadata
             metadata["score"] = result.score
-            if result.score > score_threshold:
+            if result.score >= score_threshold:
                 doc = Document(page_content=result.page_content, metadata=metadata)
                 docs.append(doc)
         return docs

api/core/rag/index_processor/processor/parent_child_index_processor.py

@@ -162,7 +162,7 @@ class ParentChildIndexProcessor(BaseIndexProcessor):
         for result in results:
             metadata = result.metadata
             metadata["score"] = result.score
-            if result.score > score_threshold:
+            if result.score >= score_threshold:
                 doc = Document(page_content=result.page_content, metadata=metadata)
                 docs.append(doc)
         return docs

api/core/rag/index_processor/processor/qa_index_processor.py

@@ -158,7 +158,7 @@ class QAIndexProcessor(BaseIndexProcessor):
         for result in results:
             metadata = result.metadata
             metadata["score"] = result.score
-            if result.score > score_threshold:
+            if result.score >= score_threshold:
                 doc = Document(page_content=result.page_content, metadata=metadata)
                 docs.append(doc)
         return docs