Merge branch 'langgenius:main' into feat-add-childchunk-api

api/core/provider_manager.py

@@ -149,6 +149,11 @@ class ProviderManager:
             provider_name = provider_entity.provider
             provider_records = provider_name_to_provider_records_dict.get(provider_entity.provider, [])
             provider_model_records = provider_name_to_provider_model_records_dict.get(provider_entity.provider, [])
+            provider_id_entity = ModelProviderID(provider_name)
+            if provider_id_entity.is_langgenius():
+                provider_model_records.extend(
+                    provider_name_to_provider_model_records_dict.get(provider_id_entity.provider_name, [])
+                )
 
             # Convert to custom configuration
             custom_configuration = self._to_custom_configuration(
@@ -190,6 +195,20 @@ class ProviderManager:
                 provider_name
             )
 
+            provider_id_entity = ModelProviderID(provider_name)
+
+            if provider_id_entity.is_langgenius():
+                if provider_model_settings is not None:
+                    provider_model_settings.extend(
+                        provider_name_to_provider_model_settings_dict.get(provider_id_entity.provider_name, [])
+                    )
+                if provider_load_balancing_configs is not None:
+                    provider_load_balancing_configs.extend(
+                        provider_name_to_provider_load_balancing_model_configs_dict.get(
+                            provider_id_entity.provider_name, []
+                        )
+                    )
+
             # Convert to model settings
             model_settings = self._to_model_settings(
                 provider_entity=provider_entity,
@@ -207,7 +226,7 @@ class ProviderManager:
                 model_settings=model_settings,
             )
 
-            provider_configurations[str(ModelProviderID(provider_name))] = provider_configuration
+            provider_configurations[str(provider_id_entity)] = provider_configuration
 
         # Return the encapsulated object
         return provider_configurations

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

@@ -194,6 +194,8 @@ class AnalyticdbVectorBySql:
 
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
         top_k = kwargs.get("top_k", 4)
+        if not isinstance(top_k, int) or top_k <= 0:
+            raise ValueError("top_k must be a positive integer")
         score_threshold = float(kwargs.get("score_threshold") or 0.0)
         with self._get_cursor() as cur:
             query_vector_str = json.dumps(query_vector)
@@ -220,6 +222,8 @@ class AnalyticdbVectorBySql:
 
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
         top_k = kwargs.get("top_k", 4)
+        if not isinstance(top_k, int) or top_k <= 0:
+            raise ValueError("top_k must be a positive integer")
         with self._get_cursor() as cur:
             cur.execute(
                 f"""SELECT id, vector, page_content, metadata_, 

api/core/rag/datasource/vdb/myscale/myscale_vector.py

@@ -125,6 +125,8 @@ class MyScaleVector(BaseVector):
 
     def _search(self, dist: str, order: SortOrder, **kwargs: Any) -> list[Document]:
         top_k = kwargs.get("top_k", 4)
+        if not isinstance(top_k, int) or top_k <= 0:
+            raise ValueError("top_k must be a positive integer")
         score_threshold = float(kwargs.get("score_threshold") or 0.0)
         where_str = (
             f"WHERE dist < {1 - score_threshold}"

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

@@ -155,7 +155,8 @@ class OpenGauss(BaseVector):
         :return: List of Documents that are nearest to the query vector.
         """
         top_k = kwargs.get("top_k", 4)
-
+        if not isinstance(top_k, int) or top_k <= 0:
+            raise ValueError("top_k must be a positive integer")
         with self._get_cursor() as cur:
             cur.execute(
                 f"SELECT meta, text, embedding <=> %s AS distance FROM {self.table_name}"
@@ -174,7 +175,8 @@ class OpenGauss(BaseVector):
 
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
         top_k = kwargs.get("top_k", 5)
-
+        if not isinstance(top_k, int) or top_k <= 0:
+            raise ValueError("top_k must be a positive integer")
         with self._get_cursor() as cur:
             cur.execute(
                 f"""SELECT meta, text, ts_rank(to_tsvector(coalesce(text, '')), plainto_tsquery(%s)) AS score

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

@@ -171,6 +171,8 @@ class PGVector(BaseVector):
         :return: List of Documents that are nearest to the query vector.
         """
         top_k = kwargs.get("top_k", 4)
+        if not isinstance(top_k, int) or top_k <= 0:
+            raise ValueError("top_k must be a positive integer")
 
         with self._get_cursor() as cur:
             cur.execute(
@@ -190,7 +192,8 @@ class PGVector(BaseVector):
 
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
         top_k = kwargs.get("top_k", 5)
-
+        if not isinstance(top_k, int) or top_k <= 0:
+            raise ValueError("top_k must be a positive integer")
         with self._get_cursor() as cur:
             if self.pg_bigm:
                 cur.execute("SET pg_bigm.similarity_limit TO 0.000001")