dataset metadata fix

api/controllers/console/datasets/datasets_document.py

@@ -617,7 +617,7 @@ class DocumentDetailApi(DocumentResource):
             raise InvalidMetadataError(f"Invalid metadata value: {metadata}")
 
         if metadata == "only":
-            response = {"id": document.id, "doc_type": document.doc_type, "doc_metadata": document.doc_metadata}
+            response = {"id": document.id, "doc_type": document.doc_type, "doc_metadata": document.doc_metadata_details}
         elif metadata == "without":
             dataset_process_rules = DatasetService.get_process_rules(dataset_id)
             document_process_rules = document.dataset_process_rule.to_dict()
@@ -678,7 +678,7 @@ class DocumentDetailApi(DocumentResource):
                 "disabled_by": document.disabled_by,
                 "archived": document.archived,
                 "doc_type": document.doc_type,
-                "doc_metadata": document.doc_metadata,
+                "doc_metadata": document.doc_metadata_details,
                 "segment_count": document.segment_count,
                 "average_segment_length": document.average_segment_length,
                 "hit_count": document.hit_count,

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

@@ -197,8 +197,8 @@ class AnalyticdbVectorBySql:
         document_ids_filter = kwargs.get("document_ids_filter")
         where_clause = "WHERE 1=1"
         if document_ids_filter:
-            doc_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
-            where_clause += f"AND metadata_->>'doc_id' IN ({doc_ids})"
+            document_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
+            where_clause += f"AND metadata_->>'document_id' IN ({document_ids})"
         score_threshold = float(kwargs.get("score_threshold") or 0.0)
         with self._get_cursor() as cur:
             query_vector_str = json.dumps(query_vector)
@@ -228,8 +228,8 @@ class AnalyticdbVectorBySql:
         document_ids_filter = kwargs.get("document_ids_filter")
         where_clause = ""
         if document_ids_filter:
-            doc_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
-            where_clause += f"AND metadata_->>'doc_id' IN ({doc_ids})"
+            document_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
+            where_clause += f"AND metadata_->>'document_id' IN ({document_ids})"
         with self._get_cursor() as cur:
             cur.execute(
                 f"""SELECT id, vector, page_content, metadata_, 

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

@@ -125,12 +125,12 @@ class BaiduVector(BaseVector):
         query_vector = [float(val) if isinstance(val, np.float64) else val for val in query_vector]
         document_ids_filter = kwargs.get("document_ids_filter")
         if document_ids_filter:
-            doc_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
+            document_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
             anns = AnnSearch(
                 vector_field=self.field_vector,
                 vector_floats=query_vector,
                 params=HNSWSearchParams(ef=kwargs.get("ef", 10), limit=kwargs.get("top_k", 4)),
-                filter=f"doc_id IN ({doc_ids})",
+                filter=f"document_id IN ({document_ids})",
             )
         else:
             anns = AnnSearch(

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

@@ -100,7 +100,7 @@ class ChromaVector(BaseVector):
             results: QueryResult = collection.query(
                 query_embeddings=query_vector,
                 n_results=kwargs.get("top_k", 4),
-                where={"doc_id": {"$in": document_ids_filter}},
+                where={"document_id": {"$in": document_ids_filter}},
             )
         else:
             results: QueryResult = collection.query(query_embeddings=query_vector, n_results=kwargs.get("top_k", 4))

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

@@ -119,7 +119,7 @@ class ElasticSearchVector(BaseVector):
         knn = {"field": Field.VECTOR.value, "query_vector": query_vector, "k": top_k, "num_candidates": num_candidates}
         document_ids_filter = kwargs.get("document_ids_filter")
         if document_ids_filter:
-            knn["filter"] = {"terms": {"metadata.doc_id": document_ids_filter}}
+            knn["filter"] = {"terms": {"metadata.document_id": document_ids_filter}}
 
         results = self._client.search(index=self._collection_name, knn=knn, size=top_k)
 
@@ -150,7 +150,7 @@ class ElasticSearchVector(BaseVector):
         query_str = {"match": {Field.CONTENT_KEY.value: query}}
         document_ids_filter = kwargs.get("document_ids_filter")
         if document_ids_filter:
-            query_str["filter"] = {"terms": {"metadata.doc_id": document_ids_filter}}
+            query_str["filter"] = {"terms": {"metadata.document_id": document_ids_filter}}
         results = self._client.search(index=self._collection_name, query=query_str, size=kwargs.get("top_k", 4))
         docs = []
         for hit in results["hits"]["hits"]:

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

@@ -171,7 +171,7 @@ class LindormVectorStore(BaseVector):
         document_ids_filter = kwargs.get("document_ids_filter")
         filters = []
         if document_ids_filter:
-            filters.append({"terms": {"metadata.doc_id": document_ids_filter}})
+            filters.append({"terms": {"metadata.document_id": document_ids_filter}})
         query = default_vector_search_query(query_vector=query_vector, k=top_k, filters=filters, **kwargs)
 
         try:
@@ -214,7 +214,7 @@ class LindormVectorStore(BaseVector):
         filters = kwargs.get("filter", [])
         document_ids_filter = kwargs.get("document_ids_filter")
         if document_ids_filter:
-            filters.append({"terms": {"metadata.doc_id": document_ids_filter}})
+            filters.append({"terms": {"metadata.document_id": document_ids_filter}})
         routing = self._routing
         full_text_query = default_text_search_query(
             query_text=query,

api/core/rag/datasource/vdb/milvus/milvus_vector.py

@@ -221,8 +221,8 @@ class MilvusVector(BaseVector):
         document_ids_filter = kwargs.get("document_ids_filter")
         filter = ""
         if document_ids_filter:
-            doc_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
-            filter = f'metadata["doc_id"] in ({doc_ids})'
+            document_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
+            filter = f'metadata["document_id"] in ({document_ids})'
         results = self._client.search(
             collection_name=self._collection_name,
             data=[query_vector],
@@ -248,8 +248,8 @@ class MilvusVector(BaseVector):
         document_ids_filter = kwargs.get("document_ids_filter")
         filter = ""
         if document_ids_filter:
-            doc_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
-            filter = f'metadata["doc_id"] in ({doc_ids})'
+            document_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
+            filter = f'metadata["document_id"] in ({document_ids})'
 
         results = self._client.search(
             collection_name=self._collection_name,

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

@@ -133,8 +133,8 @@ class MyScaleVector(BaseVector):
         )
         document_ids_filter = kwargs.get("document_ids_filter")
         if document_ids_filter:
-            doc_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
-            where_str = f"{where_str} AND metadata['doc_id'] in ({doc_ids})"
+            document_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
+            where_str = f"{where_str} AND metadata['document_id'] in ({document_ids})"
         sql = f"""
             SELECT text, vector, metadata, {dist} as dist FROM {self._config.database}.{self._collection_name}
             {where_str} ORDER BY dist {order.value} LIMIT {top_k}

api/core/rag/datasource/vdb/oceanbase/oceanbase_vector.py

@@ -157,8 +157,8 @@ class OceanBaseVector(BaseVector):
         document_ids_filter = kwargs.get("document_ids_filter")
         where_clause = None
         if document_ids_filter:
-            doc_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
-            where_clause = f"metadata->>'$.doc_id' in ({doc_ids})"
+            document_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
+            where_clause = f"metadata->>'$.document_id' in ({document_ids})"
         ef_search = kwargs.get("ef_search", self._hnsw_ef_search)
         if ef_search != self._hnsw_ef_search:
             self._client.set_ob_hnsw_ef_search(ef_search)

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

@@ -156,7 +156,7 @@ class OpenSearchVector(BaseVector):
         }
         document_ids_filter = kwargs.get("document_ids_filter")
         if document_ids_filter:
-            query["query"] = {"terms": {"metadata.doc_id": document_ids_filter}}
+            query["query"] = {"terms": {"metadata.document_id": document_ids_filter}}
 
         try:
             response = self._client.search(index=self._collection_name.lower(), body=query)
@@ -184,7 +184,7 @@ class OpenSearchVector(BaseVector):
         full_text_query = {"query": {"match": {Field.CONTENT_KEY.value: query}}}
         document_ids_filter = kwargs.get("document_ids_filter")
         if document_ids_filter:
-            full_text_query["query"]["terms"] = {"metadata.doc_id": document_ids_filter}
+            full_text_query["query"]["terms"] = {"metadata.document_id": document_ids_filter}
 
         response = self._client.search(index=self._collection_name.lower(), body=full_text_query)
 

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

@@ -188,8 +188,8 @@ class OracleVector(BaseVector):
         document_ids_filter = kwargs.get("document_ids_filter")
         where_clause = ""
         if document_ids_filter:
-            doc_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
-            where_clause = f"WHERE metadata->>'doc_id' in ({doc_ids})"
+            document_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
+            where_clause = f"WHERE metadata->>'document_id' in ({document_ids})"
         with self._get_cursor() as cur:
             cur.execute(
                 f"SELECT meta, text, vector_distance(embedding,:1) AS distance FROM {self.table_name}"
@@ -249,8 +249,8 @@ class OracleVector(BaseVector):
                 document_ids_filter = kwargs.get("document_ids_filter")
                 where_clause = ""
                 if document_ids_filter:
-                    doc_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
-                    where_clause = f" AND metadata->>'doc_id' in ({doc_ids}) "
+                    document_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
+                    where_clause = f" AND metadata->>'document_id' in ({document_ids}) "
                 cur.execute(
                     f"select meta, text, embedding FROM {self.table_name}"
                     f"WHERE CONTAINS(text, :1, 1) > 0 {where_clause} "

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

@@ -191,7 +191,7 @@ class PGVectoRS(BaseVector):
             )
             document_ids_filter = kwargs.get("document_ids_filter")
             if document_ids_filter:
-                stmt = stmt.where(self._table.meta["doc_id"].in_(document_ids_filter))
+                stmt = stmt.where(self._table.meta["document_id"].in_(document_ids_filter))
             res = session.execute(stmt)
             results = [(row[0], row[1]) for row in res]
 

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

@@ -158,8 +158,8 @@ class PGVector(BaseVector):
         document_ids_filter = kwargs.get("document_ids_filter")
         where_clause = ""
         if document_ids_filter:
-            doc_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
-            where_clause = f" WHERE metadata->>'doc_id' in ({doc_ids}) "
+            document_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
+            where_clause = f" WHERE metadata->>'document_id' in ({document_ids}) "
 
         with self._get_cursor() as cur:
             cur.execute(
@@ -185,8 +185,8 @@ class PGVector(BaseVector):
             document_ids_filter = kwargs.get("document_ids_filter")
             where_clause = ""
             if document_ids_filter:
-                doc_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
-                where_clause = f" AND metadata->>'doc_id' in ({doc_ids}) "
+                document_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
+                where_clause = f" AND metadata->>'document_id' in ({document_ids}) "
             cur.execute(
                 f"""SELECT meta, text, ts_rank(to_tsvector(coalesce(text, '')), plainto_tsquery(%s)) AS score
                 FROM {self.table_name}

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

@@ -334,7 +334,7 @@ class QdrantVector(BaseVector):
         if document_ids_filter:
             filter.must.append(
                 models.FieldCondition(
-                    key="metadata.doc_id",
+                    key="metadata.document_id",
                     match=models.MatchAny(any=document_ids_filter),
                 )
             )
@@ -388,7 +388,7 @@ class QdrantVector(BaseVector):
         if document_ids_filter:
             scroll_filter.must.append(
                 models.FieldCondition(
-                    key="metadata.doc_id",
+                    key="metadata.document_id",
                     match=models.MatchAny(any=document_ids_filter),
                 )
             )

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

@@ -226,7 +226,7 @@ class RelytVector(BaseVector):
         document_ids_filter = kwargs.get("document_ids_filter")
         filter = kwargs.get("filter", {})
         if document_ids_filter:
-            filter["doc_id"] = document_ids_filter
+            filter["document_id"] = document_ids_filter
         results = self.similarity_search_with_score_by_vector(
             k=int(kwargs.get("top_k", 4)), embedding=query_vector, filter=filter
         )

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

@@ -151,7 +151,7 @@ class TencentVector(BaseVector):
         document_ids_filter = kwargs.get("document_ids_filter")
         filter = None
         if document_ids_filter:
-            filter = Filter(Filter.In("metadata.doc_id", document_ids_filter))
+            filter = Filter(Filter.In("metadata.document_id", document_ids_filter))
         res = self._db.collection(self._collection_name).search(
             vectors=[query_vector],
             filter=filter,

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

@@ -330,7 +330,7 @@ class TidbOnQdrantVector(BaseVector):
         if document_ids_filter:
             filter.must.append(
                 models.FieldCondition(
-                    key="metadata.doc_id",
+                    key="metadata.document_id",
                     match=models.MatchAny(any=document_ids_filter),
                 )
             )
@@ -380,7 +380,7 @@ class TidbOnQdrantVector(BaseVector):
         if document_ids_filter:
             scroll_filter.must.append(
                 models.FieldCondition(
-                    key="metadata.doc_id",
+                    key="metadata.document_id",
                     match=models.MatchAny(any=document_ids_filter),
                 )
             )

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

@@ -199,8 +199,8 @@ class TiDBVector(BaseVector):
         document_ids_filter = kwargs.get("document_ids_filter")
         where_clause = ""
         if document_ids_filter:
-            doc_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
-            where_clause = f" WHERE meta->>'$.doc_id' in ({doc_ids}) "
+            document_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
+            where_clause = f" WHERE meta->>'$.document_id' in ({document_ids}) "
 
         with Session(self._engine) as session:
             select_statement = sql_text(f"""

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

@@ -90,7 +90,8 @@ class UpstashVector(BaseVector):
         top_k = kwargs.get("top_k", 4)
         document_ids_filter = kwargs.get("document_ids_filter")
         if document_ids_filter:
-            filter = f"doc_id in ({', '.join(f"'{id}'" for id in document_ids_filter)})"
+            document_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
+            filter = f"document_id in ({document_ids})"
         else:
             filter = ""
         result = self.index.query(

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

@@ -180,7 +180,7 @@ class VikingDBVector(BaseVector):
         docs = self._get_search_res(results, score_threshold)
         document_ids_filter = kwargs.get("document_ids_filter")
         if document_ids_filter:
-            docs = [doc for doc in docs if doc.metadata.get("doc_id") in document_ids_filter]
+            docs = [doc for doc in docs if doc.metadata.get("document_id") in document_ids_filter]
         return docs
 
     def _get_search_res(self, results, score_threshold) -> list[Document]:

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

@@ -189,7 +189,7 @@ class WeaviateVector(BaseVector):
         vector = {"vector": query_vector}
         document_ids_filter = kwargs.get("document_ids_filter")
         if document_ids_filter:
-            where_filter = {"operator": "ContainsAny", "path": ["doc_id"], "valueTextArray": document_ids_filter}
+            where_filter = {"operator": "ContainsAny", "path": ["document_id"], "valueTextArray": document_ids_filter}
             query_obj = query_obj.with_where(where_filter)
         result = (
             query_obj.with_near_vector(vector)
@@ -237,7 +237,7 @@ class WeaviateVector(BaseVector):
         query_obj = self._client.query.get(collection_name, properties)
         document_ids_filter = kwargs.get("document_ids_filter")
         if document_ids_filter:
-            where_filter = {"operator": "ContainsAny", "path": ["doc_id"], "valueTextArray": document_ids_filter}
+            where_filter = {"operator": "ContainsAny", "path": ["document_id"], "valueTextArray": document_ids_filter}
             query_obj = query_obj.with_where(where_filter)
         query_obj = query_obj.with_additional(["vector"])
         properties = ["text"]