main_merge

lightrag/kg/chroma_impl.py

@@ -229,3 +229,43 @@ class ChromaVectorDBStorage(BaseVectorStorage):
         except Exception as e:
             logger.error(f"Error while deleting vectors from {self.namespace}: {e}")
             raise
+
+    async def search_by_prefix(self, prefix: str) -> list[dict[str, Any]]:
+        """Search for records with IDs starting with a specific prefix.
+
+        Args:
+            prefix: The prefix to search for in record IDs
+
+        Returns:
+            List of records with matching ID prefixes
+        """
+        try:
+            # Get all records from the collection
+            # Since ChromaDB doesn't directly support prefix search on IDs,
+            # we'll get all records and filter in Python
+            results = self._collection.get(
+                include=["metadatas", "documents", "embeddings"]
+            )
+
+            matching_records = []
+
+            # Filter records where ID starts with the prefix
+            for i, record_id in enumerate(results["ids"]):
+                if record_id.startswith(prefix):
+                    matching_records.append(
+                        {
+                            "id": record_id,
+                            "content": results["documents"][i],
+                            "vector": results["embeddings"][i],
+                            **results["metadatas"][i],
+                        }
+                    )
+
+            logger.debug(
+                f"Found {len(matching_records)} records with prefix '{prefix}'"
+            )
+            return matching_records
+
+        except Exception as e:
+            logger.error(f"Error during prefix search in ChromaDB: {str(e)}")
+            raise

lightrag/kg/faiss_impl.py

@@ -371,3 +371,24 @@ class FaissVectorDBStorage(BaseVectorStorage):
                 return False  # Return error
 
         return True  # Return success
+
+    async def search_by_prefix(self, prefix: str) -> list[dict[str, Any]]:
+        """Search for records with IDs starting with a specific prefix.
+
+        Args:
+            prefix: The prefix to search for in record IDs
+
+        Returns:
+            List of records with matching ID prefixes
+        """
+        matching_records = []
+
+        # Search for records with IDs starting with the prefix
+        for faiss_id, meta in self._id_to_meta.items():
+            if "__id__" in meta and meta["__id__"].startswith(prefix):
+                # Create a copy of all metadata and add "id" field
+                record = {**meta, "id": meta["__id__"]}
+                matching_records.append(record)
+
+        logger.debug(f"Found {len(matching_records)} records with prefix '{prefix}'")
+        return matching_records

lightrag/kg/milvus_impl.py

@@ -206,3 +206,28 @@ class MilvusVectorDBStorage(BaseVectorStorage):
 
         except Exception as e:
             logger.error(f"Error while deleting vectors from {self.namespace}: {e}")
+
+    async def search_by_prefix(self, prefix: str) -> list[dict[str, Any]]:
+        """Search for records with IDs starting with a specific prefix.
+
+        Args:
+            prefix: The prefix to search for in record IDs
+
+        Returns:
+            List of records with matching ID prefixes
+        """
+        try:
+            # Use Milvus query with expression to find IDs with the given prefix
+            expression = f'id like "{prefix}%"'
+            results = self._client.query(
+                collection_name=self.namespace,
+                filter=expression,
+                output_fields=list(self.meta_fields) + ["id"],
+            )
+
+            logger.debug(f"Found {len(results)} records with prefix '{prefix}'")
+            return results
+
+        except Exception as e:
+            logger.error(f"Error searching for records with prefix '{prefix}': {e}")
+            return []

lightrag/kg/mongo_impl.py

@@ -1045,6 +1045,32 @@ class MongoVectorDBStorage(BaseVectorStorage):
         except PyMongoError as e:
             logger.error(f"Error deleting relations for {entity_name}: {str(e)}")
 
+    async def search_by_prefix(self, prefix: str) -> list[dict[str, Any]]:
+        """Search for records with IDs starting with a specific prefix.
+
+        Args:
+            prefix: The prefix to search for in record IDs
+
+        Returns:
+            List of records with matching ID prefixes
+        """
+        try:
+            # Use MongoDB regex to find documents where _id starts with the prefix
+            cursor = self._data.find({"_id": {"$regex": f"^{prefix}"}})
+            matching_records = await cursor.to_list(length=None)
+
+            # Format results
+            results = [{**doc, "id": doc["_id"]} for doc in matching_records]
+
+            logger.debug(
+                f"Found {len(results)} records with prefix '{prefix}' in {self.namespace}"
+            )
+            return results
+
+        except PyMongoError as e:
+            logger.error(f"Error searching by prefix in {self.namespace}: {str(e)}")
+            return []
+
 
 async def get_or_create_collection(db: AsyncIOMotorDatabase, collection_name: str):
     collection_names = await db.list_collection_names()

lightrag/kg/nano_vector_db_impl.py

@@ -236,3 +236,23 @@ class NanoVectorDBStorage(BaseVectorStorage):
                 return False  # Return error
 
         return True  # Return success
+
+    async def search_by_prefix(self, prefix: str) -> list[dict[str, Any]]:
+        """Search for records with IDs starting with a specific prefix.
+
+        Args:
+            prefix: The prefix to search for in record IDs
+
+        Returns:
+            List of records with matching ID prefixes
+        """
+        storage = await self.client_storage
+        matching_records = []
+
+        # Search for records with IDs starting with the prefix
+        for record in storage["data"]:
+            if "__id__" in record and record["__id__"].startswith(prefix):
+                matching_records.append({**record, "id": record["__id__"]})
+
+        logger.debug(f"Found {len(matching_records)} records with prefix '{prefix}'")
+        return matching_records

lightrag/kg/networkx_impl.py

@@ -232,19 +232,26 @@ class NetworkXStorage(BaseGraphStorage):
         return sorted(list(labels))
 
     async def get_knowledge_graph(
-        self, node_label: str, max_depth: int = 5
+        self,
+        node_label: str,
+        max_depth: int = 3,
+        min_degree: int = 0,
+        inclusive: bool = False,
     ) -> KnowledgeGraph:
         """
         Retrieve a connected subgraph of nodes where the label includes the specified `node_label`.
         Maximum number of nodes is constrained by the environment variable `MAX_GRAPH_NODES` (default: 1000).
         When reducing the number of nodes, the prioritization criteria are as follows:
-            1. Label matching nodes take precedence
-            2. Followed by nodes directly connected to the matching nodes
-            3. Finally, the degree of the nodes
+            1. min_degree does not affect nodes directly connected to the matching nodes
+            2. Label matching nodes take precedence
+            3. Followed by nodes directly connected to the matching nodes
+            4. Finally, the degree of the nodes
 
         Args:
             node_label: Label of the starting node
             max_depth: Maximum depth of the subgraph
+            min_degree: Minimum degree of nodes to include. Defaults to 0
+            inclusive: Do an inclusive search if true
 
         Returns:
             KnowledgeGraph object containing nodes and edges
@@ -255,6 +262,10 @@ class NetworkXStorage(BaseGraphStorage):
 
         graph = await self._get_graph()
 
+        # Initialize sets for start nodes and direct connected nodes
+        start_nodes = set()
+        direct_connected_nodes = set()
+
         # Handle special case for "*" label
         if node_label == "*":
             # For "*", return the entire graph including all nodes and edges
@@ -262,11 +273,16 @@ class NetworkXStorage(BaseGraphStorage):
                 graph.copy()
             )  # Create a copy to avoid modifying the original graph
         else:
-            # Find nodes with matching node id (partial match)
+            # Find nodes with matching node id based on search_mode
             nodes_to_explore = []
             for n, attr in graph.nodes(data=True):
-                if node_label in str(n):  # Use partial matching
-                    nodes_to_explore.append(n)
+                node_str = str(n)
+                if not inclusive:
+                    if node_label == node_str:  # Use exact matching
+                        nodes_to_explore.append(n)
+                else:  # inclusive mode
+                    if node_label in node_str:  # Use partial matching
+                        nodes_to_explore.append(n)
 
             if not nodes_to_explore:
                 logger.warning(f"No nodes found with label {node_label}")
@@ -277,26 +293,37 @@ class NetworkXStorage(BaseGraphStorage):
             for start_node in nodes_to_explore:
                 node_subgraph = nx.ego_graph(graph, start_node, radius=max_depth)
                 combined_subgraph = nx.compose(combined_subgraph, node_subgraph)
+
+            # Get start nodes and direct connected nodes
+            if nodes_to_explore:
+                start_nodes = set(nodes_to_explore)
+                # Get nodes directly connected to all start nodes
+                for start_node in start_nodes:
+                    direct_connected_nodes.update(
+                        combined_subgraph.neighbors(start_node)
+                    )
+
+                # Remove start nodes from directly connected nodes (avoid duplicates)
+                direct_connected_nodes -= start_nodes
+
             subgraph = combined_subgraph
 
+        # Filter nodes based on min_degree, but keep start nodes and direct connected nodes
+        if min_degree > 0:
+            nodes_to_keep = [
+                node
+                for node, degree in subgraph.degree()
+                if node in start_nodes
+                or node in direct_connected_nodes
+                or degree >= min_degree
+            ]
+            subgraph = subgraph.subgraph(nodes_to_keep)
+
         # Check if number of nodes exceeds max_graph_nodes
         if len(subgraph.nodes()) > MAX_GRAPH_NODES:
             origin_nodes = len(subgraph.nodes())
-
             node_degrees = dict(subgraph.degree())
 
-            start_nodes = set()
-            direct_connected_nodes = set()
-
-            if node_label != "*" and nodes_to_explore:
-                start_nodes = set(nodes_to_explore)
-                # Get nodes directly connected to all start nodes
-                for start_node in start_nodes:
-                    direct_connected_nodes.update(subgraph.neighbors(start_node))
-
-                # Remove start nodes from directly connected nodes (avoid duplicates)
-                direct_connected_nodes -= start_nodes
-
             def priority_key(node_item):
                 node, degree = node_item
                 # Priority order: start(2) > directly connected(1) > other nodes(0)
@@ -356,7 +383,7 @@ class NetworkXStorage(BaseGraphStorage):
             result.edges.append(
                 KnowledgeGraphEdge(
                     id=edge_id,
-                    type="RELATED",
+                    type="DIRECTED",
                     source=str(source),
                     target=str(target),
                     properties=edge_data,

lightrag/kg/oracle_impl.py

@@ -494,6 +494,41 @@ class OracleVectorDBStorage(BaseVectorStorage):
             logger.error(f"Error deleting relations for entity {entity_name}: {e}")
             raise
 
+    async def search_by_prefix(self, prefix: str) -> list[dict[str, Any]]:
+        """Search for records with IDs starting with a specific prefix.
+
+        Args:
+            prefix: The prefix to search for in record IDs
+
+        Returns:
+            List of records with matching ID prefixes
+        """
+        try:
+            # Determine the appropriate table based on namespace
+            table_name = namespace_to_table_name(self.namespace)
+
+            # Create SQL query to find records with IDs starting with prefix
+            search_sql = f"""
+                SELECT * FROM {table_name}
+                WHERE workspace = :workspace
+                AND id LIKE :prefix_pattern
+                ORDER BY id
+            """
+
+            params = {"workspace": self.db.workspace, "prefix_pattern": f"{prefix}%"}
+
+            # Execute query and get results
+            results = await self.db.query(search_sql, params, multirows=True)
+
+            logger.debug(
+                f"Found {len(results) if results else 0} records with prefix '{prefix}'"
+            )
+            return results or []
+
+        except Exception as e:
+            logger.error(f"Error searching records with prefix '{prefix}': {e}")
+            return []
+
 
 @final
 @dataclass

lightrag/kg/postgres_impl.py

@@ -585,6 +585,41 @@ class PGVectorStorage(BaseVectorStorage):
         except Exception as e:
             logger.error(f"Error deleting relations for entity {entity_name}: {e}")
 
+    async def search_by_prefix(self, prefix: str) -> list[dict[str, Any]]:
+        """Search for records with IDs starting with a specific prefix.
+
+        Args:
+            prefix: The prefix to search for in record IDs
+
+        Returns:
+            List of records with matching ID prefixes
+        """
+        table_name = namespace_to_table_name(self.namespace)
+        if not table_name:
+            logger.error(f"Unknown namespace for prefix search: {self.namespace}")
+            return []
+
+        search_sql = f"SELECT * FROM {table_name} WHERE workspace=$1 AND id LIKE $2"
+        params = {"workspace": self.db.workspace, "prefix": f"{prefix}%"}
+
+        try:
+            results = await self.db.query(search_sql, params, multirows=True)
+            logger.debug(f"Found {len(results)} records with prefix '{prefix}'")
+
+            # Format results to match the expected return format
+            formatted_results = []
+            for record in results:
+                formatted_record = dict(record)
+                # Ensure id field is available (for consistency with NanoVectorDB implementation)
+                if "id" not in formatted_record:
+                    formatted_record["id"] = record["id"]
+                formatted_results.append(formatted_record)
+
+            return formatted_results
+        except Exception as e:
+            logger.error(f"Error during prefix search for '{prefix}': {e}")
+            return []
+
 
 @final
 @dataclass
@@ -785,42 +820,85 @@ class PGGraphStorage(BaseGraphStorage):
             v = record[k]
             # agtype comes back '{key: value}::type' which must be parsed
             if isinstance(v, str) and "::" in v:
-                dtype = v.split("::")[-1]
-                v = v.split("::")[0]
-                if dtype == "vertex":
-                    vertex = json.loads(v)
-                    vertices[vertex["id"]] = vertex.get("properties")
+                if v.startswith("[") and v.endswith("]"):
+                    if "::vertex" not in v:
+                        continue
+                    v = v.replace("::vertex", "")
+                    vertexes = json.loads(v)
+                    for vertex in vertexes:
+                        vertices[vertex["id"]] = vertex.get("properties")
+                else:
+                    dtype = v.split("::")[-1]
+                    v = v.split("::")[0]
+                    if dtype == "vertex":
+                        vertex = json.loads(v)
+                        vertices[vertex["id"]] = vertex.get("properties")
 
         # iterate returned fields and parse appropriately
         for k in record.keys():
             v = record[k]
             if isinstance(v, str) and "::" in v:
-                dtype = v.split("::")[-1]
-                v = v.split("::")[0]
-            else:
-                dtype = ""
+                if v.startswith("[") and v.endswith("]"):
+                    if "::vertex" in v:
+                        v = v.replace("::vertex", "")
+                        vertexes = json.loads(v)
+                        dl = []
+                        for vertex in vertexes:
+                            prop = vertex.get("properties")
+                            if not prop:
+                                prop = {}
+                            prop["label"] = PGGraphStorage._decode_graph_label(
+                                prop["node_id"]
+                            )
+                            dl.append(prop)
+                        d[k] = dl
 
-            if dtype == "vertex":
-                vertex = json.loads(v)
-                field = vertex.get("properties")
-                if not field:
-                    field = {}
-                field["label"] = PGGraphStorage._decode_graph_label(field["node_id"])
-                d[k] = field
-            # convert edge from id-label->id by replacing id with node information
-            # we only do this if the vertex was also returned in the query
-            # this is an attempt to be consistent with neo4j implementation
-            elif dtype == "edge":
-                edge = json.loads(v)
-                d[k] = (
-                    vertices.get(edge["start_id"], {}),
-                    edge[
-                        "label"
-                    ],  # we don't use decode_graph_label(), since edge label is always "DIRECTED"
-                    vertices.get(edge["end_id"], {}),
-                )
+                    elif "::edge" in v:
+                        v = v.replace("::edge", "")
+                        edges = json.loads(v)
+                        dl = []
+                        for edge in edges:
+                            dl.append(
+                                (
+                                    vertices[edge["start_id"]],
+                                    edge["label"],
+                                    vertices[edge["end_id"]],
+                                )
+                            )
+                        d[k] = dl
+                    else:
+                        print("WARNING: unsupported type")
+                        continue
+
+                else:
+                    dtype = v.split("::")[-1]
+                    v = v.split("::")[0]
+                    if dtype == "vertex":
+                        vertex = json.loads(v)
+                        field = vertex.get("properties")
+                        if not field:
+                            field = {}
+                        field["label"] = PGGraphStorage._decode_graph_label(
+                            field["node_id"]
+                        )
+                        d[k] = field
+                    # convert edge from id-label->id by replacing id with node information
+                    # we only do this if the vertex was also returned in the query
+                    # this is an attempt to be consistent with neo4j implementation
+                    elif dtype == "edge":
+                        edge = json.loads(v)
+                        d[k] = (
+                            vertices.get(edge["start_id"], {}),
+                            edge[
+                                "label"
+                            ],  # we don't use decode_graph_label(), since edge label is always "DIRECTED"
+                            vertices.get(edge["end_id"], {}),
+                        )
             else:
-                d[k] = json.loads(v) if isinstance(v, str) else v
+                if v is None or (v.count("{") < 1 and v.count("[") < 1):
+                    d[k] = v
+                else:
+                    d[k] = json.loads(v) if isinstance(v, str) else v
 
         return d
 
@@ -1294,7 +1372,7 @@ class PGGraphStorage(BaseGraphStorage):
                          OPTIONAL MATCH p = (n)-[*..%d]-(m)
                          RETURN nodes(p) AS nodes, relationships(p) AS relationships
                          LIMIT %d
-                       $$) AS (nodes agtype[], relationships agtype[])""" % (
+                       $$) AS (nodes agtype, relationships agtype)""" % (
                 self.graph_name,
                 encoded_node_label,
                 max_depth,
@@ -1303,17 +1381,23 @@ class PGGraphStorage(BaseGraphStorage):
 
         results = await self._query(query)
 
-        nodes = set()
+        nodes = {}
         edges = []
+        unique_edge_ids = set()
 
         for result in results:
             if node_label == "*":
                 if result["n"]:
                     node = result["n"]
-                    nodes.add(self._decode_graph_label(node["node_id"]))
+                    node_id = self._decode_graph_label(node["node_id"])
+                    if node_id not in nodes:
+                        nodes[node_id] = node
+
                 if result["m"]:
                     node = result["m"]
-                    nodes.add(self._decode_graph_label(node["node_id"]))
+                    node_id = self._decode_graph_label(node["node_id"])
+                    if node_id not in nodes:
+                        nodes[node_id] = node
                 if result["r"]:
                     edge = result["r"]
                     src_id = self._decode_graph_label(edge["start_id"])
@@ -1322,16 +1406,36 @@ class PGGraphStorage(BaseGraphStorage):
             else:
                 if result["nodes"]:
                     for node in result["nodes"]:
-                        nodes.add(self._decode_graph_label(node["node_id"]))
+                        node_id = self._decode_graph_label(node["node_id"])
+                        if node_id not in nodes:
+                            nodes[node_id] = node
+
                 if result["relationships"]:
-                    for edge in result["relationships"]:
-                        src_id = self._decode_graph_label(edge["start_id"])
-                        tgt_id = self._decode_graph_label(edge["end_id"])
-                        edges.append((src_id, tgt_id))
+                    for edge in result["relationships"]:  # src --DIRECTED--> target
+                        src_id = self._decode_graph_label(edge[0]["node_id"])
+                        tgt_id = self._decode_graph_label(edge[2]["node_id"])
+                        id = src_id + "," + tgt_id
+                        if id in unique_edge_ids:
+                            continue
+                        else:
+                            unique_edge_ids.add(id)
+                        edges.append(
+                            (id, src_id, tgt_id, {"source": edge[0], "target": edge[2]})
+                        )
 
         kg = KnowledgeGraph(
-            nodes=[KnowledgeGraphNode(id=node_id) for node_id in nodes],
-            edges=[KnowledgeGraphEdge(source=src, target=tgt) for src, tgt in edges],
+            nodes=[
+                KnowledgeGraphNode(
+                    id=node_id, labels=[node_id], properties=nodes[node_id]
+                )
+                for node_id in nodes
+            ],
+            edges=[
+                KnowledgeGraphEdge(
+                    id=id, type="DIRECTED", source=src, target=tgt, properties=props
+                )
+                for id, src, tgt, props in edges
+            ],
         )
 
         return kg

lightrag/kg/qdrant_impl.py

@@ -135,7 +135,7 @@ class QdrantVectorDBStorage(BaseVectorStorage):
 
         logger.debug(f"query result: {results}")
 
-        return [{**dp.payload, "id": dp.id, "distance": dp.score} for dp in results]
+        return [{**dp.payload, "distance": dp.score} for dp in results]
 
     async def index_done_callback(self) -> None:
         # Qdrant handles persistence automatically
@@ -233,3 +233,43 @@ class QdrantVectorDBStorage(BaseVectorStorage):
                 logger.debug(f"No relations found for entity {entity_name}")
         except Exception as e:
             logger.error(f"Error deleting relations for {entity_name}: {e}")
+
+    async def search_by_prefix(self, prefix: str) -> list[dict[str, Any]]:
+        """Search for records with IDs starting with a specific prefix.
+
+        Args:
+            prefix: The prefix to search for in record IDs
+
+        Returns:
+            List of records with matching ID prefixes
+        """
+        try:
+            # Use scroll method to find records with IDs starting with the prefix
+            results = self._client.scroll(
+                collection_name=self.namespace,
+                scroll_filter=models.Filter(
+                    must=[
+                        models.FieldCondition(
+                            key="id", match=models.MatchText(text=prefix, prefix=True)
+                        )
+                    ]
+                ),
+                with_payload=True,
+                with_vectors=False,
+                limit=1000,  # Adjust as needed for your use case
+            )
+
+            # Extract matching points
+            matching_records = results[0]
+
+            # Format the results to match expected return format
+            formatted_results = [{**point.payload} for point in matching_records]
+
+            logger.debug(
+                f"Found {len(formatted_results)} records with prefix '{prefix}'"
+            )
+            return formatted_results
+
+        except Exception as e:
+            logger.error(f"Error searching for prefix '{prefix}': {e}")
+            return []

lightrag/kg/tidb_impl.py

@@ -414,6 +414,55 @@ class TiDBVectorDBStorage(BaseVectorStorage):
         # Ti handles persistence automatically
         pass
 
+    async def search_by_prefix(self, prefix: str) -> list[dict[str, Any]]:
+        """Search for records with IDs starting with a specific prefix.
+
+        Args:
+            prefix: The prefix to search for in record IDs
+
+        Returns:
+            List of records with matching ID prefixes
+        """
+        # Determine which table to query based on namespace
+        if self.namespace == NameSpace.VECTOR_STORE_ENTITIES:
+            sql_template = """
+                SELECT entity_id as id, name as entity_name, entity_type, description, content
+                FROM LIGHTRAG_GRAPH_NODES
+                WHERE entity_id LIKE :prefix_pattern AND workspace = :workspace
+            """
+        elif self.namespace == NameSpace.VECTOR_STORE_RELATIONSHIPS:
+            sql_template = """
+                SELECT relation_id as id, source_name as src_id, target_name as tgt_id,
+                       keywords, description, content
+                FROM LIGHTRAG_GRAPH_EDGES
+                WHERE relation_id LIKE :prefix_pattern AND workspace = :workspace
+            """
+        elif self.namespace == NameSpace.VECTOR_STORE_CHUNKS:
+            sql_template = """
+                SELECT chunk_id as id, content, tokens, chunk_order_index, full_doc_id
+                FROM LIGHTRAG_DOC_CHUNKS
+                WHERE chunk_id LIKE :prefix_pattern AND workspace = :workspace
+            """
+        else:
+            logger.warning(
+                f"Namespace {self.namespace} not supported for prefix search"
+            )
+            return []
+
+        # Add prefix pattern parameter with % for SQL LIKE
+        prefix_pattern = f"{prefix}%"
+        params = {"prefix_pattern": prefix_pattern, "workspace": self.db.workspace}
+
+        try:
+            results = await self.db.query(sql_template, params=params, multirows=True)
+            logger.debug(
+                f"Found {len(results) if results else 0} records with prefix '{prefix}'"
+            )
+            return results if results else []
+        except Exception as e:
+            logger.error(f"Error searching records with prefix '{prefix}': {e}")
+            return []
+
 
 @final
 @dataclass
@@ -968,4 +1017,20 @@ SQL_TEMPLATES = {
         WHERE (source_name = :source AND target_name = :target)
         AND workspace = :workspace
     """,
+    # Search by prefix SQL templates
+    "search_entity_by_prefix": """
+        SELECT entity_id as id, name as entity_name, entity_type, description, content
+        FROM LIGHTRAG_GRAPH_NODES
+        WHERE entity_id LIKE :prefix_pattern AND workspace = :workspace
+    """,
+    "search_relationship_by_prefix": """
+        SELECT relation_id as id, source_name as src_id, target_name as tgt_id, keywords, description, content
+        FROM LIGHTRAG_GRAPH_EDGES
+        WHERE relation_id LIKE :prefix_pattern AND workspace = :workspace
+    """,
+    "search_chunk_by_prefix": """
+        SELECT chunk_id as id, content, tokens, chunk_order_index, full_doc_id
+        FROM LIGHTRAG_DOC_CHUNKS
+        WHERE chunk_id LIKE :prefix_pattern AND workspace = :workspace
+    """,
 }