Merge pull request #197 from wiltshirek/main

.gitignore

@@ -5,4 +5,8 @@ book.txt
 lightrag-dev/
 .idea/
 dist/
+env/
+local_neo4jWorkDir/
+neo4jWorkDir/
+ignore_this.txt
 .venv/

Dockerfile

@@ -0,0 +1,56 @@
+FROM debian:bullseye-slim
+ENV JAVA_HOME=/opt/java/openjdk
+COPY --from=eclipse-temurin:17 $JAVA_HOME $JAVA_HOME
+ENV PATH="${JAVA_HOME}/bin:${PATH}" \
+    NEO4J_SHA256=7ce97bd9a4348af14df442f00b3dc5085b5983d6f03da643744838c7a1bc8ba7 \
+    NEO4J_TARBALL=neo4j-enterprise-5.24.2-unix.tar.gz \
+    NEO4J_EDITION=enterprise \
+    NEO4J_HOME="/var/lib/neo4j" \
+    LANG=C.UTF-8
+ARG NEO4J_URI=https://dist.neo4j.org/neo4j-enterprise-5.24.2-unix.tar.gz
+
+RUN addgroup --gid 7474 --system neo4j && adduser --uid 7474 --system --no-create-home --home "${NEO4J_HOME}" --ingroup neo4j neo4j
+
+COPY ./local-package/* /startup/
+
+RUN apt update \
+    && apt-get install -y curl gcc git jq make procps tini wget \
+    && curl --fail --silent --show-error --location --remote-name ${NEO4J_URI} \
+    && echo "${NEO4J_SHA256}  ${NEO4J_TARBALL}" | sha256sum -c --strict --quiet \
+    && tar --extract --file ${NEO4J_TARBALL} --directory /var/lib \
+    && mv /var/lib/neo4j-* "${NEO4J_HOME}" \
+    && rm ${NEO4J_TARBALL} \
+    && sed -i 's/Package Type:.*/Package Type: docker bullseye/' $NEO4J_HOME/packaging_info \
+    && mv /startup/neo4j-admin-report.sh "${NEO4J_HOME}"/bin/neo4j-admin-report \
+    && mv "${NEO4J_HOME}"/data /data \
+    && mv "${NEO4J_HOME}"/logs /logs \
+    && chown -R neo4j:neo4j /data \
+    && chmod -R 777 /data \
+    && chown -R neo4j:neo4j /logs \
+    && chmod -R 777 /logs \
+    && chown -R neo4j:neo4j "${NEO4J_HOME}" \
+    && chmod -R 777 "${NEO4J_HOME}" \
+    && chmod -R 755 "${NEO4J_HOME}/bin" \
+    && ln -s /data "${NEO4J_HOME}"/data \
+    && ln -s /logs "${NEO4J_HOME}"/logs \
+    && git clone https://github.com/ncopa/su-exec.git \
+    && cd su-exec \
+    && git checkout 4c3bb42b093f14da70d8ab924b487ccfbb1397af \
+    && echo d6c40440609a23483f12eb6295b5191e94baf08298a856bab6e15b10c3b82891 su-exec.c | sha256sum -c \
+    && echo 2a87af245eb125aca9305a0b1025525ac80825590800f047419dc57bba36b334 Makefile | sha256sum -c \
+    && make \
+    && mv /su-exec/su-exec /usr/bin/su-exec \
+    && apt-get -y purge --auto-remove curl gcc git make \
+    && rm -rf /var/lib/apt/lists/* /su-exec
+
+
+ENV PATH "${NEO4J_HOME}"/bin:$PATH
+
+WORKDIR "${NEO4J_HOME}"
+
+VOLUME /data /logs
+
+EXPOSE 7474 7473 7687
+
+ENTRYPOINT ["tini", "-g", "--", "/startup/docker-entrypoint.sh"]
+CMD ["neo4j"]

README.md

@@ -161,6 +161,39 @@ rag = LightRAG(
 ```
 </details>
 
+
+<details>
+<summary> Using Neo4J for Storage </summary>
+
+* For production level scenarios you will most likely want to leverage an enterprise solution
+* for KG storage. Running Neo4J in Docker is recommended for seamless local testing.  
+* See: https://hub.docker.com/_/neo4j
+
+
+```python
+export NEO4J_URI="neo4j://localhost:7687"
+export NEO4J_USERNAME="neo4j"
+export NEO4J_PASSWORD="password"
+
+When you launch the project be sure to override the default KG: NetworkS
+by specifying kg="Neo4JStorage".
+
+# Note: Default settings use NetworkX
+#Initialize LightRAG with Neo4J implementation. 
+WORKING_DIR = "./local_neo4jWorkDir"
+
+rag = LightRAG(
+    working_dir=WORKING_DIR,
+    llm_model_func=gpt_4o_mini_complete,  # Use gpt_4o_mini_complete LLM model
+    kg="Neo4JStorage", #<-----------override KG default
+    log_level="DEBUG"  #<-----------override log_level default
+)
+```
+see test_neo4j.py for a working example.
+</details>
+
+
+
 <details>
 <summary> Using Ollama Models </summary>
 

get_all_edges_nx.py

@@ -0,0 +1,34 @@
+import networkx as nx
+
+G = nx.read_graphml('./dickensTestEmbedcall/graph_chunk_entity_relation.graphml')
+
+def get_all_edges_and_nodes(G):
+    # Get all edges and their properties
+    edges_with_properties = []
+    for u, v, data in G.edges(data=True):
+        edges_with_properties.append({
+            'start': u,
+            'end': v,
+            'label': data.get('label', ''),  # Assuming 'label' is used for edge type
+            'properties': data,
+            'start_node_properties': G.nodes[u],
+            'end_node_properties': G.nodes[v]
+        })
+
+    return edges_with_properties
+
+# Example usage
+if __name__ == "__main__":
+    # Assume G is your NetworkX graph loaded from Neo4j
+
+    all_edges = get_all_edges_and_nodes(G)
+    
+    # Print all edges and node properties
+    for edge in all_edges:
+        print(f"Edge Label: {edge['label']}")
+        print(f"Edge Properties: {edge['properties']}")
+        print(f"Start Node: {edge['start']}")
+        print(f"Start Node Properties: {edge['start_node_properties']}")
+        print(f"End Node: {edge['end']}")
+        print(f"End Node Properties: {edge['end_node_properties']}")
+        print("---")

lightrag/kg/__init__.py

@@ -0,0 +1,3 @@
+# print ("init package vars here. ......")
+
+

lightrag/kg/neo4j_impl.py

@@ -0,0 +1,278 @@
+import asyncio
+import html
+import os
+from dataclasses import dataclass
+from typing import Any, Union, cast, Tuple, List, Dict
+import numpy as np
+import inspect
+from lightrag.utils import load_json, logger, write_json
+from ..base import (
+    BaseGraphStorage
+)
+from neo4j import AsyncGraphDatabase,exceptions as neo4jExceptions,AsyncDriver,AsyncSession, AsyncManagedTransaction
+
+from contextlib import asynccontextmanager
+
+
+from tenacity import (
+    retry,
+    stop_after_attempt,
+    wait_exponential,
+    retry_if_exception_type,
+)
+
+
+@dataclass
+class Neo4JStorage(BaseGraphStorage):
+    @staticmethod
+    def load_nx_graph(file_name):
+       print ("no preloading of graph with neo4j in production")
+
+    def __init__(self, namespace, global_config):
+        super().__init__(namespace=namespace, global_config=global_config)
+        self._driver = None
+        self._driver_lock = asyncio.Lock()
+        URI = os.environ["NEO4J_URI"]
+        USERNAME = os.environ["NEO4J_USERNAME"]
+        PASSWORD = os.environ["NEO4J_PASSWORD"]
+        self._driver: AsyncDriver = AsyncGraphDatabase.driver(URI, auth=(USERNAME, PASSWORD))
+        return None
+
+    def __post_init__(self):
+        self._node_embed_algorithms = {
+            "node2vec": self._node2vec_embed,
+        }
+
+
+    async def close(self):
+        if self._driver:
+            await self._driver.close()
+            self._driver = None
+
+    
+
+    async def __aexit__(self, exc_type, exc, tb):
+        if self._driver:
+            await self._driver.close()
+
+    async def index_done_callback(self):
+        print ("KG successfully indexed.")
+
+    
+    async def has_node(self, node_id: str) -> bool:
+        entity_name_label = node_id.strip('\"')
+
+        async with self._driver.session() as session:  
+            query = f"MATCH (n:`{entity_name_label}`) RETURN count(n) > 0 AS node_exists"  
+            result = await session.run(query)  
+            single_result = await result.single()
+            logger.debug(
+                    f'{inspect.currentframe().f_code.co_name}:query:{query}:result:{single_result["node_exists"]}'
+            )  
+            return single_result["node_exists"]
+           
+    async def has_edge(self, source_node_id: str, target_node_id: str) -> bool:
+        entity_name_label_source = source_node_id.strip('\"')
+        entity_name_label_target = target_node_id.strip('\"')
+       
+        async with self._driver.session() as session:  
+            query = (  
+                f"MATCH (a:`{entity_name_label_source}`)-[r]-(b:`{entity_name_label_target}`) "  
+                "RETURN COUNT(r) > 0 AS edgeExists"  
+            )  
+            result = await session.run(query)  
+            single_result = await result.single()
+            logger.debug(
+                    f'{inspect.currentframe().f_code.co_name}:query:{query}:result:{single_result["edgeExists"]}'
+            )  
+            return single_result["edgeExists"]
+       
+        def close(self):  
+            self._driver.close()   
+
+        
+
+
+    async def get_node(self, node_id: str) -> Union[dict, None]:
+        async with self._driver.session() as session:
+            entity_name_label = node_id.strip('\"')
+            query = f"MATCH (n:`{entity_name_label}`) RETURN n"
+            result = await session.run(query)
+            record = await result.single()
+            if record:
+                node = record["n"]
+                node_dict = dict(node)
+                logger.debug(
+                    f'{inspect.currentframe().f_code.co_name}: query: {query}, result: {node_dict}'
+                )
+                return node_dict
+            return None
+              
+
+
+    async def node_degree(self, node_id: str) -> int:
+        entity_name_label = node_id.strip('\"')
+
+        async with self._driver.session()  as session:
+            query = f"""
+                MATCH (n:`{entity_name_label}`)
+                RETURN COUNT{{ (n)--() }} AS totalEdgeCount
+            """
+            result = await session.run(query)  
+            record = await result.single()  
+            if record:
+                edge_count = record["totalEdgeCount"]  
+                logger.debug(
+                    f'{inspect.currentframe().f_code.co_name}:query:{query}:result:{edge_count}'
+                )  
+                return edge_count
+            else:  
+                return None
+                
+
+    async def edge_degree(self, src_id: str, tgt_id: str) -> int:
+        entity_name_label_source = src_id.strip('\"')
+        entity_name_label_target = tgt_id.strip('\"')
+        src_degree = await self.node_degree(entity_name_label_source)
+        trg_degree = await self.node_degree(entity_name_label_target)
+        
+        # Convert None to 0 for addition
+        src_degree = 0 if src_degree is None else src_degree
+        trg_degree = 0 if trg_degree is None else trg_degree
+
+        degrees = int(src_degree) + int(trg_degree)
+        logger.debug(
+                    f'{inspect.currentframe().f_code.co_name}:query:src_Degree+trg_degree:result:{degrees}'
+        )  
+        return degrees
+
+
+    
+    async def get_edge(self, source_node_id: str, target_node_id: str) -> Union[dict, None]:
+        entity_name_label_source = source_node_id.strip('\"')
+        entity_name_label_target = target_node_id.strip('\"')
+        """
+        Find all edges between nodes of two given labels
+        
+        Args:
+            source_node_label (str): Label of the source nodes
+            target_node_label (str): Label of the target nodes
+            
+        Returns:
+            list: List of all relationships/edges found
+        """
+        async with self._driver.session()  as session:
+            query = f"""
+            MATCH (start:`{entity_name_label_source}`)-[r]->(end:`{entity_name_label_target}`)
+            RETURN properties(r) as edge_properties
+            LIMIT 1
+            """.format(entity_name_label_source=entity_name_label_source, entity_name_label_target=entity_name_label_target)
+            
+            result = await session.run(query)           
+            record = await result.single()
+            if record:
+                result = dict(record["edge_properties"])
+                logger.debug(
+                    f'{inspect.currentframe().f_code.co_name}:query:{query}:result:{result}'
+                )     
+                return result
+            else:
+                return None
+        
+
+    async def get_node_edges(self, source_node_id: str)-> List[Tuple[str, str]]:
+        node_label = source_node_id.strip('\"')
+          
+        """
+        Retrieves all edges (relationships) for a particular node identified by its label.
+        :return: List of dictionaries containing edge information
+        """
+        query = f"""MATCH (n:`{node_label}`)
+                OPTIONAL MATCH (n)-[r]-(connected)
+                RETURN n, r, connected"""
+        async with self._driver.session()  as session:
+            results = await session.run(query)
+            edges = []
+            async for record in results:
+                source_node = record['n']
+                connected_node = record['connected']
+                
+                source_label = list(source_node.labels)[0] if source_node.labels else None
+                target_label = list(connected_node.labels)[0] if connected_node and connected_node.labels else None
+                
+                if source_label and target_label:
+                    edges.append((source_label, target_label))
+            
+            return edges
+
+
+    @retry(
+        stop=stop_after_attempt(3),
+        wait=wait_exponential(multiplier=1, min=4, max=10),
+        retry=retry_if_exception_type((neo4jExceptions.ServiceUnavailable, neo4jExceptions.TransientError, neo4jExceptions.WriteServiceUnavailable)),
+    )
+    async def upsert_node(self, node_id: str, node_data: Dict[str, Any]):
+        """
+        Upsert a node in the Neo4j database.
+
+        Args:
+            node_id: The unique identifier for the node (used as label)
+            node_data: Dictionary of node properties
+        """
+        label = node_id.strip('\"')
+        properties = node_data
+
+        async def _do_upsert(tx: AsyncManagedTransaction):
+            query = f"""
+            MERGE (n:`{label}`)
+            SET n += $properties
+            """
+            await tx.run(query, properties=properties)
+            logger.debug(f"Upserted node with label '{label}' and properties: {properties}")
+
+        try:
+            async with self._driver.session() as session:
+                await session.execute_write(_do_upsert)
+        except Exception as e:
+            logger.error(f"Error during upsert: {str(e)}")
+            raise
+               
+    @retry(
+        stop=stop_after_attempt(3),
+        wait=wait_exponential(multiplier=1, min=4, max=10),
+        retry=retry_if_exception_type((neo4jExceptions.ServiceUnavailable, neo4jExceptions.TransientError, neo4jExceptions.WriteServiceUnavailable)),
+    )
+    async def upsert_edge(self, source_node_id: str, target_node_id: str, edge_data: Dict[str, Any]):
+        """
+        Upsert an edge and its properties between two nodes identified by their labels.
+
+        Args:
+            source_node_id (str): Label of the source node (used as identifier)
+            target_node_id (str): Label of the target node (used as identifier)
+            edge_data (dict): Dictionary of properties to set on the edge
+        """
+        source_node_label = source_node_id.strip('\"')
+        target_node_label = target_node_id.strip('\"')
+        edge_properties = edge_data
+
+        async def _do_upsert_edge(tx: AsyncManagedTransaction):
+            query = f"""
+            MATCH (source:`{source_node_label}`)
+            WITH source
+            MATCH (target:`{target_node_label}`)
+            MERGE (source)-[r:DIRECTED]->(target)
+            SET r += $properties
+            RETURN r
+            """
+            await tx.run(query, properties=edge_properties)
+            logger.debug(f"Upserted edge from '{source_node_label}' to '{target_node_label}' with properties: {edge_properties}")
+
+        try:
+            async with self._driver.session() as session:
+                await session.execute_write(_do_upsert_edge)
+        except Exception as e:
+            logger.error(f"Error during edge upsert: {str(e)}")
+            raise
+    async def _node2vec_embed(self):
+        print ("Implemented but never called.")
+        

lightrag/lightrag.py

@@ -1,5 +1,6 @@
 import asyncio
 import os
+import importlib
 from dataclasses import asdict, dataclass, field
 from datetime import datetime
 from functools import partial
@@ -23,6 +24,18 @@ from .storage import (
     NanoVectorDBStorage,
     NetworkXStorage,
 )
+ 
+from .kg.neo4j_impl import (
+    Neo4JStorage 
+)
+#future KG integrations
+
+# from .kg.ArangoDB_impl import (
+#     GraphStorage as ArangoDBStorage
+# )
+
+
+
 from .utils import (
     EmbeddingFunc,
     compute_mdhash_id,
@@ -44,18 +57,27 @@ def always_get_an_event_loop() -> asyncio.AbstractEventLoop:
     try:
         loop = asyncio.get_running_loop()
     except RuntimeError:
-        logger.info("Creating a new event loop in a sub-thread.")
-        loop = asyncio.new_event_loop()
-        asyncio.set_event_loop(loop)
+        logger.info("Creating a new event loop in main thread.")
+        # loop = asyncio.new_event_loop()
+        # asyncio.set_event_loop(loop)
+        loop = asyncio.get_event_loop()
     return loop
 
 
 @dataclass
 class LightRAG:
+    
     working_dir: str = field(
         default_factory=lambda: f"./lightrag_cache_{datetime.now().strftime('%Y-%m-%d-%H:%M:%S')}"
     )
 
+    kg: str = field(default="NetworkXStorage")
+
+    current_log_level = logger.level
+    log_level: str = field(default=current_log_level)
+
+
+
     # text chunking
     chunk_token_size: int = 1200
     chunk_overlap_token_size: int = 100
@@ -94,7 +116,6 @@ class LightRAG:
     key_string_value_json_storage_cls: Type[BaseKVStorage] = JsonKVStorage
     vector_db_storage_cls: Type[BaseVectorStorage] = NanoVectorDBStorage
     vector_db_storage_cls_kwargs: dict = field(default_factory=dict)
-    graph_storage_cls: Type[BaseGraphStorage] = NetworkXStorage
     enable_llm_cache: bool = True
 
     # extension
@@ -104,11 +125,16 @@ class LightRAG:
     def __post_init__(self):
         log_file = os.path.join(self.working_dir, "lightrag.log")
         set_logger(log_file)
+        logger.setLevel(self.log_level)
+
         logger.info(f"Logger initialized for working directory: {self.working_dir}")
 
         _print_config = ",\n  ".join([f"{k} = {v}" for k, v in asdict(self).items()])
         logger.debug(f"LightRAG init with param:\n  {_print_config}\n")
 
+        #@TODO: should move all storage setup here to leverage initial start params attached to self.
+        self.graph_storage_cls: Type[BaseGraphStorage] = self._get_storage_class()[self.kg]
+
         if not os.path.exists(self.working_dir):
             logger.info(f"Creating working directory {self.working_dir}")
             os.makedirs(self.working_dir)
@@ -161,6 +187,12 @@ class LightRAG:
                 **self.llm_model_kwargs,
             )
         )
+    def _get_storage_class(self) -> Type[BaseGraphStorage]:
+        return {
+            "Neo4JStorage": Neo4JStorage,
+            "NetworkXStorage": NetworkXStorage,
+            # "ArangoDBStorage": ArangoDBStorage
+        }
 
     def insert(self, string_or_strings):
         loop = always_get_an_event_loop()
@@ -298,4 +330,4 @@ class LightRAG:
             if storage_inst is None:
                 continue
             tasks.append(cast(StorageNameSpace, storage_inst).index_done_callback())
-        await asyncio.gather(*tasks)
+        await asyncio.gather(*tasks)

lightrag/llm.py

@@ -798,4 +798,4 @@ if __name__ == "__main__":
         result = await gpt_4o_mini_complete("How are you?")
         print(result)
 
-    asyncio.run(main())
+    asyncio.run(main())

lightrag/operate.py

@@ -466,7 +466,9 @@ async def _build_local_query_context(
     text_chunks_db: BaseKVStorage[TextChunkSchema],
     query_param: QueryParam,
 ):
+
     results = await entities_vdb.query(query, top_k=query_param.top_k)
+
     if not len(results):
         return None
     node_datas = await asyncio.gather(
@@ -481,7 +483,7 @@ async def _build_local_query_context(
         {**n, "entity_name": k["entity_name"], "rank": d}
         for k, n, d in zip(results, node_datas, node_degrees)
         if n is not None
-    ]
+    ]#what is this text_chunks_db doing.  dont remember it in airvx.  check the diagram.
     use_text_units = await _find_most_related_text_unit_from_entities(
         node_datas, query_param, text_chunks_db, knowledge_graph_inst
     )
@@ -907,7 +909,6 @@ async def hybrid_query(
                 .strip()
             )
             result = "{" + result.split("{")[1].split("}")[0] + "}"
-
             keywords_data = json.loads(result)
             hl_keywords = keywords_data.get("high_level_keywords", [])
             ll_keywords = keywords_data.get("low_level_keywords", [])
@@ -927,6 +928,7 @@ async def hybrid_query(
             query_param,
         )
 
+
     if hl_keywords:
         high_level_context = await _build_global_query_context(
             hl_keywords,
@@ -937,6 +939,7 @@ async def hybrid_query(
             query_param,
         )
 
+
     context = combine_contexts(high_level_context, low_level_context)
 
     if query_param.only_need_context:
@@ -1043,6 +1046,7 @@ async def naive_query(
     chunks_ids = [r["id"] for r in results]
     chunks = await text_chunks_db.get_by_ids(chunks_ids)
 
+
     maybe_trun_chunks = truncate_list_by_token_size(
         chunks,
         key=lambda x: x["content"],
@@ -1073,4 +1077,4 @@ async def naive_query(
             .strip()
         )
 
-    return response
+    return response

lightrag/storage.py

@@ -233,6 +233,8 @@ class NetworkXStorage(BaseGraphStorage):
             raise ValueError(f"Node embedding algorithm {algorithm} not supported")
         return await self._node_embed_algorithms[algorithm]()
 
+    
+    #@TODO: NOT USED 
     async def _node2vec_embed(self):
         from graspologic import embed
 

requirements.txt

@@ -4,6 +4,7 @@ aiohttp
 graspologic
 hnswlib
 nano-vectordb
+neo4j
 networkx
 ollama
 openai

test.py

@@ -0,0 +1,35 @@
+import os
+from lightrag import LightRAG, QueryParam
+from lightrag.llm import gpt_4o_mini_complete, gpt_4o_complete
+from pprint import pprint
+#########
+# Uncomment the below two lines if running in a jupyter notebook to handle the async nature of rag.insert()
+# import nest_asyncio 
+# nest_asyncio.apply() 
+#########
+
+WORKING_DIR = "./dickens"
+
+if not os.path.exists(WORKING_DIR):
+    os.mkdir(WORKING_DIR)
+
+rag = LightRAG(
+    working_dir=WORKING_DIR,
+    llm_model_func=gpt_4o_mini_complete  # Use gpt_4o_mini_complete LLM model
+    # llm_model_func=gpt_4o_complete  # Optionally, use a stronger model
+)
+
+with open("./book.txt") as f:
+    rag.insert(f.read())
+
+# Perform naive search
+print(rag.query("What are the top themes in this story?", param=QueryParam(mode="naive")))
+
+# Perform local search
+print(rag.query("What are the top themes in this story?", param=QueryParam(mode="local")))
+
+# Perform global search
+print(rag.query("What are the top themes in this story?", param=QueryParam(mode="global")))
+
+# Perform hybrid search
+print(rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid")))

test_neo4j.py

@@ -0,0 +1,38 @@
+import os
+from lightrag import LightRAG, QueryParam
+from lightrag.llm import gpt_4o_mini_complete, gpt_4o_complete
+
+
+#########
+# Uncomment the below two lines if running in a jupyter notebook to handle the async nature of rag.insert()
+# import nest_asyncio 
+# nest_asyncio.apply() 
+#########
+
+WORKING_DIR = "./local_neo4jWorkDir"
+
+if not os.path.exists(WORKING_DIR):
+    os.mkdir(WORKING_DIR)
+
+rag = LightRAG(
+    working_dir=WORKING_DIR,
+    llm_model_func=gpt_4o_mini_complete,  # Use gpt_4o_mini_complete LLM model
+    kg="Neo4JStorage",
+    log_level="INFO"
+    # llm_model_func=gpt_4o_complete  # Optionally, use a stronger model
+)
+
+with open("./book.txt") as f:
+    rag.insert(f.read())
+
+# Perform naive search
+print(rag.query("What are the top themes in this story?", param=QueryParam(mode="naive")))
+
+# Perform local search
+print(rag.query("What are the top themes in this story?", param=QueryParam(mode="local")))
+
+# Perform global search
+print(rag.query("What are the top themes in this story?", param=QueryParam(mode="global")))
+
+# Perform hybrid search
+print(rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid")))