refactor(llm): replace vid by full vertexes info (#189)

hugegraph-llm/src/hugegraph_llm/api/models/rag_requests.py

@@ -76,7 +76,7 @@ class GraphRAGRequest(BaseModel):
                                     from the query, by default only the most similar one is returned.")
 
     client_config : Optional[GraphConfigRequest] = Query(None, description="hugegraph server config.")
-    get_vid_only: bool = Query(False, description="return only keywords & vid (early stop).")
+    get_vertex_only: bool = Query(False, description="return only keywords & vertex (early stop).")
 
     gremlin_tmpl_num: int = Query(
         1, description="Number of Gremlin templates to use. If num <=0 means template is not provided"

hugegraph-llm/src/hugegraph_llm/api/rag_api.py

@@ -32,7 +32,7 @@
 from hugegraph_llm.config import llm_settings, prompt
 from hugegraph_llm.utils.log import log
 
-
+# pylint: disable=too-many-statements
 def rag_http_api(
     router: APIRouter,
     rag_answer_func,
@@ -101,9 +101,18 @@ def graph_rag_recall_api(req: GraphRAGRequest):
                 near_neighbor_first=req.near_neighbor_first,
                 custom_related_information=req.custom_priority_info,
                 gremlin_prompt=req.gremlin_prompt or prompt.gremlin_generate_prompt,
-                get_vid_only=req.get_vid_only
+                get_vertex_only=req.get_vertex_only
             )
 
+            if req.get_vertex_only:
+                from hugegraph_llm.operators.hugegraph_op.graph_rag_query import GraphRAGQuery
+                graph_rag = GraphRAGQuery()
+                graph_rag.init_client(result)
+                vertex_details = graph_rag.get_vertex_details(result["match_vids"])
+
+                if vertex_details:
+                    result["match_vids"] = vertex_details
+
             if isinstance(result, dict):
                 params = [
                     "query",

hugegraph-llm/src/hugegraph_llm/demo/rag_demo/text2gremlin_block.py

@@ -192,15 +192,16 @@ def graph_rag_recall(
     topk_return_results: int,
     vector_dis_threshold: float,
     topk_per_keyword: int,
-    get_vid_only: bool
+    get_vertex_only: bool = False,
 ) -> dict:
     store_schema(prompt.text2gql_graph_schema, query, gremlin_prompt)
     rag = RAGPipeline()
     rag.extract_keywords().keywords_to_vid(
             vector_dis_threshold=vector_dis_threshold,
             topk_per_keyword=topk_per_keyword,
         )
-    if not get_vid_only:
+
+    if not get_vertex_only:
         rag.import_schema(huge_settings.graph_name).query_graphdb(
             num_gremlin_generate_example=gremlin_tmpl_num,
             gremlin_prompt=gremlin_prompt,

hugegraph-llm/src/hugegraph_llm/operators/hugegraph_op/graph_rag_query.py

@@ -110,7 +110,7 @@ def __init__(
         self._gremlin_prompt = gremlin_prompt or prompt.gremlin_generate_prompt
 
     def run(self, context: Dict[str, Any]) -> Dict[str, Any]:
-        self._init_client(context)
+        self.init_client(context)
 
         # initial flag: -1 means no result, 0 means subgraph query, 1 means gremlin query
         context["graph_result_flag"] = -1
@@ -239,7 +239,9 @@ def _subgraph_query(self, context: Dict[str, Any]) -> Dict[str, Any]:
             )
         return context
 
-    def _init_client(self, context):
+    # TODO: move this method to a util file for reuse (remove self param)
+    def init_client(self, context):
+        """Initialize the HugeGraph client from context or default settings."""
         # pylint: disable=R0915 (too-many-statements)
         if self._client is None:
             if isinstance(context.get("graph_client"), PyHugeClient):
@@ -254,6 +256,15 @@ def _init_client(self, context):
                 self._client = PyHugeClient(ip, port, graph, user, pwd, gs)
         assert self._client is not None, "No valid graph to search."
 
+    def get_vertex_details(self, vertex_ids: List[str]) -> List[Dict[str, Any]]:
+        if not vertex_ids:
+            return []
+
+        formatted_ids = ", ".join(f"'{vid}'" for vid in vertex_ids)
+        gremlin_query = f"g.V({formatted_ids}).limit(20)"
+        result = self._client.gremlin().exec(gremlin=gremlin_query)["data"]
+        return result
+
     def _format_graph_from_vertex(self, query_result: List[Any]) -> Set[str]:
         knowledge = set()
         for item in query_result:
@@ -374,8 +385,8 @@ def _extract_labels_from_schema(self) -> Tuple[List[str], List[str]]:
         schema = self._get_graph_schema()
         vertex_props_str, edge_props_str = schema.split("\n")[:2]
         # TODO: rename to vertex (also need update in the schema)
-        vertex_props_str = vertex_props_str[len("Vertex properties: ") :].strip("[").strip("]")
-        edge_props_str = edge_props_str[len("Edge properties: ") :].strip("[").strip("]")
+        vertex_props_str = vertex_props_str[len("Vertex properties: "):].strip("[").strip("]")
+        edge_props_str = edge_props_str[len("Edge properties: "):].strip("[").strip("]")
         vertex_labels = self._extract_label_names(vertex_props_str)
         edge_labels = self._extract_label_names(edge_props_str)
         return vertex_labels, edge_labels