fix(llm): fix parametername in docs

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

@@ -17,7 +17,7 @@
 import json
 from typing import Literal, Optional
 
-from fastapi import status, APIRouter, HTTPException, Query
+from fastapi import status, APIRouter, HTTPException, Query, Body
 
 from hugegraph_llm.api.exceptions.rag_exceptions import generate_response
 from hugegraph_llm.api.models.rag_requests import (
@@ -64,26 +64,34 @@ def rag_http_api(
 ):
     @router.post("/rag", status_code=status.HTTP_200_OK)
     def rag_answer_api(
-        req: RAGRequest,
-        query_param: str = Query("", description="Query you want to ask"),
-        raw_answer_param: bool = Query(False, description="Use LLM to generate answer directly"),
-        vector_only_param: bool = Query(False, description="Use LLM to generate answer with vector"),
-        graph_only_param: bool = Query(False, description="Use LLM to generate answer with graph RAG only"),
-        graph_vector_answer_param: bool = Query(False, description="Use LLM to generate answer with vector & GraphRAG"),
-        with_gremlin_tmpl_param: bool = Query(True, description="Use exapmle template in text2gremlin"),
-        graph_ratio_param: float = Query(0.5, description="The ratio of GraphRAG ans & vector ans"),
-        rerank_method_param: Literal["bleu", "reranker"] = Query("bleu", description="Method to rerank the results."),
-        near_neighbor_first_param: bool = Query(False, description="Prioritize near neighbors in the search results."),
-        custom_priority_info_param: str = Query("", description="Custom information to prioritize certain results."),
-        answer_prompt_param: Optional[str] = Query(
+        req: RAGRequest = Body(...),
+        query: Optional[str] = Query("", description="Query you want to ask"),
+        raw_answer: Optional[bool] = Query(False, description="Use LLM to generate answer directly"),
+        vector_only: Optional[bool] = Query(False, description="Use LLM to generate answer with vector"),
+        graph_only: Optional[bool] = Query(False, description="Use LLM to generate answer with graph RAG only"),
+        graph_vector_answer: Optional[bool] = Query(
+            True, description="Use LLM to generate answer with vector & GraphRAG"
+        ),
+        with_gremlin_tmpl: Optional[bool] = Query(True, description="Use example template in text2gremlin"),
+        graph_ratio: Optional[float] = Query(0.5, description="The ratio of GraphRAG ans & vector ans"),
+        rerank_method: Optional[Literal["bleu", "reranker"]] = Query(
+            "bleu", description="Method to rerank the results."
+        ),
+        near_neighbor_first: Optional[bool] = Query(
+            True, description="Prioritize near neighbors in the search results."
+        ),
+        custom_priority_info: Optional[str] = Query(
+            "", description="Custom information to prioritize certain results."
+        ),
+        answer_prompt: Optional[str] = Query(
             prompt.answer_prompt, description="Prompt to guide the answer generation."
         ),
-        keywords_extract_prompt_param: Optional[str] = Query(
-            prompt.keywords_extract_prompt, description="Prompt for extracting keywords from the query."
+        keywords_extract_prompt: Optional[str] = Query(
+            prompt.keywords_extract_prompt, description="Prompt for extracting keywords from query."
         ),
-        gremlin_tmpl_num_param: int = Query(1, description="Number of Gremlin templates to use."),
-        gremlin_prompt_param: Optional[str] = Query(
-            prompt.gremlin_generate_prompt, description="Prompt for the Gremlin query. Don't change it casually"
+        gremlin_tmpl_num: Optional[int] = Query(1, description="Number of Gremlin templates to use."),
+        gremlin_prompt: Optional[str] = Query(
+            prompt.gremlin_generate_prompt, description="Prompt for the Gremlin query."
         ),
     ):
         result = rag_answer_func(
@@ -107,20 +115,31 @@ def rag_answer_api(
             for key, value in zip(["raw_answer", "vector_only", "graph_only", "graph_vector_answer"], result)
             if getattr(req, key)
         }
+        return {
+            key: value
+            for key, value in zip(["raw_answer", "vector_only", "graph_only", "graph_vector_answer"], result)
+            if getattr(req, key)
+        }
 
     @router.post("/rag/graph", status_code=status.HTTP_200_OK)
     def graph_rag_recall_api(
         req: GraphRAGRequest,
-        query_param: str = Query("", description="Query you want to ask"),
-        with_gremlin_templ_param: bool = Query(True, description="Use exapmle template in text2gremlin"),
-        rerank_method_param: Literal["bleu", "reranker"] = Query("bleu", description="Method to rerank the results."),
-        near_neighbor_first_param: bool = Query(False, description="Prioritize near neighbors in the search results."),
-        custom_priority_info_param: str = Query("", description="Custom information to prioritize certain results."),
-        answer_prompt_param: Optional[str] = Query(
+        query: Optional[str] = Query("", description="Query you want to ask"),
+        with_gremlin_tmpl: Optional[bool] = Query(True, description="Use exapmle template in text2gremlin"),
+        rerank_method: Optional[Literal["bleu", "reranker"]] = Query(
+            "bleu", description="Method to rerank the results."
+        ),
+        near_neighbor_first: Optional[bool] = Query(
+            False, description="Prioritize near neighbors in the search results."
+        ),
+        custom_priority_info: Optional[str] = Query(
+            "", description="Custom information to prioritize certain results."
+        ),
+        answer_prompt: Optional[str] = Query(
             prompt.answer_prompt, description="Prompt to guide the answer generation."
         ),
-        gremlin_tmpl_num_param: int = Query(1, description="Number of Gremlin templates to use."),
-        gremlin_prompt_param: Optional[str] = Query(
+        gremlin_tmpl_num: Optional[int] = Query(1, description="Number of Gremlin templates to use."),
+        gremlin_prompt: Optional[str] = Query(
             prompt.gremlin_generate_prompt, description="Prompt for the Gremlin query. Don't change it casually"
         ),
     ):