def rag_answer_api()

in hugegraph-llm/src/hugegraph_llm/api/rag_api.py [0:0]

• 29 lines of code
• 7 McCabe index (conditional complexity)

    def rag_answer_api(req: RAGRequest):
        set_graph_config(req)

        result = rag_answer_func(
            text=req.query,
            raw_answer=req.raw_answer,
            vector_only_answer=req.vector_only,
            graph_only_answer=req.graph_only,
            graph_vector_answer=req.graph_vector_answer,
            graph_ratio=req.graph_ratio,
            rerank_method=req.rerank_method,
            near_neighbor_first=req.near_neighbor_first,
            gremlin_tmpl_num=req.gremlin_tmpl_num,
            max_graph_items=req.max_graph_items,
            topk_return_results=req.topk_return_results,
            vector_dis_threshold=req.vector_dis_threshold,
            topk_per_keyword=req.topk_per_keyword,
            # Keep prompt params in the end
            custom_related_information=req.custom_priority_info,
            answer_prompt=req.answer_prompt or prompt.answer_prompt,
            keywords_extract_prompt=req.keywords_extract_prompt or prompt.keywords_extract_prompt,
            gremlin_prompt=req.gremlin_prompt or prompt.gremlin_generate_prompt,
        )
        # TODO: we need more info in the response for users to understand the query logic
        return {
            "query": req.query,
            **{
                key: value
                for key, value in zip(["raw_answer", "vector_only", "graph_only", "graph_vector_answer"], result)
                if getattr(req, key)
            },
        }