Function query_graph

query_graph.py:125–162

(global_config,db,query)

        aggregation_descriptions+="\n".join([information[0]+"\t\t"+str(information[1]) for information in aggregation_results])
    return aggregation_descriptions,communities
def query_graph(global_config,db,query):
    use_llm_func: callable = global_config["use_llm_func"]
    embedding: callable=global_config["embeddings_func"]
    b=time.time()
    level_mode=global_config['level_mode']
    topk=global_config['topk']
    chunks_file=global_config["chunks_file"]
    entity_results=search_vector_search(global_config['working_dir'],embedding(query),topk=topk,level_mode=level_mode)
    v=time.time()
    res_entity=[i[0]for i in entity_results]
    chunks=[i[-1]for i in entity_results]
    entity_descriptions=get_entity_description(global_config,entity_results)
    reasoning_path,reasoning_path_information_description=get_reasoning_chain(global_config,res_entity)
    # reasoning_path,reasoning_path_information_description=get_path_chain(global_config,res_entity)
    aggregation_descriptions,aggregation=get_aggregation_description(global_config,reasoning_path)
    # chunks=search_chunks(global_config['working_dir'],aggregation)
    text_units=get_text_units(global_config['working_dir'],chunks,chunks_file,k=5)
    describe=f"""
    entity_information:
    {entity_descriptions}
    aggregation_entity_information:
    {aggregation_descriptions}
    reasoning_path_information:
    {reasoning_path_information_description}
    text_units:
    {text_units}
    """
    e=time.time()
    
    # print(describe)
    sys_prompt =PROMPTS["rag_response"].format(context_data=describe)
    response=use_llm_func(query,system_prompt=sys_prompt)
    g=time.time()
    print(f"embedding time: {v-b:.2f}s")
    print(f"query time: {e-v:.2f}s")
    
    print(f"response time: {g-e:.2f}s")
    return describe,response
if __name__=="__main__":
    db = pymysql.connect(host='localhost', user='root',port=4321,
                      passwd='123',  charset='utf8mb4')