from configs.model_config import * from chains.local_doc_qa import LocalDocQA import os import nltk nltk.data.path = [os.path.join(os.path.dirname(__file__), "nltk_data")] + nltk.data.path # return top-k text chunk from vector store VECTOR_SEARCH_TOP_K = 6 # LLM input history length LLM_HISTORY_LEN = 3 # Show reply with source text from input document REPLY_WITH_SOURCE = True if __name__ == "__main__": local_doc_qa = LocalDocQA() local_doc_qa.init_cfg(llm_model=LLM_MODEL, embedding_model=EMBEDDING_MODEL, embedding_device=EMBEDDING_DEVICE, llm_history_len=LLM_HISTORY_LEN, top_k=VECTOR_SEARCH_TOP_K) vs_path = None while not vs_path: filepath = input("Input your local knowledge file path 请输入本地知识文件路径:") vs_path, _ = local_doc_qa.init_knowledge_vector_store(filepath) history = [] while True: query = input("Input your question 请输入问题:") resp, history = local_doc_qa.get_knowledge_based_answer(query=query, vs_path=vs_path, chat_history=history) if REPLY_WITH_SOURCE: print(resp) else: print(resp["result"])