Spaces:
Running
on
CPU Upgrade
Running
on
CPU Upgrade
| import os | |
| import logging | |
| from llama_index import GPTSimpleVectorIndex | |
| from llama_index import download_loader | |
| from llama_index import ( | |
| Document, | |
| LLMPredictor, | |
| PromptHelper, | |
| QuestionAnswerPrompt, | |
| RefinePrompt, | |
| ) | |
| from langchain.llms import OpenAI | |
| import colorama | |
| from presets import * | |
| from utils import * | |
| def get_documents(file_src): | |
| documents = [] | |
| index_name = "" | |
| logging.debug("Loading documents...") | |
| logging.debug(f"file_src: {file_src}") | |
| for file in file_src: | |
| logging.debug(f"file: {file.name}") | |
| index_name += file.name | |
| if os.path.splitext(file.name)[1] == ".pdf": | |
| logging.debug("Loading PDF...") | |
| CJKPDFReader = download_loader("CJKPDFReader") | |
| loader = CJKPDFReader() | |
| documents += loader.load_data(file=file.name) | |
| elif os.path.splitext(file.name)[1] == ".docx": | |
| logging.debug("Loading DOCX...") | |
| DocxReader = download_loader("DocxReader") | |
| loader = DocxReader() | |
| documents += loader.load_data(file=file.name) | |
| elif os.path.splitext(file.name)[1] == ".epub": | |
| logging.debug("Loading EPUB...") | |
| EpubReader = download_loader("EpubReader") | |
| loader = EpubReader() | |
| documents += loader.load_data(file=file.name) | |
| else: | |
| logging.debug("Loading text file...") | |
| with open(file.name, "r", encoding="utf-8") as f: | |
| text = add_space(f.read()) | |
| documents += [Document(text)] | |
| index_name = sha1sum(index_name) | |
| return documents, index_name | |
| def construct_index( | |
| api_key, | |
| file_src, | |
| max_input_size=4096, | |
| num_outputs=1, | |
| max_chunk_overlap=20, | |
| chunk_size_limit=600, | |
| embedding_limit=None, | |
| separator=" ", | |
| num_children=10, | |
| max_keywords_per_chunk=10, | |
| ): | |
| os.environ["OPENAI_API_KEY"] = api_key | |
| chunk_size_limit = None if chunk_size_limit == 0 else chunk_size_limit | |
| embedding_limit = None if embedding_limit == 0 else embedding_limit | |
| separator = " " if separator == "" else separator | |
| llm_predictor = LLMPredictor( | |
| llm=OpenAI(model_name="gpt-3.5-turbo-0301", openai_api_key=api_key) | |
| ) | |
| prompt_helper = PromptHelper( | |
| max_input_size, | |
| num_outputs, | |
| max_chunk_overlap, | |
| embedding_limit, | |
| chunk_size_limit, | |
| separator=separator, | |
| ) | |
| documents, index_name = get_documents(file_src) | |
| if os.path.exists(f"./index/{index_name}.json"): | |
| logging.info("找到了缓存的索引文件，加载中……") | |
| return GPTSimpleVectorIndex.load_from_disk(f"./index/{index_name}.json") | |
| else: | |
| try: | |
| logging.debug("构建索引中……") | |
| index = GPTSimpleVectorIndex( | |
| documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper | |
| ) | |
| os.makedirs("./index", exist_ok=True) | |
| index.save_to_disk(f"./index/{index_name}.json") | |
| return index | |
| except Exception as e: | |
| print(e) | |
| return None | |
| def chat_ai( | |
| api_key, | |
| index, | |
| question, | |
| context, | |
| chatbot, | |
| ): | |
| os.environ["OPENAI_API_KEY"] = api_key | |
| logging.info(f"Question: {question}") | |
| response, chatbot_display, status_text = ask_ai( | |
| api_key, | |
| index, | |
| question, | |
| replace_today(PROMPT_TEMPLATE), | |
| REFINE_TEMPLATE, | |
| SIM_K, | |
| INDEX_QUERY_TEMPRATURE, | |
| context, | |
| ) | |
| if response is None: | |
| status_text = "查询失败，请换个问法试试" | |
| return context, chatbot | |
| response = response | |
| context.append({"role": "user", "content": question}) | |
| context.append({"role": "assistant", "content": response}) | |
| chatbot.append((question, chatbot_display)) | |
| os.environ["OPENAI_API_KEY"] = "" | |
| return context, chatbot, status_text | |
| def ask_ai( | |
| api_key, | |
| index, | |
| question, | |
| prompt_tmpl, | |
| refine_tmpl, | |
| sim_k=1, | |
| temprature=0, | |
| prefix_messages=[], | |
| ): | |
| os.environ["OPENAI_API_KEY"] = api_key | |
| logging.debug("Index file found") | |
| logging.debug("Querying index...") | |
| llm_predictor = LLMPredictor( | |
| llm=OpenAI( | |
| temperature=temprature, | |
| model_name="gpt-3.5-turbo-0301", | |
| prefix_messages=prefix_messages, | |
| ) | |
| ) | |
| response = None # Initialize response variable to avoid UnboundLocalError | |
| qa_prompt = QuestionAnswerPrompt(prompt_tmpl) | |
| rf_prompt = RefinePrompt(refine_tmpl) | |
| response = index.query( | |
| question, | |
| llm_predictor=llm_predictor, | |
| similarity_top_k=sim_k, | |
| text_qa_template=qa_prompt, | |
| refine_template=rf_prompt, | |
| response_mode="compact", | |
| ) | |
| if response is not None: | |
| logging.info(f"Response: {response}") | |
| ret_text = response.response | |
| nodes = [] | |
| for index, node in enumerate(response.source_nodes): | |
| brief = node.source_text[:25].replace("\n", "") | |
| nodes.append( | |
| f"<details><summary>[{index+1}]\t{brief}...</summary><p>{node.source_text}</p></details>" | |
| ) | |
| new_response = ret_text + "\n----------\n" + "\n\n".join(nodes) | |
| logging.info( | |
| f"Response: {colorama.Fore.BLUE}{ret_text}{colorama.Style.RESET_ALL}" | |
| ) | |
| os.environ["OPENAI_API_KEY"] = "" | |
| return ret_text, new_response, f"查询消耗了{llm_predictor.last_token_usage} tokens" | |
| else: | |
| logging.warning("No response found, returning None") | |
| os.environ["OPENAI_API_KEY"] = "" | |
| return None | |
| def add_space(text): | |
| punctuations = {"，": "， ", "。": "。 ", "？": "？ ", "！": "！ ", "：": "： ", "；": "； "} | |
| for cn_punc, en_punc in punctuations.items(): | |
| text = text.replace(cn_punc, en_punc) | |
| return text | |