diff --git a/README.md b/README.md index cbf40e6..859cfa0 100644 --- a/README.md +++ b/README.md @@ -178,6 +178,6 @@ Web UI 可以实现如下功能： - [ ] 实现调用 API 的 Web UI Demo ## 项目交流群 -![二维码](img/qr_code_14.jpg) +![二维码](img/qr_code_15.jpg) 🎉 langchain-ChatGLM 项目交流群，如果你也对本项目感兴趣，欢迎加入群聊参与讨论交流。 diff --git a/chains/local_doc_qa.py b/chains/local_doc_qa.py index 02e0958..ea4b0e1 100644 --- a/chains/local_doc_qa.py +++ b/chains/local_doc_qa.py @@ -12,43 +12,33 @@ from utils import torch_gc from tqdm import tqdm from pypinyin import lazy_pinyin - DEVICE_ = EMBEDDING_DEVICE DEVICE_ID = "0" if torch.cuda.is_available() else None DEVICE = f"{DEVICE_}:{DEVICE_ID}" if DEVICE_ID else DEVICE_ -def load_file(filepath): +def load_file(filepath, sentence_size=SENTENCE_SIZE): if filepath.lower().endswith(".md"): loader = UnstructuredFileLoader(filepath, mode="elements") docs = loader.load() elif filepath.lower().endswith(".pdf"): loader = UnstructuredFileLoader(filepath) - textsplitter = ChineseTextSplitter(pdf=True) + textsplitter = ChineseTextSplitter(pdf=True, sentence_size=sentence_size) docs = loader.load_and_split(textsplitter) else: loader = UnstructuredFileLoader(filepath, mode="elements") - textsplitter = ChineseTextSplitter(pdf=False) + textsplitter = ChineseTextSplitter(pdf=False, sentence_size=sentence_size) docs = loader.load_and_split(text_splitter=textsplitter) return docs -def generate_prompt(related_docs: List[str], - query: str, +def generate_prompt(related_docs: List[str], query: str, prompt_template=PROMPT_TEMPLATE) -> str: context = "\n".join([doc.page_content for doc in related_docs]) prompt = prompt_template.replace("{question}", query).replace("{context}", context) return prompt -def get_docs_with_score(docs_with_score): - docs = [] - for doc, score in docs_with_score: - doc.metadata["score"] = score - docs.append(doc) - return docs - - def seperate_list(ls: List[int]) -> List[List[int]]: lists = [] ls1 = [ls[0]] @@ -63,18 +53,24 @@ def seperate_list(ls: List[int]) -> List[List[int]]: def similarity_search_with_score_by_vector( - self, embedding: List[float], k: int = 4, + self, embedding: List[float], k: int = 4 ) -> List[Tuple[Document, float]]: scores, indices = self.index.search(np.array([embedding], dtype=np.float32), k) docs = [] id_set = set() store_len = len(self.index_to_docstore_id) for j, i in enumerate(indices[0]): - if i == -1: + if i == -1 or 0 < self.score_threshold < scores[0][j]: # This happens when not enough docs are returned. continue _id = self.index_to_docstore_id[i] doc = self.docstore.search(_id) + if not self.chunk_conent: + if not isinstance(doc, Document): + raise ValueError(f"Could not find document for id {_id}, got {doc}") + doc.metadata["score"] = int(scores[0][j]) + docs.append(doc) + continue id_set.add(i) docs_len = len(doc.page_content) for k in range(1, max(i, store_len - i)): @@ -91,6 +87,10 @@ def similarity_search_with_score_by_vector( id_set.add(l) if break_flag: break + if not self.chunk_conent: + return docs + if len(id_set) == 0 and self.score_threshold > 0: + return [] id_list = sorted(list(id_set)) id_lists = seperate_list(id_list) for id_seq in id_lists: @@ -105,7 +105,8 @@ def similarity_search_with_score_by_vector( if not isinstance(doc, Document): raise ValueError(f"Could not find document for id {_id}, got {doc}") doc_score = min([scores[0][id] for id in [indices[0].tolist().index(i) for i in id_seq if i in indices[0]]]) - docs.append((doc, doc_score)) + doc.metadata["score"] = int(doc_score) + docs.append(doc) torch_gc() return docs @@ -115,6 +116,8 @@ class LocalDocQA: embeddings: object = None top_k: int = VECTOR_SEARCH_TOP_K chunk_size: int = CHUNK_SIZE + chunk_conent: bool = True + score_threshold: int = VECTOR_SEARCH_SCORE_THRESHOLD def init_cfg(self, embedding_model: str = EMBEDDING_MODEL, @@ -137,7 +140,8 @@ class LocalDocQA: def init_knowledge_vector_store(self, filepath: str or List[str], - vs_path: str or os.PathLike = None): + vs_path: str or os.PathLike = None, + sentence_size=SENTENCE_SIZE): loaded_files = [] failed_files = [] if isinstance(filepath, str): @@ -147,40 +151,41 @@ class LocalDocQA: elif os.path.isfile(filepath): file = os.path.split(filepath)[-1] try: - docs = load_file(filepath) - print(f"{file} 已成功加载") + docs = load_file(filepath, sentence_size) + logger.info(f"{file} 已成功加载") loaded_files.append(filepath) except Exception as e: - print(e) - print(f"{file} 未能成功加载") + logger.error(e) + logger.info(f"{file} 未能成功加载") return None elif os.path.isdir(filepath): docs = [] for file in tqdm(os.listdir(filepath), desc="加载文件"): fullfilepath = os.path.join(filepath, file) try: - docs += load_file(fullfilepath) + docs += load_file(fullfilepath, sentence_size) loaded_files.append(fullfilepath) except Exception as e: + logger.error(e) failed_files.append(file) if len(failed_files) > 0: - print("以下文件未能成功加载：") + logger.info("以下文件未能成功加载：") for file in failed_files: - print(file, end="\n") + logger.info(file, end="\n") else: docs = [] for file in filepath: try: docs += load_file(file) - print(f"{file} 已成功加载") + logger.info(f"{file} 已成功加载") loaded_files.append(file) except Exception as e: - print(e) - print(f"{file} 未能成功加载") + logger.error(e) + logger.info(f"{file} 未能成功加载") if len(docs) > 0: - print("文件加载完毕，正在生成向量库") + logger.info("文件加载完毕，正在生成向量库") if vs_path and os.path.isdir(vs_path): vector_store = FAISS.load_local(vs_path, self.embeddings) vector_store.add_documents(docs) @@ -189,38 +194,46 @@ class LocalDocQA: if not vs_path: vs_path = os.path.join(VS_ROOT_PATH, f"""{"".join(lazy_pinyin(os.path.splitext(file)[0]))}_FAISS_{datetime.datetime.now().strftime("%Y%m%d_%H%M%S")}""") - vector_store = FAISS.from_documents(docs, self.embeddings) + vector_store = FAISS.from_documents(docs, self.embeddings) # docs 为Document列表 torch_gc() vector_store.save_local(vs_path) return vs_path, loaded_files else: - print("文件均未成功加载，请检查依赖包或替换为其他文件再次上传。") + logger.info("文件均未成功加载，请检查依赖包或替换为其他文件再次上传。") return None, loaded_files - def get_knowledge_based_answer(self, - query, - vs_path, - chat_history=[], - streaming: bool = STREAMING): + def one_knowledge_add(self, vs_path, one_title, one_conent, one_content_segmentation, sentence_size): + try: + if not vs_path or not one_title or not one_conent: + logger.info("知识库添加错误，请确认知识库名字、标题、内容是否正确！") + return None, [one_title] + docs = [Document(page_content=one_conent+"\n", metadata={"source": one_title})] + if not one_content_segmentation: + text_splitter = ChineseTextSplitter(pdf=False, sentence_size=sentence_size) + docs = text_splitter.split_documents(docs) + if os.path.isdir(vs_path): + vector_store = FAISS.load_local(vs_path, self.embeddings) + vector_store.add_documents(docs) + else: + vector_store = FAISS.from_documents(docs, self.embeddings) ##docs 为Document列表 + torch_gc() + vector_store.save_local(vs_path) + return vs_path, [one_title] + except Exception as e: + logger.error(e) + return None, [one_title] + + def get_knowledge_based_answer(self, query, vs_path, chat_history=[], streaming: bool = STREAMING): vector_store = FAISS.load_local(vs_path, self.embeddings) FAISS.similarity_search_with_score_by_vector = similarity_search_with_score_by_vector vector_store.chunk_size = self.chunk_size - related_docs_with_score = vector_store.similarity_search_with_score(query, - k=self.top_k) - related_docs = get_docs_with_score(related_docs_with_score) + vector_store.chunk_conent = self.chunk_conent + vector_store.score_threshold = self.score_threshold + related_docs_with_score = vector_store.similarity_search_with_score(query, k=self.top_k) torch_gc() - prompt = generate_prompt(related_docs, query) + prompt = generate_prompt(related_docs_with_score, query) - # if streaming: - # for result, history in self.llm._stream_call(prompt=prompt, - # history=chat_history): - # history[-1][0] = query - # response = {"query": query, - # "result": result, - # "source_documents": related_docs} - # yield response, history - # else: for result, history in self.llm._call(prompt=prompt, history=chat_history, streaming=streaming): @@ -228,10 +241,35 @@ class LocalDocQA: history[-1][0] = query response = {"query": query, "result": result, - "source_documents": related_docs} + "source_documents": related_docs_with_score} yield response, history torch_gc() + # query 查询内容 + # vs_path 知识库路径 + # chunk_conent 是否启用上下文关联 + # score_threshold 搜索匹配score阈值 + # vector_search_top_k 搜索知识库内容条数，默认搜索5条结果 + # chunk_sizes 匹配单段内容的连接上下文长度 + def get_knowledge_based_conent_test(self, query, vs_path, chunk_conent, + score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD, + vector_search_top_k=VECTOR_SEARCH_TOP_K, chunk_size=CHUNK_SIZE): + vector_store = FAISS.load_local(vs_path, self.embeddings) + FAISS.similarity_search_with_score_by_vector = similarity_search_with_score_by_vector + vector_store.chunk_conent = chunk_conent + vector_store.score_threshold = score_threshold + vector_store.chunk_size = chunk_size + related_docs_with_score = vector_store.similarity_search_with_score(query, k=vector_search_top_k) + if not related_docs_with_score: + response = {"query": query, + "source_documents": []} + return response, "" + torch_gc() + prompt = "\n".join([doc.page_content for doc in related_docs_with_score]) + response = {"query": query, + "source_documents": related_docs_with_score} + return response, prompt + if __name__ == "__main__": local_doc_qa = LocalDocQA() @@ -243,11 +281,11 @@ if __name__ == "__main__": vs_path=vs_path, chat_history=[], streaming=True): - print(resp["result"][last_print_len:], end="", flush=True) + logger.info(resp["result"][last_print_len:], end="", flush=True) last_print_len = len(resp["result"]) source_text = [f"""出处 [{inum + 1}] {os.path.split(doc.metadata['source'])[-1]}：\n\n{doc.page_content}\n\n""" # f"""相关度：{doc.metadata['score']}\n\n""" for inum, doc in enumerate(resp["source_documents"])] - print("\n\n" + "\n\n".join(source_text)) + logger.info("\n\n" + "\n\n".join(source_text)) pass diff --git a/cli_demo.py b/cli_demo.py index 245412e..eb0f7e2 100644 --- a/cli_demo.py +++ b/cli_demo.py @@ -31,7 +31,7 @@ if __name__ == "__main__": chat_history=history, streaming=STREAMING): if STREAMING: - logger.info(resp["result"][last_print_len:], end="", flush=True) + logger.info(resp["result"][last_print_len:]) last_print_len = len(resp["result"]) else: logger.info(resp["result"]) diff --git a/configs/model_config.py b/configs/model_config.py index 41f32de..48bca8e 100644 --- a/configs/model_config.py +++ b/configs/model_config.py @@ -69,6 +69,9 @@ LLM_HISTORY_LEN = 3 # return top-k text chunk from vector store VECTOR_SEARCH_TOP_K = 5 +# 如果为0，则不生效，经测试小于500值的结果更精准 +VECTOR_SEARCH_SCORE_THRESHOLD = 0 + NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data") FLAG_USER_NAME = uuid.uuid4().hex @@ -79,4 +82,4 @@ llm device: {LLM_DEVICE} embedding device: {EMBEDDING_DEVICE} dir: {os.path.dirname(os.path.dirname(__file__))} flagging username: {FLAG_USER_NAME} -""") \ No newline at end of file +""") diff --git a/img/qr_code_14.jpg b/img/qr_code_14.jpg deleted file mode 100644 index 5aa8e2e..0000000 Binary files a/img/qr_code_14.jpg and /dev/null differ diff --git a/img/qr_code_15.jpg b/img/qr_code_15.jpg new file mode 100644 index 0000000..74d5c89 Binary files /dev/null and b/img/qr_code_15.jpg differ diff --git a/models/chatglm_llm.py b/models/chatglm_llm.py index 0cac961..ec2c04e 100644 --- a/models/chatglm_llm.py +++ b/models/chatglm_llm.py @@ -125,7 +125,7 @@ class ChatGLM(LLM): prefix_encoder_file.close() model_config.pre_seq_len = prefix_encoder_config['pre_seq_len'] model_config.prefix_projection = prefix_encoder_config['prefix_projection'] - except Exception as e: + except Exception as e: logger.error(f"加载PrefixEncoder config.json失败: {e}") self.model = AutoModel.from_pretrained(model_name_or_path, config=model_config, trust_remote_code=True, **kwargs) @@ -163,7 +163,7 @@ class ChatGLM(LLM): new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v self.model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict) self.model.transformer.prefix_encoder.float() - except Exception as e: + except Exception as e: logger.error(f"加载PrefixEncoder模型参数失败:{e}") self.model = self.model.eval() diff --git a/textsplitter/chinese_text_splitter.py b/textsplitter/chinese_text_splitter.py index c544f93..e7694df 100644 --- a/textsplitter/chinese_text_splitter.py +++ b/textsplitter/chinese_text_splitter.py @@ -5,9 +5,10 @@ from configs.model_config import SENTENCE_SIZE class ChineseTextSplitter(CharacterTextSplitter): - def __init__(self, pdf: bool = False, **kwargs): + def __init__(self, pdf: bool = False, sentence_size: int = None, **kwargs): super().__init__(**kwargs) self.pdf = pdf + self.sentence_size = sentence_size def split_text1(self, text: str) -> List[str]: if self.pdf: @@ -23,7 +24,7 @@ class ChineseTextSplitter(CharacterTextSplitter): sent_list.append(ele) return sent_list - def split_text(self, text: str) -> List[str]: + def split_text(self, text: str) -> List[str]: ##此处需要进一步优化逻辑 if self.pdf: text = re.sub(r"\n{3,}", r"\n", text) text = re.sub('\s', " ", text) @@ -38,15 +39,15 @@ class ChineseTextSplitter(CharacterTextSplitter): # 很多规则中会考虑分号;，但是这里我把它忽略不计，破折号、英文双引号等同样忽略，需要的再做些简单调整即可。 ls = [i for i in text.split("\n") if i] for ele in ls: - if len(ele) > SENTENCE_SIZE: + if len(ele) > self.sentence_size: ele1 = re.sub(r'([,，.]["’”」』]{0,2})([^,，.])', r'\1\n\2', ele) ele1_ls = ele1.split("\n") for ele_ele1 in ele1_ls: - if len(ele_ele1) > SENTENCE_SIZE: + if len(ele_ele1) > self.sentence_size: ele_ele2 = re.sub(r'([\n]{1,}| {2,}["’”」』]{0,2})([^\s])', r'\1\n\2', ele_ele1) ele2_ls = ele_ele2.split("\n") for ele_ele2 in ele2_ls: - if len(ele_ele2) > SENTENCE_SIZE: + if len(ele_ele2) > self.sentence_size: ele_ele3 = re.sub('( ["’”」』]{0,2})([^ ])', r'\1\n\2', ele_ele2) ele2_id = ele2_ls.index(ele_ele2) ele2_ls = ele2_ls[:ele2_id] + [i for i in ele_ele3.split("\n") if i] + ele2_ls[ diff --git a/webui.py b/webui.py index ff431a4..3788a18 100644 --- a/webui.py +++ b/webui.py @@ -4,9 +4,10 @@ import shutil from chains.local_doc_qa import LocalDocQA from configs.model_config import * import nltk - + nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path + def get_vs_list(): lst_default = ["新建知识库"] if not os.path.exists(VS_ROOT_PATH): @@ -28,14 +29,13 @@ local_doc_qa = LocalDocQA() flag_csv_logger = gr.CSVLogger() -def get_answer(query, vs_path, history, mode, - streaming: bool = STREAMING): - if mode == "知识库问答" and vs_path: + +def get_answer(query, vs_path, history, mode, score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD, + vector_search_top_k=VECTOR_SEARCH_TOP_K, chunk_conent: bool = True, + chunk_size=CHUNK_SIZE, streaming: bool = STREAMING): + if mode == "知识库问答" and os.path.exists(vs_path): for resp, history in local_doc_qa.get_knowledge_based_answer( - query=query, - vs_path=vs_path, - chat_history=history, - streaming=streaming): + query=query, vs_path=vs_path, chat_history=history, streaming=streaming): source = "\n\n" source += "".join( [f"""
出处 [{i + 1}] {os.path.split(doc.metadata["source"])[-1]}\n""" @@ -45,15 +45,34 @@ def get_answer(query, vs_path, history, mode, enumerate(resp["source_documents"])]) history[-1][-1] += source yield history, "" + elif mode == "知识库测试" and os.path.exists(vs_path): + resp, prompt = local_doc_qa.get_knowledge_based_conent_test(query=query, vs_path=vs_path, + score_threshold=score_threshold, + vector_search_top_k=vector_search_top_k, + chunk_conent=chunk_conent, + chunk_size=chunk_size) + if not resp["source_documents"]: + yield history + [[query, + "根据您的设定，没有匹配到任何内容，请确认您设置的score阈值是否过小或其他参数是否正确！"]], "" + else: + source = "".join( + [ + f"""
[score值]：{doc.metadata["score"]} - （{i + 1}）[出处]： {os.path.split(doc.metadata["source"])[-1]}\n""" + f"""{doc.page_content}\n""" + f"""
""" + for i, doc in + enumerate(resp["source_documents"])]) + history.append([query, prompt + source]) + yield history, "" else: - for resp, history in local_doc_qa.llm._call(query, history, - streaming=streaming): + for resp, history in local_doc_qa.llm._call(query, history, streaming=streaming): history[-1][-1] = resp + ( "\n\n当前知识库为空，如需基于知识库进行问答，请先加载知识库后，再进行提问。" if mode == "知识库问答" else "") yield history, "" logger.info(f"flagging: username={FLAG_USER_NAME},query={query},vs_path={vs_path},mode={mode},history={history}") flag_csv_logger.flag([query, vs_path, history, mode], username=FLAG_USER_NAME) + def init_model(): try: local_doc_qa.init_cfg() @@ -66,7 +85,7 @@ def init_model(): reply = """模型未成功加载，请到页面左上角"模型配置"选项卡中重新选择后点击"加载模型"按钮""" if str(e) == "Unknown platform: darwin": logger.info("该报错可能因为您使用的是 macOS 操作系统，需先下载模型至本地后执行 Web UI，具体方法请参考项目 README 中本地部署方法及常见问题：" - " https://github.com/imClumsyPanda/langchain-ChatGLM") + " https://github.com/imClumsyPanda/langchain-ChatGLM") else: logger.info(reply) return reply @@ -89,19 +108,23 @@ def reinit_model(llm_model, embedding_model, llm_history_len, use_ptuning_v2, us return history + [[None, model_status]] -def get_vector_store(vs_id, files, history): +def get_vector_store(vs_id, files, sentence_size, history, one_conent, one_content_segmentation): vs_path = os.path.join(VS_ROOT_PATH, vs_id) filelist = [] if not os.path.exists(os.path.join(UPLOAD_ROOT_PATH, vs_id)): os.makedirs(os.path.join(UPLOAD_ROOT_PATH, vs_id)) - for file in files: - filename = os.path.split(file.name)[-1] - shutil.move(file.name, os.path.join(UPLOAD_ROOT_PATH, vs_id, filename)) - filelist.append(os.path.join(UPLOAD_ROOT_PATH, vs_id, filename)) if local_doc_qa.llm and local_doc_qa.embeddings: - vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(filelist, vs_path) + if isinstance(files, list) and one_conent is None: + for file in files: + filename = os.path.split(file.name)[-1] + shutil.move(file.name, os.path.join(UPLOAD_ROOT_PATH, vs_id, filename)) + filelist.append(os.path.join(UPLOAD_ROOT_PATH, vs_id, filename)) + vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(filelist, vs_path, sentence_size) + else: + vs_path, loaded_files = local_doc_qa.one_knowledge_add(vs_path, files, one_conent, one_content_segmentation, + sentence_size) if len(loaded_files): - file_status = f"已上传 {'、'.join([os.path.split(i)[-1] for i in loaded_files])} 至知识库，并已加载知识库，请开始提问" + file_status = f"已添加 {'、'.join([os.path.split(i)[-1] for i in loaded_files])} 内容至知识库，并已加载知识库，请开始提问" else: file_status = "文件未成功加载，请重新上传文件" else: @@ -111,7 +134,6 @@ def get_vector_store(vs_id, files, history): return vs_path, None, history + [[None, file_status]] - def change_vs_name_input(vs_id, history): if vs_id == "新建知识库": return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), None, history @@ -119,25 +141,45 @@ def change_vs_name_input(vs_id, history): file_status = f"已加载知识库{vs_id}，请开始提问" return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), os.path.join(VS_ROOT_PATH, vs_id), history + [ - [None, file_status]] + [None, file_status]] -def change_mode(mode): +def change_mode(mode, history): if mode == "知识库问答": - return gr.update(visible=True) + return gr.update(visible=True), gr.update(visible=False), history + [[None, + "【注意】：现在是知识库问答模式，您输入的任何查询都将进行知识库查询，然后会自动整理知识库关联内容进入模型查询！！！"]] + elif mode == "知识库测试": + return gr.update(visible=True), gr.update(visible=True), [[None, + "【注意】：现在是知识库测试模式，您输入的任何查询都将进行知识库查询，并仅输出知识库匹配出的内容及相似度分值和及输入的文本源路径，查询的内容并不会进入模型查询！！！如果单条内容入库，内容如未分段，则内容越多越会稀释各查询内容与之关联的score阈值。单条内容长度在100-150左右较为合理。"]] else: - return gr.update(visible=False) + return gr.update(visible=False), gr.update(visible=False), history + + +def change_chunk_conent(mode, label_conent, history): + conent = "" + if "chunk_conent" in label_conent: + conent = "搜索结果上下文关联" + elif "one_content_segmentation" in label_conent: # 这里没用上，可以先留着 + conent = "内容分段入库" + + if mode: + return gr.update(visible=True), history + [[None, f"【已开启{conent}】"]] + else: + return gr.update(visible=False), history + [[None, f"[已关闭{conent}]"]] def add_vs_name(vs_name, vs_list, chatbot): if vs_name in vs_list: vs_status = "与已有知识库名称冲突，请重新选择其他名称后提交" chatbot = chatbot + [[None, vs_status]] - return gr.update(visible=True), vs_list,gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), chatbot + return gr.update(visible=True), vs_list, gr.update(visible=True), gr.update(visible=True), gr.update( + visible=False), chatbot else: vs_status = f"""已新增知识库"{vs_name}",将在上传文件并载入成功后进行存储。请在开始对话前，先完成文件上传。 """ chatbot = chatbot + [[None, vs_status]] - return gr.update(visible=True, choices= [vs_name] + vs_list, value=vs_name), [vs_name]+vs_list, gr.update(visible=False), gr.update(visible=False), gr.update(visible=True),chatbot + return gr.update(visible=True, choices=[vs_name] + vs_list, value=vs_name), [vs_name] + vs_list, gr.update( + visible=False), gr.update(visible=False), gr.update(visible=True), chatbot + block_css = """.importantButton { background: linear-gradient(45deg, #7e0570,#5d1c99, #6e00ff) !important; @@ -168,24 +210,25 @@ default_path = os.path.join(VS_ROOT_PATH, vs_list[0]) if len(vs_list) > 1 else " with gr.Blocks(css=block_css) as demo: vs_path, file_status, model_status, vs_list = gr.State(default_path), gr.State(""), gr.State( model_status), gr.State(vs_list) - + gr.Markdown(webui_title) with gr.Tab("对话"): with gr.Row(): with gr.Column(scale=10): chatbot = gr.Chatbot([[None, init_message], [None, model_status.value]], elem_id="chat-box", - show_label=False).style(height=750) + show_label=False).style(height=650) query = gr.Textbox(show_label=False, placeholder="请输入提问内容，按回车进行提交").style(container=False) with gr.Column(scale=5): mode = gr.Radio(["LLM 对话", "知识库问答"], label="请选择使用模式", value="知识库问答", ) + knowledge_set = gr.Accordion("知识库设定", visible=False) vs_setting = gr.Accordion("配置知识库") mode.change(fn=change_mode, - inputs=mode, - outputs=vs_setting) + inputs=[mode, chatbot], + outputs=[vs_setting, knowledge_set, chatbot]) with vs_setting: select_vs = gr.Dropdown(vs_list.value, label="请选择要加载的知识库", @@ -195,12 +238,96 @@ with gr.Blocks(css=block_css) as demo: vs_name = gr.Textbox(label="请输入新建知识库名称", lines=1, interactive=True, - visible=True if default_path=="" else False) - vs_add = gr.Button(value="添加至知识库选项", visible=True if default_path=="" else False) - file2vs = gr.Column(visible=False if default_path=="" else True) + visible=True) + vs_add = gr.Button(value="添加至知识库选项", visible=True) + file2vs = gr.Column(visible=False) with file2vs: # load_vs = gr.Button("加载知识库") gr.Markdown("向知识库中添加文件") + sentence_size = gr.Number(value=SENTENCE_SIZE, precision=0, + label="文本入库分句长度限制", + interactive=True, visible=True) + with gr.Tab("上传文件"): + files = gr.File(label="添加文件", + file_types=['.txt', '.md', '.docx', '.pdf'], + file_count="multiple", + show_label=False) + load_file_button = gr.Button("上传文件并加载知识库") + with gr.Tab("上传文件夹"): + folder_files = gr.File(label="添加文件", + # file_types=['.txt', '.md', '.docx', '.pdf'], + file_count="directory", + show_label=False) + load_folder_button = gr.Button("上传文件夹并加载知识库") + vs_add.click(fn=add_vs_name, + inputs=[vs_name, vs_list, chatbot], + outputs=[select_vs, vs_list, vs_name, vs_add, file2vs, chatbot]) + select_vs.change(fn=change_vs_name_input, + inputs=[select_vs, chatbot], + outputs=[vs_name, vs_add, file2vs, vs_path, chatbot]) + load_file_button.click(get_vector_store, + show_progress=True, + inputs=[select_vs, files, sentence_size, chatbot, vs_setting, file2vs], + outputs=[vs_path, files, chatbot], ) + load_folder_button.click(get_vector_store, + show_progress=True, + inputs=[select_vs, folder_files, sentence_size, chatbot, vs_setting, + file2vs], + outputs=[vs_path, folder_files, chatbot], ) + flag_csv_logger.setup([query, vs_path, chatbot, mode], "flagged") + query.submit(get_answer, + [query, vs_path, chatbot, mode], + [chatbot, query]) + with gr.Tab("知识库测试"): + with gr.Row(): + with gr.Column(scale=10): + chatbot = gr.Chatbot([[None, + "【注意】：现在是知识库测试模式，您输入的任何查询都将进行知识库查询，并仅输出知识库匹配出的内容及相似度分值和及输入的文本源路径，查询的内容并不会进入模型查询！！！如果单条内容入库，内容如未分段，则内容越多越会稀释各查询内容与之关联的score阈值。单条内容长度在100-150左右较为合理。"]], + elem_id="chat-box", + show_label=False).style(height=750) + query = gr.Textbox(show_label=False, + placeholder="请输入提问内容，按回车进行提交").style(container=False) + with gr.Column(scale=5): + mode = gr.Radio(["知识库问答", "知识库测试"], + label="请选择使用模式", + value="知识库测试", ) + knowledge_set = gr.Accordion("知识库设定", visible=True) + vs_setting = gr.Accordion("配置知识库", visible=True) + mode.change(fn=change_mode, + inputs=[mode, chatbot], + outputs=[vs_setting, knowledge_set, chatbot]) + with knowledge_set: + score_threshold = gr.Number(value=VECTOR_SEARCH_SCORE_THRESHOLD, + label="score阈值，分值越低匹配度越高", + precision=0, interactive=True) + vector_search_top_k = gr.Number(value=VECTOR_SEARCH_TOP_K, precision=0, + label="获取知识库内容条数", interactive=True) + chunk_conent = gr.Checkbox(value=False, + label="是否启用上下文关联", + interactive=True) + chunk_sizes = gr.Number(value=CHUNK_SIZE, precision=0, + label="匹配单段内容的连接上下文长度", + interactive=True, visible=False) + chunk_conent.change(fn=change_chunk_conent, + inputs=[chunk_conent, gr.Textbox(value="chunk_conent", visible=False), chatbot], + outputs=[chunk_sizes, chatbot]) + with vs_setting: + select_vs = gr.Dropdown(vs_list.value, + label="请选择要加载的知识库", + interactive=True, + value=vs_list.value[0] if len(vs_list.value) > 0 else None) + vs_name = gr.Textbox(label="请输入新建知识库名称", + lines=1, + interactive=True, + visible=True) + vs_add = gr.Button(value="添加至知识库选项", visible=True) + file2vs = gr.Column(visible=False) + with file2vs: + # load_vs = gr.Button("加载知识库") + gr.Markdown("向知识库中添加单条内容或文件") + sentence_size = gr.Number(value=SENTENCE_SIZE, precision=0, + label="文本入库分句长度限制", + interactive=True, visible=True) with gr.Tab("上传文件"): files = gr.File(label="添加文件", file_types=['.txt', '.md', '.docx', '.pdf'], @@ -212,38 +339,46 @@ with gr.Blocks(css=block_css) as demo: folder_files = gr.File(label="添加文件", # file_types=['.txt', '.md', '.docx', '.pdf'], file_count="directory", - show_label=False - ) + show_label=False) load_folder_button = gr.Button("上传文件夹并加载知识库") - # load_vs.click(fn=) + with gr.Tab("添加单条内容"): + one_title = gr.Textbox(label="标题", placeholder="请输入要添加单条段落的标题", lines=1) + one_conent = gr.Textbox(label="内容", placeholder="请输入要添加单条段落的内容", lines=5) + one_content_segmentation = gr.Checkbox(value=True, label="禁止内容分句入库", + interactive=True) + load_conent_button = gr.Button("添加内容并加载知识库") + # 将上传的文件保存到content文件夹下,并更新下拉框 vs_add.click(fn=add_vs_name, inputs=[vs_name, vs_list, chatbot], - outputs=[select_vs, vs_list,vs_name,vs_add, file2vs,chatbot]) + outputs=[select_vs, vs_list, vs_name, vs_add, file2vs, chatbot]) select_vs.change(fn=change_vs_name_input, inputs=[select_vs, chatbot], outputs=[vs_name, vs_add, file2vs, vs_path, chatbot]) - # 将上传的文件保存到content文件夹下,并更新下拉框 load_file_button.click(get_vector_store, show_progress=True, - inputs=[select_vs, files, chatbot], - outputs=[vs_path, files, chatbot], - ) + inputs=[select_vs, files, sentence_size, chatbot, vs_setting, file2vs], + outputs=[vs_path, files, chatbot], ) load_folder_button.click(get_vector_store, show_progress=True, - inputs=[select_vs, folder_files, chatbot], - outputs=[vs_path, folder_files, chatbot], - ) + inputs=[select_vs, folder_files, sentence_size, chatbot, vs_setting, + file2vs], + outputs=[vs_path, folder_files, chatbot], ) + load_conent_button.click(get_vector_store, + show_progress=True, + inputs=[select_vs, one_title, sentence_size, chatbot, + one_conent, one_content_segmentation], + outputs=[vs_path, files, chatbot], ) flag_csv_logger.setup([query, vs_path, chatbot, mode], "flagged") query.submit(get_answer, - [query, vs_path, chatbot, mode], + [query, vs_path, chatbot, mode, score_threshold, vector_search_top_k, chunk_conent, + chunk_sizes], [chatbot, query]) with gr.Tab("模型配置"): llm_model = gr.Radio(llm_model_dict_list, label="LLM 模型", value=LLM_MODEL, interactive=True) - llm_history_len = gr.Slider(0, - 10, + llm_history_len = gr.Slider(0, 10, value=LLM_HISTORY_LEN, step=1, label="LLM 对话轮数", @@ -258,19 +393,12 @@ with gr.Blocks(css=block_css) as demo: label="Embedding 模型", value=EMBEDDING_MODEL, interactive=True) - top_k = gr.Slider(1, - 20, - value=VECTOR_SEARCH_TOP_K, - step=1, - label="向量匹配 top k", - interactive=True) + top_k = gr.Slider(1, 20, value=VECTOR_SEARCH_TOP_K, step=1, + label="向量匹配 top k", interactive=True) load_model_button = gr.Button("重新加载模型") - load_model_button.click(reinit_model, - show_progress=True, - inputs=[llm_model, embedding_model, llm_history_len, use_ptuning_v2, use_lora, top_k, - chatbot], - outputs=chatbot - ) + load_model_button.click(reinit_model, show_progress=True, + inputs=[llm_model, embedding_model, llm_history_len, use_ptuning_v2, use_lora, + top_k, chatbot], outputs=chatbot) (demo .queue(concurrency_count=3) @@ -278,4 +406,4 @@ with gr.Blocks(css=block_css) as demo: server_port=7860, show_api=False, share=False, - inbrowser=False)) \ No newline at end of file + inbrowser=False))