merge master

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。

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],
+def generate_prompt(related_docs: List[str], query: 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)
+                    docs = load_file(filepath, sentence_size)
-                    print(f"{file} 已成功加载")
+                    logger.info(f"{file} 已成功加载")
                     loaded_files.append(filepath)
                 except Exception as e:
-                    print(e)
+                    logger.error(e)
-                    print(f"{file} 未能成功加载")
+                    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)
+                    logger.error(e)
-                    print(f"{file} 未能成功加载")
+                    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,
+    def one_knowledge_add(self, vs_path, one_title, one_conent, one_content_segmentation, sentence_size):
-                                   query,
+        try:
-                                   vs_path,
+            if not vs_path or not one_title or not one_conent:
-                                   chat_history=[],
+                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！")
-                                   streaming: bool = STREAMING):
+                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,
+        vector_store.chunk_conent = self.chunk_conent
-                                                                            k=self.top_k)
+        vector_store.score_threshold = self.score_threshold
-        related_docs = get_docs_with_score(related_docs_with_score)
+        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

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"])

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

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[

webui.py

@@ -7,6 +7,7 @@ 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):
+def get_answer(query, vs_path, history, mode, score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
-    if mode == "知识库问答" and vs_path:
+               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,
+                query=query, vs_path=vs_path, chat_history=history, streaming=streaming):
-                vs_path=vs_path,
-                chat_history=history,
-                streaming=streaming):
             source = "\n\n"
             source += "".join(
                 [f"""<details> <summary>出处 [{i + 1}] {os.path.split(doc.metadata["source"])[-1]}</summary>\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:
-        for resp, history in local_doc_qa.llm._call(query, history,
+            source = "".join(
-                                                    streaming=streaming):
+                [
+                    f"""<details> <summary>[score值]：{doc.metadata["score"]} - （{i + 1}）[出处]：  {os.path.split(doc.metadata["source"])[-1]}</summary>\n"""
+                    f"""{doc.page_content}\n"""
+                    f"""</details>"""
+                    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):
             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()
@@ -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))
+    if local_doc_qa.llm and local_doc_qa.embeddings:
+        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))
-    if local_doc_qa.llm and local_doc_qa.embeddings:
+            vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(filelist, vs_path, sentence_size)
-        vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(filelist, vs_path)
+        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
@@ -122,22 +144,42 @@ def change_vs_name_input(vs_id, history):
             [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;
@@ -175,17 +217,18 @@ with gr.Blocks(css=block_css) as demo:
             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,
+                            inputs=[mode, chatbot],
-                            outputs=vs_setting)
+                            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)
+                                         visible=True)
-                    vs_add = gr.Button(value="添加至知识库选项",  visible=True if default_path=="" else False)                   
+                    vs_add = gr.Button(value="添加至知识库选项", visible=True)
-                    file2vs = gr.Column(visible=False if default_path=="" else 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])
                     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],
+                                           inputs=[select_vs, files, sentence_size, chatbot, vs_setting, file2vs],
-                                           outputs=[vs_path, files, chatbot],
+                                           outputs=[vs_path, files, chatbot], )
-                                           )
                     load_folder_button.click(get_vector_store,
                                              show_progress=True,
-                                             inputs=[select_vs, folder_files, chatbot],
+                                             inputs=[select_vs, folder_files, sentence_size, chatbot, vs_setting,
-                                             outputs=[vs_path, folder_files, chatbot],
+                                                     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,
+        llm_history_len = gr.Slider(0, 10,
-                                    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,
+        top_k = gr.Slider(1, 20, value=VECTOR_SEARCH_TOP_K, step=1,
-                          20,
+                          label="向量匹配 top k", interactive=True)
-                          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,
+        load_model_button.click(reinit_model, show_progress=True,
-                            show_progress=True,
+                                inputs=[llm_model, embedding_model, llm_history_len, use_ptuning_v2, use_lora,
-                            inputs=[llm_model, embedding_model, llm_history_len, use_ptuning_v2, use_lora, top_k,
+                                        top_k, chatbot], outputs=chatbot)
-                                    chatbot],
-                            outputs=chatbot
-                            )
 
 (demo
  .queue(concurrency_count=3)