merge master
This commit is contained in:
imClumsyPanda
commit
fbdc62d910
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@ -178,6 +178,6 @@ Web UI 可以实现如下功能：
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- [ ] 实现调用 API 的 Web UI Demo
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## 项目交流群
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🎉 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。
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@ -12,43 +12,33 @@ from utils import torch_gc
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from tqdm import tqdm
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from pypinyin import lazy_pinyin
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DEVICE_ = EMBEDDING_DEVICE
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DEVICE_ID = "0" if torch.cuda.is_available() else None
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DEVICE = f"{DEVICE_}:{DEVICE_ID}" if DEVICE_ID else DEVICE_
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def load_file(filepath):
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def load_file(filepath, sentence_size=SENTENCE_SIZE):
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if filepath.lower().endswith(".md"):
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loader = UnstructuredFileLoader(filepath, mode="elements")
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docs = loader.load()
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elif filepath.lower().endswith(".pdf"):
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loader = UnstructuredFileLoader(filepath)
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textsplitter = ChineseTextSplitter(pdf=True)
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textsplitter = ChineseTextSplitter(pdf=True, sentence_size=sentence_size)
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docs = loader.load_and_split(textsplitter)
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else:
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loader = UnstructuredFileLoader(filepath, mode="elements")
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textsplitter = ChineseTextSplitter(pdf=False)
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textsplitter = ChineseTextSplitter(pdf=False, sentence_size=sentence_size)
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docs = loader.load_and_split(text_splitter=textsplitter)
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return docs
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def generate_prompt(related_docs: List[str],
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query: str,
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def generate_prompt(related_docs: List[str], query: str,
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prompt_template=PROMPT_TEMPLATE) -> str:
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context = "\n".join([doc.page_content for doc in related_docs])
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prompt = prompt_template.replace("{question}", query).replace("{context}", context)
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return prompt
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def get_docs_with_score(docs_with_score):
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docs = []
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for doc, score in docs_with_score:
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doc.metadata["score"] = score
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docs.append(doc)
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return docs
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def seperate_list(ls: List[int]) -> List[List[int]]:
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lists = []
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ls1 = [ls[0]]
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@ -63,18 +53,24 @@ def seperate_list(ls: List[int]) -> List[List[int]]:
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def similarity_search_with_score_by_vector(
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self, embedding: List[float], k: int = 4,
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self, embedding: List[float], k: int = 4
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) -> List[Tuple[Document, float]]:
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scores, indices = self.index.search(np.array([embedding], dtype=np.float32), k)
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docs = []
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id_set = set()
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store_len = len(self.index_to_docstore_id)
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for j, i in enumerate(indices[0]):
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if i == -1:
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if i == -1 or 0 < self.score_threshold < scores[0][j]:
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# This happens when not enough docs are returned.
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continue
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_id = self.index_to_docstore_id[i]
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doc = self.docstore.search(_id)
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if not self.chunk_conent:
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if not isinstance(doc, Document):
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raise ValueError(f"Could not find document for id {_id}, got {doc}")
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doc.metadata["score"] = int(scores[0][j])
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docs.append(doc)
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continue
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id_set.add(i)
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docs_len = len(doc.page_content)
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for k in range(1, max(i, store_len - i)):
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@ -91,6 +87,10 @@ def similarity_search_with_score_by_vector(
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id_set.add(l)
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if break_flag:
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break
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if not self.chunk_conent:
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return docs
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if len(id_set) == 0 and self.score_threshold > 0:
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return []
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id_list = sorted(list(id_set))
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id_lists = seperate_list(id_list)
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for id_seq in id_lists:
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@ -105,7 +105,8 @@ def similarity_search_with_score_by_vector(
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if not isinstance(doc, Document):
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raise ValueError(f"Could not find document for id {_id}, got {doc}")
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doc_score = min([scores[0][id] for id in [indices[0].tolist().index(i) for i in id_seq if i in indices[0]]])
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docs.append((doc, doc_score))
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doc.metadata["score"] = int(doc_score)
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docs.append(doc)
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torch_gc()
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return docs
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@ -115,6 +116,8 @@ class LocalDocQA:
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embeddings: object = None
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top_k: int = VECTOR_SEARCH_TOP_K
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chunk_size: int = CHUNK_SIZE
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chunk_conent: bool = True
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score_threshold: int = VECTOR_SEARCH_SCORE_THRESHOLD
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def init_cfg(self,
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embedding_model: str = EMBEDDING_MODEL,
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@ -137,7 +140,8 @@ class LocalDocQA:
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def init_knowledge_vector_store(self,
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filepath: str or List[str],
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vs_path: str or os.PathLike = None):
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vs_path: str or os.PathLike = None,
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sentence_size=SENTENCE_SIZE):
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loaded_files = []
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failed_files = []
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if isinstance(filepath, str):
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@ -147,40 +151,41 @@ class LocalDocQA:
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elif os.path.isfile(filepath):
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file = os.path.split(filepath)[-1]
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try:
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docs = load_file(filepath)
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print(f"{file} 已成功加载")
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docs = load_file(filepath, sentence_size)
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logger.info(f"{file} 已成功加载")
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loaded_files.append(filepath)
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except Exception as e:
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print(e)
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print(f"{file} 未能成功加载")
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logger.error(e)
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logger.info(f"{file} 未能成功加载")
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return None
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elif os.path.isdir(filepath):
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docs = []
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for file in tqdm(os.listdir(filepath), desc="加载文件"):
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fullfilepath = os.path.join(filepath, file)
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try:
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docs += load_file(fullfilepath)
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docs += load_file(fullfilepath, sentence_size)
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loaded_files.append(fullfilepath)
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except Exception as e:
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logger.error(e)
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failed_files.append(file)
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if len(failed_files) > 0:
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print("以下文件未能成功加载：")
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logger.info("以下文件未能成功加载：")
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for file in failed_files:
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print(file, end="\n")
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logger.info(file, end="\n")
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else:
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docs = []
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for file in filepath:
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try:
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docs += load_file(file)
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print(f"{file} 已成功加载")
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logger.info(f"{file} 已成功加载")
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loaded_files.append(file)
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except Exception as e:
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print(e)
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print(f"{file} 未能成功加载")
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logger.error(e)
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logger.info(f"{file} 未能成功加载")
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if len(docs) > 0:
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print("文件加载完毕，正在生成向量库")
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logger.info("文件加载完毕，正在生成向量库")
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if vs_path and os.path.isdir(vs_path):
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vector_store = FAISS.load_local(vs_path, self.embeddings)
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vector_store.add_documents(docs)
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@ -189,38 +194,46 @@ class LocalDocQA:
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if not vs_path:
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vs_path = os.path.join(VS_ROOT_PATH,
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f"""{"".join(lazy_pinyin(os.path.splitext(file)[0]))}_FAISS_{datetime.datetime.now().strftime("%Y%m%d_%H%M%S")}""")
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vector_store = FAISS.from_documents(docs, self.embeddings)
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vector_store = FAISS.from_documents(docs, self.embeddings) # docs 为Document列表
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torch_gc()
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vector_store.save_local(vs_path)
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return vs_path, loaded_files
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else:
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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。")
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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。")
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return None, loaded_files
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def get_knowledge_based_answer(self,
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query,
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vs_path,
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chat_history=[],
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streaming: bool = STREAMING):
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def one_knowledge_add(self, vs_path, one_title, one_conent, one_content_segmentation, sentence_size):
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try:
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if not vs_path or not one_title or not one_conent:
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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！")
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return None, [one_title]
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docs = [Document(page_content=one_conent+"\n", metadata={"source": one_title})]
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if not one_content_segmentation:
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text_splitter = ChineseTextSplitter(pdf=False, sentence_size=sentence_size)
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docs = text_splitter.split_documents(docs)
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if os.path.isdir(vs_path):
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vector_store = FAISS.load_local(vs_path, self.embeddings)
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vector_store.add_documents(docs)
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else:
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vector_store = FAISS.from_documents(docs, self.embeddings) ##docs 为Document列表
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torch_gc()
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vector_store.save_local(vs_path)
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return vs_path, [one_title]
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except Exception as e:
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logger.error(e)
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return None, [one_title]
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def get_knowledge_based_answer(self, query, vs_path, chat_history=[], streaming: bool = STREAMING):
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vector_store = FAISS.load_local(vs_path, self.embeddings)
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FAISS.similarity_search_with_score_by_vector = similarity_search_with_score_by_vector
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vector_store.chunk_size = self.chunk_size
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related_docs_with_score = vector_store.similarity_search_with_score(query,
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k=self.top_k)
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related_docs = get_docs_with_score(related_docs_with_score)
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vector_store.chunk_conent = self.chunk_conent
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vector_store.score_threshold = self.score_threshold
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related_docs_with_score = vector_store.similarity_search_with_score(query, k=self.top_k)
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torch_gc()
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prompt = generate_prompt(related_docs, query)
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prompt = generate_prompt(related_docs_with_score, query)
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# if streaming:
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# for result, history in self.llm._stream_call(prompt=prompt,
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# history=chat_history):
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# history[-1][0] = query
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# response = {"query": query,
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# "result": result,
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# "source_documents": related_docs}
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# yield response, history
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# else:
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for result, history in self.llm._call(prompt=prompt,
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history=chat_history,
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streaming=streaming):
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@ -228,10 +241,35 @@ class LocalDocQA:
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history[-1][0] = query
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response = {"query": query,
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"result": result,
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"source_documents": related_docs}
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"source_documents": related_docs_with_score}
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yield response, history
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torch_gc()
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# query 查询内容
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# vs_path 知识库路径
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# chunk_conent 是否启用上下文关联
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# score_threshold 搜索匹配score阈值
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# vector_search_top_k 搜索知识库内容条数，默认搜索5条结果
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# chunk_sizes 匹配单段内容的连接上下文长度
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def get_knowledge_based_conent_test(self, query, vs_path, chunk_conent,
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score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
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vector_search_top_k=VECTOR_SEARCH_TOP_K, chunk_size=CHUNK_SIZE):
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vector_store = FAISS.load_local(vs_path, self.embeddings)
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FAISS.similarity_search_with_score_by_vector = similarity_search_with_score_by_vector
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vector_store.chunk_conent = chunk_conent
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vector_store.score_threshold = score_threshold
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vector_store.chunk_size = chunk_size
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related_docs_with_score = vector_store.similarity_search_with_score(query, k=vector_search_top_k)
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if not related_docs_with_score:
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response = {"query": query,
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"source_documents": []}
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return response, ""
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torch_gc()
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prompt = "\n".join([doc.page_content for doc in related_docs_with_score])
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response = {"query": query,
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"source_documents": related_docs_with_score}
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return response, prompt
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if __name__ == "__main__":
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local_doc_qa = LocalDocQA()
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@ -243,11 +281,11 @@ if __name__ == "__main__":
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vs_path=vs_path,
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chat_history=[],
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streaming=True):
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print(resp["result"][last_print_len:], end="", flush=True)
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logger.info(resp["result"][last_print_len:], end="", flush=True)
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last_print_len = len(resp["result"])
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source_text = [f"""出处 [{inum + 1}] {os.path.split(doc.metadata['source'])[-1]}：\n\n{doc.page_content}\n\n"""
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# f"""相关度：{doc.metadata['score']}\n\n"""
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for inum, doc in
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enumerate(resp["source_documents"])]
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print("\n\n" + "\n\n".join(source_text))
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logger.info("\n\n" + "\n\n".join(source_text))
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pass
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@ -31,7 +31,7 @@ if __name__ == "__main__":
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chat_history=history,
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streaming=STREAMING):
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if STREAMING:
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logger.info(resp["result"][last_print_len:], end="", flush=True)
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logger.info(resp["result"][last_print_len:])
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last_print_len = len(resp["result"])
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else:
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logger.info(resp["result"])
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@ -69,6 +69,9 @@ LLM_HISTORY_LEN = 3
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# return top-k text chunk from vector store
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VECTOR_SEARCH_TOP_K = 5
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# 如果为0，则不生效，经测试小于500值的结果更精准
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VECTOR_SEARCH_SCORE_THRESHOLD = 0
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NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")
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FLAG_USER_NAME = uuid.uuid4().hex
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After Width: | Height: | Size: 268 KiB |
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@ -5,9 +5,10 @@ from configs.model_config import SENTENCE_SIZE
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class ChineseTextSplitter(CharacterTextSplitter):
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def __init__(self, pdf: bool = False, **kwargs):
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def __init__(self, pdf: bool = False, sentence_size: int = None, **kwargs):
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super().__init__(**kwargs)
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self.pdf = pdf
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self.sentence_size = sentence_size
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def split_text1(self, text: str) -> List[str]:
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if self.pdf:
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@ -23,7 +24,7 @@ class ChineseTextSplitter(CharacterTextSplitter):
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sent_list.append(ele)
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return sent_list
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def split_text(self, text: str) -> List[str]:
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def split_text(self, text: str) -> List[str]: ##此处需要进一步优化逻辑
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if self.pdf:
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text = re.sub(r"\n{3,}", r"\n", text)
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text = re.sub('\s', " ", text)
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@ -38,15 +39,15 @@ class ChineseTextSplitter(CharacterTextSplitter):
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# 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。
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ls = [i for i in text.split("\n") if i]
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for ele in ls:
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if len(ele) > SENTENCE_SIZE:
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if len(ele) > self.sentence_size:
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ele1 = re.sub(r'([,，.]["’”」』]{0,2})([^,，.])', r'\1\n\2', ele)
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ele1_ls = ele1.split("\n")
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for ele_ele1 in ele1_ls:
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if len(ele_ele1) > SENTENCE_SIZE:
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if len(ele_ele1) > self.sentence_size:
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ele_ele2 = re.sub(r'([\n]{1,}| {2,}["’”」』]{0,2})([^\s])', r'\1\n\2', ele_ele1)
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ele2_ls = ele_ele2.split("\n")
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for ele_ele2 in ele2_ls:
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if len(ele_ele2) > SENTENCE_SIZE:
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if len(ele_ele2) > self.sentence_size:
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ele_ele3 = re.sub('( ["’”」』]{0,2})([^ ])', r'\1\n\2', ele_ele2)
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ele2_id = ele2_ls.index(ele_ele2)
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ele2_ls = ele2_ls[:ele2_id] + [i for i in ele_ele3.split("\n") if i] + ele2_ls[
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|
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240
webui.py
240
webui.py
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|
@ -7,6 +7,7 @@ import nltk
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nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
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def get_vs_list():
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lst_default = ["新建知识库"]
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if not os.path.exists(VS_ROOT_PATH):
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@ -28,14 +29,13 @@ local_doc_qa = LocalDocQA()
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flag_csv_logger = gr.CSVLogger()
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def get_answer(query, vs_path, history, mode,
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streaming: bool = STREAMING):
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if mode == "知识库问答" and vs_path:
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def get_answer(query, vs_path, history, mode, score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
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vector_search_top_k=VECTOR_SEARCH_TOP_K, chunk_conent: bool = True,
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chunk_size=CHUNK_SIZE, streaming: bool = STREAMING):
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if mode == "知识库问答" and os.path.exists(vs_path):
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for resp, history in local_doc_qa.get_knowledge_based_answer(
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query=query,
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vs_path=vs_path,
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chat_history=history,
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streaming=streaming):
|
||||
query=query, 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:
|
||||
source = "".join(
|
||||
[
|
||||
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):
|
||||
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;
|
||||
|
|
@ -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,
|
||||
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)
|
||||
|
|
|
|||
Loading…
Reference in New Issue