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jinchat-server
提交 635aa2d2 作者: imClumsyPanda
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update webui.py

上级 57d78bd1
正在显示 包含 77 行增加 和 74 行删除
chains/local_doc_qa.py
......@@ -17,10 +17,6 @@ VECTOR_SEARCH_TOP_K = 6
# LLM input history length
LLM_HISTORY_LEN = 3
<<<<<<< HEAD
<<<<<<< HEAD
=======
>>>>>>> 7cc03c3 (feat: add api for knowledge_based QA)
def load_file(filepath):
if filepath.lower().endswith(".pdf"):
......@@ -33,11 +29,6 @@ def load_file(filepath):
docs = loader.load_and_split(text_splitter=textsplitter)
return docs
<<<<<<< HEAD
=======
>>>>>>> cba44ca (修复 webui.py 中 llm_history_len 和 vector_search_top_k 显示值与启动设置默认值不一致的问题)
=======
>>>>>>> 7cc03c3 (feat: add api for knowledge_based QA)
class LocalDocQA:
llm: object = None
......
webui.py
......@@ -12,18 +12,7 @@ VECTOR_SEARCH_TOP_K = 6
# LLM input history length
LLM_HISTORY_LEN = 3
<<<<<<< HEAD
=======
<<<<<<< HEAD
>>>>>>> f87a5f5 (fix bug in webui.py)
=======
# return top-k text chunk from vector store
VECTOR_SEARCH_TOP_K = 6
# LLM input history length
LLM_HISTORY_LEN = 3
>>>>>>> cba44ca (修复 webui.py 中 llm_history_len 和 vector_search_top_k 显示值与启动设置默认值不一致的问题)
def get_file_list():
if not os.path.exists("content"):
......@@ -31,7 +20,14 @@ def get_file_list():
return [f for f in os.listdir("content")]
def get_vs_list():
if not os.path.exists("vector_store"):
return []
return [f for f in os.listdir("vector_store")]
file_list = get_file_list()
vs_list = get_vs_list()
embedding_model_dict_list = list(embedding_model_dict.keys())
......@@ -40,22 +36,30 @@ llm_model_dict_list = list(llm_model_dict.keys())
local_doc_qa = LocalDocQA()
def upload_file(file):
def upload_file(file, chatbot):
if not os.path.exists("content"):
os.mkdir("content")
filename = os.path.basename(file.name)
shutil.move(file.name, "content/" + filename)
# file_list首位插入新上传的文件
file_list.insert(0, filename)
return gr.Dropdown.update(choices=file_list, value=filename)
status = "已将xx上传至xxx"
return chatbot + [None, status]
def get_answer(query, vs_path, history):
if vs_path:
resp, history = local_doc_qa.get_knowledge_based_answer(
query=query, vs_path=vs_path, chat_history=history)
source = "".join([f"""<details> <summary>出处 {i + 1}</summary>
{doc.page_content}
<b>所属文件：</b>{doc.metadata["source"]}
</details>""" for i, doc in enumerate(resp["source_documents"])])
history[-1][-1] += source
else:
history = history + [[None, "请先加载文件后，再进行提问。"]]
resp = local_doc_qa.llm._call(query)
history = history + [[None, resp + "\n如需基于知识库进行问答，请先加载知识库后，再进行提问。"]]
return history, ""
......@@ -68,6 +72,7 @@ def update_status(history, status):
def init_model():
try:
local_doc_qa.init_cfg()
local_doc_qa.llm._call("你好")
return """模型已成功加载，请选择文件后点击"加载文件"按钮"""
except Exception as e:
print(e)
......@@ -88,7 +93,6 @@ def reinit_model(llm_model, embedding_model, llm_history_len, use_ptuning_v2, to
return history + [[None, model_status]]
def get_vector_store(filepath, history):
if local_doc_qa.llm and local_doc_qa.embeddings:
vs_path = local_doc_qa.init_knowledge_vector_store(["content/" + filepath])
......@@ -120,71 +124,79 @@ webui_title = """
"""
init_message = """欢迎使用 langchain-ChatGLM Web UI，开始提问前，请依次如下 3 个步骤：
1. 选择语言模型、Embedding 模型及相关参数，如果使用ptuning-v2方式微调过模型，将PrefixEncoder模型放在ptuning-v2文件夹里并勾选相关选项，然后点击"重新加载模型"，并等待加载完成提示
1. 选择语言模型、Embedding 模型及相关参数，如果使用 ptuning-v2 方式微调过模型，将 PrefixEncoder 模型放在 ptuning-v2 文件夹里并勾选相关选项，然后点击"重新加载模型"，并等待加载完成提示
2. 上传或选择已有文件作为本地知识文档输入后点击"重新加载文档"，并等待加载完成提示
3. 输入要提交的问题后，点击回车提交 """
model_status = init_model()
with gr.Blocks(css=block_css) as demo:
vs_path, file_status, model_status = gr.State(""), gr.State(""), gr.State(model_status)
gr.Markdown(webui_title)
with gr.Row():
with gr.Column(scale=2):
chatbot = gr.Chatbot([[None, init_message], [None, model_status.value]],
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=1):
llm_model = gr.Radio(llm_model_dict_list,
label="LLM 模型",
value=LLM_MODEL,
interactive=True)
llm_history_len = gr.Slider(0,
10,
value=LLM_HISTORY_LEN,
step=1,
label="LLM history len",
interactive=True)
use_ptuning_v2 = gr.Checkbox(USE_PTUNING_V2,
label="使用p-tuning-v2微调过的模型",
interactive=True)
embedding_model = gr.Radio(embedding_model_dict_list,
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)
load_model_button = gr.Button("重新加载模型")
# with gr.Column():
with gr.Tab("select"):
selectFile = gr.Dropdown(file_list,
label="content file",
with gr.Tab("聊天"):
with gr.Row():
with gr.Column(scale=2):
chatbot = gr.Chatbot([[None, init_message], [None, model_status.value]],
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=1):
# with gr.Column():
# with gr.Tab("select"):
selectFile = gr.Dropdown(vs_list,
label="请选择要加载的知识库",
interactive=True,
value=file_list[0] if len(file_list) > 0 else None)
with gr.Tab("upload"):
file = gr.File(label="content file",
file_types=['.txt', '.md', '.docx', '.pdf']
) # .style(height=100)
load_file_button = gr.Button("加载文件")
value=vs_list[0] if len(vs_list) > 0 else None)
#
gr.Markdown("向知识库中添加文件")
with gr.Tab("上传文件"):
files = gr.File(label="向知识库中添加文件",
file_types=['.txt', '.md', '.docx', '.pdf'],
file_count="multiple"
) # .style(height=100)
with gr.Tab("上传文件夹"):
files = gr.File(label="向知识库中添加文件",
file_types=['.txt', '.md', '.docx', '.pdf'],
file_count="directory"
) # .style(height=100)
load_file_button = gr.Button("加载知识库")
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,
value=LLM_HISTORY_LEN,
step=1,
label="LLM history len",
interactive=True)
use_ptuning_v2 = gr.Checkbox(USE_PTUNING_V2,
label="使用p-tuning-v2微调过的模型",
interactive=True)
embedding_model = gr.Radio(embedding_model_dict_list,
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)
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, top_k, chatbot],
outputs=chatbot
)
# 将上传的文件保存到content文件夹下,并更新下拉框
file.upload(upload_file,
inputs=file,
outputs=selectFile)
files.upload(upload_file,
inputs=[files, chatbot],
outputs=chatbot)
load_file_button.click(get_vector_store,
show_progress=True,
inputs=[selectFile, chatbot],
......
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