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update:merge dev

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aries-ckt
2023-05-25 22:54:12 +08:00
parent d735240ec6
commit 23929d1c42
1 changed files with 273 additions and 242 deletions
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515
pilot/server/webserver.py
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@@ -18,9 +18,10 @@ import requests
ROOT_PATH = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
sys.path.append(ROOT_PATH)
from pilot.commands.command import execute_ai_response_json
from pilot.commands.command_mange import CommandRegistry
from pilot.commands.exception_not_commands import NotCommands
from pilot.scene.base_chat import BaseChat
from pilot.configs.config import Config
from pilot.configs.model_config import (
DATASETS_DIR,
@@ -29,7 +30,6 @@ from pilot.configs.model_config import (
LOGDIR,
VECTOR_SEARCH_TOP_K,
)
from pilot.server.vectordb_qa import KnownLedgeBaseQA
from pilot.connections.mysql import MySQLOperator
from pilot.conversation import (
SeparatorStyle,
@@ -41,15 +41,22 @@ from pilot.conversation import (
)
from pilot.plugins import scan_plugins
from pilot.prompts.auto_mode_prompt import AutoModePrompt
from pilot.prompts.generator import PromptGenerator
from pilot.server.gradio_css import code_highlight_css
from pilot.server.gradio_patch import Chatbot as grChatbot
from pilot.server.vectordb_qa import KnownLedgeBaseQA
from pilot.source_embedding.knowledge_embedding import KnowledgeEmbedding
from pilot.utils import build_logger, server_error_msg
from pilot.vector_store.extract_tovec import (
get_vector_storelist,
knownledge_tovec_st,
load_knownledge_from_doc,
)
from pilot.commands.command import execute_ai_response_json
from pilot.scene.base import ChatScene
from pilot.scene.chat_factory import ChatFactory
logger = build_logger("webserver", LOGDIR + "webserver.log")
headers = {"User-Agent": "dbgpt Client"}
@@ -69,6 +76,7 @@ priority = {"vicuna-13b": "aaa"}
# 加载插件
CFG = Config()
CHAT_FACTORY = ChatFactory()
DB_SETTINGS = {
"user": CFG.LOCAL_DB_USER,
@@ -125,6 +133,10 @@ def load_demo(url_params, request: gr.Request):
gr.Dropdown.update(choices=dbs)
state = default_conversation.copy()
unique_id = uuid.uuid1()
state.conv_id = str(unique_id)
return (
state,
dropdown_update,
@@ -166,6 +178,8 @@ def add_text(state, text, request: gr.Request):
state.append_message(state.roles[0], text)
state.append_message(state.roles[1], None)
state.skip_next = False
### TODO
state.last_user_input = text
return (state, state.to_gradio_chatbot(), "") + (disable_btn,) * 5
@@ -180,255 +194,271 @@ def post_process_code(code):
return code
def get_chat_mode(mode, sql_mode, db_selector) -> ChatScene:
if mode == conversation_types["default_knownledge"] and not db_selector:
return ChatScene.ChatKnowledge
elif mode == conversation_types["custome"] and not db_selector:
return ChatScene.ChatNewKnowledge
elif sql_mode == conversation_sql_mode["auto_execute_ai_response"] and db_selector:
return ChatScene.ChatWithDb
elif mode == conversation_types["auto_execute_plugin"] and not db_selector:
return ChatScene.ChatExecution
else:
return ChatScene.ChatNormal
def http_bot(
state, mode, sql_mode, db_selector, temperature, max_new_tokens, request: gr.Request
):
if sql_mode == conversation_sql_mode["auto_execute_ai_response"]:
print("AUTO DB-GPT模式.")
if sql_mode == conversation_sql_mode["dont_execute_ai_response"]:
print("标准DB-GPT模式.")
print("是否是AUTO-GPT模式.", autogpt)
logger.info(f"User message send!{state.conv_id},{sql_mode},{db_selector}")
start_tstamp = time.time()
scene: ChatScene = get_chat_mode(mode, sql_mode, db_selector)
print(f"当前对话模式:{scene.value}")
model_name = CFG.LLM_MODEL
dbname = db_selector
# TODO 这里的请求需要拼接现有知识库, 使得其根据现有知识库作答, 所以prompt需要继续优化
if state.skip_next:
# This generate call is skipped due to invalid inputs
yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5
return
cfg = Config()
auto_prompt = AutoModePrompt()
auto_prompt.command_registry = cfg.command_registry
# TODO when tab mode is AUTO_GPT, Prompt need to rebuild.
if len(state.messages) == state.offset + 2:
query = state.messages[-2][1]
# 第一轮对话需要加入提示Prompt
if sql_mode == conversation_sql_mode["auto_execute_ai_response"]:
# autogpt模式的第一轮对话需要 构建专属prompt
system_prompt = auto_prompt.construct_first_prompt(
fisrt_message=[query], db_schemes=gen_sqlgen_conversation(dbname)
)
logger.info("[TEST]:" + system_prompt)
template_name = "auto_dbgpt_one_shot"
new_state = conv_templates[template_name].copy()
new_state.append_message(role="USER", message=system_prompt)
# new_state.append_message(new_state.roles[0], query)
new_state.append_message(new_state.roles[1], None)
else:
template_name = "conv_one_shot"
new_state = conv_templates[template_name].copy()
# prompt 中添加上下文提示, 根据已有知识对话, 上下文提示是否也应该放在第一轮, 还是每一轮都添加上下文?
# 如果用户侧的问题跨度很大, 应该每一轮都加提示。
if db_selector:
new_state.append_message(
new_state.roles[0], gen_sqlgen_conversation(dbname) + query
)
new_state.append_message(new_state.roles[1], None)
else:
new_state.append_message(new_state.roles[0], query)
new_state.append_message(new_state.roles[1], None)
new_state.conv_id = uuid.uuid4().hex
state = new_state
else:
### 后续对话
query = state.messages[-2][1]
# 第一轮对话需要加入提示Prompt
if mode == conversation_types["custome"]:
template_name = "conv_one_shot"
new_state = conv_templates[template_name].copy()
# prompt 中添加上下文提示, 根据已有知识对话, 上下文提示是否也应该放在第一轮, 还是每一轮都添加上下文?
# 如果用户侧的问题跨度很大, 应该每一轮都加提示。
if db_selector:
new_state.append_message(
new_state.roles[0], gen_sqlgen_conversation(dbname) + query
)
new_state.append_message(new_state.roles[1], None)
else:
new_state.append_message(new_state.roles[0], query)
new_state.append_message(new_state.roles[1], None)
state = new_state
elif sql_mode == conversation_sql_mode["auto_execute_ai_response"]:
## 获取最后一次插件的返回
follow_up_prompt = auto_prompt.construct_follow_up_prompt([query])
state.messages[0][0] = ""
state.messages[0][1] = ""
state.messages[-2][1] = follow_up_prompt
prompt = state.get_prompt()
skip_echo_len = len(prompt.replace("</s>", " ")) + 1
if mode == conversation_types["default_knownledge"] and not db_selector:
vector_store_config = {
"vector_store_name": "default",
"vector_store_path": KNOWLEDGE_UPLOAD_ROOT_PATH,
if ChatScene.ChatWithDb == scene:
logger.info("基于DB对话走新的模式！")
chat_param = {
"chat_session_id": state.conv_id,
"db_name": db_selector,
"user_input": state.last_user_input,
}
knowledge_embedding_client = KnowledgeEmbedding(
file_path="",
model_name=LLM_MODEL_CONFIG["text2vec"],
local_persist=False,
vector_store_config=vector_store_config,
)
query = state.messages[-2][1]
docs = knowledge_embedding_client.similar_search(query, VECTOR_SEARCH_TOP_K)
prompt = KnownLedgeBaseQA.build_knowledge_prompt(query, docs, state)
state.messages[-2][1] = query
skip_echo_len = len(prompt.replace("</s>", " ")) + 1
if mode == conversation_types["custome"] and not db_selector:
print("vector store name: ", vector_store_name["vs_name"])
vector_store_config = {
"vector_store_name": vector_store_name["vs_name"],
"text_field": "content",
"vector_store_path": KNOWLEDGE_UPLOAD_ROOT_PATH,
}
knowledge_embedding_client = KnowledgeEmbedding(
file_path="",
model_name=LLM_MODEL_CONFIG["text2vec"],
local_persist=False,
vector_store_config=vector_store_config,
)
query = state.messages[-2][1]
docs = knowledge_embedding_client.similar_search(query, VECTOR_SEARCH_TOP_K)
prompt = KnownLedgeBaseQA.build_knowledge_prompt(query, docs, state)
state.messages[-2][1] = query
skip_echo_len = len(prompt.replace("</s>", " ")) + 1
# Make requests
payload = {
"model": model_name,
"prompt": prompt,
"temperature": float(temperature),
"max_new_tokens": int(max_new_tokens),
"stop": state.sep if state.sep_style == SeparatorStyle.SINGLE else state.sep2,
}
logger.info(f"Requert: \n{payload}")
if sql_mode == conversation_sql_mode["auto_execute_ai_response"]:
response = requests.post(
urljoin(CFG.MODEL_SERVER, "generate"),
headers=headers,
json=payload,
timeout=120,
)
print(response.json())
print(str(response))
try:
text = response.text.strip()
text = text.rstrip()
respObj = json.loads(text)
xx = respObj["response"]
xx = xx.strip(b"\x00".decode())
respObj_ex = json.loads(xx)
if respObj_ex["error_code"] == 0:
ai_response = None
all_text = respObj_ex["text"]
### 解析返回文本，获取AI回复部分
tmpResp = all_text.split(state.sep)
last_index = -1
for i in range(len(tmpResp)):
if tmpResp[i].find("ASSISTANT:") != -1:
last_index = i
ai_response = tmpResp[last_index]
ai_response = ai_response.replace("ASSISTANT:", "")
ai_response = ai_response.replace("\n", "")
ai_response = ai_response.replace("\_", "_")
print(ai_response)
if ai_response == None:
state.messages[-1][-1] = "ASSISTANT未能正确回复，回复结果为:\n" + all_text
yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5
else:
plugin_resp = execute_ai_response_json(
auto_prompt.prompt_generator, ai_response
)
cfg.set_last_plugin_return(plugin_resp)
print(plugin_resp)
state.messages[-1][-1] = (
"Model推理信息:\n" + ai_response + "\n\nDB-GPT执行结果:\n" + plugin_resp
)
yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5
except NotCommands as e:
print("命令执行:" + e.message)
state.messages[-1][-1] = (
"命令执行:" + e.message + "\n模型输出:\n" + str(ai_response)
)
yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5
else:
# 流式输出
state.messages[-1][-1] = "▌"
yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5
try:
# Stream output
response = requests.post(
urljoin(CFG.MODEL_SERVER, "generate_stream"),
headers=headers,
json=payload,
stream=True,
timeout=20,
)
for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
if chunk:
data = json.loads(chunk.decode())
""" TODO Multi mode output handler, rewrite this for multi model, use adapter mode.
"""
if data["error_code"] == 0:
if "vicuna" in CFG.LLM_MODEL:
output = data["text"][skip_echo_len:].strip()
else:
output = data["text"].strip()
output = post_process_code(output)
state.messages[-1][-1] = output + "▌"
yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5
else:
output = data["text"] + f" (error_code: {data['error_code']})"
state.messages[-1][-1] = output
yield (state, state.to_gradio_chatbot()) + (
disable_btn,
disable_btn,
disable_btn,
enable_btn,
enable_btn,
)
return
except requests.exceptions.RequestException as e:
state.messages[-1][-1] = server_error_msg + f" (error_code: 4)"
yield (state, state.to_gradio_chatbot()) + (
disable_btn,
disable_btn,
disable_btn,
enable_btn,
enable_btn,
)
return
state.messages[-1][-1] = state.messages[-1][-1][:-1]
chat: BaseChat = CHAT_FACTORY.get_implementation(scene.value, **chat_param)
chat.call()
state.messages[-1][-1] = f"{chat.current_ai_response()}"
yield (state, state.to_gradio_chatbot()) + (enable_btn,) * 5
# 记录运行日志
finish_tstamp = time.time()
logger.info(f"{output}")
else:
dbname = db_selector
# TODO 这里的请求需要拼接现有知识库, 使得其根据现有知识库作答, 所以prompt需要继续优化
if state.skip_next:
# This generate call is skipped due to invalid inputs
yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5
return
with open(get_conv_log_filename(), "a") as fout:
data = {
"tstamp": round(finish_tstamp, 4),
"type": "chat",
"model": model_name,
"start": round(start_tstamp, 4),
"finish": round(start_tstamp, 4),
"state": state.dict(),
"ip": request.client.host,
cfg = Config()
auto_prompt = AutoModePrompt()
auto_prompt.command_registry = cfg.command_registry
# TODO when tab mode is AUTO_GPT, Prompt need to rebuild.
if len(state.messages) == state.offset + 2:
query = state.messages[-2][1]
# 第一轮对话需要加入提示Prompt
if sql_mode == conversation_sql_mode["auto_execute_ai_response"]:
# autogpt模式的第一轮对话需要 构建专属prompt
system_prompt = auto_prompt.construct_first_prompt(
fisrt_message=[query], db_schemes=gen_sqlgen_conversation(dbname)
)
logger.info("[TEST]:" + system_prompt)
template_name = "auto_dbgpt_one_shot"
new_state = conv_templates[template_name].copy()
new_state.append_message(role="USER", message=system_prompt)
# new_state.append_message(new_state.roles[0], query)
new_state.append_message(new_state.roles[1], None)
else:
template_name = "conv_one_shot"
new_state = conv_templates[template_name].copy()
# prompt 中添加上下文提示, 根据已有知识对话, 上下文提示是否也应该放在第一轮, 还是每一轮都添加上下文?
# 如果用户侧的问题跨度很大, 应该每一轮都加提示。
if db_selector:
new_state.append_message(
new_state.roles[0], gen_sqlgen_conversation(dbname) + query
)
new_state.append_message(new_state.roles[1], None)
else:
new_state.append_message(new_state.roles[0], query)
new_state.append_message(new_state.roles[1], None)
new_state.conv_id = uuid.uuid4().hex
state = new_state
else:
### 后续对话
query = state.messages[-2][1]
# 第一轮对话需要加入提示Prompt
if mode == conversation_types["custome"]:
template_name = "conv_one_shot"
new_state = conv_templates[template_name].copy()
# prompt 中添加上下文提示, 根据已有知识对话, 上下文提示是否也应该放在第一轮, 还是每一轮都添加上下文?
# 如果用户侧的问题跨度很大, 应该每一轮都加提示。
if db_selector:
new_state.append_message(
new_state.roles[0], gen_sqlgen_conversation(dbname) + query
)
new_state.append_message(new_state.roles[1], None)
else:
new_state.append_message(new_state.roles[0], query)
new_state.append_message(new_state.roles[1], None)
state = new_state
elif sql_mode == conversation_sql_mode["auto_execute_ai_response"]:
## 获取最后一次插件的返回
follow_up_prompt = auto_prompt.construct_follow_up_prompt([query])
state.messages[0][0] = ""
state.messages[0][1] = ""
state.messages[-2][1] = follow_up_prompt
prompt = state.get_prompt()
skip_echo_len = len(prompt.replace("</s>", " ")) + 1
if mode == conversation_types["default_knownledge"] and not db_selector:
vector_store_config = {
"vector_store_name": "default",
"vector_store_path": KNOWLEDGE_UPLOAD_ROOT_PATH,
}
fout.write(json.dumps(data) + "\n")
knowledge_embedding_client = KnowledgeEmbedding(
file_path="",
model_name=LLM_MODEL_CONFIG["text2vec"],
local_persist=False,
vector_store_config=vector_store_config,
)
query = state.messages[-2][1]
docs = knowledge_embedding_client.similar_search(query, VECTOR_SEARCH_TOP_K)
prompt = KnownLedgeBaseQA.build_knowledge_prompt(query, docs, state)
state.messages[-2][1] = query
skip_echo_len = len(prompt.replace("</s>", " ")) + 1
if mode == conversation_types["custome"] and not db_selector:
print("vector store name: ", vector_store_name["vs_name"])
vector_store_config = {
"vector_store_name": vector_store_name["vs_name"],
"text_field": "content",
"vector_store_path": KNOWLEDGE_UPLOAD_ROOT_PATH,
}
knowledge_embedding_client = KnowledgeEmbedding(
file_path="",
model_name=LLM_MODEL_CONFIG["text2vec"],
local_persist=False,
vector_store_config=vector_store_config,
)
query = state.messages[-2][1]
docs = knowledge_embedding_client.similar_search(query, VECTOR_SEARCH_TOP_K)
prompt = KnownLedgeBaseQA.build_knowledge_prompt(query, docs, state)
state.messages[-2][1] = query
skip_echo_len = len(prompt.replace("</s>", " ")) + 1
# Make requests
payload = {
"model": model_name,
"prompt": prompt,
"temperature": float(temperature),
"max_new_tokens": int(max_new_tokens),
"stop": state.sep
if state.sep_style == SeparatorStyle.SINGLE
else state.sep2,
}
logger.info(f"Requert: \n{payload}")
if sql_mode == conversation_sql_mode["auto_execute_ai_response"]:
response = requests.post(
urljoin(CFG.MODEL_SERVER, "generate"),
headers=headers,
json=payload,
timeout=120,
)
print(response.json())
print(str(response))
try:
text = response.text.strip()
text = text.rstrip()
respObj = json.loads(text)
xx = respObj["response"]
xx = xx.strip(b"\x00".decode())
respObj_ex = json.loads(xx)
if respObj_ex["error_code"] == 0:
ai_response = None
all_text = respObj_ex["text"]
### 解析返回文本，获取AI回复部分
tmpResp = all_text.split(state.sep)
last_index = -1
for i in range(len(tmpResp)):
if tmpResp[i].find("ASSISTANT:") != -1:
last_index = i
ai_response = tmpResp[last_index]
ai_response = ai_response.replace("ASSISTANT:", "")
ai_response = ai_response.replace("\n", "")
ai_response = ai_response.replace("\_", "_")
print(ai_response)
if ai_response == None:
state.messages[-1][-1] = "ASSISTANT未能正确回复，回复结果为:\n" + all_text
yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5
else:
plugin_resp = execute_ai_response_json(
auto_prompt.prompt_generator, ai_response
)
cfg.set_last_plugin_return(plugin_resp)
print(plugin_resp)
state.messages[-1][-1] = (
"Model推理信息:\n" + ai_response + "\n\nDB-GPT执行结果:\n" + plugin_resp
)
yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5
except NotCommands as e:
print("命令执行:" + e.message)
state.messages[-1][-1] = (
"命令执行:" + e.message + "\n模型输出:\n" + str(ai_response)
)
yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5
else:
# 流式输出
state.messages[-1][-1] = "▌"
yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5
try:
# Stream output
response = requests.post(
urljoin(CFG.MODEL_SERVER, "generate_stream"),
headers=headers,
json=payload,
stream=True,
timeout=20,
)
for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
if chunk:
data = json.loads(chunk.decode())
""" TODO Multi mode output handler, rewrite this for multi model, use adapter mode.
"""
if data["error_code"] == 0:
if "vicuna" in CFG.LLM_MODEL:
output = data["text"][skip_echo_len:].strip()
else:
output = data["text"].strip()
output = post_process_code(output)
state.messages[-1][-1] = output + "▌"
yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5
else:
output = data["text"] + f" (error_code: {data['error_code']})"
state.messages[-1][-1] = output
yield (state, state.to_gradio_chatbot()) + (
disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
return
except requests.exceptions.RequestException as e:
state.messages[-1][-1] = server_error_msg + f" (error_code: 4)"
yield (state, state.to_gradio_chatbot()) + (
disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
return
state.messages[-1][-1] = state.messages[-1][-1][:-1]
yield (state, state.to_gradio_chatbot()) + (enable_btn,) * 5
# 记录运行日志
finish_tstamp = time.time()
logger.info(f"{output}")
with open(get_conv_log_filename(), "a") as fout:
data = {
"tstamp": round(finish_tstamp, 4),
"type": "chat",
"model": model_name,
"start": round(start_tstamp, 4),
"finish": round(start_tstamp, 4),
"state": state.dict(),
"ip": request.client.host,
}
fout.write(json.dumps(data) + "\n")
block_css = (
@@ -685,7 +715,8 @@ if __name__ == "__main__":
# 配置初始化
cfg = Config()
# dbs = get_database_list()
dbs = cfg.local_db.get_database_list()
cfg.set_plugins(scan_plugins(cfg, cfg.debug_mode))
# 加载插件可执行命令