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import os
import ssl
import gradio as gr
import nltk
from config_utils import parse_configuration
from custom_prompt import CustomPromptGenerator
from custom_prompt_zh import ZhCustomPromptGenerator
from langchain.embeddings import ModelScopeEmbeddings
from langchain.vectorstores import FAISS
from modelscope_agent import prompt_generator_register
from modelscope_agent.agent import AgentExecutor
from modelscope_agent.agent_types import AgentType
from modelscope_agent.llm import LLMFactory
from modelscope_agent.retrieve import KnowledgeRetrieval
from modelscope_agent.tools.openapi_plugin import OpenAPIPluginTool
from modelscope_agent.utils.logger import agent_logger as logger
prompts = {
'CustomPromptGenerator': CustomPromptGenerator,
'ZhCustomPromptGenerator': ZhCustomPromptGenerator,
}
prompt_generator_register(prompts)
# try:
# _create_unverified_https_context = ssl._create_unverified_context
# except AttributeError:
# pass
# else:
# ssl._create_default_https_context = _create_unverified_https_context
#
# nltk.download('punkt')
# nltk.download('averaged_perceptron_tagger')
# init user chatbot_agent
def init_user_chatbot_agent(uuid_str=''):
builder_cfg, model_cfg, tool_cfg, available_tool_list, plugin_cfg, available_plugin_list = parse_configuration(
uuid_str)
# set top_p and stop_words for role play
model_cfg[builder_cfg.model]['generate_cfg']['top_p'] = 0.5
model_cfg[builder_cfg.model]['generate_cfg']['stop'] = 'Observation'
# build model
logger.info(
uuid=uuid_str,
message=f'using model {builder_cfg.model}',
content={'model_config': model_cfg[builder_cfg.model]})
# # check configuration
# if builder_cfg.model in ['qwen-max', 'qwen-72b-api', 'qwen-14b-api', 'qwen-plus']:
# if 'DASHSCOPE_API_KEY' not in os.environ:
# raise gr.Error('DASHSCOPE_API_KEY should be set via setting environment variable')
try:
llm = LLMFactory.build_llm(builder_cfg.model, model_cfg)
except Exception as e:
raise gr.Error(str(e))
# build prompt with zero shot react template
prompt_generator = builder_cfg.get('prompt_generator', None)
if builder_cfg.model.startswith('qwen') and not prompt_generator:
prompt_generator = 'CustomPromptGenerator'
language = builder_cfg.get('language', 'en')
if language == 'zh':
prompt_generator = 'ZhCustomPromptGenerator'
prompt_cfg = {
'prompt_generator':
prompt_generator,
'add_addition_round':
True,
'knowledge_file_name':
os.path.basename(builder_cfg.knowledge[0]
if len(builder_cfg.knowledge) > 0 else ''),
'uuid_str':
uuid_str
}
# get knowledge
# 开源版本的向量库配置
model_id = 'damo/nlp_gte_sentence-embedding_chinese-base'
embeddings = ModelScopeEmbeddings(model_id=model_id)
available_knowledge_list = []
for item in builder_cfg.knowledge:
# if isfile and end with .txt, .md, .pdf, support only those file
if os.path.isfile(item) and item.endswith(('.txt', '.md', '.pdf')):
available_knowledge_list.append(item)
if len(available_knowledge_list) > 0:
knowledge_retrieval = KnowledgeRetrieval.from_file(
available_knowledge_list, embeddings, FAISS)
else:
knowledge_retrieval = None
additional_tool_list = add_openapi_plugin_to_additional_tool(
plugin_cfg, available_plugin_list)
# build agent
agent = AgentExecutor(
llm,
additional_tool_list=additional_tool_list,
tool_cfg=tool_cfg,
agent_type=AgentType.MRKL,
knowledge_retrieval=knowledge_retrieval,
tool_retrieval=False,
**prompt_cfg)
agent.set_available_tools(available_tool_list + available_plugin_list)
return agent
def add_openapi_plugin_to_additional_tool(plugin_cfgs, available_plugin_list):
additional_tool_list = {}
for name, cfg in plugin_cfgs.items():
openapi_plugin_object = OpenAPIPluginTool(name=name, cfg=plugin_cfgs)
additional_tool_list[name] = openapi_plugin_object
return additional_tool_list
def user_chatbot_single_run(query, agent):
agent.run(query)
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