Comments (1)
可以参考以下代码:
`
from modelscope_agent.memory import MemoryWithRag
from modelscope_agent.agents import RolePlay
role_template = '知识库查询小助手,可以优先通过查询本地知识库来回答用户的问题'
llm_config = {
'model': 'GLM-4',
'model_server': 'zhipu'
}
function_list = []
file_paths = ['./tests/samples/常见QA.pdf']
bot = RolePlay(function_list=function_list,llm=llm_config, instruction=role_template)
memory = MemoryWithRag(urls=file_paths, use_knowledge_cache=False)
use_llm = True if len(function_list) else False
query = "高德天气API在哪申请"
ref_doc = memory.run(query, use_llm=use_llm)
response = bot.run(query, remote=False, print_info=True, ref_doc=ref_doc)
text = ''
for chunk in response:
text += chunk
print(text)
`
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Related Issues (20)
- Tool Nonimplementation Error HOT 2
- TypeError: Can't instantiate abstract class Vllm with abstract method _chat_no_stream HOT 2
- [framework] Refactor Non-Core Requirements to Runtime/On-Demand Dependencies HOT 1
- qwen1.5和qwen2调用agent方式不一样吗?? HOT 5
- Bad example: With Modelscope-Agent-Server, Qwen2 could be used by OpenAI SDK with tool calling ability, please find detail in doc. HOT 3
- [<Agent component: framework|tool|llm|etc...>] ollama._types.ResponseError: pull model manifest: file does not exist
- llm:调用vllm部署的大模型openai接口时报错 HOT 2
- 无法用dashscope model server使用OpenAI SDK HOT 13
- max_tokens must be at least 1, got -160 HOT 1
- 调用qwen-max模型,还需要消耗显存吗 HOT 2
- MSAgent-Bench的benchmark有没有具体的评估脚本?
- 使用这个 sh scripts/run_assistant_server.sh 部署模型之后,会不会比VLLM速度慢很多 HOT 8
- 参照文档搭建支持function calling的qwen2 openai server时报错openai.BadRequestError: Error code: 400
- MemoryWithRag 方法传了本地的llm模型,但是还是提示AssertionError: DASHSCOPE_API_KEY should be set in environ. HOT 2
- 本地部署之后,AgentFabric上操作就会报错。preview_send_message user_agent = _state['user_agent'] KeyError: 'user_agent' HOT 11
- [Agentfabric]: need stop current round, retry last round HOT 1
- 如何可以使用http://0.0.0.0:7860本地访问,而不是默认的http://127.0.0.1:7860访问 HOT 1
- [tool] 运行 langchian_as_third_party_tools.ipynb 和 openapi_schema_tool.ipynb 时工具注册失败 HOT 1
- 知识库文件上传一个可以,但是两个以上会报错
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