95636cad97
* Add AutoGen example * Adjust AutoGen README * Adjust AutoGen README * Change AutoGen README * Change AutoGen README
92 lines
No EOL
3 KiB
Python
92 lines
No EOL
3 KiB
Python
#
|
|
# Copyright 2016 The BigDL Authors.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
#
|
|
|
|
import autogen
|
|
from autogen import ConversableAgent, UserProxyAgent
|
|
from autogen.agentchat.contrib.capabilities.teachability import Teachability
|
|
|
|
autogen.Completion.clear_cache()
|
|
|
|
config_list = [
|
|
{
|
|
"api_key": "NULL",
|
|
|
|
# ----------- fastchat
|
|
"model": "bigdl",
|
|
"base_url": "http://localhost:8000/v1",
|
|
|
|
# ----------- vllm
|
|
# "model": "hello",
|
|
# "base_url": "http://localhost:65533/v1",
|
|
}]
|
|
|
|
llm_config={
|
|
"config_list": config_list,
|
|
"timeout": 1000,
|
|
"max_tokens": 256,
|
|
"cache_seed": None, # Disable caching.
|
|
"seed": 2024,
|
|
"temperature": 0,
|
|
}
|
|
|
|
|
|
# Start by instantiating any agent that inherits from ConversableAgent.
|
|
teachable_agent = ConversableAgent(
|
|
name="teachable_agent", # The name is flexible, but should not contain spaces to work in group chat.
|
|
llm_config=llm_config,
|
|
)
|
|
|
|
# Instantiate the Teachability capability. Its parameters are all optional.
|
|
teachability = Teachability(
|
|
verbosity=0, # 0 for basic info, 1 to add memory operations, 2 for analyzer messages, 3 for memo lists.
|
|
reset_db=True,
|
|
path_to_db_dir="./tmp/autogen/teachability_db",
|
|
recall_threshold=1.5, # Higher numbers allow more (but less relevant) memos to be recalled.
|
|
)
|
|
|
|
# Now add the Teachability capability to the agent.
|
|
teachability.add_to_agent(teachable_agent)
|
|
|
|
try:
|
|
from termcolor import colored
|
|
except ImportError:
|
|
|
|
def colored(x, *args, **kwargs):
|
|
return x
|
|
|
|
|
|
# Instantiate a UserProxyAgent to represent the user. But in this notebook, all user input will be simulated.
|
|
user = UserProxyAgent(
|
|
name="user",
|
|
human_input_mode="NEVER",
|
|
is_termination_msg=lambda x: True if "TERMINATE" in x.get("content") else False,
|
|
max_consecutive_auto_reply=0,
|
|
)
|
|
|
|
text = "What is the Vicuna model?"
|
|
user.initiate_chat(teachable_agent, message=text, clear_history=True)
|
|
|
|
text = "Vicuna is a 13B-parameter language model released by Meta."
|
|
user.initiate_chat(teachable_agent, message=text, clear_history=False)
|
|
|
|
text = "What is the Orca model?"
|
|
user.initiate_chat(teachable_agent, message=text, clear_history=False)
|
|
|
|
text = "Orca is a 13B-parameter language model developed by Microsoft. It outperforms Vicuna on most tasks."
|
|
user.initiate_chat(teachable_agent, message=text, clear_history=False)
|
|
|
|
text = "How does the Vicuna model compare to the Orca model?"
|
|
user.initiate_chat(teachable_agent, message=text, clear_history=True) |