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