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Gorilla functions and document examples testing
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‎document_examples/cloudproxyserver_huggingface_ie.py

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import autogen
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# Update with your Inference Endpoint and Access Token
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hf_inference_endpoint_url = "https://inowtlifsx7oieb7.us-east-1.aws.endpoints.huggingface.cloud/v1/"
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hf_api_key = ''
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llm_config={
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"config_list": [
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{
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# Available together.ai model strings:
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# https://docs.together.ai/docs/inference-models
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# "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
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"model": "tgi", #"mistralai/Mistral-7B-Instruct-v0.2",
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"api_key": hf_api_key,
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"base_url": hf_inference_endpoint_url
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}
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],
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"cache_seed": None
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}
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# User Proxy will execute code and finish the chat upon typing 'exit'
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user_proxy = autogen.UserProxyAgent(
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name="UserProxy",
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system_message="A human admin",
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code_execution_config={
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"last_n_messages": 2,
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"work_dir": "groupchat",
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"use_docker": False,
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}, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.
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human_input_mode="TERMINATE",
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is_termination_msg=lambda x: "TERMINATE" in x.get("content"),
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)
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# Python Coder agent
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coder = autogen.AssistantAgent(
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name="softwareCoder",
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description="Software Coder, writes Python code as required and reiterates with feedback from the Code Reviewer.",
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system_message="You are a senior Python developer, a specialist in writing succinct Python functions.",
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llm_config=llm_config,
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)
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# Code Reviewer agent
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reviewer = autogen.AssistantAgent(
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name="codeReviewer",
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description="Code Reviewer, reviews written code for correctness, efficiency, and security. Asks the Software Coder to address issues.",
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system_message="You are a Code Reviewer, experienced in checking code for correctness, efficiency, and security. Review and provide feedback to the Software Coder until you are satisfied, then return the word TERMINATE",
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is_termination_msg=lambda x: "TERMINATE" in x.get("content"),
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llm_config=llm_config,
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)
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# Establish the Group Chat and disallow a speaker being selected consecutively
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groupchat = autogen.GroupChat(agents=[user_proxy, coder, reviewer], messages=[], max_round=12, allow_repeat_speaker=False)
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# Manages the group of multiple agents
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manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)
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# Start the chat with a request to write a function
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user_proxy.initiate_chat(
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manager,
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message="Write a Python function for the Fibonacci sequence, the function will have one parameter for the number in the sequence, which the function will return the Fibonacci number for."
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)
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# type exit to terminate the chat

‎document_examples/cloudproxyserver_togetherai.py

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import autogen
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import os
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llm_config={
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"config_list": [
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{
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# Available together.ai model strings:
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# https://docs.together.ai/docs/inference-models
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"model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
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"api_key": os.environ['TOGETHER_API_KEY'],
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"base_url": "https://api.together.xyz/v1"
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}
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],
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"cache_seed": 42
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}
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# User Proxy will execute code and finish the chat upon typing 'exit'
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user_proxy = autogen.UserProxyAgent(
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name="UserProxy",
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system_message="A human admin",
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code_execution_config={
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"last_n_messages": 2,
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"work_dir": "groupchat",
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"use_docker": False,
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}, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.
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human_input_mode="TERMINATE",
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is_termination_msg=lambda x: "TERMINATE" in x.get("content"),
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)
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# Python Coder agent
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coder = autogen.AssistantAgent(
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name="softwareCoder",
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description="Software Coder, writes Python code as required and reiterates with feedback from the Code Reviewer.",
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system_message="You are a senior Python developer, a specialist in writing succinct Python functions.",
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llm_config=llm_config,
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)
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# Code Reviewer agent
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reviewer = autogen.AssistantAgent(
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name="codeReviewer",
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description="Code Reviewer, reviews written code for correctness, efficiency, and security. Asks the Software Coder to address issues.",
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system_message="You are a Code Reviewer, experienced in checking code for correctness, efficiency, and security. Review and provide feedback to the Software Coder until you are satisfied, then return the word TERMINATE",
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is_termination_msg=lambda x: "TERMINATE" in x.get("content"),
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llm_config=llm_config,
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)
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# Establish the Group Chat and disallow a speaker being selected consecutively
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groupchat = autogen.GroupChat(agents=[user_proxy, coder, reviewer], messages=[], max_round=12, allow_repeat_speaker=False)
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# Manages the group of multiple agents
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manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)
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# Start the chat with a request to write a function
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user_proxy.initiate_chat(
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manager,
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message="Write a Python function for the Fibonacci sequence, the function will have one parameter for the number in the sequence, which the function will return the Fibonacci number for."
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)
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# type exit to terminate the chat

‎document_examples/cloudproxyserver_togetherai_fc.py

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import os
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from typing import Literal
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from pydantic import BaseModel, Field
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from typing_extensions import Annotated
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import autogen
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llm_config={
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# Together.ai (supports function calling, requires API key in environment variable "TOGETHER_API_KEY")
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# More info here: https://docs.together.ai/docs/function-calling including the models it supports
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"config_list": [{"model": "mistralai/Mixtral-8x7B-Instruct-v0.1", "api_key": os.environ['TOGETHER_API_KEY'], "base_url": f"https://api.together.xyz/v1"}],
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"cache_seed": None,
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}
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chatbot = autogen.AssistantAgent(
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name="chatbot",
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system_message="""For currency exchange tasks,
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only use the functions you have been provided with.
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Output 'TERMINATE' with the answer to indicate an answer has been provided.
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Do not include the function name or result in the JSON.
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Example of the return JSON is:
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{
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"parameter_1_name": 100.00,
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"parameter_2_name": "ABC",
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"parameter_3_name": "DEF",
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}.
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Another example of the return JSON is:
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{
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"parameter_1_name": "GHI",
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"parameter_2_name": "ABC",
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"parameter_3_name": "DEF",
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"parameter_4_name": 123.00,
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}.
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When finished, return a non-JSON format string of 'TERMINATE'""",
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llm_config=llm_config,
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)
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user_proxy = autogen.UserProxyAgent(
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name="user_proxy",
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is_termination_msg=lambda x: x.get("content", "") and "TERMINATE" in x.get("content", ""),
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human_input_mode="TERMINATE",
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max_consecutive_auto_reply=1,
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)
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CurrencySymbol = Literal["USD", "EUR"]
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def exchange_rate(base_currency: CurrencySymbol, quote_currency: CurrencySymbol) -> float:
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if base_currency == quote_currency:
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return 1.0
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elif base_currency == "USD" and quote_currency == "EUR":
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return 1 / 1.1
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elif base_currency == "EUR" and quote_currency == "USD":
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return 1.1
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else:
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raise ValueError(f"Unknown currencies {base_currency}, {quote_currency}")
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@user_proxy.register_for_execution()
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@chatbot.register_for_llm(description="Currency exchange calculator.")
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def currency_calculator(
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base_amount: Annotated[float, "Amount of currency in base_currency"],
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base_currency: Annotated[CurrencySymbol, "Base currency"] = "USD",
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quote_currency: Annotated[CurrencySymbol, "Quote currency"] = "EUR",
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) -> str:
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quote_amount = exchange_rate(base_currency, quote_currency) * base_amount
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return f"{format(quote_amount, '.2f')} {quote_currency}"
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# Test that the function map is the function
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# assert user_proxy.function_map["currency_calculator"]._origin == currency_calculator
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# start the conversation
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res = user_proxy.initiate_chat(
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chatbot,
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message="How much is 123.45 EUR in USD?",
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summary_method="reflection_with_llm",
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)

‎document_examples/localproxyserver_litellmollama.py

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from autogen import UserProxyAgent, ConversableAgent
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local_llm_config={
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"config_list": [
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{
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"model": "NotRequired", # Loaded with LiteLLM command
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"api_key": "NotRequired", # Not needed
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"base_url": "http://192.168.0.115:4000" # Your LiteLLM URL
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}
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],
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"cache_seed": None # Turns off caching, useful for testing different models
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}
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# Create the agent that uses the LLM.
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assistant = ConversableAgent("agent", llm_config=local_llm_config)
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# Create the agent that represents the user in the conversation.
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user_proxy = UserProxyAgent("user", code_execution_config=False)
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# Let the assistant start the conversation. It will end when the user types exit.
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assistant.initiate_chat(user_proxy, message="How can I help you today?")

‎document_examples/localproxyserver_litellmollama_fc.py

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import autogen
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from typing import Literal
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from typing_extensions import Annotated
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local_llm_config={
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"config_list": [
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{
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"model": "NotRequired", # Loaded with LiteLLM command
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"api_key": "NotRequired", # Not needed
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"base_url": "http://192.168.0.115:4000" # Your LiteLLM URL
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}
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],
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"cache_seed": None # Turns off caching, useful for testing different models
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}
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chatbot = autogen.AssistantAgent(
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name="chatbot",
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system_message="""For currency exchange tasks,
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only use the functions you have been provided with.
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Output 'TERMINATE' when an answer has been provided.
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Do not include the function name or result in the JSON.
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Example of the return JSON is:
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{
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"parameter_1_name": 100.00,
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"parameter_2_name": "ABC",
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"parameter_3_name": "DEF",
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}.
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Another example of the return JSON is:
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{
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"parameter_1_name": "GHI",
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"parameter_2_name": "ABC",
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"parameter_3_name": "DEF",
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"parameter_4_name": 123.00,
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}. """,
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llm_config=local_llm_config,
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)
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user_proxy = autogen.UserProxyAgent(
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name="user_proxy",
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is_termination_msg=lambda x: x.get("content", "") and "TERMINATE" in x.get("content", ""),
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human_input_mode="NEVER",
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max_consecutive_auto_reply=1,
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)
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CurrencySymbol = Literal["USD", "EUR"]
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def exchange_rate(base_currency: CurrencySymbol, quote_currency: CurrencySymbol) -> float:
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if base_currency == quote_currency:
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return 1.0
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elif base_currency == "USD" and quote_currency == "EUR":
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return 1 / 1.1
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elif base_currency == "EUR" and quote_currency == "USD":
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return 1.1
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else:
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raise ValueError(f"Unknown currencies {base_currency}, {quote_currency}")
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@user_proxy.register_for_execution()
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@chatbot.register_for_llm(description="Currency exchange calculator.")
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def currency_calculator(
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base_amount: Annotated[float, "Amount of currency in base_currency"],
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base_currency: Annotated[CurrencySymbol, "Base currency"] = "USD",
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quote_currency: Annotated[CurrencySymbol, "Quote currency"] = "EUR",
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) -> str:
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quote_amount = exchange_rate(base_currency, quote_currency) * base_amount
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return f"{format(quote_amount, '.2f')} {quote_currency}"
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# print(chatbot.llm_config["tools"])
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# Test that the function map is the function
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assert user_proxy.function_map["currency_calculator"]._origin == currency_calculator
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# start the conversation
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res = user_proxy.initiate_chat(
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chatbot,
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message="How much is 123.45 EUR in USD?",
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summary_method="reflection_with_llm",
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)

‎document_examples/localproxyserver_lmstudiomultimodel.py

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gemma = {
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"config_list": [
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{
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"model": "lmstudio-ai/gemma-2b-it-GGUF/gemma-2b-it-q8_0.gguf",
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"base_url": "http://192.168.0.115:1234/v1",
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"api_key": "NULL",
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},
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],
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"cache_seed": None, # Disable caching.
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}
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phi2 = {
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"config_list": [
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{
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"model": "TheBloke/phi-2-GGUF/phi-2.Q4_K_S.gguf",
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"base_url": "http://192.168.0.115:1234/v1",
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"api_key": "NULL",
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},
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],
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"cache_seed": None, # Disable caching.
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}
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from autogen import ConversableAgent
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jack = ConversableAgent(
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"Jack (Phi-2)",
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llm_config=phi2,
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system_message="Your name is Jack and you are a comedian in a two-person comedy show.",
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)
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emma = ConversableAgent(
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"Emma (Gemma)",
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llm_config=gemma,
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system_message="Your name is Emma and you are a comedian in two-person comedy show.",
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)
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chat_result = jack.initiate_chat(emma, message="Emma, tell me a joke.", max_turns=2)

‎document_examples/localproxyserver_vllm.py

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from autogen import UserProxyAgent, ConversableAgent
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local_llm_config={
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"config_list": [
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{
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"model": "TheBloke/Llama-2-7b-Chat-AWQ", # Loaded with vLLM command
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"api_key": "NotRequired", # Not needed
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"base_url": "http://192.168.0.115:8000/v1" # Your vLLM URL
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}
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],
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"cache_seed": None # Turns off caching, useful for testing different models
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}
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# Create the agent that uses the LLM.
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assistant = ConversableAgent("agent", llm_config=local_llm_config,system_message="")
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# Create the agent that represents the user in the conversation.
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user_proxy = UserProxyAgent("user", code_execution_config=False,system_message="")
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# Let the assistant start the conversation. It will end when the user types exit.
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assistant.initiate_chat(user_proxy, message="How can I help you today?")

‎function_calling/function_calling_test.py

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# "config_list": [{"model": model_name, "api_key": "NotRequired", "base_url": f"http://{network_address}:11434/v1"}],
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# LiteLLM (supports function calling but may have Ollama function calling bug)
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"config_list": [{"model": "litellmnotneeded", "api_key": "NotRequired", "base_url": f"http://{network_address}:8801"}],
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# "config_list": [{"model": "litellmnotneeded", "api_key": "NotRequired", "base_url": f"http://{network_address}:8801"}],
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# Gorilla OpenFunctions
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"config_list": [{"model": "gorilla-7b-hf-v1", "api_key": "EMPTY", "base_url": f"http://zanino.millennium.berkeley.edu:8000/v1"}],
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# Functionary (not tested)
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# "config_list": [{"model": "litellmnotneeded", "api_key": "NotRequired", "base_url": f"http://{network_address}:8000/v1"}],

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