added updated version of a chatbot with human intervention. Includes …

…Ollama and LMStudio configuration for local source llms

autogen_human_interaction/OAI_CONFIG_LIST.json

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+[
+    {
+        "model": "lmstudio",
+        "api_key": "sk-1111",
+        "base_url": "http://localhost:1234/v1"
+    },
+    {
+        "model": "phi",
+        "api_key": "sk-1111",
+        "base_url": "http://localhost:11434/v1"
+    },
+    {
+        "model": "gpt-3.5-turbo",
+        "api_key": "sk-1111"
+    }
+]

autogen_human_interaction/main.py

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+import autogen
+import json
+
+filter_criteria = {"model": ["lmstudio"]}
+
+# this is used to retrieve the api key and model we want to use for the LLM
+config_list = autogen.config_list_from_json(
+    "OAI_CONFIG_LIST.json",
+    filter_dict=filter_criteria
+)
+
+print(config_list)
+
+# the assistant agent takes in the name, and then the llm_config
+assistant = autogen.AssistantAgent(
+    name="Assistant Agent",
+    llm_config={
+        "cache_seed": 44,           # the cache is saved with each seed number, if unchanged on next run, will produce same result
+        "temperature": 0.7,
+        "config_list": config_list  # the configuration for the LLM (openapi, lm studio, ollama, etc.)
+    }
+)
+
+# an agent for the user that can execute code and provide feedback to the other agents
+user = autogen.UserProxyAgent(
+    name="User",
+    human_input_mode="ALWAYS",      # 3 types, ALWAYS means we will have input every LLM call, NEVER means we never reply
+    is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
+    code_execution_config={
+        "work_dir": "tested_code",  # the directory the user agent will save tested code to
+        "use_docker": False         # if false, won't look for docker to run code inside of, otherwise it will
+    }
+)
+
+# if you ask it to save to disk first, it happens more often
+message = """
+Save the code to disk.
+
+Can you write a total of 3 functions:
+
+1. The first function takes in a string, and outputs the number of characters.
+2. The second function checks a string to test if it is a palindrome.
+3. The third function will take in a number, and return the square root of it.
+"""
+
+# this is how we execute our chat!
+# we want to initiate a chat with an agent, ask it to do something in the message parameter
+# when set "last_msg", it returns the last message of the dialog as the summary.
+# when set "reflection_with_llm", it returns a summary extracted using an llm client
+user.initiate_chat(
+    recipient=assistant,
+    message=message,
+    silent=False,
+    summary_prompt="Summarize takeaway from the conversation. Do not add any introductory phrases. If the intended "
+                   "request is NOT properly addressed, please point it out.",
+    summary_method="reflection_with_llm"
+)
+
+# this will dump the chat into a json file to look at
+json.dump(user.chat_messages[assistant], open("conversations.json", "w"), indent=2)