first pass at turn based conversation

vocode/turn_based/agent/base_agent.py

@@ -0,0 +1,9 @@
+from typing import Optional
+
+
+class BaseAgent:
+    def __init__(self, initial_message: Optional[str] = None):
+        self.initial_message = initial_message
+
+    def respond(self, human_input: str):
+        raise NotImplementedError

vocode/turn_based/agent/chat_gpt_agent.py

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+from typing import Optional
+from langchain.prompts import (
+    ChatPromptTemplate,
+    MessagesPlaceholder,
+    SystemMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+from langchain.chains import ConversationChain
+from langchain.chat_models import ChatOpenAI
+from langchain.memory import ConversationBufferMemory
+
+from vocode.turn_based.agent.base_agent import BaseAgent
+
+
+class ChatGPTAgent(BaseAgent):
+    def __init__(
+        self,
+        system_prompt: str,
+        initial_message: Optional[str] = None,
+        model_name: str = "gpt-3.5-turbo",
+        temperature: float = 0.7,
+        max_tokens: int = 100,
+    ):
+        super().__init__(initial_message=initial_message)
+        self.prompt = ChatPromptTemplate.from_messages(
+            [
+                SystemMessagePromptTemplate.from_template(system_prompt),
+                MessagesPlaceholder(variable_name="history"),
+                HumanMessagePromptTemplate.from_template("{input}"),
+            ]
+        )
+        self.memory = ConversationBufferMemory(return_messages=True)
+        if initial_message:
+            self.memory.chat_memory.add_ai_message(initial_message)
+        self.llm = ChatOpenAI(
+            model_name=model_name,
+            temperature=temperature,
+            max_tokens=max_tokens,
+        )
+        self.conversation = ConversationChain(
+            memory=self.memory, prompt=self.prompt, llm=self.llm
+        )
+
+    def respond(self, human_input: str):
+        return self.conversation.predict(input=human_input)

vocode/turn_based/agent/echo_agent.py

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+from vocode.turn_based.agent.base_agent import BaseAgent
+
+
+class EchoAgent(BaseAgent):
+    def respond(self, human_input: str):
+        return human_input