fix openai env vars

vocode/streaming/agent/bot_sentiment_analyser.py

@@ -3,6 +3,8 @@ from langchain.llms import OpenAI
 from langchain.prompts import PromptTemplate
 from pydantic import BaseModel
 
+from vocode import getenv
+
 TEMPLATE = """
 Read the following conversation classify the final emotion of the Bot as one of [{emotions}].
 Output the degree of emotion as a value between 0 and 1 in the format EMOTION,DEGREE: ex. {example_emotion},0.5
@@ -22,7 +24,7 @@ class BotSentimentAnalyser:
     def __init__(self, emotions: list[str], model_name: str = "text-davinci-003"):
         self.model_name = model_name
         self.llm = OpenAI(
-            model_name=self.model_name,
+            model_name=self.model_name, openai_api_key=getenv("OPENAI_API_KEY")
         )
         assert len(emotions) > 0
         self.emotions = [e.lower() for e in emotions]

vocode/streaming/agent/chat_gpt_agent.py

@@ -57,6 +57,7 @@ class ChatGPTAgent(BaseAgent):
             model_name=self.agent_config.model_name,
             temperature=self.agent_config.temperature,
             max_tokens=self.agent_config.max_tokens,
+            openai_api_key=openai.api_key,
         )
         self.conversation = ConversationChain(
             memory=self.memory, prompt=self.prompt, llm=self.llm

vocode/streaming/agent/llm_agent.py

@@ -5,6 +5,7 @@ from langchain import OpenAI
 from langchain.llms import OpenAIChat
 from typing import Generator
 import logging
+from vocode import getenv
 
 from vocode.streaming.agent.base_agent import BaseAgent
 from vocode.streaming.agent.utils import stream_llm_response
@@ -43,6 +44,7 @@ class LLMAgent(BaseAgent):
             model_name=self.agent_config.model_name,
             temperature=self.agent_config.temperature,
             max_tokens=self.agent_config.max_tokens,
+            openai_api_key=getenv("OPENAI_API_KEY"),
         )
         self.stop_tokens = [f"{recipient}:"]
         self.first_response = (