Update helper/utils.py

helper/utils.py

@@ -100,7 +100,7 @@ def read_and_textify(
     return text_list, sources_list
 
 
-client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
+openai_client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
 
 
 def list_to_nums(sentences: List[str]) -> List[List[float]]:
@@ -121,7 +121,7 @@ def list_to_nums(sentences: List[str]) -> List[List[float]]:
     for sentence in sentences:
         # Use the OpenAI API to get embeddings for the sentence
 
-        response = client.embeddings.create(
+        response = openai_client.embeddings.create(
             input=sentence, model="text-embedding-3-small"
         )
 
@@ -146,12 +146,12 @@ def call_gpt(prompt: str, content: str) -> str:
     Returns:
         str: The generated response from the GPT model based on the given prompts and content.
     
-    Note: 'client' is assumed to be an already created and authenticated instance of the OpenAI
-          client, which should be set up prior to calling this function.
+    Note: 'openai_client' is assumed to be an already created and authenticated instance of the OpenAI
+          openai_client, which should be set up prior to calling this function.
     """
     
     # Generates a response from the model based on the interactive messages provided
-    response = client.chat.completions.create(
+    response = openai_client.chat.completions.create(
         model="gpt-3.5-turbo",  # The AI model being queried for a response
         messages=[
             # System message defining the assistant's role
@@ -169,7 +169,7 @@ def call_gpt(prompt: str, content: str) -> str:
     return response.choices[0].message.content
 
 
-client = Together(api_key=os.environ["TOGETHER_API_KEY"])
+together_client = Together(api_key=os.environ["TOGETHER_API_KEY"])
 
 def call_llama(prompt: str) -> str:
     """
@@ -181,7 +181,7 @@ def call_llama(prompt: str) -> str:
     """
 
     # Create a completion request with the prompt
-    response = client.chat.completions.create(
+    response = together_client.chat.completions.create(
 
         # Use the Llama-3-8b-chat-hf model
         model="meta-llama/Llama-3-8b-chat-hf",