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app.py

@@ -64,12 +64,12 @@ print(f"endpoint::{endpoint}" )
 #MEM0_api_key = config.get('MEM0_API_KEY') # MEM0_api_key = os.environ['mem0']
 #my_api_key = config.get("MY_API_KEY")
 
-#groq_api_key = userdata.get('LLAMA_API_KEY') # os.environ['LLAMA_API_KEY'] # llama_api_key = os.environ['GROQ_API_KEY']
-#print(groq_api_key)
-#MEM0_api_key = userdata.get('MEM0_API_KEY') # ['MEM0_API_KEY'] # MEM0_api_key = os.environ['mem0']
-#print(MEM0_api_key)
-#my_api_key = userdata.get('MY_API_KEY') #os.environ["MY_API_KEY"]
-#print(my_api_key)
+groq_api_key = os.environ['LLAMA_API_KEY'] # llama_api_key = os.environ['GROQ_API_KEY']
+print(f"groq_api_key::{groq_api_key}")
+MEM0_api_key = os.environ['MEM0_API_KEY'] # MEM0_api_key = os.environ['mem0']
+print(f"MEM0_api_key::{MEM0_api_key}")
+my_api_key = os.environ["MY_API_KEY"]
+print(f"my_api_key::{my_api_key}")
 
 # Initialize the OpenAI embedding function for Chroma
 embedding_function = chromadb.utils.embedding_functions.OpenAIEmbeddingFunction(
@@ -77,6 +77,7 @@ embedding_function = chromadb.utils.embedding_functions.OpenAIEmbeddingFunction(
     api_key=api_key, # Complete the code to define the API key
     model_name='text-embedding-ada-002' # This is a fixed value and does not need modification
 )
+print("embedding_function initialized...")
 
 # This initializes the OpenAI embedding function for the Chroma vectorstore, using the provided endpoint and API key.
 
@@ -86,6 +87,7 @@ embedding_model = OpenAIEmbeddings(
     openai_api_key=api_key,
     model='text-embedding-ada-002'
 )
+print("embedding_model initialized...")
 
 # Initialize the Chat OpenAI model
 llm = ChatOpenAI(
@@ -94,6 +96,8 @@ llm = ChatOpenAI(
     model="gpt-4o", # used gpt4o instead of gpt-4o-mini to get improved results
     streaming=False
 )
+print("llm initialized...")
+
 # This initializes the Chat OpenAI model with the provided endpoint, API key, deployment name, and a temperature setting of 0 (to control response variability).
 
 # set the LLM and embedding model in the LlamaIndex settings.