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app.py

@@ -67,18 +67,18 @@ my_api_key = os.environ["MY_API_KEY"]
 # Initialize the OpenAI embedding function for Chroma
 embedding_function = chromadb.utils.embedding_functions.OpenAIEmbeddingFunction(
     api_base=endpoint, # Complete the code to define the API base endpoint
-    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
+    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
 
 # This initializes the OpenAI embedding function for the Chroma vectorstore, using the provided endpoint and API key.
 
 # Initialize the OpenAI Embeddings
 embedding_model = OpenAIEmbeddings(
     openai_api_base=endpoint,
-    openai_api_key=api_key,
-    model='text-embedding-ada-002'
+    openai_api_key=api_key
 )
+#model='text-embedding-ada-002'
 
 # Initialize the Chat OpenAI model
 llm = ChatOpenAI(
@@ -529,11 +529,11 @@ def agentic_rag(query: str):
 
     return output
 
-
 #================================ Guardrails ===========================#
 llama_guard_client = Groq(api_key=groq_api_key) # Groq(api_key=llama_api_key)
 # Function to filter user input with Llama Guard
-def filter_input_with_llama_guard(user_input, model="llama-guard-3-8b"):
+#def filter_input_with_llama_guard(user_input, model="llama-guard-3-8b"):
+def filter_input_with_llama_guard(user_input, model="meta-llama/llama-guard-4-12b"):
     """
     Filters user input using Llama Guard to ensure it is safe.
 
@@ -569,9 +569,12 @@ class NutritionBot:
         self.memory = MemoryClient(api_key=MEM0_api_key) # userdata.get("mem0"))  # Complete the code to define the memory client API key
 
         self.client = ChatOpenAI(
-            model_name="gpt-4o",  # Specify the model to use (e.g., GPT-4 optimized version)
-            api_key = api_key, # config.get("API_KEY"),  # API key for authentication
-            base_url = endpoint, # config.get("OPENAI_API_BASE"),
+            #model_name="gpt-4o",  # Specify the model to use (e.g., GPT-4 optimized version)
+            model="gpt-4o-mini",
+            #api_key = api_key, # config.get("API_KEY"),  # API key for authentication
+            openai_api_key=api_key,  # Fill in the API key
+            #base_url = endpoint, # config.get("OPENAI_API_BASE"),
+            openai_api_base=endpoint,  # Fill in the endpoint
             temperature=0  # Controls randomness in responses; 0 ensures deterministic results
         )