Upload app.py

app.py

@@ -10,7 +10,10 @@ from huggingface_hub import CommitScheduler
 from pathlib import Path
 from langchain.embeddings import SentenceTransformerEmbeddings
 from langchain.vectorstores import Chroma
-from langchain.llms import OpenAI
+# from langchain.llms import OpenAI
+
+from langchain_openai import ChatOpenAI
+from langchain.schema import HumanMessage, AIMessage, SystemMessage
 
 # Create Client
 import os
@@ -18,7 +21,11 @@ os.environ['OPENAI_API_KEY']  = "gl-U2FsdGVkX1+0bNWD6YsVLZUYsn0m1WfLxUzrP0xUFbtW
 os.environ["OPENAI_BASE_URL"] =  "https://aibe.mygreatlearning.com/openai/v1" # e.g. "https://aibe.mygreatlearning.com/openai/v1";
 
 model_name = 'gpt-4o-mini' # e.g. 'gpt-3.5-turbo'
-llm_client = OpenAI()
+# llm_client = OpenAI()
+# Initialize the ChatOpenAI model
+llm = ChatOpenAI(model_name=model_name, temperature=0)  # Set temperature to 0 for deterministic output
+# Create a HumanMessage
+user_message = HumanMessage(content="What's the weather like today?")
 
 # Define the embedding model and the vectorstore
 embedding_model = SentenceTransformerEmbeddings(model_name='thenlper/gte-large')
@@ -111,11 +118,14 @@ def llm_query(user_input,company):
 
     # Get response from the LLM
     try:
-        response = llm_client.chat.completions.create(
-            model=model_name,
-            messages=prompt,
-            temperature=0
-        )
+        # Call the chat model with the message
+        response = llm([prompt])
+        
+        # response = llm_client.chat.completions.create(
+        #     model=model_name,
+        #     messages=prompt,
+        #     temperature=0
+        # )
 
         llm_response = response.choices[0].message.content.strip()