Update app.py

app.py

@@ -3,21 +3,21 @@ import chainlit as cl
 from dotenv import load_dotenv
 from operator import itemgetter
 from langchain_community.vectorstores import Qdrant
+from qdrant_client import QdrantClient
 from langchain_openai.chat_models import ChatOpenAI
 from langchain_text_splitters import RecursiveCharacterTextSplitter
 from langchain.prompts import ChatPromptTemplate
 from langchain.schema.output_parser import StrOutputParser
 from langchain_openai import OpenAIEmbeddings
-from helpers import process_file
+from helpers import process_file, add_to_qdrant
 
 
 load_dotenv()
-# HF_LLM_ENDPOINT = os.environ["HF_LLM_ENDPOINT"]
-# HF_EMBED_ENDPOINT = os.environ["HF_EMBED_ENDPOINT"]
-# HF_TOKEN = os.environ["HF_TOKEN"]
 
 embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
 llm = ChatOpenAI(model="gpt-4")
+qdrant_client = QdrantClient(url=constants.QDRANT_ENDPOINT, api_key=constants.QDRANT_API_KEY) # TO DO: Add constants, info from Mark
+collection_name = "marketing_data"
 
 RAG_PROMPT_TEMPLATE = """\
 <|start_header_id|>system<|end_header_id|>
@@ -57,7 +57,7 @@ async def on_chat_start():
     docs = process_file(file)
     for i, doc in enumerate(docs):
         doc.metadata["source"] = f"source_{i}" # TO DO: Add metadata
-        
+        add_to_qdrant(doc, embeddings, qdrant_client, collection_name)
     print(f"Processing {len(docs)} text chunks")
 
     # Create the vectorstore