uipto

app.py

@@ -2,7 +2,6 @@ import os
 import streamlit as st
 from together import Together
 from langchain_community.vectorstores import Chroma
-# Use the updated HuggingFace Embeddings class
 from langchain_huggingface import HuggingFaceEmbeddings
 
 # --- Configuration ---
@@ -15,7 +14,7 @@ if not TOGETHER_API_KEY:
 # Initialize TogetherAI client
 client = Together(api_key=TOGETHER_API_KEY)
 
-# Embeddings setup (new huggingface integration)
+# Embeddings setup
 EMBED_MODEL_NAME = "BAAI/bge-base-en"
 embeddings = HuggingFaceEmbeddings(
     model_name=EMBED_MODEL_NAME,
@@ -55,7 +54,8 @@ vectorstore = Chroma(
 )
 retriever = vectorstore.as_retriever(search_kwargs={"k": 20})  # k=20
 
-# System prompt template for long, detailed answers
+# System prompt template
+
 def build_system(context: str) -> dict:
     """
     Build a comprehensive system prompt:
@@ -81,8 +81,6 @@ Retain memory of previous user messages to support follow-up interactions.
 """
     return {"role": "system", "content": prompt}
 
- {"role": "system", "content": prompt}
-
 st.title("🩺 DocChatter RAG (Streaming & Memory)")
 
 # Initialize chat history
@@ -137,7 +135,6 @@ with chat_tab:
                     answer += delta
                     stream_placeholder.write(answer)
             except (IndexError, AttributeError):
-                # Skip empty or malformed token
                 continue
 
         # Save assistant response