Update app.py

app.py

@@ -5,8 +5,8 @@ from groq import Groq
 from PyPDF2 import PdfReader
 
 # Set your Groq API key here or use environment variable
-GROQ_API_TOKEN = os.getenv("groq_api")
-client = Groq(api_key=GROQ_API_TOKEN)
+GROQ_API_TOKEN = os.getenv("GROQ_API_TOKEN")
+client = Groq(api_key=groq_api)
 
 # Initialize the SentenceTransformer model for embeddings
 retriever = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
@@ -16,16 +16,21 @@ documents = []
 document_embeddings = None
 
 # Function to retrieve top relevant document
-def retrieve(query, top_k=1):
+def retrieve(query, top_k=3):  # Retrieve top 3 relevant documents
     if document_embeddings is None:
         return None
     query_embedding = retriever.encode(query, convert_to_tensor=True)
     hits = util.semantic_search(query_embedding, document_embeddings, top_k=top_k)
     top_docs = [documents[hit['corpus_id']] for hit in hits[0]]
-    return top_docs[0] if hits[0] else None
+    return ' '.join(top_docs) if hits[0] else None  # Concatenate the top documents
 
 # Function to generate response using Groq
 def generate_response(query, context):
+    # Limit context size to prevent exceeding token limits
+    max_context_length = 200  # Adjust this number based on your needs
+    if len(context.split()) > max_context_length:
+        context = ' '.join(context.split()[:max_context_length])  # Truncate context to the first N words
+
     response = client.chat.completions.create(
         messages=[{
             "role": "user",
@@ -78,10 +83,6 @@ if question:
 
 
 
-
-
-
-
 # import os
 # import streamlit as st
 # from sentence_transformers import SentenceTransformer, util