Update app.py

app.py

@@ -8,6 +8,7 @@ from langchain.chains import create_retrieval_chain
 from langchain_community.vectorstores import FAISS
 from langchain_community.document_loaders import PyPDFLoader
 from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.prompts import PromptTemplate
 from dotenv import load_dotenv
 
 # Load environment variables
@@ -52,8 +53,6 @@ def clear_knowledge_base():
     vectors = None  # Reset the vector store
     return "Knowledge base cleared."
 
-# Function to process questions
-# Function to process questions
 def process_question(question):
     global vectors
     if vectors is None:
@@ -62,7 +61,7 @@ def process_question(question):
     # Create document retrieval chain
     retriever = vectors.as_retriever(search_type="similarity", search_kwargs={"k": 5})
     
-    # Use the invoke method instead of get_relevant_documents
+    # Use the invoke method for retrieving relevant documents
     documents = retriever.invoke(question)
     
     if not documents:
@@ -70,16 +69,18 @@ def process_question(question):
     
     # Create context from retrieved documents
     context = "\n\n".join([doc.page_content for doc in documents])
+    
+    # Combine context and question into a single string (formatted input for LLM)
+    prompt = f"Answer the question based on the provided context.\n\nContext: {context}\n\nQuestion: {question}"
 
-    # Use the invoke method for the LLM
-    response = llm.invoke({"context": context, "input": question})
+    # Pass the string to llm.invoke
+    response = llm.invoke(prompt)
 
     # Confidence score as average relevance
     confidence_score = sum([doc.metadata.get('score', 0) for doc in documents]) / len(documents)
 
     return response, context, round(confidence_score, 2)
 
-
 # CSS styling
 CSS = """
 .duplicate-button { margin: auto !important; color: white !important; background: black !important; border-radius: 100vh !important;}