Update app.py

app.py

@@ -50,7 +50,7 @@ def process_answer(question, full_text):
     docs = loader.load()
 
     # Chunk the documents with increased size and overlap
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=300)
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=300)
     splits = text_splitter.split_documents(docs)
 
     # Load embeddings
@@ -64,14 +64,25 @@ def process_answer(question, full_text):
     llm = load_model()
 
     # Create a custom prompt
-    prompt_template = PromptTemplate.from_template("""
-    You are a helpful assistant. Use the following context to answer the question as accurately and thoroughly as possible.
-
-    Context: {context}
-
-    Question: {question}
-
-    Answer in detail:""")
+    prompt_template = PromptTemplate(
+    input_variables=["context", "question"],
+    template="""
+    You are a helpful assistant. Carefully analyze the given context and extract direct answers ONLY from it.
+     
+    Context:
+    {context}
+     
+    Question:
+    {question}
+     
+    Important Instructions:
+    - If the question asks for a URL (e.g., LinkedIn link), provide the exact URL as it appears.
+    - Do NOT summarize or paraphrase.
+    - If the information is not in the context, say "Not found in the document."
+     
+    Answer:
+    """)
+     
 
     # Retrieval QA with custom prompt
     qa_chain = RetrievalQA.from_chain_type(