Update chunking config & web-app prompt

cdk/examples/generative_ai_rag/web-app/pages/rag_integration.py

@@ -78,9 +78,9 @@ def get_prompt_template(retrieved_passages: List[str]) -> str:
        - Use the information from the retrieved passages to recommend sessions.
        - Prioritize recommendations based on:
          a) Exact matches in the "Related AWS Services" field (highest priority)
-         b) Relevance in the "Description" and "Title" fields
-         c) Relevance to the Topic and Areas of Interest
-       - Always include "Description", "Time" and "Venue" information for recommended sessions and summary it.
+         b) Relevance in the "Related AWS Services", "Description" and "Title" fields
+         c) Relevance to the "Topic" and "Areas of Interest"
+       - Must to include "Description", "Time" and "Venue" information for recommended sessions.
 
     3. For re:Invent session information questions:
        - Use the information from the retrieved passages to provide detailed session information.
@@ -101,20 +101,41 @@ def get_prompt_template(retrieved_passages: List[str]) -> str:
     - Verify user assertions against search results; don't assume user statements are factual.
 
     Here are the retrieved passages:
+
     {retrieved_passages}
 
-    IMPORTANT: Your final response should only contain the actual answer to the user's question. 
+    IMPORTANT:
+    Your final response should only contain the actual answer to the user's question. 
     Do not include any explanation of your thought process, categorization, or analysis in the final response. 
+    If retrieved passages are empty and question type is not GENERAL, respond with "Sorry. I couldn't find any related information."
     
-    Format your response as follows:
+    CRITICAL RESPONSE FORMAT:
+    You MUST format your entire response EXACTLY as follows, with no exceptions:
+
+    [QUESTION_TYPE]
+    GENERAL or REINVENT_RECOMMENDATION or REINVENT_INFORMATION
+    [/QUESTION_TYPE]
+    [RESPONSE]
+    Your complete answer here, with no text outside these tags.
+    [/RESPONSE]
+
+    IMPORTANT RULES:
+    1. ALWAYS include both [QUESTION_TYPE] and [RESPONSE] tags.
+    2. [QUESTION_TYPE] must contain ONLY ONE of the three specified types, nothing else.
+    3. [RESPONSE] must contain your COMPLETE answer and nothing else.
+    4. DO NOT include ANY text outside of these tags.
+    5. If you cannot provide an answer, still use the tags and put your "Sorry" message inside the [RESPONSE] tags.
+    6. Never use [GENERAL], [REINVENT_RECOMMENDATION], or [REINVENT_INFORMATION] as standalone tags.
+
+    EXAMPLE OF CORRECT FORMAT:
     [QUESTION_TYPE]
-    The type of question (GENERAL, REINVENT_RECOMMENDATION, or REINVENT_INFORMATION)
+    GENERAL
     [/QUESTION_TYPE]
     [RESPONSE]
-    Your actual response here, without any preamble or explanation of your thought process.
+    This is where your complete answer would go, with no other text outside these tags.
     [/RESPONSE]
 
-    Everything outside the [RESPONSE] tags will be discarded, so ensure your complete answer is within these tags.
+    Failure to follow this format exactly will result in an error. Double-check your response before submitting.
     """
 
 def retrieve_from_knowledge_base(client: Any, knowledge_base_id: str, prompt: str) -> List[Dict[str, Any]]:
@@ -164,14 +185,36 @@ def main():
             retrieved_passages = [result['content']['text'] for result in retrieved_results]
             formatted_passages = "\n\n".join(f"Passage {i+1}:\n{passage}" for i, passage in enumerate(retrieved_passages))
 
+            print(formatted_passages)
+
+            # Initialize variables to store the full response and the content after the [RESPONSE] tag
+            full_response = ""
+            response_content = ""
+            response_started = False
+
+            message_placeholder = st.chat_message("assistant").empty()
+
             # Generate the final response using the invoke_model API
             system_prompt = get_prompt_template(formatted_passages)
-
-            message_placeholder = st.chat_message("assistant").empty()
-            full_response = ""
+
+            # Process each chunk from the Bedrock stream
             for chunk in invoke_bedrock_stream(bedrock_runtime_client, system_prompt, prompt):
+                # Accumulate the full response
                 full_response += chunk
-                message_placeholder.markdown(full_response + "▌")
+
+                # Check if we've reached the [RESPONSE] tag
+                if '[RESPONSE]' in chunk:
+                    response_started = True
+                    # Extract the content after the [RESPONSE] tag
+                    response_content = chunk.split('[RESPONSE]')[1]
+                elif response_started:
+                    # If we're past the [RESPONSE] tag, continue accumulating the response content
+                    response_content += chunk
+
+                # Display the response content if we've started collecting it
+                if response_started:
+                    # Remove the [/RESPONSE] tag if present and display the content
+                    message_placeholder.markdown(response_content.replace('[/RESPONSE]', '') + "▌")
 
             # Extract question type and response
             import re
@@ -181,12 +224,16 @@ def main():
             question_type = question_type_match.group(1).strip() if question_type_match else "UNKNOWN"
             final_response = response_match.group(1).strip() if response_match else "I apologize. There was an issue generating an appropriate response."
 
+            print(full_response)
+            print(question_type)
+            print(final_response)
+
             message_placeholder.markdown(final_response)
 
             st.session_state.messages.append({"role": "assistant", "content": final_response})
 
             # Display citations only for non-general questions
-            if question_type != "GENERAL":
+            if question_type not in ["GENERAL", "UNKNOWN"]:
                 with st.expander("Data Sources"):
                     for i, result in enumerate(retrieved_results, 1):
                         content = result['content']['text']

cdk/examples/other_stack/bedrock_agent_stack.py

@@ -58,7 +58,10 @@ def __init__(
             bucket=self.bucket,
             knowledge_base=self.knowledge_base,
             data_source_name='ReinventSessionInformationText',
-            chunking_strategy= bedrock.ChunkingStrategy.FIXED_SIZE,
+            chunking_strategy= bedrock.ChunkingStrategy.fixed_size(
+                max_tokens= 512,
+                overlap_percentage= 20
+            )
         )
 
         # create parameter store