Update app.py

app.py

@@ -36,7 +36,7 @@ def create_web_search_vectors(search_results):
             documents.append(Document(page_content=content, metadata={"source": result['href']}))
     return FAISS.from_documents(documents, embed)
 
-async def get_response_with_search(query, model, use_embeddings, num_calls=3, temperature=0.2):
+async def get_response_with_search(query, model, use_embeddings, conversation_history, num_calls=3, temperature=0.2):
     search_results = duckduckgo_search(query)
     
     if not search_results:
@@ -51,14 +51,20 @@ async def get_response_with_search(query, model, use_embeddings, num_calls=3, te
     else:
         context = "\n".join([f"{result['title']}\n{result['body']}\nSource: {result['href']}" for result in search_results])
 
-    system_message = """ You are a world-class AI system, capable of complex reasoning and reflection. 
+    system_message = """You are a world-class AI system, capable of complex reasoning and reflection. 
     Reason through the query inside <thinking> tags, and then provide your final response inside <output> tags.
     Providing comprehensive and accurate information based on web search results is essential. 
     Your goal is to synthesize the given context into a coherent and detailed response that directly addresses the user's query. 
     Please ensure that your response is well-structured, factual, and cites sources where appropriate.
-    If you detect that you made a mistake in your reasoning at any point, correct yourself inside <reflection> tags."""
+    If you detect that you made a mistake in your reasoning at any point, correct yourself inside <reflection> tags.
+    Consider the conversation history when formulating your response to maintain context and coherence."""
 
-    user_message = f"""Using the following context from web search results:
+    conversation_context = "\n".join([f"Human: {msg['human']}\nAI: {msg['ai']}" for msg in conversation_history])
+    
+    user_message = f"""Conversation history:
+{conversation_context}
+
+Using the following context from web search results:
 {context}
 
 Write a detailed and complete research document that fulfills the following user request: '{query}'
@@ -104,8 +110,11 @@ async def respond(message, history, model, temperature, num_calls, use_embedding
     logging.info(f"Number of API Calls: {num_calls}")
     logging.info(f"Use Embeddings: {use_embeddings}")
 
+    # Convert Gradio history to a list of dictionaries
+    conversation_history = [{"human": h, "ai": a} for h, a in history]
+
     try:
-        async for main_content, sources in get_response_with_search(message, model, use_embeddings, num_calls=num_calls, temperature=temperature):
+        async for main_content, sources in get_response_with_search(message, model, use_embeddings, conversation_history, num_calls=num_calls, temperature=temperature):
             response = f"{main_content}\n\n{sources}"
             yield response
     except asyncio.CancelledError:
@@ -138,8 +147,8 @@ def create_gradio_interface():
             gr.Slider(minimum=1, maximum=5, value=1, step=1, label="Number of API Calls"),
             gr.Checkbox(label="Use Embeddings", value=False),
         ],
-        title="AI-powered Web Search Assistant",
-        description="Use web search to answer questions or generate summaries.",
+        title="AI-powered Conversational Web Search Assistant",
+        description="Use web search to answer questions or generate summaries. The assistant remembers previous interactions.",
         theme=gr.Theme.from_hub("allenai/gradio-theme"),
         css=css,
         examples=[
@@ -168,6 +177,7 @@ def create_gradio_interface():
         5. Check or uncheck the "Use Embeddings" box to toggle between using embeddings or direct text summarization.
         6. Press Enter or click the submit button to get your answer.
         7. Use the provided examples or ask your own questions.
+        8. The assistant will remember previous interactions and maintain context throughout the conversation.
         """)
 
     return demo