Precription feature completed with safeguards

agents/get_prescription_text_agent.py

@@ -28,4 +28,6 @@ def get_prescription_text(messages):
         messages=cleaned_messages
     )   
     prescription_text = response.choices[0].message.content.strip()
+    prepend_text = "Lab tests extracted from prescription by the LLM agent: \n"
+    prescription_text =  prepend_text + prescription_text
     return prescription_text

app.py

@@ -51,13 +51,17 @@ with gr.Blocks() as demo:
             return base64.b64encode(f.read()).decode("utf-8")
     
     def load_welcome():
-        return [
-            {"role": "assistant", "content": bot_welcome_message}
-        ]
+        history = []
+        history.append({"role": "system", "content": openai_opening_system_message})
+        history.append({"role": "assistant", "content": bot_welcome_message})
+        return history
     
     def clear_and_load():
         # Return the welcome message
-        return [{"role": "assistant", "content": bot_welcome_message}], None
+        history = []
+        history.append({"role": "system", "content": openai_opening_system_message})
+        history.append({"role": "assistant", "content": bot_welcome_message})
+        return history, None
         
     def add_message(history, message):
         # Send the image to the agent4_get_prescription_text
@@ -74,9 +78,12 @@ with gr.Blocks() as demo:
                 "image_url": {"url": f"data:image/jpeg;base64,{encoded_content}"}
             })
             history.append({"role": "user", "content": {"path": x}})
-        # call agent4_get_prescription_text to get prescription text
-        prescription_text = "Extracted lab tests from prescription: \n" + agent4_get_prescription_text(messages)
-        history.append({"role": "system", "content": prescription_text})
+        
+        # call agent4_get_prescription_text if there is an image_url in the message
+        has_image_url = any("image_url" in item for item in messages[0]["content"])
+        if has_image_url:
+            prescription_text = agent4_get_prescription_text(messages)
+            history.append({"role": "system", "content": prescription_text})
         
         if message["text"] is not None:
             history.append({"role": "user", "content": message["text"]})
@@ -84,6 +91,7 @@ with gr.Blocks() as demo:
         return history, gr.MultimodalTextbox(value=None, interactive=False, file_count="multiple", placeholder="Enter message or upload file...")
     
     def respond(history):
+
         if len(history) == 2:
             history.insert(0,{"role": "system", "content": openai_opening_system_message})
         messages = copy.deepcopy(history)
@@ -108,7 +116,6 @@ with gr.Blocks() as demo:
                 "content": msg["content"]
             }
             clean_messages.append(clean_msg)
-
         ########################### AGENTIC WORKFLOW ##########################
         # Call Agent1- the RAG Decision Agent
         if clean_messages[-1]["role"] == "system" and "No prescription found" in clean_messages[-1]["content"]:

prompts.py

@@ -11,19 +11,38 @@ and may vary from the information provided here.
 **Can I help you with anything?**
 """
 openai_opening_system_message = """"You are a helpful assistant of a diagnostic 
-services business in an agentic AI framework. Provide the response in markdown format.
+services business in an agentic AI framework. 
 The system uses RAG to retrieve relevant information from a knowledge base.
-You can also answer questions based on the information provided by the user.
-If the user has uploaded an image and an agent has replied in the message that No prescription
-found, then just reply "The image does not seem to that of a prescription, please 
-upload good quality staright images of prescription" and nothing else.
+You can also answer questions based on the information provided by the user but 
+only regarding the diagnostic services business.
+If the user has uploaded an image and an agent has replied in the message that 
+No prescription found, then just reply "The image does not seem like a prescription, please 
+upload good quality staright images of prescription without any private information" 
+and nothing else.
 Do not provide any medical advice or diagnosis. 
+
+Safeguards:
+1. If the user uploads a prescription image, and the agent which reads the 
+prescription has replied with some tests, then after your reply, always ask if 
+you had missed any test.
+2. Do not provide any information other than the information available in the
+knowledge base, even if the user asks for it.
+2. If you are giving information about the cost of a test or any offers, always 
+mention that the final cost will be available at the time of booking, and may 
+vary from the information provided by you.
+3. If the user asks for a medical advice or diagnosis, politely inform them that
+you are not a medical professional and cannot provide medical advice.
+4. Do not add Laboratory specific information which is not present in the
+knowledge base. For example, if there is no information about bringing an ID in 
+the knowledge base, do not ask users to bring an ID.
+5. When in doubt, ask user to browse the website or contact customer care.
+
+Provide the response in markdown format.
 """
 
 prescription_text_user = f"""You are an agent of a Diagnostics Lab agentic AI 
 Chatbot system using RAG on a knowledge base. Your job is to extract the
 lab tests advised by the doctor in the prescription image uploaded by the user.
-The user has uploaded an image. 
 Check if the image contains a medical prescription, if not, just reply 
 "No prescription found". DO NOT provide any other information about the image 
 if the image is not a prescription.