Update app.py

app.py

@@ -33,16 +33,21 @@ def create_web_search_vectors(search_results):
             documents.append(Document(page_content=content, metadata={"source": result['href']}))
     return FAISS.from_documents(documents, embed)
 
-def get_response_with_search(query, model, num_calls=3, temperature=0.2):
+def get_response_with_search(query, model, use_embeddings, num_calls=3, temperature=0.2):
     search_results = duckduckgo_search(query)
-    web_search_database = create_web_search_vectors(search_results)
-    if not web_search_database:
+    
+    if not search_results:
         yield "No web search results available. Please try again.", ""
         return
 
-    retriever = web_search_database.as_retriever(search_kwargs={"k": 5})
-    relevant_docs = retriever.get_relevant_documents(query)
-    context = "\n".join([doc.page_content for doc in relevant_docs])
+    if use_embeddings:
+        web_search_database = create_web_search_vectors(search_results)
+        retriever = web_search_database.as_retriever(search_kwargs={"k": 5})
+        relevant_docs = retriever.get_relevant_documents(query)
+        context = "\n".join([doc.page_content for doc in relevant_docs])
+    else:
+        context = "\n".join([f"{result['title']}\n{result['body']}\nSource: {result['href']}" for result in search_results])
+
     prompt = f"""Using the following context from web search results:
 {context}
 Write a detailed and complete research document that fulfills the following user request: '{query}'
@@ -63,14 +68,15 @@ After writing the document, please provide a list of sources used in your respon
                 main_content += chunk
                 yield main_content, ""
 
-def respond(message, history, model, temperature, num_calls):
+def respond(message, history, model, temperature, num_calls, use_embeddings):
     logging.info(f"User Query: {message}")
     logging.info(f"Model Used: {model}")
     logging.info(f"Temperature: {temperature}")
     logging.info(f"Number of API Calls: {num_calls}")
+    logging.info(f"Use Embeddings: {use_embeddings}")
 
     try:
-        for main_content, sources in get_response_with_search(message, model, num_calls=num_calls, temperature=temperature):
+        for main_content, sources in get_response_with_search(message, model, use_embeddings, num_calls=num_calls, temperature=temperature):
             response = f"{main_content}\n\n{sources}"
             yield response
     except Exception as e:
@@ -99,6 +105,7 @@ def create_gradio_interface():
             gr.Dropdown(choices=MODELS, label="Select Model", value=MODELS[2]),
             gr.Slider(minimum=0.1, maximum=1.0, value=0.2, step=0.1, label="Temperature"),
             gr.Slider(minimum=1, maximum=5, value=1, step=1, label="Number of API Calls"),
+            gr.Checkbox(label="Use Embeddings", value=True),
         ],
         title="AI-powered Web Search Assistant",
         description="Use web search to answer questions or generate summaries.",
@@ -127,8 +134,9 @@ def create_gradio_interface():
         2. Select the model you want to use from the dropdown.
         3. Adjust the Temperature to control the randomness of the response.
         4. Set the Number of API Calls to determine how many times the model will be queried.
-        5. Press Enter or click the submit button to get your answer.
-        6. Use the provided examples or ask your own questions.
+        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.
         """)
 
     return demo