Update app.py

app.py

@@ -18,11 +18,6 @@ import getpass
 if 'OPENAI_API_KEY' not in os.environ:
     os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API key:')
 
-# Set up embedding function
-@pxt.expr_udf
-def e5_embed(text: str) -> np.ndarray:
-    return sentence_transformer(text, model_id='intfloat/e5-large-v2')
-
 # Create prompt function
 @pxt.udf
 def create_prompt(top_k_list: list[dict], question: str) -> str:
@@ -70,8 +65,8 @@ def process_files(token, pdf_files, chunk_limit, chunk_separator):
     t = pxt.create_table(
         'chatbot_demo.documents',
         {
-            'document': pxt.DocumentType(nullable=True),
-            'question': pxt.StringType(nullable=True)
+            'document': pxt.Document,
+            'question': pxt.String
         }
     )
 
@@ -90,8 +85,11 @@ def process_files(token, pdf_files, chunk_limit, chunk_separator):
         )
     )
 
-    # Add an embedding index to the chunks for similarity search
-    chunks_t.add_embedding_index('text', string_embed=e5_embed)
+    # Add embedding index with updated pattern
+    chunks_t.add_embedding_index(
+        'text', 
+        string_embed=sentence_transformer.using(model_id='intfloat/e5-large-v2')
+    )
 
     @chunks_t.query
     def top_k(query_text: str):
@@ -102,20 +100,20 @@ def process_files(token, pdf_files, chunk_limit, chunk_separator):
                 .limit(5)
         )
 
-    # Add computed columns to create the chain of transformations
-    t['question_context'] = chunks_t.queries.top_k(t.question)
-    t['prompt'] = create_prompt(t.question_context, t.question)
-    t['messages'] = create_messages(t.prompt)  # New computed column for messages
+    # Add computed columns using keyword argument syntax
+    t.add_computed_column(question_context=chunks_t.queries.top_k(t.question))
+    t.add_computed_column(prompt=create_prompt(t.question_context, t.question))
+    t.add_computed_column(messages=create_messages(t.prompt))
     
     # Add the response column using the messages computed column
-    t['response'] = openai.chat_completions(
+    t.add_computed_column(response=openai.chat_completions(
         model='gpt-4o-mini-2024-07-18',
-        messages=t.messages,  # Use the computed messages column
+        messages=t.messages,
         max_tokens=300,
         top_p=0.9,
         temperature=0.7
-    )
-    t['gpt4omini'] = t.response.choices[0].message.content
+    ))
+    t.add_computed_column(gpt4omini=t.response.choices[0].message.content)
 
     return "Files processed successfully. You can start the discussion."
 
@@ -142,7 +140,7 @@ with gr.Blocks(theme=gr.themes.Base()) as demo:
     gr.Markdown(
         """
         <div>
-            <img src="https://raw.githubusercontent.com/pixeltable/pixeltable/main/docs/source/data/pixeltable-logo-large.png" alt="Pixeltable" style="max-width: 200px; margin-bottom: 20px;" />
+            <img src="https://raw.githubusercontent.com/pixeltable/pixeltable/main/docs/resources/pixeltable-logo-large.png" alt="Pixeltable" style="max-width: 200px; margin-bottom: 20px;" />
             <h1 style="margin-bottom: 0.5em;">AI Chatbot With Retrieval-Augmented Generation (RAG)</h1>
         </div>
         """