Update app.py

app.py

@@ -67,55 +67,27 @@ def get_titan_embedding(bedrock_client, doc_name, text, attempt=0, cutoff=10000)
     
     retries = 5
     
-    try:
-        model_id = 'amazon.titan-embed-text-v1' 
-        accept = 'application/json' 
-        content_type = 'application/json'
-        
-        body = json.dumps({
-            "inputText": text,
-        })
-
-        # Invoke model 
-        response = bedrock_client.invoke_model(
-            body=body, 
-            modelId=model_id, 
-            accept=accept, 
-            contentType=content_type
-        )
-        
-        # Print response
-        response_body = json.loads(response['body'].read())
+    model_id = 'amazon.titan-embed-text-v1' 
+    accept = 'application/json' 
+    content_type = 'application/json'
+    
+    body = json.dumps({
+        "inputText": text,
+    })
+
+    # Invoke model 
+    response = bedrock_client.invoke_model(
+        body=body, 
+        modelId=model_id, 
+        accept=accept, 
+        contentType=content_type
+    )
+    
+    # Print response
+    response_body = json.loads(response['body'].read())
 
 
-    # Handle a few common client exceptions
-    except botocore.exceptions.ClientError as error: 
-        if error.response['Error']['Code'] == 'ThrottlingException':
-            if attempt + 1 == retries:
-                return None
-                
-            delay = 2 ** (attempt + 1);               
-            time.sleep(delay)
-            return get_titan_embedding(bedrock_client, doc_name, text, attempt=attempt + 1)
-            
-        elif error.response['Error']['Code'] == 'ValidationException':
-            # get chunks of text length 20000 characters
-            text_chunks = [text[i:i+cutoff] for i in range(0, len(text), cutoff)]
-            embeddings = []
-            for chunk in text_chunks:
-                embeddings.append(get_titan_embedding(bedrock_client, doc_name, chunk))
-            
-            # return the average of the embeddinngs
-            return np.mean(embeddings, axis=0)
-        
-        else:
-            yield f"Unhandled Exception when processing {doc_name}! : {error.response['Error']['Code']}"
-            return None
 
-    # Catch-all for any other exceptions
-    except Exception as error:
-        yield f"Unhandled Exception when processing {doc_name}: {type(error).__name__}"
-        return None
 
     return response_body.get('embedding')
 
@@ -129,6 +101,8 @@ def ask_ds(message, history):
 
     # RAG
     question_embedding = get_titan_embedding(bedrock_client, 'question', question)
+
+    yield f"question embedding: {question_embedding}"
     
     similar_documents = []
     for file, data in extractions.items():