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app.py
CHANGED
@@ -10,6 +10,8 @@ from streamlit_feedback import streamlit_feedback
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from utils import thumbs_feedback, send_amplitude_data, escape_dollars_outside_latex
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max_examples = 6
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languages = {'English': 'eng', 'Spanish': 'spa', 'French': 'fra', 'Chinese': 'zho', 'German': 'deu', 'Hindi': 'hin', 'Arabic': 'ara',
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@@ -32,7 +34,7 @@ def isTrue(x) -> bool:
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def launch_bot():
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def reset():
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st.session_state.messages = [{"role": "assistant", "content": "How may I help you?"}]
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st.session_state.ex_prompt = None
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st.session_state.first_turn = True
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@@ -84,7 +86,7 @@ def launch_bot():
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image = Image.open('Vectara-logo.png')
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st.image(image, width=175)
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st.markdown(f"## About\n\n"
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f"This demo uses
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cfg.language = st.selectbox('Language:', languages.keys())
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if st.session_state.language != cfg.language:
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@@ -103,11 +105,8 @@ def launch_bot():
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st.markdown(
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"## How this works?\n"
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"This app was built with [Vectara](https://vectara.com).\n"
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"Vectara's [Indexing API](https://docs.vectara.com/docs/api-reference/indexing-apis/indexing) was used to ingest the data into a Vectara corpus (or index).\n\n"
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"This app uses Vectara [Chat API](https://docs.vectara.com/docs/console-ui/vectara-chat-overview) to query the corpus and present the results to you, answering your question.\n\n"
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)
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st.markdown("---")
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st.markdown(f"<center> <h2> Vectara AI Assistant: {cfg.title} </h2> </center>", unsafe_allow_html=True)
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@@ -116,7 +115,7 @@ def launch_bot():
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# Display chat messages
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for message in st.session_state.messages:
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with st.chat_message(message["role"]):
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st.write(message["content"])
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example_container = st.empty()
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@@ -131,14 +130,14 @@ def launch_bot():
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else:
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prompt = st.chat_input()
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if prompt:
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st.session_state.messages.append({"role": "user", "content": prompt})
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with st.chat_message("user"):
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st.write(prompt)
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st.session_state.ex_prompt = None
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# Generate a new response if last message is not from assistant
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if st.session_state.messages[-1]["role"] != "assistant":
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with st.chat_message("assistant"):
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if cfg.streaming:
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stream = generate_streaming_response(prompt)
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response = st.write_stream(stream)
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@@ -148,7 +147,7 @@ def launch_bot():
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st.write(response)
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response = escape_dollars_outside_latex(response)
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message = {"role": "assistant", "content": response}
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st.session_state.messages.append(message)
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# Send query and response to Amplitude Analytics
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from utils import thumbs_feedback, send_amplitude_data, escape_dollars_outside_latex
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from dotenv import load_dotenv
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load_dotenv(override=True)
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max_examples = 6
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languages = {'English': 'eng', 'Spanish': 'spa', 'French': 'fra', 'Chinese': 'zho', 'German': 'deu', 'Hindi': 'hin', 'Arabic': 'ara',
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def launch_bot():
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def reset():
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st.session_state.messages = [{"role": "assistant", "content": "How may I help you?", "avatar": 'π€'}]
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st.session_state.ex_prompt = None
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st.session_state.first_turn = True
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image = Image.open('Vectara-logo.png')
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st.image(image, width=175)
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st.markdown(f"## About\n\n"
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f"This demo uses Vectara RAG to ask questions about {cfg.source_data_desc}\n")
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cfg.language = st.selectbox('Language:', languages.keys())
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if st.session_state.language != cfg.language:
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st.markdown(
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"## How this works?\n"
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"This app was built with [Vectara](https://vectara.com).\n"
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"This app uses Vectara [Chat API](https://docs.vectara.com/docs/console-ui/vectara-chat-overview) to query the corpus and present the results to you, answering your question.\n\n"
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)
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st.markdown(f"<center> <h2> Vectara AI Assistant: {cfg.title} </h2> </center>", unsafe_allow_html=True)
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# Display chat messages
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for message in st.session_state.messages:
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with st.chat_message(message["role"], avatar=message["avatar"]):
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st.write(message["content"])
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example_container = st.empty()
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else:
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prompt = st.chat_input()
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if prompt:
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st.session_state.messages.append({"role": "user", "content": prompt, "avatar": 'π§βπ»'})
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with st.chat_message("user", avatar="π§βπ»"):
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st.write(prompt)
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st.session_state.ex_prompt = None
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# Generate a new response if last message is not from assistant
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if st.session_state.messages[-1]["role"] != "assistant":
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with st.chat_message("assistant", avatar="π€"):
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if cfg.streaming:
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stream = generate_streaming_response(prompt)
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response = st.write_stream(stream)
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st.write(response)
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response = escape_dollars_outside_latex(response)
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message = {"role": "assistant", "content": response, "avatar": 'π€'}
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st.session_state.messages.append(message)
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# Send query and response to Amplitude Analytics
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query.py
CHANGED
@@ -32,20 +32,30 @@ class VectaraQuery():
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},
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'reranker':
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{
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},
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},
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'generation':
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{
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'
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'max_used_search_results':
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'response_language': response_lang,
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'citations':
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{
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'style': '
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},
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'enable_factual_consistency_score':
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},
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'chat':
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{
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@@ -79,7 +89,6 @@ class VectaraQuery():
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endpoint = "https://api.vectara.io/v2/chats"
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body = self.get_body(query_str, language, stream=False)
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response = requests.post(endpoint, data=json.dumps(body), verify=True, headers=self.get_headers())
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if response.status_code != 200:
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@@ -125,5 +134,7 @@ class VectaraQuery():
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chunk = line['generation_chunk']
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chunks.append(chunk)
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yield chunk
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return ''.join(chunks)
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},
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'reranker':
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{
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"type": "chain",
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"rerankers": [
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{
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"type": "customer_reranker",
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"reranker_name": "Rerank_Multilingual_v1"
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},
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{
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"type": "mmr",
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"diversity_bias": 0.05
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}
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]
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},
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},
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'generation':
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{
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'generation_preset_name': self.prompt_name,
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'max_used_search_results': 7,
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'response_language': response_lang,
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'citations':
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{
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'style': 'markdown',
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'url_pattern': '{doc.url}'
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},
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'enable_factual_consistency_score': True
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},
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'chat':
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{
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endpoint = "https://api.vectara.io/v2/chats"
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body = self.get_body(query_str, language, stream=False)
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response = requests.post(endpoint, data=json.dumps(body), verify=True, headers=self.get_headers())
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if response.status_code != 200:
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chunk = line['generation_chunk']
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chunks.append(chunk)
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yield chunk
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elif line['type'] == 'chat_info':
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self.conv_id = line['chat_id']
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return ''.join(chunks)
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utils.py
CHANGED
@@ -25,9 +25,11 @@ def thumbs_feedback(feedback, **kwargs):
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st.session_state.feedback_key += 1
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def send_amplitude_data(user_query, chat_response, demo_name, language, feedback=None):
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data = {
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"api_key":
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"events": [{
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"device_id": st.session_state.device_id,
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"event_type": "submitted_query",
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st.session_state.feedback_key += 1
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def send_amplitude_data(user_query, chat_response, demo_name, language, feedback=None):
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amplitude_api_key = os.getenv('AMPLITUDE_TOKEN')
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if not amplitude_api_key:
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return
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data = {
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"api_key": amplitude_api_key,
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"events": [{
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"device_id": st.session_state.device_id,
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"event_type": "submitted_query",
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