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Files changed (3) hide show
  1. app.py +10 -11
  2. query.py +18 -7
  3. utils.py +4 -2
app.py CHANGED
@@ -10,6 +10,8 @@ from streamlit_feedback import streamlit_feedback
10
 
11
  from utils import thumbs_feedback, send_amplitude_data, escape_dollars_outside_latex
12
 
 
 
13
 
14
  max_examples = 6
15
  languages = {'English': 'eng', 'Spanish': 'spa', 'French': 'fra', 'Chinese': 'zho', 'German': 'deu', 'Hindi': 'hin', 'Arabic': 'ara',
@@ -32,7 +34,7 @@ def isTrue(x) -> bool:
32
 
33
  def launch_bot():
34
  def reset():
35
- st.session_state.messages = [{"role": "assistant", "content": "How may I help you?"}]
36
  st.session_state.ex_prompt = None
37
  st.session_state.first_turn = True
38
 
@@ -84,7 +86,7 @@ def launch_bot():
84
  image = Image.open('Vectara-logo.png')
85
  st.image(image, width=175)
86
  st.markdown(f"## About\n\n"
87
- f"This demo uses Retrieval Augmented Generation to ask questions about {cfg.source_data_desc}\n")
88
 
89
  cfg.language = st.selectbox('Language:', languages.keys())
90
  if st.session_state.language != cfg.language:
@@ -103,11 +105,8 @@ def launch_bot():
103
  st.markdown(
104
  "## How this works?\n"
105
  "This app was built with [Vectara](https://vectara.com).\n"
106
- "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"
107
  "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"
108
- )
109
- st.markdown("---")
110
-
111
 
112
  st.markdown(f"<center> <h2> Vectara AI Assistant: {cfg.title} </h2> </center>", unsafe_allow_html=True)
113
 
@@ -116,7 +115,7 @@ def launch_bot():
116
 
117
  # Display chat messages
118
  for message in st.session_state.messages:
119
- with st.chat_message(message["role"]):
120
  st.write(message["content"])
121
 
122
  example_container = st.empty()
@@ -131,14 +130,14 @@ def launch_bot():
131
  else:
132
  prompt = st.chat_input()
133
  if prompt:
134
- st.session_state.messages.append({"role": "user", "content": prompt})
135
- with st.chat_message("user"):
136
  st.write(prompt)
137
  st.session_state.ex_prompt = None
138
 
139
  # Generate a new response if last message is not from assistant
140
  if st.session_state.messages[-1]["role"] != "assistant":
141
- with st.chat_message("assistant"):
142
  if cfg.streaming:
143
  stream = generate_streaming_response(prompt)
144
  response = st.write_stream(stream)
@@ -148,7 +147,7 @@ def launch_bot():
148
  st.write(response)
149
 
150
  response = escape_dollars_outside_latex(response)
151
- message = {"role": "assistant", "content": response}
152
  st.session_state.messages.append(message)
153
 
154
  # Send query and response to Amplitude Analytics
 
10
 
11
  from utils import thumbs_feedback, send_amplitude_data, escape_dollars_outside_latex
12
 
13
+ from dotenv import load_dotenv
14
+ load_dotenv(override=True)
15
 
16
  max_examples = 6
17
  languages = {'English': 'eng', 'Spanish': 'spa', 'French': 'fra', 'Chinese': 'zho', 'German': 'deu', 'Hindi': 'hin', 'Arabic': 'ara',
 
34
 
35
  def launch_bot():
36
  def reset():
37
+ st.session_state.messages = [{"role": "assistant", "content": "How may I help you?", "avatar": 'πŸ€–'}]
38
  st.session_state.ex_prompt = None
39
  st.session_state.first_turn = True
40
 
 
86
  image = Image.open('Vectara-logo.png')
87
  st.image(image, width=175)
88
  st.markdown(f"## About\n\n"
89
+ f"This demo uses Vectara RAG to ask questions about {cfg.source_data_desc}\n")
90
 
91
  cfg.language = st.selectbox('Language:', languages.keys())
92
  if st.session_state.language != cfg.language:
 
105
  st.markdown(
106
  "## How this works?\n"
107
  "This app was built with [Vectara](https://vectara.com).\n"
 
108
  "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"
109
+ )
 
 
110
 
111
  st.markdown(f"<center> <h2> Vectara AI Assistant: {cfg.title} </h2> </center>", unsafe_allow_html=True)
112
 
 
115
 
116
  # Display chat messages
117
  for message in st.session_state.messages:
118
+ with st.chat_message(message["role"], avatar=message["avatar"]):
119
  st.write(message["content"])
120
 
121
  example_container = st.empty()
 
130
  else:
131
  prompt = st.chat_input()
132
  if prompt:
133
+ st.session_state.messages.append({"role": "user", "content": prompt, "avatar": 'πŸ§‘β€πŸ’»'})
134
+ with st.chat_message("user", avatar="πŸ§‘β€πŸ’»"):
135
  st.write(prompt)
136
  st.session_state.ex_prompt = None
137
 
138
  # Generate a new response if last message is not from assistant
139
  if st.session_state.messages[-1]["role"] != "assistant":
140
+ with st.chat_message("assistant", avatar="πŸ€–"):
141
  if cfg.streaming:
142
  stream = generate_streaming_response(prompt)
143
  response = st.write_stream(stream)
 
147
  st.write(response)
148
 
149
  response = escape_dollars_outside_latex(response)
150
+ message = {"role": "assistant", "content": response, "avatar": 'πŸ€–'}
151
  st.session_state.messages.append(message)
152
 
153
  # Send query and response to Amplitude Analytics
query.py CHANGED
@@ -32,20 +32,30 @@ class VectaraQuery():
32
  },
33
  'reranker':
34
  {
35
- 'type': 'customer_reranker',
36
- 'reranker_id': 'rnk_272725719'
 
 
 
 
 
 
 
 
 
37
  },
38
  },
39
  'generation':
40
  {
41
- 'prompt_name': self.prompt_name,
42
- 'max_used_search_results': 10,
43
  'response_language': response_lang,
44
  'citations':
45
  {
46
- 'style': 'none'
 
47
  },
48
- 'enable_factual_consistency_score': False
49
  },
50
  'chat':
51
  {
@@ -79,7 +89,6 @@ class VectaraQuery():
79
  endpoint = "https://api.vectara.io/v2/chats"
80
 
81
  body = self.get_body(query_str, language, stream=False)
82
-
83
  response = requests.post(endpoint, data=json.dumps(body), verify=True, headers=self.get_headers())
84
 
85
  if response.status_code != 200:
@@ -125,5 +134,7 @@ class VectaraQuery():
125
  chunk = line['generation_chunk']
126
  chunks.append(chunk)
127
  yield chunk
 
 
128
 
129
  return ''.join(chunks)
 
32
  },
33
  'reranker':
34
  {
35
+ "type": "chain",
36
+ "rerankers": [
37
+ {
38
+ "type": "customer_reranker",
39
+ "reranker_name": "Rerank_Multilingual_v1"
40
+ },
41
+ {
42
+ "type": "mmr",
43
+ "diversity_bias": 0.05
44
+ }
45
+ ]
46
  },
47
  },
48
  'generation':
49
  {
50
+ 'generation_preset_name': self.prompt_name,
51
+ 'max_used_search_results': 7,
52
  'response_language': response_lang,
53
  'citations':
54
  {
55
+ 'style': 'markdown',
56
+ 'url_pattern': '{doc.url}'
57
  },
58
+ 'enable_factual_consistency_score': True
59
  },
60
  'chat':
61
  {
 
89
  endpoint = "https://api.vectara.io/v2/chats"
90
 
91
  body = self.get_body(query_str, language, stream=False)
 
92
  response = requests.post(endpoint, data=json.dumps(body), verify=True, headers=self.get_headers())
93
 
94
  if response.status_code != 200:
 
134
  chunk = line['generation_chunk']
135
  chunks.append(chunk)
136
  yield chunk
137
+ elif line['type'] == 'chat_info':
138
+ self.conv_id = line['chat_id']
139
 
140
  return ''.join(chunks)
utils.py CHANGED
@@ -25,9 +25,11 @@ def thumbs_feedback(feedback, **kwargs):
25
  st.session_state.feedback_key += 1
26
 
27
  def send_amplitude_data(user_query, chat_response, demo_name, language, feedback=None):
28
- # Send query and response to Amplitude Analytics
 
 
29
  data = {
30
- "api_key": os.getenv('AMPLITUDE_TOKEN'),
31
  "events": [{
32
  "device_id": st.session_state.device_id,
33
  "event_type": "submitted_query",
 
25
  st.session_state.feedback_key += 1
26
 
27
  def send_amplitude_data(user_query, chat_response, demo_name, language, feedback=None):
28
+ amplitude_api_key = os.getenv('AMPLITUDE_TOKEN')
29
+ if not amplitude_api_key:
30
+ return
31
  data = {
32
+ "api_key": amplitude_api_key,
33
  "events": [{
34
  "device_id": st.session_state.device_id,
35
  "event_type": "submitted_query",