Spaces:
Sleeping
Sleeping
File size: 4,886 Bytes
7f46a81 8d7a085 7f46a81 8a46321 7f46a81 8a46321 f26592e 7f46a81 8a46321 bbb0dfd 8a46321 7f46a81 8d7a085 8a46321 86ad827 8a46321 86ad827 bbb0dfd 8a46321 86ad827 bbb0dfd 86ad827 8a46321 7f46a81 e63fa66 8d7a085 8a46321 8d7a085 bbb0dfd 8d7a085 8a46321 bbb0dfd 8a46321 55fd9a2 7f46a81 8a46321 7f46a81 7ff5239 8a46321 39e2176 8a46321 39e2176 8d7a085 bbb0dfd 8d7a085 8a46321 bbb0dfd 8a46321 55fd9a2 8d7a085 8a46321 7f46a81 8d7a085 8a46321 8d7a085 8a46321 86ad827 8a46321 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 |
import requests
import json
class VectaraQuery():
def __init__(self, api_key: str, corpus_keys: list[str], prompt_name: str = None):
self.corpus_keys = corpus_keys
self.api_key = api_key
self.prompt_name = prompt_name if prompt_name else "vectara-summary-ext-24-05-sml"
self.conv_id = None
def get_body(self, query_str: str, response_lang: str, stream: False):
corpora_list = [{
'corpus_key': corpus_key, 'lexical_interpolation': 0.005
} for corpus_key in self.corpus_keys
]
return {
'query': query_str,
'search':
{
'corpora': corpora_list,
'offset': 0,
'limit': 50,
'context_configuration':
{
'sentences_before': 2,
'sentences_after': 2,
'start_tag': "%START_SNIPPET%",
'end_tag': "%END_SNIPPET%",
},
'reranker':
{
"type": "chain",
"rerankers": [
{
"type": "customer_reranker",
"reranker_name": "Rerank_Multilingual_v1"
},
{
"type": "mmr",
"diversity_bias": 0.05
}
]
},
},
'generation':
{
'generation_preset_name': self.prompt_name,
'max_used_search_results': 7,
'response_language': response_lang,
'citations':
{
'style': 'markdown',
'url_pattern': '{doc.url}'
},
'enable_factual_consistency_score': True
},
'chat':
{
'store': True
},
'stream_response': stream
}
def get_headers(self):
return {
"Content-Type": "application/json",
"Accept": "application/json",
"x-api-key": self.api_key,
"grpc-timeout": "60S"
}
def get_stream_headers(self):
return {
"Content-Type": "application/json",
"Accept": "text/event-stream",
"x-api-key": self.api_key,
"grpc-timeout": "60S"
}
def submit_query(self, query_str: str, language: str):
if self.conv_id:
endpoint = f"https://api.vectara.io/v2/chats/{self.conv_id}/turns"
else:
endpoint = "https://api.vectara.io/v2/chats"
body = self.get_body(query_str, language, stream=False)
response = requests.post(endpoint, data=json.dumps(body), verify=True, headers=self.get_headers())
if response.status_code != 200:
print(f"Query failed with code {response.status_code}, reason {response.reason}, text {response.text}")
if response.status_code == 429:
return "Sorry, Vectara chat turns exceeds plan limit."
return "Sorry, something went wrong in my brain. Please try again later."
res = response.json()
if self.conv_id is None:
self.conv_id = res['chat_id']
summary = res['answer']
return summary
def submit_query_streaming(self, query_str: str, language: str):
if self.conv_id:
endpoint = f"https://api.vectara.io/v2/chats/{self.conv_id}/turns"
else:
endpoint = "https://api.vectara.io/v2/chats"
body = self.get_body(query_str, language, stream=True)
response = requests.post(endpoint, data=json.dumps(body), verify=True, headers=self.get_stream_headers(), stream=True)
if response.status_code != 200:
print(f"Query failed with code {response.status_code}, reason {response.reason}, text {response.text}")
if response.status_code == 429:
return "Sorry, Vectara chat turns exceeds plan limit."
return "Sorry, something went wrong in my brain. Please try again later."
chunks = []
for line in response.iter_lines():
line = line.decode('utf-8')
if line: # filter out keep-alive new lines
key, value = line.split(':', 1)
if key == 'data':
line = json.loads(value)
if line['type'] == 'generation_chunk':
chunk = line['generation_chunk']
chunks.append(chunk)
yield chunk
elif line['type'] == 'chat_info':
self.conv_id = line['chat_id']
return ''.join(chunks) |