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import requests | |
import json | |
class VectaraQuery(): | |
def __init__(self, api_key: str, customer_id: str, corpus_ids: list[str], prompt_name: str = None): | |
self.customer_id = customer_id | |
self.corpus_ids = corpus_ids | |
self.api_key = api_key | |
self.prompt_name = prompt_name if prompt_name else "vectara-experimental-summary-ext-2023-12-11-sml" | |
self.conv_id = None | |
def get_body(self, query_str: str): | |
corpora_key_list = [{ | |
'customer_id': self.customer_id, 'corpus_id': corpus_id, 'lexical_interpolation_config': {'lambda': 0.005} | |
} for corpus_id in self.corpus_ids | |
] | |
return { | |
'query': [ | |
{ | |
'query': query_str, | |
'start': 0, | |
'numResults': 50, | |
'corpusKey': corpora_key_list, | |
'context_config': { | |
'sentences_before': 2, | |
'sentences_after': 2, | |
'start_tag': "%START_SNIPPET%", | |
'end_tag': "%END_SNIPPET%", | |
}, | |
'rerankingConfig': | |
{ | |
'rerankerId': 272725719, | |
}, | |
'summary': [ | |
{ | |
'responseLang': 'eng', | |
'maxSummarizedResults': 10, | |
'summarizerPromptName': self.prompt_name, | |
'chat': { | |
'store': True, | |
'conversationId': self.conv_id | |
}, | |
'citationParams': { | |
"style": "NONE", | |
} | |
} | |
] | |
} | |
] | |
} | |
def get_headers(self): | |
return { | |
"Content-Type": "application/json", | |
"Accept": "application/json", | |
"customer-id": self.customer_id, | |
"x-api-key": self.api_key, | |
"grpc-timeout": "60S" | |
} | |
def submit_query(self, query_str: str): | |
endpoint = f"https://api.vectara.io/v1/query" | |
body = self.get_body(query_str) | |
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}") | |
return "Sorry, something went wrong in my brain. Please try again later." | |
res = response.json() | |
summary = res['responseSet'][0]['summary'][0]['text'] | |
chat = res['responseSet'][0]['summary'][0].get('chat', None) | |
if chat and chat['status'] is not None: | |
st_code = chat['status'] | |
print(f"Chat query failed with code {st_code}") | |
if st_code == 'RESOURCE_EXHAUSTED': | |
self.conv_id = None | |
return 'Sorry, Vectara chat turns exceeds plan limit.' | |
return 'Sorry, something went wrong in my brain. Please try again later.' | |
self.conv_id = chat['conversationId'] if chat else None | |
return summary | |
def submit_query_streaming(self, query_str: str): | |
endpoint = "https://api.vectara.io/v1/stream-query" | |
body = self.get_body(query_str) | |
response = requests.post(endpoint, data=json.dumps(body), verify=True, headers=self.get_headers(), stream=True) | |
if response.status_code != 200: | |
print(f"Query failed with code {response.status_code}, reason {response.reason}, text {response.text}") | |
return "Sorry, something went wrong in my brain. Please try again later." | |
chunks = [] | |
for line in response.iter_lines(): | |
if line: # filter out keep-alive new lines | |
data = json.loads(line.decode('utf-8')) | |
res = data['result'] | |
response_set = res['responseSet'] | |
if response_set is None: | |
# grab next chunk and yield it as output | |
summary = res.get('summary', None) | |
if summary is None or len(summary)==0: | |
continue | |
else: | |
chat = summary.get('chat', None) | |
if chat and chat.get('status', None): | |
st_code = chat['status'] | |
print(f"Chat query failed with code {st_code}") | |
if st_code == 'RESOURCE_EXHAUSTED': | |
self.conv_id = None | |
return 'Sorry, Vectara chat turns exceeds plan limit.' | |
return 'Sorry, something went wrong in my brain. Please try again later.' | |
conv_id = chat.get('conversationId', None) if chat else None | |
if conv_id: | |
self.conv_id = conv_id | |
chunk = summary['text'] | |
chunks.append(chunk) | |
yield chunk | |
if summary['done']: | |
break | |
return ''.join(chunks) | |