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import requests |
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import json |
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class VectaraQuery(): |
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def __init__(self, api_key: str, corpus_keys: list[str], prompt_name: str = None): |
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self.corpus_keys = corpus_keys |
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self.api_key = api_key |
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self.prompt_name = prompt_name if prompt_name else "vectara-summary-ext-24-05-sml" |
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self.conv_id = None |
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def get_body(self, query_str: str, response_lang: str, stream: False): |
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corpora_list = [{ |
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'corpus_key': corpus_key, 'lexical_interpolation': 0.005 |
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} for corpus_key in self.corpus_keys |
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] |
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return { |
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'query': query_str, |
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'search': |
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{ |
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'corpora': corpora_list, |
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'offset': 0, |
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'limit': 50, |
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'context_configuration': |
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{ |
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'sentences_before': 2, |
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'sentences_after': 2, |
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'start_tag': "%START_SNIPPET%", |
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'end_tag': "%END_SNIPPET%", |
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}, |
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'reranker': |
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{ |
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'type': 'customer_reranker', |
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'reranker_id': 'rnk_272725719' |
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}, |
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}, |
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'generation': |
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{ |
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'prompt_name': self.prompt_name, |
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'max_used_search_results': 10, |
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'response_language': response_lang, |
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'citations': |
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{ |
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'style': 'none' |
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}, |
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'enable_factual_consistency_score': False |
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}, |
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'chat': |
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{ |
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'store': True |
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}, |
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'stream_response': stream |
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} |
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def get_headers(self): |
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return { |
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"Content-Type": "application/json", |
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"Accept": "application/json", |
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"x-api-key": self.api_key, |
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"grpc-timeout": "60S" |
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} |
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def get_stream_headers(self): |
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return { |
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"Content-Type": "application/json", |
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"Accept": "text/event-stream", |
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"x-api-key": self.api_key, |
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"grpc-timeout": "60S" |
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} |
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def submit_query(self, query_str: str, language: str): |
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if self.conv_id: |
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endpoint = f"https://api.vectara.io/v2/chats/{self.conv_id}/turns" |
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else: |
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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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print(f"Query failed with code {response.status_code}, reason {response.reason}, text {response.text}") |
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if response.status_code == 429: |
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return "Sorry, Vectara chat turns exceeds plan limit." |
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return "Sorry, something went wrong in my brain. Please try again later." |
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res = response.json() |
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if self.conv_id is None: |
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self.conv_id = res['chat_id'] |
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summary = res['answer'] |
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return summary |
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def submit_query_streaming(self, query_str: str, language: str): |
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if self.conv_id: |
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endpoint = f"https://api.vectara.io/v2/chats/{self.conv_id}/turns" |
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else: |
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endpoint = "https://api.vectara.io/v2/chats" |
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body = self.get_body(query_str, language, stream=True) |
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response = requests.post(endpoint, data=json.dumps(body), verify=True, headers=self.get_stream_headers(), stream=True) |
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if response.status_code != 200: |
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print(f"Query failed with code {response.status_code}, reason {response.reason}, text {response.text}") |
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if response.status_code == 429: |
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return "Sorry, Vectara chat turns exceeds plan limit." |
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return "Sorry, something went wrong in my brain. Please try again later." |
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chunks = [] |
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for line in response.iter_lines(): |
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line = line.decode('utf-8') |
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if line: |
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key, value = line.split(':', 1) |
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if key == 'data': |
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line = json.loads(value) |
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if line['type'] == 'generation_chunk': |
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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) |
