| 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': 'customer_reranker', |
| 'reranker_id': 'rnk_272725719' |
| }, |
| }, |
| 'generation': |
| { |
| 'prompt_name': self.prompt_name, |
| 'max_used_search_results': 10, |
| 'response_language': response_lang, |
| 'citations': |
| { |
| 'style': 'none' |
| }, |
| 'enable_factual_consistency_score': False |
| }, |
| '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: |
| 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 |
|
|
| return ''.join(chunks) |