import requests import json class VectaraQuery(): def __init__(self, api_key: str, customer_id: str, corpus_id: str, prompt_name: str = None): self.customer_id = customer_id self.corpus_id = corpus_id self.api_key = api_key self.prompt_name = prompt_name if prompt_name else "vectara-experimental-summary-ext-2023-12-11-large" self.conv_id = None def get_body(self, user_response: str): corpora_key_list = [{ 'customer_id': self.customer_id, 'corpus_id': self.corpus_id, 'lexical_interpolation_config': {'lambda': 0.025} }] user_response = user_response.replace('"', '\\"') # Escape double quotes prompt = f''' [ {{ "role": "system", "content": "You are an assistant that provides information about drink names based on a given corpus. \ Format the response in the following way:\n\ Reason: \n\ Alternative: \n\ Notes: \n\n\ Example:\n\ Reason: The name 'Vodka Sunrise' cannot be used because it is trademarked.\n\ Alternative: Use 'Morning Delight' instead.\n\ Notes: Ensure the drink contains vodka to match the alternative name." }}, {{ "role": "user", "content": "{user_response}" }} ] ''' return { 'query': [ { 'query': user_response, 'start': 0, 'numResults': 10, 'corpusKey': corpora_key_list, 'context_config': { 'sentences_before': 2, 'sentences_after': 2, 'start_tag': "%START_SNIPPET%", 'end_tag': "%END_SNIPPET%", }, 'summary': [ { 'responseLang': 'eng', 'maxSummarizedResults': 1, 'summarizerPromptName': self.prompt_name, 'promptText': prompt, 'chat': { 'store': True, 'conversationId': self.conv_id }, } ] } ] } 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/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. Please try again later." accumulated_text = "" for line in response.iter_lines(): if line: # filter out keep-alive new lines data = json.loads(line.decode('utf-8')) print(f"Received data chunk: {json.dumps(data, indent=2)}") # Debugging line if 'result' not in data: print("No 'result' in data") continue res = data['result'] if 'responseSet' not in res: print("No 'responseSet' in result") continue response_set = res['responseSet'] if response_set: for result in response_set['response']: if 'text' not in result: print("No 'text' in result") continue text = result['text'] print(f"Processing text: {text}") # Debugging line accumulated_text += text if accumulated_text: return self.summarize_text(accumulated_text) return "No relevant information found." def summarize_text(self, text): endpoint = f"https://api.vectara.io/v1/stream-summary" prompt = f''' [ {{ "role": "system", "content": "You are an assistant that provides information about drink names based on a given corpus. \ Format the response in the following way:\n\ Reason: \n\ Alternative: \n\ Notes: \n\n\ Example:\n\ Reason: The name 'Vodka Sunrise' cannot be used because it is trademarked.\n\ Alternative: Use 'Morning Delight' instead.\n\ Notes: Ensure the drink contains vodka to match the alternative name." }}, {{ "role": "user", "content": "{text}" }} ] ''' body = { 'text': text, 'summary': { 'responseLang': 'eng', 'maxSummarizedResults': 1, 'summarizerPromptName': self.prompt_name, 'promptText': prompt, 'chat': { 'store': True, 'conversationId': self.conv_id }, } } response = requests.post(endpoint, data=json.dumps(body), verify=True, headers=self.get_headers(), stream=True) if response.status_code != 200: print(f"Summary query failed with code {response.status_code}, reason {response.reason}, text {response.text}") return "Sorry, something went wrong. Please try again later." for line in response.iter_lines(): if line: # filter out keep-alive new lines data = json.loads(line.decode('utf-8')) print(f"Received summary data chunk: {json.dumps(data, indent=2)}") # Debugging line if 'summary' in data: return data['summary']['text'] return "No relevant information found."