import requests import json import re from urllib.parse import quote def extract_between_tags(text, start_tag, end_tag): start_index = text.find(start_tag) end_index = text.find(end_tag, start_index) return text[start_index+len(start_tag):end_index-len(end_tag)] class VectaraQuery(): def __init__(self, api_key: str, customer_id: int, corpus_ids: list): self.customer_id = customer_id self.corpus_ids = corpus_ids self.api_key = api_key self.conv_id = None def submit_query(self, query_str: str): corpora_key_list = [{ 'customer_id': str(self.customer_id), 'corpus_id': str(corpus_id), 'lexical_interpolation_config': {'lambda': 0.025} } for corpus_id in self.corpus_ids ] endpoint = f"https://api.vectara.io/v1/query" start_tag = "%START_SNIPPET%" end_tag = "%END_SNIPPET%" headers = { "Content-Type": "application/json", "Accept": "application/json", "customer-id": str(self.customer_id), "x-api-key": self.api_key, "grpc-timeout": "60S" } body = { 'query': [ { 'query': query_str, 'start': 0, 'numResults': 50, 'corpusKey': corpora_key_list, 'context_config': { 'sentences_before': 2, 'sentences_after': 2, 'start_tag': start_tag, 'end_tag': end_tag, }, 'rerankingConfig': { 'rerankerId': '272725718', 'mmrConfig': { 'diversityBias': 0.2 } } 'summary': [ { 'responseLang': 'eng', 'maxSummarizedResults': 7, 'summarizerPromptName': 'vectara-experimental-summary-ext-2023-10-23-med', 'chat': { 'store': True, 'conversationId': self.conv_id }, 'debug': True, } ] } ] } response = requests.post(endpoint, data=json.dumps(body), verify=True, headers=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() print(f"DEBUG request body = {body}") print(f"DEBUG response summary = {res['responseSet'][0]['summary']}") top_k = 10 summary = res['responseSet'][0]['summary'][0]['text'] responses = res['responseSet'][0]['response'][:top_k] docs = res['responseSet'][0]['document'] self.conv_id = res['responseSet'][0]['summary'][0]['chat']['conversationId'] pattern = r'\[\d{1,2}\]' matches = [match.span() for match in re.finditer(pattern, summary)] # figure out unique list of references refs = [] for match in matches: start, end = match response_num = int(summary[start+1:end-1]) doc_num = responses[response_num-1]['documentIndex'] metadata = {item['name']: item['value'] for item in docs[doc_num]['metadata']} text = extract_between_tags(responses[response_num-1]['text'], start_tag, end_tag) url = f"{metadata['url']}#:~:text={quote(text)}" if url not in refs: refs.append(url) # replace references with markdown links refs_dict = {url:(inx+1) for inx,url in enumerate(refs)} for match in reversed(matches): start, end = match response_num = int(summary[start+1:end-1]) doc_num = responses[response_num-1]['documentIndex'] metadata = {item['name']: item['value'] for item in docs[doc_num]['metadata']} text = extract_between_tags(responses[response_num-1]['text'], start_tag, end_tag) url = f"{metadata['url']}#:~:text={quote(text)}" citation_inx = refs_dict[url] summary = summary[:start] + f'[\[{citation_inx}\]]({url})' + summary[end:] return summary