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import requests | |
import json | |
import re | |
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" | |
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': 10, | |
'corpusKey': corpora_key_list, | |
'context_config': { | |
'sentences_before': 2, | |
'sentences_after': 2, | |
'start_tag': "%START_SNIPPET%", | |
'end_tag': "%END_SNIPPET%", | |
}, | |
'rerankingConfig': { 'rerankerId': 272725719 } | |
} | |
] | |
} | |
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 = "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() | |
top_k = 3 | |
responses = res['responseSet'][0]['response'][:top_k] | |
documents = res['responseSet'][0]['document'] | |
metadatas = [] | |
for x in responses: | |
md = {m["name"]: m["value"] for m in x["metadata"]} | |
doc_num = x["documentIndex"] | |
doc_md = {f'doc_{m["name"]}': m["value"] for m in documents[doc_num]["metadata"]} | |
md.update(doc_md) | |
metadatas.append(md) | |
movie_title = metadatas[0].get("doc_title", None) | |
snippet_url = metadatas[0].get("url", None) | |
score = responses[0]["score"] | |
return movie_title, snippet_url, score | |