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]

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."
            }},
            {{
                "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%",
                    }
                } 
            ]
        }

    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 in my brain. Please try again later."

        chunks = []
        accumulated_text = ""  # Initialize text accumulation
        pattern_max_length = 50  # Example heuristic
        for line in response.iter_lines():
            if line:  # filter out keep-alive new lines
                data = json.loads(line.decode('utf-8'))                
                res = data['result']
                response_set = res['responseSet']                
                if response_set is None:
                    # grab next chunk and yield it as output
                    summary = res.get('summary', None)
                    if summary is None or len(summary)==0:
                        continue
                    else:
                        chat = summary.get('chat', None)
                        if chat and chat.get('status', None):
                            st_code = chat['status']
                            print(f"Chat query failed with code {st_code}")
                            if st_code == 'RESOURCE_EXHAUSTED':
                                self.conv_id = None
                                return 'Sorry, Vectara chat turns exceeds plan limit.'
                            return 'Sorry, something went wrong in my brain. Please try again later.'
                        conv_id = chat.get('conversationId', None) if chat else None
                        if conv_id:
                            self.conv_id = conv_id
                        
                    chunk = summary['text']
                    accumulated_text += chunk  # Append current chunk to accumulation
                    if len(accumulated_text) > pattern_max_length:
                        accumulated_text = re.sub(r"\[\d+\]", "", accumulated_text)
                        accumulated_text = re.sub(r"\s+\.", ".", accumulated_text)
                        out_chunk = accumulated_text[:-pattern_max_length]
                        chunks.append(out_chunk)
                        yield out_chunk
                        accumulated_text = accumulated_text[-pattern_max_length:]

                    if summary['done']:
                        break

        # yield the last piece
        if len(accumulated_text) > 0:
            accumulated_text = re.sub(r" \[\d+\]\.", ".", accumulated_text)
            chunks.append(accumulated_text)
            yield accumulated_text        
        
        return ''.join(chunks)