if conditional

total_cost = (tokens * cost_per_token) * requests return total_cost # Example usage: tokens = 1000 requests = 1000000 estimated_cost = estimate_cost(tokens, requests) print(f"Estimated cost: ${estimated_cost}") ``` ### Output Runn…

- Based on feedback, iterate on the POC to refine the role assignments and responsibilities. - Ensure that the final assignments are well-documented and understood by all stakeholders. If you encounter any issues or have any question…

```python from kafka import KafkaProducer, KafkaConsumer from kafka.errors import KafkaError, TimeoutError import json import time # Kafka producer configuration producer = KafkaProducer( bootstrap_servers='localhost:9092', value_s…

import keycloak import asyncio from aiocache import caches, SimpleMemoryCache from aiocache.serializers import PickleSerializer from ratelimiter import RateLimiter # Initialize Keycloak keycloak_url = "https://my-keycloak-instance.com" rea…

def get_transition_id(issue, desired_status): transitions = jira.transitions(issue) for transition in transitions: if transition['name'] == desired_status: return transition['id'] return None def update_task…

print(failure.decode('utf-8')) # Optionally clear logs clear_logs() ``` ### Explanation: 1. **Connect to Redis**: Establish a connection to the Redis server. 2. **Log Rollback Failure**: Use `r.lpush` to add log entries to a list nam…

But I'm not sure what to put in the test code section, can someone help me? ->-> 10,10 [Turn 9733] Assistant: Certainly! To test the performance of your documentation tools, you'll want to measure how long it takes to perform specific task…

if strategy.select_strategy(query): best_strategy = strategy break return best_strategy # Define strategies strategies = [ SynonymStrategy("strategy1", "context1"), SynonymStrategy("strategy2", "…

self.correction = None class Trie: def __init__(self): self.root = TrieNode() def insert(self, word, correction): node = self.root for char in word: if char not in node.children: …

corrected_text = tokenizer.decode(corrected_text) return corrected_text def spell_correction(input_text): """ Combine dictionary lookups and context-aware correction. """ words_list = word_tokenize(input_text) c…

1. **Specific Exception Handling**: - Each type of exception is caught and logged with a specific message indicating the type of error and the stage where it occurred. - This helps in pinpointing the exact issue and the stage causing …

- **Profiling**: Use profiling tools to measure the performance of your code and identify any remaining bottlenecks. By implementing these optimizations, you should be able to reduce the processing time for your text chunks significantly. …

for token_in_dict in dictionary: distance = levenshtein_distance(token, token_in_dict) if distance < min_distance: min_distance = distance closest_token = token_in_dict return closest_token #…

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertModel.from_pretrained('bert-base-uncased') def get_context_aware_synonyms(word, context_sentence): inputs = tokenizer(context_sentence, return_tensors='pt', pad…

# Check if the reformulated query matches the expected intent if check_intent_match(query, reformulated_query): correct_count += 1 precision = correct_count / len(test_queries) return precision def …

# Process the query with spaCy doc = nlp(query) # Correct each word corrected_words = [] for token in doc: if not token.is_oov: corrected_words.append(token.text) else: correc…