Example usage

query_embedding = query_output.last_hidden_state.mean(dim=1) document_embeddings = document_output.last_hidden_state.mean(dim=1) similarities = torch.nn.functional.cosine_similarity(query_embedding, document_embeddings, dim=-1)…

def meets_requirement_2(tool): # Implementation for requirement 2 return False # Replace with actual implementation # Example tool classes class Tool: def __init__(self, name): self.name = name class Tool1(Tool): …

[Turn 1144] User: I'm designing a system for proposing 7 index fields to reduce search times by 15%, and I want to make sure my design is compatible with the existing system. Can you help me review my data modeling? I've got a list of field…

"temperature": temperature, "top_k": top_k, "top_p": top_p, "repetition_penalty": repetition_penalty, "seed": seed } try: response = requests.post(endpoint, headers=headers, json=…

def update_weights(engine1_accuracy, engine2_accuracy): total_accuracy = engine1_accuracy + engine2_accuracy if total_accuracy == 0: return (0.5, 0.5) # Default equal weights if both accuracies are zero new_weights = (e…

time.sleep(60) # Example usage: instance_ids = ['instance1', 'instance2', 'instance3'] monitor_uptime(instance_ids) ``` ->-> 5,15 [Turn 2723] Assistant: Certainly! To monitor the uptime of Azure VM instances and alert you if i…

- Test the pipeline to ensure it handles errors and retries correctly. - Verify that the system can handle 3,500 documents per hour with under 200ms processing time. 3. **Monitor Performance**: - Monitor the system to ensure it ac…

[Turn 4884] User: I'm collaborating with Patricia on sprint planning, and we're addressing vector bugs for 40% error reduction. One of the issues we're facing is with vector normalization. Here's the code: ```python import numpy as np def …

es = Elasticsearch() def create_pipeline(index_name): # Create a new pipeline pipeline = { 'description': 'My pipeline', 'processors': [ {'set': {'field': '_index', 'value': index_name}}, {'r…

4. **Proper Exception Handling**: Include proper exception handling and resource cleanup. ### Additional Considerations - **Scroll API**: If you need to fetch large result sets, consider using the Scroll API. - **Bulk Requests**: If you a…

Here's an example of how you might perform real-time analytics using Elasticsearch: ```python from elasticsearch import Elasticsearch es = Elasticsearch() def search_with_aggregation(es, index_name, query): # Create a new search quer…

Ensure that you log any errors or critical information related to embedding generation and indexing. ```python from sentence_transformers import SentenceTransformer import faiss import numpy as np model = SentenceTransformer('sentence-tra…

except jwt.InvalidTokenError: return "Token is invalid" # Example usage token = create_jwt(1) print(f"Token: {token}") payload = verify_jwt(token) print(f"Payload: {payload}") ``` This example demonstrates how to create and v…

# Example users users = { "admin": User("admin", roles["Admin"]), "editor": User("editor", roles["Editor"]), "viewer": User("viewer", roles["Viewer"]), } # Function to check permissions def check_permission(user: User, permissi…

- Create a route that accepts language and query parameters. - Generate a dynamic cache key based on the language and query parameters. - Use the cache to store and retrieve results. ### Example Code ```python from flask import F…

# Create a Redis client client = redis.Redis(host='localhost', port=6379, db=0) # Function to set a log summary in Redis def set_log_summary(summary_id, summary_data): key = f"log_summary:{summary_id}" client.set(key, json.dumps(su…

print(f"Current skill level: {current_skill_level:.2f}. Target: {target_skill_level:.2f}") # Example usage review_and_apply_strategies(context_window) # Assume initial skill level and target skill level initial_skill_level = 0.8 t…

best_strategy = max(performance_data, key=lambda k: np.mean(performance_data[k])) print(f"The best strategy is {best_strategy} with performance: Mean={np.mean(performance_data[best_strategy]):.2f}") # Example usage initial_skill_le…

[Turn 9122] User: In my current project, I need to ensure that 100% of 80,000 model files are encrypted using AES-256, and I'm considering using a library like `cryptography` to handle the encryption; can you provide an example of how to us…

from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score # Load dataset and split into training and testing sets X_train, X_test, y_train, y_test = …

- **Joblib**: The `joblib` library is used for parallel computing in Python. It provides a simple interface to parallelize tasks and manage the parallel execution of functions. By using this parallel implementation, you can significantly r…

4. **Caching**: Use caching to reduce the load on the underlying data store. ### Optimized Implementation Here's an improved version of your `SynonymLookupModule`: 1. **Use `defaultdict` for Multiple Synonyms**: This allows storing multi…

Here's an optimized version of your code that incorporates these strategies: ```python import torch from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from concurrent.futures import ThreadPoolExecutor, as_completed class Reform…

for future in as_completed(futures): results.extend(future.result()) return results # Example usage: queries = ["What is the capital of France?", "Who is the president of the United States?", ...] reformulated_q…

outputs = self.model.generate(**inputs) reformulated_query = self.tokenizer.decode(outputs[0], skip_special_tokens=True) self.redis_client.set(query, reformulated_query, ex=3600) # Cache for 1 hour return re…

redis_client = redis.Redis(host='localhost', port=6379, db=0) @lru_cache(maxsize=1000) def cached_reformulate_query(query): cached_result = redis_client.get(query) if cached_result: return cached_result.decode('utf-8') …

[Turn 10446] User: I'm using Jira 9.6.0 to manage my sprint planning, and I've logged 16 tasks for contextual reformulation, aiming for 85% sprint completion, but I'm not sure how to prioritize my tasks effectively, can you give me some adv…

def search_reformulated_query(query): return es.search(index="reformulated_queries", body={"query": {"match": {"query": query}}}) # Example usage: query = "This is a sample query" reformulated_query = "This is a reformulated query" ind…