Verify Alerts

apiVersion: apps/v1 kind: Deployment name: retrieval-module minReplicas: 1 maxReplicas: 10 metrics: - type: Resource resource: name: cpu target: type: Utilization averageUtilization: 50 ``…

Subject: '[Alertmanager] {{ .CommonAnnotations.summary }}' ``` ### Step 5: Start Prometheus and Alertmanager 1. **Start Prometheus**: ```sh ./prometheus --config.file=prometheus.yml ``` 2. **Start Alertmanager**: ``…

5. **Make the API call**: - `response = requests.post(...)`: - Use `requests.post` to send a POST request to the API endpoint. - Include the `Authorization` header with your API key. - Pass the parameters as JSON data. 6.…

6. **Extensibility**: - NiFi is highly extensible with a rich set of processors and custom processors can be developed to meet specific needs. ### Example Integration with Existing Pipeline To integrate Apache NiFi into your existing p…

run: terraform init - name: Apply Terraform run: terraform apply -auto-approve ``` ### Step 5: Store Generated Secrets Store the generated secrets in a file that Terraform can read. In the example above, the secrets are s…

embeddings = pool.apply_async(process_batch, args=(batch,)) results.append(embeddings) return [result.get() for result in results] # Main function to handle the entire process def handle_texts(texts): start_…

# Sort the words by average latency in descending order latency_freq_sorted = latency_freq.sort_values(by="latency", ascending=False) return latency_freq_sorted # Example usage: log_file = "latency_log.csv" result = analyz…

matchLabels: app: dense-retrieval template: metadata: labels: app: dense-retrieval spec: containers: - name: dense-retrieval image: your-image:dense-retrieval ports: - co…

- Define training arguments for the `Trainer` to control the training process. 5. **Trainer**: - Use the `Trainer` from the `transformers` library to fine-tune the model. 6. **Fine-Tuning and Evaluation**: - Fine-tune the model o…

vectorizer = TfidfVectorizer() tfidf_matrix = vectorizer.fit_transform(documents) query_vector = vectorizer.transform([query]) similarity_scores = (query_vector * tfidf_matrix.T).toarray() return similarity_scores def h…

datasets = pd.read_csv('datasets.csv') # Convert columns to appropriate data types datasets['some_column'] = pd.to_numeric(datasets['some_column'], errors='coerce') # Define secure tuning function def secure_tuning(row): # Implement s…

model = T5ForConditionalGeneration.from_pretrained('./fine_tuned_model') def reformulate_query(query): inputs = tokenizer(f"reformulate: {query}", return_tensors="pt", max_length=512, truncation=True) outputs = model.generate(input…