Slicing Operation

tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") # Move the model to the GPU device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) # Define a function to perform retrieval def retrieve(…

if 'max_value' in constraints: data_model[field] = data_model[field].apply(lambda x: min(x, constraints['max_value'])) elif data_type == 'str': …

retrieval_results = parallel_process_queries(queries, retrieval_layer, max_workers=10) generation_responses = parallel_process_queries(prompts, generation_layer, max_workers=10) # Print the results print("Retrieval Results:", retrieval_res…

# Calculate the similarity between the query vector and each vector in the database similarities = [np.dot(query_vector, vector) for vector in self.vectors] # Return the indices of the top 10 most similar vectors …

# Load pre-trained model and tokenizer model_name = "bert-base-uncased" model = AutoModel.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) # Move the model to GPU if available device = torch.device("cuda" …

def __init__(self, challenges): self.challenges = challenges def assess_challenges(self): # Assess the challenges based on their complexity and impact for challenge in self.challenges: complexity…

index = faiss.IndexIVFPQ(quantizer, 128, nlist, m, nbits) # Train the index index.train(vectors) # Add vectors to the index index.add(vectors) # Set the number of probes index.nprobe = nprobe # Search for the nearest neighbors D, I = in…

entities = [(ent.text, ent.label_) for ent in doc.ents] # Extract synonyms for each token synonyms = [] for token in tokens: pos = get_wordnet_pos(nltk.pos_tag([token])[0][1]) synsets = wordnet.synsets(t…

from fastapi.middleware.trustedhost import TrustedHostMiddleware from fastapi.middleware.cors import CORSMiddleware from fastapi.middleware.gzip import GZipMiddleware from fastapi.middleware.httpsredirect import HTTPSRedirectMiddleware app…

handler = logging.StreamHandler() formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') handler.setFormatter(formatter) self.logger.addHandler(handler) def segment_input(s…

[Turn 7890] User: I'm working on optimizing the performance of my context window management module, I've noticed that the `segment_input` function is taking a long time to execute, can you help me optimize it, here's the current implementat…

formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') handler.setFormatter(formatter) self.logger.addHandler(handler) def segment_input(self, input_sequence): """ …

self.cache.popitem(last=False) # Remove the least recently used item self.cache[input_sequence] = result def handle_token_overflow(self, input_sequence): """ Handle token overflow by segmenting the …

raise ValueError(f"WindowSizeMismatchError: Query length ({len(query)}) exceeds window size ({window_size})") return query[:window_size] # Example usage query = "What is the capital of France?" try: resized_query = res…

- Add additional checks to ensure the query length does not exceed the window size. ### Example Adjusted Logic ```python def resize_window(query, complexity): # Resize context window based on complexity base_window_size = 768 …

[Turn 8160] User: I'm trying to implement a dynamic context window resizing algorithm based on query complexity, but I'm not sure how to handle edge cases, can you provide an example of how to handle queries with high complexity and low com…

def forward(self, input_ids, attention_mask): # Resize the context window dynamically resized_window = self.resize_window(input_ids, attention_mask) return resized_window def resize_window(self,…

from tensorflow.keras.models import Model import numpy as np # Define a function to implement context window concepts with dynamic context size def implement_dynamic_context_window_concepts(input_ids): # Define the input layer inpu…

optimized_input_ids = self.optimize_input_ids(input_ids) optimized_attention_mask = self.optimize_attention_mask(attention_mask) return optimized_input_ids, optimized_attention_mask def optimize_inp…

quantized_model.to(device) # Define a function to perform batch inference with the quantized model def perform_quantized_batch_inference(texts): # Tokenize the input texts inputs = tokenizer(texts, return_tensors="pt", padding=True…

for word, synonyms in thesaurus.items(): word_embedding = get_contextual_embeddings(word) similarities = [np.dot(term_embedding, get_contextual_embeddings(syn)) for syn in synonyms] closest_synonyms.extend([synon…

# Test the implementation with different query loads test_queries = ["What is the meening of life?"] * 2500 # Example queries # Test with different batch sizes and worker counts batch_sizes = [100, 200, 500, 1000, 2500] worker_counts = [5…

segments = ["This is an example segment."] * 800 # Simulate 800 segments start_time = time.time() processed_segments = process_segment_batches(segments) end_time = time.time() print(f"Processed 800 segments in {end_time - start_time} sec…