.numpy()

# Simulate a more efficient search query with a reduced response time # Assume a normal distribution centered around 100ms with a standard deviation of 20ms response_time = max(0, random.normalvariate(100, 20)) time.sleep(re…

with torch.no_grad(): doc_outputs = model(**doc_inputs) query_outputs = model(**query_inputs) doc_embeddings = doc_outputs.last_hidden_state.mean(dim=1) query_embedding = query_outputs.last_hidden_state.mean(dim…

- Experiment with different weighting schemes to find the optimal balance. 3. **Normalization:** - Normalize the scores to ensure they are comparable and to avoid bias towards one type of scoring. 4. **Evaluation:** - Evaluate th…

By following these guidelines, you can design a modular and scalable query rewriting pipeline with clear interfaces and efficient data flows. Let me know if you need further assistance or have any specific concerns! [Turn 6920] User: I'm t…

def add_token(self, token): self.tokens.append(token) self.token_count += 1 def get_context(self): if self.token_count in self.cache: return self.cache[self.token_count] context = list(s…

for i in tf.range(seq_len): start_idx = tf.maximum(i - context_size // 2, 0) end_idx = tf.minimum(i + context_size // 2 + 1, seq_len) context_window = context_window.write(i, x[:, start_idx:end_id…

### Step 3: Initialize Redis for Caching Initialize Redis to cache the contextual embeddings and synonyms: ```python import redis redis_client = redis.Redis(host='localhost', port=6379, db=0) ``` ### Step 4: Generate Contextual Embeddin…