Asynchronous Execution

query = "How do I optimize LLM retrieval latency?" results = retrieve(query) print(results) ``` ### 4. **Efficient Tokenization** - **Tokenization Settings**: Ensure that tokenization settings are optimized. For example, usi…

1. **Parallel Processing:** Use Python's `concurrent.futures` module to process tasks in parallel. 2. **Batch Processing:** Split the documents into batches to manage memory and processing load. 3. **Asynchronous Execution:** Use `asyncio` …

futures = {executor.submit(vectorize_document, doc): doc for doc in docs} for future in as_completed(futures): try: vectors.append(future.result()) except Exception as e: …

return [1.0, 2.0, 3.0] def process_documents(documents): vectors = [] with ThreadPoolExecutor(max_workers=10) as executor: futures = [executor.submit(vectorize_document, document) for document in documents] for …

rate_limiter = RateLimiter(max_calls=100, period=60) # 100 calls per minute # Define a function to handle authentication async def authenticate(username, password): try: # Check cache first token = await caches.get(f"t…

async def predict(self, text): await self.load() return self._model.predict(text) # Create an asynchronous model instance async_model = AsyncLanguageModel() # Measure the time it takes to load the model start_time = ti…

1. **Data Preprocessing**: Ensure your data is preprocessed correctly for the reranking model. 2. **Pipeline Modification**: Integrate the reranking step into your existing pipeline. 3. **Performance Optimization**: Use batch processing, as…

Here's how you can implement parallel processing using Python's `concurrent.futures` module, which provides a high-level interface for asynchronously executing callables: ### Example Implementation ```python import time from concurrent.fu…