search_vectors

from concurrent.futures import ThreadPoolExecutor # Create a FAISS index d = 128 # dimension index = faiss.IndexFlatL2(d) # Add vectors to the index vectors = np.random.rand(10000, d).astype('float32') index.add(vectors) # Function to p…

# Search the vectors using a vector search algorithm results = search_algorithm(query) # Log memory usage after the search mem_after = psutil.virtual_memory().used logging.debug(f"Memory usage after …

# Define the vector search function. def search_vectors(tokens): # Create a FAISS query. query = np.array([vector for vector in tokens]).astype('float32') # Search for similar vectors. distances, indices = index.search(quer…

# Define the API endpoint @app.route('/api/v1/tokenize-language', methods=['POST']) def tokenize_language(): try: # Get the input text data = request.get_json() text = data['text'] # Tokenize the text …

[Turn 7434] User: I'm designing an API endpoint for tokenizing language data, and I want to propose `/api/v1/tokenize-language` with a 2-second timeout for 550 req/sec throughput. Can you help me craft a well-structured API using Flask, con…

# Define the API endpoint @app.route('/api/v1/tokenize-language', methods=['POST']) def tokenize_language(): # Start the debugger here pdb.set_trace() # Get the input text data = request.get_json() text = data['text'] …

6. **Define API Endpoint**: - Define the `/api/v1/tokenize-language` endpoint to handle POST requests. - Place `pdb.set_trace()` at the beginning of the route handler to start debugging. - Retrieve the input text from the request J…

3. **Create FAISS Index**: - Initialize the FAISS index using `faiss.IndexFlatL2(128)`. 4. **Create Redis Client**: - Create a Redis client using `redis.Redis(host='localhost', port=6379, db=0)`. 5. **Define Tokenization Function**:…