There are many approaches to perform parallel queries in your application, from using the Python SDK to making REST calls. Below is one example of an approach using multi-threaded, asynchronous requests in Python. For guidance on using asyncio for single-threaded, asynchronous requests in Python, see Asyncio support.
This example assumes the following:
import os
from pinecone import Pinecone
from concurrent.futures import ThreadPoolExecutor
# Get the API key from the environment variable and initialize Pinecone
api_key = os.environ.get("PINECONE_API_KEY")
pc = Pinecone(api_key=api_key)
# Define the index name
index_name = "docs-example"
# Define the index
index = pc.Index(index_name)
# Define the function to run parallel queries
def run_parallel_queries(vectors):
"""
Run a list of vectors in parallel using ThreadPoolExecutor.
Parameters:
vectors (list): A list of vectors.
Returns:
list: A list of query results.
"""
# Define the maximum number of concurrent queries
MAX_CONCURRENT_QUERIES = 4
def run_query(vector):
"""
Run a single query.
"""
return index.query(
namespace="",
vector=vector,
top_k=3,
include_values=True
)
# Run the queries in parallel
with ThreadPoolExecutor(max_workers=MAX_CONCURRENT_QUERIES) as executor:
"""
Run the queries in parallel.
"""
results = list(executor.map(run_query, vectors))
return results
def test_parallel_queries():
"""
Test the run_parallel_queries function with 20 random vectors.
"""
import numpy as np
# Generate 20 random vectors of size 1536 and convert them to lists
vectors = [np.random.rand(1536).tolist() for _ in range(20)]
# Define the batch size
QUERY_BATCH_SIZE = 20
# Run the parallel queries
results = run_parallel_queries(vectors)
# Print the results
for i, result in enumerate(results):
print(f"Query {i+1} results: {result}")
if __name__ == "__main__":
test_parallel_queries()
