baseten-performance-client 0.0.11

A ultra-high performance package for sending requests to Baseten Embedding Inference'

High performance client for Baseten.co

This library provides a high-performance Python client for Baseten.co endpoints including embeddings, reranking, and classification. It was built for massive concurrent post requests to any URL, also outside of baseten.co. PerformanceClient releases the GIL while performing requests in the Rust, and supports simulaneous sync and async usage. It was benchmarked with >1200 rps per client in our blog. PerformanceClient is built on top of pyo3, reqwest and tokio and is MIT licensed.

Installation

pip install baseten_performance_client

Usage

import os
import asyncio
from baseten_performance_client import PerformanceClient, OpenAIEmbeddingsResponse, RerankResponse, ClassificationResponse

api_key = os.environ.get("BASETEN_API_KEY")
base_url_embed = "https://model-yqv4yjjq.api.baseten.co/environments/production/sync"
# Also works with OpenAI or Mixedbread.
# base_url_embed = "https://api.openai.com" or "https://api.mixedbread.com"
client = PerformanceClient(base_url=base_url_embed, api_key=api_key)

Embeddings

Synchronous Embedding

texts = ["Hello world", "Example text", "Another sample"]
response = client.embed(
    input=texts,
    model="my_model",
    batch_size=4,
    max_concurrent_requests=32,
    timeout_s=360
)

# Accessing embedding data
print(f"Model used: {response.model}")
print(f"Total tokens used: {response.usage.total_tokens}")
print(f"Total time: {response.total_time:.4f}s")
if response.individual_batch_request_times:
    for i, batch_time in enumerate(response.individual_batch_request_times):
        print(f"  Time for batch {i}: {batch_time:.4f}s")

for i, embedding_data in enumerate(response.data):
    print(f"Embedding for text {i} (original input index {embedding_data.index}):")
    # embedding_data.embedding can be List[float] or str (base64)
    if isinstance(embedding_data.embedding, list):
        print(f"  First 3 dimensions: {embedding_data.embedding[:3]}")
        print(f"  Length: {len(embedding_data.embedding)}")

# Using the numpy() method (requires numpy to be installed)
import numpy as np
numpy_array = response.numpy()
print("\nEmbeddings as NumPy array:")
print(f"  Shape: {numpy_array.shape}")
print(f"  Data type: {numpy_array.dtype}")
if numpy_array.shape[0] > 0:
    print(f"  First 3 dimensions of the first embedding: {numpy_array[0][:3]}")

Note: The embed method is versatile and can be used with any embeddings service, e.g. OpenAI API embeddings, not just for Baseten deployments.

Asynchronous Embedding

async def async_embed():
    texts = ["Async hello", "Async example"]
    response = await client.async_embed(
        input=texts,
        model="my_model",
        batch_size=2,
        max_concurrent_requests=16,
        timeout_s=360
    )
    print("Async embedding response:", response.data)

# To run:
# asyncio.run(async_embed())

Embedding Benchmarks

Comparison against pip install openai for /v1/embeddings. Tested with the ./scripts/compare_latency_openai.py with mini_batch_size of 128, and 4 server-side replicas. Results with OpenAI similar, OpenAI allows a max mini_batch_size of 2048.

Number of inputs / embeddings	Number of Tasks	PerformanceClient (s)	AsyncOpenAI (s)	Speedup
128	1	0.12	0.13	1.08×
512	4	0.14	0.21	1.50×
8 192	64	0.83	1.95	2.35×
131 072	1 024	4.63	39.07	8.44×
2 097 152	16 384	70.92	903.68	12.74×

Gerneral Batch POST

The batch_post method is generic. It can be used to send POST requests to any URL, not limited to Baseten endpoints. The input and output can be any JSON item.

Synchronous Batch POST

payload1 = {"model": "my_model", "input": ["Batch request sample 1"]}
payload2 = {"model": "my_model", "input": ["Batch request sample 2"]}
response_obj = client.batch_post(
    url_path="/v1/embeddings", # Example path, adjust to your needs
    payloads=[payload1, payload2],
    max_concurrent_requests=96,
    timeout_s=360
)
print(f"Total time for batch POST: {response_obj.total_time:.4f}s")
for i, (resp_data, headers, time_taken) in enumerate(zip(response_obj.data, response_obj.response_headers, response_obj.individual_request_times)):
    print(f"Response {i+1}:")
    print(f"  Data: {resp_data}")
    print(f"  Headers: {headers}")
    print(f"  Time taken: {time_taken:.4f}s")

Asynchronous Batch POST

async def async_batch_post_example():
    payload1 = {"model": "my_model", "input": ["Async batch sample 1"]}
    payload2 = {"model": "my_model", "input": ["Async batch sample 2"]}
    response_obj = await client.async_batch_post(
        url_path="/v1/embeddings",
        payloads=[payload1, payload2],
        max_concurrent_requests=4,
        timeout_s=360
    )
    print(f"Async total time for batch POST: {response_obj.total_time:.4f}s")
    for i, (resp_data, headers, time_taken) in enumerate(zip(response_obj.data, response_obj.response_headers, response_obj.individual_request_times)):
        print(f"Async Response {i+1}:")
        print(f"  Data: {resp_data}")
        print(f"  Headers: {headers}")
        print(f"  Time taken: {time_taken:.4f}s")

# To run:
# asyncio.run(async_batch_post_example())

Reranking

Reranking compatible with BEI or text-embeddings-inference.

Synchronous Reranking

query = "What is the best framework?"
documents = ["Doc 1 text", "Doc 2 text", "Doc 3 text"]
rerank_response = client.rerank(
    query=query,
    texts=documents,
    return_text=True,
    batch_size=2,
    max_concurrent_requests=16,
    timeout_s=360
)
for res in rerank_response.data:
    print(f"Index: {res.index} Score: {res.score}")

Asynchronous Reranking

async def async_rerank():
    query = "Async query sample"
    docs = ["Async doc1", "Async doc2"]
    response = await client.async_rerank(
        query=query,
        texts=docs,
        return_text=True,
        batch_size=1,
        max_concurrent_requests=8,
        timeout_s=360
    )
    for res in response.data:
        print(f"Async Index: {res.index} Score: {res.score}")

# To run:
# asyncio.run(async_rerank())

Classification

Predicy (classification endpoint) compatible with BEI or text-embeddings-inference.

Synchronous Classification

texts_to_classify = [
    "This is great!",
    "I did not like it.",
    "Neutral experience."
]
classify_response = client.classify(
    inputs=texts_to_classify,
    batch_size=2,
    max_concurrent_requests=16,
    timeout_s=360
)
for group in classify_response.data:
    for result in group:
        print(f"Label: {result.label}, Score: {result.score}")

Asynchronous Classification

async def async_classify():
    texts = ["Async positive", "Async negative"]
    response = await client.async_classify(
        inputs=texts,
        batch_size=1,
        max_concurrent_requests=8,
        timeout_s=360
    )
    for group in response.data:
        for res in group:
            print(f"Async Label: {res.label}, Score: {res.score}")

# To run:
# asyncio.run(async_classify())

Error Handling

The client can raise several types of errors. Here's how to handle common ones:

• requests.exceptions.HTTPError: This error is raised for HTTP issues, such as authentication failures (e.g., 403 Forbidden if the API key is wrong), server errors (e.g., 5xx), or if the endpoint is not found (404). You can inspect e.response.status_code and e.response.text (or e.response.json() if the body is JSON) for more details.
• ValueError: This error can occur due to invalid input parameters (e.g., an empty input list for embed, invalid batch_size or max_concurrent_requests values). It can also be raised by response.numpy() if embeddings are not float vectors or have inconsistent dimensions.

Here's an example demonstrating how to catch these errors for the embed method:

import requests

# client = PerformanceClient(base_url="your_b10_url", api_key="your_b10_api_key")

texts_to_embed = ["Hello world", "Another text example"]
try:
    response = client.embed(
        input=texts_to_embed,
        model="your_embedding_model", # Replace with your actual model name
        batch_size=2,
        max_concurrent_requests=4,
        timeout_s=60 # Timeout in seconds
    )
    # Process successful response
    print(f"Model used: {response.model}")
    print(f"Total tokens: {response.usage.total_tokens}")
    for item in response.data:
        embedding_preview = item.embedding[:3] if isinstance(item.embedding, list) else "Base64 Data"
        print(f"Index {item.index}, Embedding (first 3 dims or type): {embedding_preview}")

except requests.exceptions.HTTPError as e:
    print(f"An HTTP error occurred: {e}, code {e.args[0]}")

For asynchronous methods (async_embed, async_rerank, async_classify, async_batch_post), the same exceptions will be raised by the await call and can be caught using a try...except block within an async def function.

Development

# Install prerequisites
sudo apt-get install patchelf
# Install cargo if not already installed.

# Set up a Python virtual environment
python -m venv .venv
source .venv/bin/activate

# Install development dependencies
pip install maturin[patchelf] pytest requests numpy

# Build and install the Rust extension in development mode
maturin develop
cargo fmt
# Run tests
pytest tests

Contributions

Feel free to contribute to this repo, tag @michaelfeil for review.

License

MIT License