Optimizing API Response Times: Delay in Initial vs. Continuous Calls

[priyanshuverifast]

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Example Code

python
 = AzureOpenAIEmbeddings(
                deployment=target['deployment'],
                azure_endpoint=target['azure_endpoint'],
                openai_api_key=target['openai_api_key'],
                openai_api_type="azure",
                http_client= self.client,
                timeout=0.8
            )

Description

I’m looking for some insights or solutions. When making continuous calls within a 2-second interval, the average response time is around 0.3 seconds. However, if there’s a gap between the calls, the response time for the next call increases to about 1.5 seconds.
how can I replicate behavior of continuous calls for calls after sometime. How to do this? I am currently using azure embeddings i am also using same httpxclient. these are p95 values of all calls.

System Info

i'm using python 3.10
langchain version is 0.1.11
langchain-openai is 0.0.8

[dosubot[bot]]

Hey @priyanshuverifast! I'm here to assist you with any bugs, questions, or contributions. Let's work together to resolve the issue you're facing.

To replicate the behavior of continuous calls for calls made after some time and optimize API response times using AzureOpenAIEmbeddings with the same httpx client, you can configure the http_client parameter of the AzureOpenAIEmbeddings class to use a shared httpx.AsyncClient. This allows you to reuse the same HTTP client for multiple requests, which can improve performance by maintaining persistent connections. Additionally, you can implement retry logic to handle transient errors and optimize the API calls further.

Here is an example of how you can set this up:

import os
import httpx
from langchain_openai.embeddings.azure import AzureOpenAIEmbeddings
from tenacity import AsyncRetrying, retry_if_exception_type, stop_after_attempt, wait_exponential, before_sleep_log
import logging

# Set environment variables for Azure OpenAI
os.environ["AZURE_OPENAI_API_KEY"] = "your AzureOpenAI key"
os.environ["AZURE_OPENAI_ENDPOINT"] = "https://<your-endpoint>.openai.azure.com/"
os.environ["AZURE_OPENAI_API_VERSION"] = "2024-02-01"

# Create a shared httpx.AsyncClient
shared_http_client = httpx.AsyncClient()

# Initialize the AzureOpenAIEmbeddings with the shared httpx client
embeddings = AzureOpenAIEmbeddings(
    model="your-embeddings-model-name",
    azure_endpoint="https://your-endpoint.openai.azure.com/",
    http_client=shared_http_client
)

# Configure retry logic
logger = logging.getLogger(__name__)
min_seconds = 4
max_seconds = 10

async_retrying = AsyncRetrying(
    reraise=True,
    stop=stop_after_attempt(6),
    wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
    retry=(
        retry_if_exception_type(httpx.TimeoutException)
        | retry_if_exception_type(httpx.HTTPStatusError)
        | retry_if_exception_type(httpx.RequestError)
    ),
    before_sleep=before_sleep_log(logger, logging.WARNING),
)

async def get_embedding_with_retry(text: str):
    async for attempt in async_retrying:
        with attempt:
            return await embeddings.aembed_query(text)

# Example usage
async def main():
    embedding = await get_embedding_with_retry("your text here")
    print(embedding)

# Don't forget to close the shared client when done
await shared_http_client.aclose()

In this example, the httpx.AsyncClient is created once and passed to the AzureOpenAIEmbeddings instance. This client will be reused for all API calls made through the embeddings object, optimizing the response times by maintaining persistent connections. Additionally, the retry logic is implemented using the tenacity library to handle transient errors and ensure the API calls are retried with exponential backoff in case of failures ^[1][2][3].

To continue talking to Dosu, mention @dosu.

[priyanshuverifast]

i am already uing different http client which is same, but still second response is slower.

…

On Tue, 3 Sept 2024 at 17:41, dosubot[bot] ***@***.***> wrote: Hey @priyanshuverifast <https://github.com/priyanshuverifast>! I'm here to assist you with any bugs, questions, or contributions. Let's work together to resolve the issue you're facing. To replicate the behavior of continuous calls for calls made after some time and optimize API response times using AzureOpenAIEmbeddings with the same httpx client, you can configure the http_client parameter of the AzureOpenAIEmbeddings class to use a shared httpx.AsyncClient. This allows you to reuse the same HTTP client for multiple requests, which can improve performance by maintaining persistent connections. Additionally, you can implement retry logic to handle transient errors and optimize the API calls further. Here is an example of how you can set this up: import osimport httpxfrom langchain_openai.embeddings.azure import AzureOpenAIEmbeddingsfrom tenacity import AsyncRetrying, retry_if_exception_type, stop_after_attempt, wait_exponential, before_sleep_logimport logging # Set environment variables for Azure OpenAIos.environ["AZURE_OPENAI_API_KEY"] = "your AzureOpenAI key"os.environ["AZURE_OPENAI_ENDPOINT"] = "https://<your-endpoint>.openai.azure.com/"os.environ["AZURE_OPENAI_API_VERSION"] = "2024-02-01" # Create a shared httpx.AsyncClientshared_http_client = httpx.AsyncClient() # Initialize the AzureOpenAIEmbeddings with the shared httpx clientembeddings = AzureOpenAIEmbeddings( model="your-embeddings-model-name", azure_endpoint="https://your-endpoint.openai.azure.com/", http_client=shared_http_client ) # Configure retry logiclogger = logging.getLogger(__name__)min_seconds = 4max_seconds = 10 async_retrying = AsyncRetrying( reraise=True, stop=stop_after_attempt(6), wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds), retry=( retry_if_exception_type(httpx.TimeoutException) | retry_if_exception_type(httpx.HTTPStatusError) | retry_if_exception_type(httpx.RequestError) ), before_sleep=before_sleep_log(logger, logging.WARNING), ) async def get_embedding_with_retry(text: str): async for attempt in async_retrying: with attempt: return await embeddings.aembed_query(text) # Example usageasync def main(): embedding = await get_embedding_with_retry("your text here") print(embedding) # Don't forget to close the shared client when doneawait shared_http_client.aclose() In this example, the httpx.AsyncClient is created once and passed to the AzureOpenAIEmbeddings instance. This client will be reused for all API calls made through the embeddings object, optimizing the response times by maintaining persistent connections. Additionally, the retry logic is implemented using the tenacity library to handle transient errors and ensure the API calls are retried with exponential backoff in case of failures [1] <https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/embeddings/openai.py> [2] <https://github.com/langchain-ai/langchain/blob/master/libs/partners/openai/langchain_openai/embeddings/azure.py> [3] <https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/embeddings/localai.py> . *To continue talking to Dosu <https://dosu.dev>, mention @dosu <https://github.com/dosu>.* — Reply to this email directly, view it on GitHub <#25961 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/BJDASFYSNENALCX5WYD47WLZUWRNXAVCNFSM6AAAAABNR7VS2SVHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTANJTGE2DCNY> . You are receiving this because you were mentioned.Message ID: ***@***.*** com>

[dosubot[bot]]

To optimize the response time for the second API call using AzureOpenAIEmbeddings with the same httpx client, ensure that the httpx client is reused properly and that the client parameters are set correctly to avoid unnecessary overhead. Additionally, leverage the retry mechanism provided by the AzureOpenAIEmbeddings class to handle transient errors more efficiently.

Here is an example of how you can achieve this:

1. Reuse the httpx client: Ensure that the httpx client is instantiated once and reused for subsequent requests.
2. Set appropriate client parameters: Adjust parameters such as timeout, max_retries, retry_min_seconds, and retry_max_seconds to optimize performance.
3. Use the retry mechanism: Utilize the built-in retry mechanism to handle transient errors and reduce the impact on response time.

Here is a code snippet demonstrating how to reuse the httpx client, set the client parameters, and use the retry mechanism:

import httpx
from langchain_community.embeddings.openai import AzureOpenAIEmbeddings

# Create a shared httpx client
shared_httpx_client = httpx.Client()

# Initialize the AzureOpenAIEmbeddings with the shared client
embeddings = AzureOpenAIEmbeddings(
    model="text-embedding-3-large",
    azure_endpoint="https://<your-endpoint>.openai.azure.com/",
    api_key="your-api-key",
    http_client=shared_httpx_client,
    request_timeout=10,  # Set a reasonable timeout
    max_retries=3,  # Set the number of retries
    retry_min_seconds=2,  # Minimum wait time between retries
    retry_max_seconds=10  # Maximum wait time between retries
)

# First API call
input_text_1 = "The meaning of life is 42"
vector_1 = embeddings.embed_query(input_text_1)
print(vector_1[:3])

# Second API call
input_text_2 = "Another text to embed"
vector_2 = embeddings.embed_query(input_text_2)
print(vector_2[:3])

By reusing the httpx client, setting appropriate parameters, and leveraging the retry mechanism, you can reduce the overhead and improve the response time for subsequent API calls.

Additionally, you can consider using asynchronous requests to further optimize performance, especially if you are making multiple API calls concurrently. Here is an example of how to use the asynchronous client:

import httpx
import asyncio
from langchain_community.embeddings.openai import AzureOpenAIEmbeddings

# Create a shared async httpx client
shared_async_httpx_client = httpx.AsyncClient()

# Initialize the AzureOpenAIEmbeddings with the shared async client
embeddings = AzureOpenAIEmbeddings(
    model="text-embedding-3-large",
    azure_endpoint="https://<your-endpoint>.openai.azure.com/",
    api_key="your-api-key",
    async_http_client=shared_async_httpx_client,
    request_timeout=10,  # Set a reasonable timeout
    max_retries=3,  # Set the number of retries
    retry_min_seconds=2,  # Minimum wait time between retries
    retry_max_seconds=10  # Maximum wait time between retries
)

async def main():
    # First API call
    input_text_1 = "The meaning of life is 42"
    vector_1 = await embeddings.aembed_query(input_text_1)
    print(vector_1[:3])

    # Second API call
    input_text_2 = "Another text to embed"
    vector_2 = await embeddings.aembed_query(input_text_2)
    print(vector_2[:3])

# Run the async main function
asyncio.run(main())

Using asynchronous requests can help improve the response time, especially when dealing with multiple API calls ^[1][2][3].

To continue talking to Dosu, mention @dosu.

[priyanshuverifast]

it will just increase time as we are increasing the retry time, how to take that in account.

…

On Tue, 3 Sept 2024 at 17:45, dosubot[bot] ***@***.***> wrote: To optimize the response time for the second API call using AzureOpenAIEmbeddings with the same httpx client, ensure that the httpx client is reused properly and that the client parameters are set correctly to avoid unnecessary overhead. Additionally, leverage the retry mechanism provided by the AzureOpenAIEmbeddings class to handle transient errors more efficiently. Here is an example of how you can achieve this: 1. *Reuse the httpx client*: Ensure that the httpx client is instantiated once and reused for subsequent requests. 2. *Set appropriate client parameters*: Adjust parameters such as timeout, max_retries, retry_min_seconds, and retry_max_seconds to optimize performance. 3. *Use the retry mechanism*: Utilize the built-in retry mechanism to handle transient errors and reduce the impact on response time. Here is a code snippet demonstrating how to reuse the httpx client, set the client parameters, and use the retry mechanism: import httpxfrom langchain_community.embeddings.openai import AzureOpenAIEmbeddings # Create a shared httpx clientshared_httpx_client = httpx.Client() # Initialize the AzureOpenAIEmbeddings with the shared clientembeddings = AzureOpenAIEmbeddings( model="text-embedding-3-large", azure_endpoint="https://<your-endpoint>.openai.azure.com/", api_key="your-api-key", http_client=shared_httpx_client, request_timeout=10, # Set a reasonable timeout max_retries=3, # Set the number of retries retry_min_seconds=2, # Minimum wait time between retries retry_max_seconds=10 # Maximum wait time between retries ) # First API callinput_text_1 = "The meaning of life is 42"vector_1 = embeddings.embed_query(input_text_1)print(vector_1[:3]) # Second API callinput_text_2 = "Another text to embed"vector_2 = embeddings.embed_query(input_text_2)print(vector_2[:3]) By reusing the httpx client, setting appropriate parameters, and leveraging the retry mechanism, you can reduce the overhead and improve the response time for subsequent API calls. Additionally, you can consider using asynchronous requests to further optimize performance, especially if you are making multiple API calls concurrently. Here is an example of how to use the asynchronous client: import httpximport asynciofrom langchain_community.embeddings.openai import AzureOpenAIEmbeddings # Create a shared async httpx clientshared_async_httpx_client = httpx.AsyncClient() # Initialize the AzureOpenAIEmbeddings with the shared async clientembeddings = AzureOpenAIEmbeddings( model="text-embedding-3-large", azure_endpoint="https://<your-endpoint>.openai.azure.com/", api_key="your-api-key", async_http_client=shared_async_httpx_client, request_timeout=10, # Set a reasonable timeout max_retries=3, # Set the number of retries retry_min_seconds=2, # Minimum wait time between retries retry_max_seconds=10 # Maximum wait time between retries ) async def main(): # First API call input_text_1 = "The meaning of life is 42" vector_1 = await embeddings.aembed_query(input_text_1) print(vector_1[:3]) # Second API call input_text_2 = "Another text to embed" vector_2 = await embeddings.aembed_query(input_text_2) print(vector_2[:3]) # Run the async main functionasyncio.run(main()) Using asynchronous requests can help improve the response time, especially when dealing with multiple API calls [1] <https://github.com/langchain-ai/langchain/blob/master/libs/partners/openai/langchain_openai/embeddings/azure.py> [2] <https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/embeddings/openai.py> [3] <https://github.com/langchain-ai/langchain/blob/master/libs/partners/mistralai/langchain_mistralai/embeddings.py> . *To continue talking to Dosu <https://dosu.dev>, mention @dosu <https://github.com/dosu>.* — Reply to this email directly, view it on GitHub <#25961 (reply in thread)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/BJDASF74EXFYI4H7WVAFABTZUWR7FAVCNFSM6AAAAABNR7VS2SVHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTANJTGE2DOMA> . You are receiving this because you were mentioned.Message ID: ***@***.*** com>

[dosubot[bot]]

To optimize the retry mechanism without significantly increasing the overall response time when using AzureOpenAIEmbeddings with the same httpx client, you can fine-tune the retry parameters to balance between retry attempts and wait times. Here’s how you can do it:

1. Set Reasonable Retry Parameters: Adjust retry_min_seconds, retry_max_seconds, and max_retries to minimize the impact on response time.
2. Use Exponential Backoff with Jitter: This helps in spreading out the retries and avoiding thundering herd problems.

Here’s an example configuration:

import httpx
import logging
from langchain_community.embeddings.openai import AzureOpenAIEmbeddings
from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type, before_sleep_log
import openai

# Configure logging
logger = logging.getLogger(__name__)

# Set the retry parameters
retry_min_seconds = 1  # Minimum number of seconds to wait between retries
retry_max_seconds = 4  # Maximum number of seconds to wait between retries
max_retries = 3        # Maximum number of retries

# Configure the retry mechanism
retry_decorator = retry(
    reraise=True,
    stop=stop_after_attempt(max_retries),
    wait=wait_exponential(multiplier=1, min=retry_min_seconds, max=retry_max_seconds),
    retry=(
        retry_if_exception_type(openai.error.Timeout)
        | retry_if_exception_type(openai.error.APIError)
        | retry_if_exception_type(openai.error.APIConnectionError)
        | retry_if_exception_type(openai.error.RateLimitError)
        | retry_if_exception_type(openai.error.ServiceUnavailableError)
    ),
    before_sleep=before_sleep_log(logger, logging.WARNING),
)

# Create a shared httpx client
shared_httpx_client = httpx.Client()

# Initialize the AzureOpenAIEmbeddings with the shared client
embeddings = AzureOpenAIEmbeddings(
    deployment="your-embeddings-deployment-name",
    model="your-embeddings-model-name",
    openai_api_base="https://your-endpoint.openai.azure.com/",
    openai_api_type="azure",
    http_client=shared_httpx_client,
    max_retries=max_retries,
    request_timeout=10  # Set a reasonable timeout
)

# Example usage with retry decorator
@retry_decorator
def embed_query_with_retry(text):
    return embeddings.embed_query(text)

# First API call
input_text_1 = "The meaning of life is 42"
vector_1 = embed_query_with_retry(input_text_1)
print(vector_1[:3])

# Second API call
input_text_2 = "Another text to embed"
vector_2 = embed_query_with_retry(input_text_2)
print(vector_2[:3])

By setting retry_min_seconds and retry_max_seconds to lower values and limiting the number of retries, you can reduce the additional time added by retries while still handling transient errors effectively ^[1][2][3].

To continue talking to Dosu, mention @dosu.

[priyanshuverifast]

but why retry? its not a issue of rety, i already have retry implemented, and I have never seen then embeddings have retrying.

…

On Tue, 3 Sept 2024 at 17:51, dosubot[bot] ***@***.***> wrote: To optimize the retry mechanism without significantly increasing the overall response time when using AzureOpenAIEmbeddings with the same httpx client, you can fine-tune the retry parameters to balance between retry attempts and wait times. Here’s how you can do it: 1. *Set Reasonable Retry Parameters*: Adjust retry_min_seconds, retry_max_seconds, and max_retries to minimize the impact on response time. 2. *Use Exponential Backoff with Jitter*: This helps in spreading out the retries and avoiding thundering herd problems. Here’s an example configuration: import httpximport loggingfrom langchain_community.embeddings.openai import AzureOpenAIEmbeddingsfrom tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type, before_sleep_logimport openai # Configure logginglogger = logging.getLogger(__name__) # Set the retry parametersretry_min_seconds = 1 # Minimum number of seconds to wait between retriesretry_max_seconds = 4 # Maximum number of seconds to wait between retriesmax_retries = 3 # Maximum number of retries # Configure the retry mechanismretry_decorator = retry( reraise=True, stop=stop_after_attempt(max_retries), wait=wait_exponential(multiplier=1, min=retry_min_seconds, max=retry_max_seconds), retry=( retry_if_exception_type(openai.error.Timeout) | retry_if_exception_type(openai.error.APIError) | retry_if_exception_type(openai.error.APIConnectionError) | retry_if_exception_type(openai.error.RateLimitError) | retry_if_exception_type(openai.error.ServiceUnavailableError) ), before_sleep=before_sleep_log(logger, logging.WARNING), ) # Create a shared httpx clientshared_httpx_client = httpx.Client() # Initialize the AzureOpenAIEmbeddings with the shared clientembeddings = AzureOpenAIEmbeddings( deployment="your-embeddings-deployment-name", model="your-embeddings-model-name", openai_api_base="https://your-endpoint.openai.azure.com/", openai_api_type="azure", http_client=shared_httpx_client, max_retries=max_retries, request_timeout=10 # Set a reasonable timeout ) # Example usage with retry ***@***.***_decoratordef embed_query_with_retry(text): return embeddings.embed_query(text) # First API callinput_text_1 = "The meaning of life is 42"vector_1 = embed_query_with_retry(input_text_1)print(vector_1[:3]) # Second API callinput_text_2 = "Another text to embed"vector_2 = embed_query_with_retry(input_text_2)print(vector_2[:3]) By setting retry_min_seconds and retry_max_seconds to lower values and limiting the number of retries, you can reduce the additional time added by retries while still handling transient errors effectively [1] <https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/embeddings/openai.py> [2] <https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/embeddings/localai.py> [3] <https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/embeddings/dashscope.py> . *To continue talking to Dosu <https://dosu.dev>, mention @dosu <https://github.com/dosu>.* — Reply to this email directly, view it on GitHub <#25961 (reply in thread)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/BJDASF73P4W2Z54FRS5B4DDZUWSVDAVCNFSM6AAAAABNR7VS2SVHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTANJTGE2TGMY> . You are receiving this because you were mentioned.Message ID: ***@***.*** com>

[dosubot[bot]]

The increased latency for the second API response when using AzureOpenAIEmbeddings with the same httpx client might be due to the azure_ad_token_provider function being invoked on every request. This function is designed to return an Azure Active Directory token and could introduce latency if it involves network calls or complex computations.

azure_ad_token_provider: Union[Callable[[], str], None] = None
"""A function that returns an Azure Active Directory token.

    Will be invoked on every request.
"""

If the azure_ad_token_provider is set and it performs time-consuming operations, it could cause the second API call to be slower ^[1].

To mitigate this, you can:

1. Cache the Token: Implement a caching mechanism for the Azure AD token to avoid fetching it on every request.
2. Pre-warm Connections: Ensure that the httpx client maintains persistent connections by configuring it properly.

Here’s an example of how you can cache the token:

import httpx
from langchain_openai.embeddings.azure import AzureOpenAIEmbeddings
from datetime import datetime, timedelta

# Token caching mechanism
class TokenCache:
    def __init__(self, token_provider, expiry_seconds=3600):
        self.token_provider = token_provider
        self.expiry_seconds = expiry_seconds
        self.token = None
        self.expiry_time = datetime.min

    def get_token(self):
        if datetime.now() >= self.expiry_time:
            self.token = self.token_provider()
            self.expiry_time = datetime.now() + timedelta(seconds=self.expiry_seconds)
        return self.token

# Example token provider function
def azure_ad_token_provider():
    # Replace with actual token fetching logic
    return "your-azure-ad-token"

# Initialize token cache
token_cache = TokenCache(azure_ad_token_provider)

# Create a shared httpx client
shared_httpx_client = httpx.Client()

# Initialize the AzureOpenAIEmbeddings with the shared client and token cache
embeddings = AzureOpenAIEmbeddings(
    deployment="your-embeddings-deployment-name",
    model="your-embeddings-model-name",
    openai_api_base="https://your-endpoint.openai.azure.com/",
    openai_api_type="azure",
    http_client=shared_httpx_client,
    azure_ad_token_provider=token_cache.get_token
)

# Example usage
input_text = "The meaning of life is 42"
vector = embeddings.embed_query(input_text)
print(vector[:3])

By caching the token and reusing the httpx client, you can reduce the latency for subsequent API calls ^[1][2].

To continue talking to Dosu, mention @dosu.