langcache 0.8.0

Python Client SDK for LangCache Redis Service

langcache

Developer-friendly & type-safe Python SDK specifically catered to leverage langcache API.

[!IMPORTANT] This SDK is not yet ready for production use. To complete setup please follow the steps outlined in your workspace. Delete this section before > publishing to a package manager.

Summary

Redis LangCache Service: API for managing a Redis LangCache

Table of Contents

• langcache
  • SDK Installation
  • IDE Support
  • SDK Example Usage
  • Authentication
  • Available Resources and Operations
  • Global Parameters
  • Retries
  • Error Handling
  • Custom HTTP Client
  • Resource Management
  • Debugging
• Development
  • Maturity
  • Contributions

SDK Installation

[!NOTE] Python version upgrade policy

Once a Python version reaches its official end of life date, a 3-month grace period is provided for users to upgrade. Following this grace period, the minimum python version supported in the SDK will be updated.

The SDK can be installed with either pip or poetry package managers.

PIP

PIP is the default package installer for Python, enabling easy installation and management of packages from PyPI via the command line.

pip install langcache

Poetry

Poetry is a modern tool that simplifies dependency management and package publishing by using a single pyproject.toml file to handle project metadata and dependencies.

poetry add langcache

Shell and script usage with uv

You can use this SDK in a Python shell with uv and the uvx command that comes with it like so:

uvx --from langcache python

It's also possible to write a standalone Python script without needing to set up a whole project like so:

#!/usr/bin/env -S uv run --script
# /// script
# requires-python = ">=3.9"
# dependencies = [
#     "langcache",
# ]
# ///

from langcache import LangCache

sdk = LangCache(
  # SDK arguments
)

# Rest of script here...

Once that is saved to a file, you can run it with uv run script.py where script.py can be replaced with the actual file name.

IDE Support

PyCharm

Generally, the SDK will work well with most IDEs out of the box. However, when using PyCharm, you can enjoy much better integration with Pydantic by installing an additional plugin.

• PyCharm Pydantic Plugin

SDK Example Usage

Save an entry

Save an entry to the cache

# Synchronous Example
from langcache import LangCache
import os


with LangCache(
    server_url="https://api.example.com",
    cache_id="<id>",
    service_key=os.getenv("LANGCACHE_SERVICE_KEY", ""),
) as lang_cache:

    res = lang_cache.set(prompt="How does semantic caching work?", response="Semantic caching stores and retrieves data based on meaning, not exact matches.", attributes={
        "language": "en",
        "topic": "ai",
    })

    # Handle response
    print(res)

The same SDK client can also be used to make asychronous requests by importing asyncio.

# Asynchronous Example
import asyncio
from langcache import LangCache
import os

async def main():

    async with LangCache(
        server_url="https://api.example.com",
        cache_id="<id>",
        service_key=os.getenv("LANGCACHE_SERVICE_KEY", ""),
    ) as lang_cache:

        res = await lang_cache.set_async(prompt="How does semantic caching work?", response="Semantic caching stores and retrieves data based on meaning, not exact matches.", attributes={
            "language": "en",
            "topic": "ai",
        })

        # Handle response
        print(res)

asyncio.run(main())

Search for entries

Search for entries in the cache

# Synchronous Example
from langcache import LangCache
import os


with LangCache(
    server_url="https://api.example.com",
    cache_id="<id>",
    service_key=os.getenv("LANGCACHE_SERVICE_KEY", ""),
) as lang_cache:

    res = lang_cache.search(prompt="How does semantic caching work?", similarity_threshold=0.9, attributes={
        "language": "en",
        "topic": "ai",
    })

    # Handle response
    print(res)

The same SDK client can also be used to make asychronous requests by importing asyncio.

# Asynchronous Example
import asyncio
from langcache import LangCache
import os

async def main():

    async with LangCache(
        server_url="https://api.example.com",
        cache_id="<id>",
        service_key=os.getenv("LANGCACHE_SERVICE_KEY", ""),
    ) as lang_cache:

        res = await lang_cache.search_async(prompt="How does semantic caching work?", similarity_threshold=0.9, attributes={
            "language": "en",
            "topic": "ai",
        })

        # Handle response
        print(res)

asyncio.run(main())

Delete an entry

Delete an entry from the cache by id

# Synchronous Example
from langcache import LangCache
import os


with LangCache(
    server_url="https://api.example.com",
    cache_id="<id>",
    service_key=os.getenv("LANGCACHE_SERVICE_KEY", ""),
) as lang_cache:

    lang_cache.delete_by_id(entry_id="<id>")

    # Use the SDK ...

The same SDK client can also be used to make asychronous requests by importing asyncio.

# Asynchronous Example
import asyncio
from langcache import LangCache
import os

async def main():

    async with LangCache(
        server_url="https://api.example.com",
        cache_id="<id>",
        service_key=os.getenv("LANGCACHE_SERVICE_KEY", ""),
    ) as lang_cache:

        await lang_cache.delete_by_id_async(entry_id="<id>")

        # Use the SDK ...

asyncio.run(main())

Delete entries

Delete entries based on attributes

# Synchronous Example
from langcache import LangCache
import os


with LangCache(
    server_url="https://api.example.com",
    cache_id="<id>",
    service_key=os.getenv("LANGCACHE_SERVICE_KEY", ""),
) as lang_cache:

    res = lang_cache.delete_query(attributes={
        "language": "en",
        "topic": "ai",
    })

    # Handle response
    print(res)

The same SDK client can also be used to make asychronous requests by importing asyncio.

# Asynchronous Example
import asyncio
from langcache import LangCache
import os

async def main():

    async with LangCache(
        server_url="https://api.example.com",
        cache_id="<id>",
        service_key=os.getenv("LANGCACHE_SERVICE_KEY", ""),
    ) as lang_cache:

        res = await lang_cache.delete_query_async(attributes={
            "language": "en",
            "topic": "ai",
        })

        # Handle response
        print(res)

asyncio.run(main())

Authentication

Per-Client Security Schemes

This SDK supports the following security scheme globally:

Name	Type	Scheme	Environment Variable
service_key	http	HTTP Bearer	LANGCACHE_SERVICE_KEY

To authenticate with the API the service_key parameter must be set when initializing the SDK client instance. For example:

from langcache import LangCache
import os


with LangCache(
    server_url="https://api.example.com",
    service_key=os.getenv("LANGCACHE_SERVICE_KEY", ""),
    cache_id="<id>",
) as lang_cache:

    res = lang_cache.search(prompt="How does semantic caching work?", similarity_threshold=0.9, attributes={
        "language": "en",
        "topic": "ai",
    })

    # Handle response
    print(res)

Available Resources and Operations

Available methods

LangCache SDK

• search - Search the cache
• set - Add a new cache entry to the cache
• delete_query - Delete cache entries based on the request parameters
• delete_by_id - Delete a cache entry by ID

Global Parameters

A parameter is configured globally. This parameter may be set on the SDK client instance itself during initialization. When configured as an option during SDK initialization, This global value will be used as the default on the operations that use it. When such operations are called, there is a place in each to override the global value, if needed.

For example, you can set cacheId to "<id>" at SDK initialization and then you do not have to pass the same value on calls to operations like search. But if you want to do so you may, which will locally override the global setting. See the example code below for a demonstration.

Available Globals

The following global parameter is available. Global parameters can also be set via environment variable.

Name	Type	Description	Environment
cache_id	str	The cache_id parameter.	LANGCACHE_CACHE_ID

Example

from langcache import LangCache
import os


with LangCache(
    server_url="https://api.example.com",
    cache_id="<id>",
    service_key=os.getenv("LANGCACHE_SERVICE_KEY", ""),
) as lang_cache:

    res = lang_cache.search(prompt="How does semantic caching work?", similarity_threshold=0.9, attributes={
        "language": "en",
        "topic": "ai",
    })

    # Handle response
    print(res)

Retries

Some of the endpoints in this SDK support retries. If you use the SDK without any configuration, it will fall back to the default retry strategy provided by the API. However, the default retry strategy can be overridden on a per-operation basis, or across the entire SDK.

To change the default retry strategy for a single API call, simply provide a RetryConfig object to the call:

from langcache import LangCache
from langcache.utils import BackoffStrategy, RetryConfig
import os


with LangCache(
    server_url="https://api.example.com",
    cache_id="<id>",
    service_key=os.getenv("LANGCACHE_SERVICE_KEY", ""),
) as lang_cache:

    res = lang_cache.search(prompt="How does semantic caching work?", similarity_threshold=0.9, attributes={
        "language": "en",
        "topic": "ai",
    },
        RetryConfig("backoff", BackoffStrategy(1, 50, 1.1, 100), False))

    # Handle response
    print(res)

If you'd like to override the default retry strategy for all operations that support retries, you can use the retry_config optional parameter when initializing the SDK:

from langcache import LangCache
from langcache.utils import BackoffStrategy, RetryConfig
import os


with LangCache(
    server_url="https://api.example.com",
    retry_config=RetryConfig("backoff", BackoffStrategy(1, 50, 1.1, 100), False),
    cache_id="<id>",
    service_key=os.getenv("LANGCACHE_SERVICE_KEY", ""),
) as lang_cache:

    res = lang_cache.search(prompt="How does semantic caching work?", similarity_threshold=0.9, attributes={
        "language": "en",
        "topic": "ai",
    })

    # Handle response
    print(res)

Error Handling

LangCacheError is the base class for all HTTP error responses. It has the following properties:

Property	Type	Description
err.message	str	Error message
err.status_code	int	HTTP response status code eg 404
err.headers	httpx.Headers	HTTP response headers
err.body	str	HTTP body. Can be empty string if no body is returned.
err.raw_response	httpx.Response	Raw HTTP response
err.data		Optional. Some errors may contain structured data. See Error Classes.

Example

from langcache import LangCache, errors
import os


with LangCache(
    server_url="https://api.example.com",
    cache_id="<id>",
    service_key=os.getenv("LANGCACHE_SERVICE_KEY", ""),
) as lang_cache:
    res = None
    try:

        res = lang_cache.search(prompt="How does semantic caching work?", similarity_threshold=0.9, attributes={
            "language": "en",
            "topic": "ai",
        })

        # Handle response
        print(res)


    except errors.LangCacheError as e:
        # The base class for HTTP error responses
        print(e.message)
        print(e.status_code)
        print(e.body)
        print(e.headers)
        print(e.raw_response)

        # Depending on the method different errors may be thrown
        if isinstance(e, errors.BadRequestErrorResponseContent):
            print(e.data.title)  # str
            print(e.data.status)  # Optional[int]
            print(e.data.detail)  # Optional[str]
            print(e.data.type)  # models.BadRequestErrorURI

Error Classes

Primary errors:

• LangCacheError: The base class for HTTP error responses.
  • BadRequestErrorResponseContent: Bad Request. Status code 400.
  • AuthenticationErrorResponseContent: Unauthorized. Status code 401.
  • ForbiddenErrorResponseContent: Forbidden. Status code 403.
  • InternalServerErrorResponseContent: An unexpected error occurred. Status code 500.
  • ServiceUnavailableErrorResponseContent: An internal error occurred. Status code 503.

Less common errors (6)

Network errors:

• httpx.RequestError: Base class for request errors.
  • httpx.ConnectError: HTTP client was unable to make a request to a server.
  • httpx.TimeoutException: HTTP request timed out.

Inherit from LangCacheError:

• NotFoundErrorResponseContent: Cache entry not found. Status code 404. Applicable to 1 of 4 methods.*
• ResponseValidationError: Type mismatch between the response data and the expected Pydantic model. Provides access to the Pydantic validation error via the cause attribute.

* Check the method documentation to see if the error is applicable.

Custom HTTP Client

The Python SDK makes API calls using the httpx HTTP library. In order to provide a convenient way to configure timeouts, cookies, proxies, custom headers, and other low-level configuration, you can initialize the SDK client with your own HTTP client instance. Depending on whether you are using the sync or async version of the SDK, you can pass an instance of HttpClient or AsyncHttpClient respectively, which are Protocol's ensuring that the client has the necessary methods to make API calls. This allows you to wrap the client with your own custom logic, such as adding custom headers, logging, or error handling, or you can just pass an instance of httpx.Client or httpx.AsyncClient directly.

For example, you could specify a header for every request that this sdk makes as follows:

from langcache import LangCache
import httpx

http_client = httpx.Client(headers={"x-custom-header": "someValue"})
s = LangCache(client=http_client)

or you could wrap the client with your own custom logic:

from langcache import LangCache
from langcache.httpclient import AsyncHttpClient
import httpx

class CustomClient(AsyncHttpClient):
    client: AsyncHttpClient

    def __init__(self, client: AsyncHttpClient):
        self.client = client

    async def send(
        self,
        request: httpx.Request,
        *,
        stream: bool = False,
        auth: Union[
            httpx._types.AuthTypes, httpx._client.UseClientDefault, None
        ] = httpx.USE_CLIENT_DEFAULT,
        follow_redirects: Union[
            bool, httpx._client.UseClientDefault
        ] = httpx.USE_CLIENT_DEFAULT,
    ) -> httpx.Response:
        request.headers["Client-Level-Header"] = "added by client"

        return await self.client.send(
            request, stream=stream, auth=auth, follow_redirects=follow_redirects
        )

    def build_request(
        self,
        method: str,
        url: httpx._types.URLTypes,
        *,
        content: Optional[httpx._types.RequestContent] = None,
        data: Optional[httpx._types.RequestData] = None,
        files: Optional[httpx._types.RequestFiles] = None,
        json: Optional[Any] = None,
        params: Optional[httpx._types.QueryParamTypes] = None,
        headers: Optional[httpx._types.HeaderTypes] = None,
        cookies: Optional[httpx._types.CookieTypes] = None,
        timeout: Union[
            httpx._types.TimeoutTypes, httpx._client.UseClientDefault
        ] = httpx.USE_CLIENT_DEFAULT,
        extensions: Optional[httpx._types.RequestExtensions] = None,
    ) -> httpx.Request:
        return self.client.build_request(
            method,
            url,
            content=content,
            data=data,
            files=files,
            json=json,
            params=params,
            headers=headers,
            cookies=cookies,
            timeout=timeout,
            extensions=extensions,
        )

s = LangCache(async_client=CustomClient(httpx.AsyncClient()))

Resource Management

The LangCache class implements the context manager protocol and registers a finalizer function to close the underlying sync and async HTTPX clients it uses under the hood. This will close HTTP connections, release memory and free up other resources held by the SDK. In short-lived Python programs and notebooks that make a few SDK method calls, resource management may not be a concern. However, in longer-lived programs, it is beneficial to create a single SDK instance via a context manager and reuse it across the application.

from langcache import LangCache
import os
def main():

    with LangCache(
        server_url="https://api.example.com",
        cache_id="<id>",
        service_key=os.getenv("LANGCACHE_SERVICE_KEY", ""),
    ) as lang_cache:
        # Rest of application here...


# Or when using async:
async def amain():

    async with LangCache(
        server_url="https://api.example.com",
        cache_id="<id>",
        service_key=os.getenv("LANGCACHE_SERVICE_KEY", ""),
    ) as lang_cache:
        # Rest of application here...

Debugging

You can setup your SDK to emit debug logs for SDK requests and responses.

You can pass your own logger class directly into your SDK.

from langcache import LangCache
import logging

logging.basicConfig(level=logging.DEBUG)
s = LangCache(server_url="https://example.com", debug_logger=logging.getLogger("langcache"))

You can also enable a default debug logger by setting an environment variable LANGCACHE_DEBUG to true.

Development

Maturity

This SDK is in beta, and there may be breaking changes between versions without a major version update. Therefore, we recommend pinning usage to a specific package version. This way, you can install the same version each time without breaking changes unless you are intentionally looking for the latest version.

Contributions

While we value open-source contributions to this SDK, this library is generated programmatically. Any manual changes added to internal files will be overwritten on the next generation. We look forward to hearing your feedback. Feel free to open a PR or an issue with a proof of concept and we'll do our best to include it in a future release.

SDK Created by Speakeasy