This release is a pre-release and may not be stable for production use.
Zep Python Library
The Zep Python library provides convenient access to the Zep APIs from Python.
Table of Contents
- What Is Zep 💬
- Installation
- Reference
- Usage
- Async Client
- Exception Handling
- Pagination
- Advanced
- Contributing
What is Zep? 💬
Zep is a context engineering platform for AI Assistant apps. With Zep, you can provide AI assistants with the ability to recall past conversations, no matter how distant, while also reducing hallucinations, latency, and cost.
Cloud Installation
You can install the Zep Cloud SDK by running:
pip install zep-cloud
Community Installation
pip install zep-python
Zep v0.x Compatible SDK
You can install Zep v0.x compatible sdk by running:
pip install "zep-python>=1.5.0,<2.0.0"
How Zep works
Zep persists and recalls chat histories, and automatically generates summaries and other artifacts from these chat histories. It also embeds messages and summaries, enabling you to search Zep for relevant context from past conversations. Zep does all of this asynchronously, ensuring these operations don't impact your user's chat experience. Data is persisted to database, allowing you to scale out when growth demands.
Zep also provides a simple, easy to use abstraction for document vector search called Document Collections. This is designed to complement Zep's core context features, but is not designed to be a general purpose vector database.
Zep allows you to be more intentional about constructing your prompt:
- automatically adding a few recent messages, with the number customized for your app;
- a summary of recent conversations prior to the messages above;
- and/or contextually relevant summaries or messages surfaced from the entire chat session.
- and/or relevant Business data from Zep Document Collections.
Zep Cloud offers:
- Fact Extraction: Automatically build fact tables from conversations, without having to define a data schema upfront.
- Dialog Classification: Instantly and accurately classify chat dialog. Understand user intent and emotion, segment users, and more. Route chains based on semantic context, and trigger events.
- Structured Data Extraction: Quickly extract business data from chat conversations using a schema you define. Understand what your Assistant should ask for next in order to complete its task.
You will also need to provide a Zep Project API key to your zep client. You can find out about zep projects in our cloud docs
Using LangChain Zep Classes with zep-python
(Currently only available on release candidate versions)
In the pre-release version zep-python sdk comes with ZepChatMessageHistory and ZepVectorStore
classes that are compatible with LangChain's Python expression language
In order to use these classes in your application, you need to make sure that you have
langchain_core package installed, please refer to Langchain's docs installation section.
We support langchain_core@>=0.1.3<0.2.0
You can import these classes in the following way:
from zep_cloud.langchain import ZepChatMessageHistory, ZepVectorStore
Running Examples
You will need to set the following environment variables to run examples in the examples directory:
# Please use examples/.env.example as a template for .env file
# Required
ZEP_API_KEY=<zep-project-api-key># Your Zep Project API Key
ZEP_COLLECTION=<zep-collection-name># used in ingestion script and in vector store examples
OPENAI_API_KEY=<openai-api-key># Your OpenAI API Key
# Optional (If you want to use langsmith with LangServe Sample App)
LANGCHAIN_TRACING_V2=true
LANGCHAIN_API_KEY=<your-langchain-api-key>
LANGCHAIN_PROJECT=<your-langchain-project-name># If not specified, defaults to "default"
Installation
pip install zep-cloud
Reference
A full reference for this library is available here.
Usage
Instantiate and use the client with the following:
from zep_cloud import Zep
client = Zep(
api_key="<value>",
)
client.batch.create()
Async Client
The SDK also exports an async client so that you can make non-blocking calls to our API. Note that if you are constructing an Async httpx client class to pass into this client, use httpx.AsyncClient() instead of httpx.Client() (e.g. for the httpx_client parameter of this client).
import asyncio
from zep_cloud import AsyncZep
client = AsyncZep(
api_key="<value>",
)
async def main() -> None:
await client.batch.create()
asyncio.run(main())
Exception Handling
When the API returns a non-success status code (4xx or 5xx response), a subclass of the following error will be thrown.
from zep_cloud.core.api_error import ApiError
try:
client.batch.create(...)
except ApiError as e:
print(e.status_code)
print(e.body)
Pagination
Paginated requests will return a SyncPager or AsyncPager, which can be used as generators for the underlying object.
from zep_cloud import Zep
client = Zep(
api_key="<value>",
)
client.batch.list(
limit=1,
cursor="cursor",
status="status",
)
# You can also iterate through pages and access the typed response per page
pager = client.batch.list(...)
for page in pager.iter_pages():
print(page.response) # access the typed response for each page
for item in page:
print(item)
Advanced
Access Raw Response Data
The SDK provides access to raw response data, including headers, through the .with_raw_response property.
The .with_raw_response property returns a "raw" client that can be used to access the .headers and .data attributes.
from zep_cloud import Zep
client = Zep(...)
response = client.batch.with_raw_response.create(...)
print(response.headers) # access the response headers
print(response.status_code) # access the response status code
print(response.data) # access the underlying object
Retries
The SDK is instrumented with automatic retries with exponential backoff. A request will be retried as long as the request is deemed retryable and the number of retry attempts has not grown larger than the configured retry limit (default: 2).
A request is deemed retryable when any of the following HTTP status codes is returned:
Use the max_retries request option to configure this behavior.
client.batch.create(..., request_options={
"max_retries": 1
})
Timeouts
The SDK defaults to a 60 second timeout. You can configure this with a timeout option at the client or request level.
from zep_cloud import Zep
client = Zep(..., timeout=20.0)
# Override timeout for a specific method
client.batch.create(..., request_options={
"timeout_in_seconds": 1
})
Custom Client
You can override the httpx client to customize it for your use-case. Some common use-cases include support for proxies
and transports.
import httpx
from zep_cloud import Zep
client = Zep(
...,
httpx_client=httpx.Client(
proxy="http://my.test.proxy.example.com",
transport=httpx.HTTPTransport(local_address="0.0.0.0"),
),
)
Contributing
While we value open-source contributions to this SDK, this library is generated programmatically. Additions made directly to this library would have to be moved over to our generation code, otherwise they would be overwritten upon the next generated release. Feel free to open a PR as a proof of concept, but know that we will not be able to merge it as-is. We suggest opening an issue first to discuss with us!
On the other hand, contributions to the README are always very welcome!
1 maintainer
Release files for zep-cloud 4.0.0a1
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Total release size: 249.5 kB
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