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# docs `ecosystem/integrations` update 4 Added missed integrations. Fixed inconsistencies. ## Who can review? @hwchase17 @dev2049
55 lines
1.7 KiB
Markdown
55 lines
1.7 KiB
Markdown
# Momento
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>[Momento Cache](https://docs.momentohq.com/) is the world's first truly serverless caching service. It provides instant elasticity, scale-to-zero
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> capability, and blazing-fast performance.
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> With Momento Cache, you grab the SDK, you get an end point, input a few lines into your code, and you're off and running.
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This page covers how to use the [Momento](https://gomomento.com) ecosystem within LangChain.
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## Installation and Setup
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- Sign up for a free account [here](https://docs.momentohq.com/getting-started) and get an auth token
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- Install the Momento Python SDK with `pip install momento`
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## Cache
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The Cache wrapper allows for [Momento](https://gomomento.com) to be used as a serverless, distributed, low-latency cache for LLM prompts and responses.
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The standard cache is the go-to use case for [Momento](https://gomomento.com) users in any environment.
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Import the cache as follows:
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```python
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from langchain.cache import MomentoCache
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```
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And set up like so:
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```python
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from datetime import timedelta
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from momento import CacheClient, Configurations, CredentialProvider
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import langchain
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# Instantiate the Momento client
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cache_client = CacheClient(
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Configurations.Laptop.v1(),
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CredentialProvider.from_environment_variable("MOMENTO_AUTH_TOKEN"),
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default_ttl=timedelta(days=1))
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# Choose a Momento cache name of your choice
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cache_name = "langchain"
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# Instantiate the LLM cache
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langchain.llm_cache = MomentoCache(cache_client, cache_name)
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```
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## Memory
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Momento can be used as a distributed memory store for LLMs.
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### Chat Message History Memory
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See [this notebook](../modules/memory/examples/momento_chat_message_history.ipynb) for a walkthrough of how to use Momento as a memory store for chat message history.
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