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templates/README.md
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# LangServe Templates # LangChain Templates
LangServe Templates are the easiest and fastest way to build a production-ready LLM application. LangChain Templates are the easiest and fastest way to build a production-ready LLM application.
These templates serve as a set of reference architectures for a wide variety of popular LLM use cases. These templates serve as a set of reference architectures for a wide variety of popular LLM use cases.
They are all in a standard format which make it easy to deploy them with [LangServe](https://github.com/langchain-ai/langserve).
🚩 We will be releasing a hosted version of LangServe for one-click deployments of LangChain applications. [Sign up here](https://airtable.com/app0hN6sd93QcKubv/shrAjst60xXa6quV2) to get on the waitlist.
## Quick Start ## Quick Start
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add_routes(app, pirate_speak_chain, path="/pirate_speak") add_routes(app, pirate_speak_chain, path="/pirate_speak")
``` ```
(Optional) Let's now configure LangSmith.
LangSmith will help us trace, monitor and debug LangChain applications.
LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/).
If you don't have access, you can skip this section
```shell
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
```
For this particular application, we will use OpenAI as the LLM, so we need to export our OpenAI API key:
```shell
export OPENAI_API_KEY=sk-...
```
You can then spin up production-ready endpoints, along with a playground, by running: You can then spin up production-ready endpoints, along with a playground, by running:
```shell ```shell