Ask HN: How are you managing LLM APIs in production?

https://news.ycombinator.com/item?id=36937772

by lamroger • 3 years ago

3 8 3 years ago

news.ycombinator.com

Looks like LangChain has LangSmith but it's in closed beta.

I saw a couple YC launches like Hegel AI.

I'm personally interested in deployments in small teams or teams with a lot of freedom to pick and choose their own tooling.

ianpurton• 3 years ago

I'm currently writing up a deployment architecture for LLM's and the API question is answered here https://fine-tuna.com/docs/choosing-a-model/model/

Basically you can get a Docker container that will publish an Open AI API compatible end point. You can then choose the model that sits behind that API.

As deployment will be in Kuberenetes we will clusters with GPU resources to maxz out performance but we're not there yet.

lamroger3• 3 years ago

Very cool!

XGBoost• 3 years ago

Play around with langchain and then convert all of that into decent code. After a few prototypes, you'll realize langchains or other pipelining are just for non-coders. You can architect elegant solutions yourself.

lamroger3• 3 years ago

I'll give it a shot and keep that in mind

retrovrv2• 3 years ago

Langsmith is broadly for tracing the chains - are you looking for prompt deployment solutions?

lamroger3• 3 years ago

Interesting - If I'm running a "single" chain, do you know if langsmith would be helpful? Deployment solutions would be interesting - things are so new it would be hard to choose so my default would be just to wrap the API and keep it simple.

retrovrv2• 3 years ago

Langsmith would be useful in that case to see the traces for your chain. (Of course given that you are using Langchain for chaining the prompts)

Makes sense on the deployment thing. I like what Vellum is doing. Helicone and Portkey also let you do deployments of prompt templates through APIs.

tab_jockey• 3 years ago

We built an AWS serverless app that handles:

- Configurable context and cases mapped to a RESTful API

- Multi-account and high throughput error handling

- DDB backed records of all requests and responses for evaluation, debugging & training

- One-click devops deploy

Has helped us deploy and maintain LLM apps into production quite easily. Let me know if you would like access to the repo.

Ask HN: How are you managing LLM APIs in production?

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