Bridging the Last Mile in LangChain Application Development

• langchain

  36 86,215 10.0 Python

  🦜🔗 Build context-aware reasoning applications

  

Undoubtedly, LangChain is the most popular framework for AI application development at the moment. The advent of LangChain has greatly simplified the construction of AI applications based on Large Language Models (LLM). If we compare an AI application to a person, the LLM would be the "brain," while LangChain acts as the "limbs" by providing various tools and abstractions. Combined, they enable the creation of AI applications capable of "thinking." However, this article does not delve into the specific usage of LangChain but aims to discuss with readers the last-mile issue in LangChain application development—how to deploy LangChain applications, using AWS as an example. Why deploy on AWS? The free tier is simply too appealing for daily use.

• langserve

  4 1,657 9.6 JavaScript

  LangServe 🦜️🏓

  

Those familiar with the LangChain ecosystem might think of LangChain's sub-project LangServe upon reading this. LangServe's goal is to simplify the deployment of LangChain applications. It can package LangChain apps into API servers and provides default endpoints such as stream, async, docs, and playground. But LangServe alone does not resolve the deployment issues of LangChain applications. It ultimately provides an API server based on FastAPI, akin to frameworks like Flask and Django. How to deploy LangServe applications to the cloud and how to create and manage the dependent backend services remain unanswered by LangServe.

• InfluxDB

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• langchain-aws-template

  1 167 2.1 Python

  Build Generative AI applications with Langchain on AWS

  

However, I found a langchain-aws-template GitHub repository in the Resources list at the end of the video. It contains two example applications integrating AWS with LangChain, complete with deployment guides. The deployment process includes four steps:

• pluto

  7 76 9.6 TypeScript

  Pluto provides a unified programming interface that allows you to seamlessly tap into cloud capabilities and develop your business logic. (by pluto-lang)

  

The analysis above shows that despite the powerful services offered by large cloud service providers like AWS, there's still a significant learning curve for developers to effectively utilize these services. This led us to an idea: what if we could deduce the infrastructure resource requirements of an application directly from the LangChain application code, and then automatically create corresponding resource instances on cloud platforms like AWS? This approach could simplify the process of resource creation and application deployment. Based on this idea, we developed a research and development tool named Pluto.

• community

  1 2 3.1

  The Pluto Community (by pluto-lang)

  

Lastly, if you like the Pluto project and want to give it a try, you can visit our Getting Started guide, which offers various usage options including containers and online. If you have any questions or suggestions, or would like to contribute (very welcome!), feel free to join our community to participate in discussions and co-creation.

• SaaSHub

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