This repository provides adapters for LangChain developers to integrate with Steamship, a platform for building and deploying full-lifecycle language AI applications. It enables rapid deployment with production-ready API endpoints, horizontal scaling, persistent storage, authentication, multi-tenancy, and integrated logging and metrics.

How It Works

Steamship acts as a deployment and infrastructure layer for LangChain applications. It offers adapters for various LangChain components, including LLMs (OpenAI), caches, callbacks, document loaders, tools (SERPAPI), memory, vector stores, and text splitters. These adapters allow LangChain applications to leverage Steamship's managed services for scaling, persistence, and deployment, abstracting away infrastructure complexities.

Quick Start & Requirements

• Install via pip: pip install steamship-langchain
• Requires a Steamship API key, configurable via the STEAMSHIP_API_KEY environment variable or directly in code.
• See Documentation for detailed setup and examples.

Highlighted Details

• Adapters for LLMs, Caching, Callbacks, Document Loaders (including GitHub, YouTube), Tools (SERPAPI), Memory, Vector Stores, and a Python AST-based Text Splitter.
• Examples demonstrate basic prompting, self-ask-with-search, chatbots with persistent memory, audio summarization via async chaining, and question-answering with persistent embeddings.
• Supports temporary and persistent workspaces for isolated development and production deployments.
• Deployment via the ship it command.

Maintenance & Community

• Contact: hello@steamship.com for feedback.
• Documentation and examples are available.

Licensing & Compatibility

• The license is not explicitly stated in the README. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

• Audio transcription support is noted as not yet fully production-ready, with potential issues.
• The license is not specified, which may impact commercial adoption.