Question 1

How do I use Langchain.rb with a Rails app?

Accepted Answer

Use the companion langchainrb_rails gem for deep Rails integration, which simplifies adding LLM capabilities to models and controllers. The README mentions it for seamless Rails workflows.

Question 2

Langchain.rb vs Python LangChain – which should I choose?

Accepted Answer

Langchain.rb brings LangChain's concepts to Ruby, offering similar features but tailored for Ruby developers. The Python version has a larger community and more tools, but Langchain.rb is best for Ruby-centric teams wanting native integration.

Question 3

How to implement a RAG system with Langchain.rb?

Accepted Answer

Pick a vector database like Weaviate, install its gem, then use methods like add_texts to index documents and similarity_search for retrieval, followed by integrating with LLM completions, as outlined in the RAG section.

Question 4

Does Langchain.rb support streaming for real-time responses?

Accepted Answer

Yes, streaming is supported through the assistants framework for some LLMs, but the README cautions it's not available for all providers, so check compatibility with your chosen LLM.

Question 5

What's the best way to create custom tools in Langchain.rb?

Accepted Answer

Extend Langchain::ToolDefinition, define functions with descriptions, and implement methods—like the MovieInfoTool example—then integrate them into assistants for enhanced functionality.

Question 6

Is Langchain.rb good for production use?

Accepted Answer

With features like RAGAS evaluations and multi-provider support, it's production-ready, but ensure robust testing due to dependencies on external APIs and gems, and monitor for updates.

langchain.rb

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