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LangChain’s ChatOpenAI class accepts a custom base_url, making Meliai a drop-in provider for any LangChain application. You keep every chain, agent, tool, and memory component exactly as-is — only the endpoint and API key change. Your data stays within the EU on every call.
1

Install LangChain

Install the langchain-openai package, which provides the ChatOpenAI and OpenAIEmbeddings classes used throughout this guide.
2

Configure ChatOpenAI with Meliai

Pass your Meliai API key and base URL when instantiating ChatOpenAI. Everything else — temperature, streaming, callbacks — works the same way.
3

Use in a chain

Compose your ChatOpenAI instance with prompts and other runnables using LangChain Expression Language (LCEL). No changes are needed compared to a standard OpenAI setup.
4

Use embeddings

OpenAIEmbeddings accepts the same base_url parameter, giving you Meliai-hosted embedding models for vector search, RAG pipelines, and semantic similarity tasks.
Append a routing suffix to any model ID to control how Meliai selects a provider. For example, use <MODEL_ID>:eco to prefer the lowest-carbon data center, or <MODEL_ID>:speed to prioritise the fastest available provider.