langchain-kjarni

LangChain integration for Kjarni: local embeddings in your own process, with no PyTorch, no ONNX Runtime, no GPU and no server.

Kjarni produces the same vectors as sentence-transformers for the same model. The model downloads once on first use and is cached; after that everything works offline.

Install

pip install -U langchain-kjarni

Embeddings

from langchain_kjarni import KjarniEmbeddings

embeddings = KjarniEmbeddings(model="minilm-l6-v2")

embeddings.embed_query("What is the capital of Iceland?")      # 384 floats
embeddings.embed_documents(["Reykjavik", "Akureyri"])          # 2 x 384

Use it anywhere LangChain takes an Embeddings, for example a vector store:

from langchain_core.vectorstores import InMemoryVectorStore

store = InMemoryVectorStore.from_texts(
    ["Reykjavik is the capital of Iceland", "Paris is the capital of France"],
    embedding=embeddings,
)
store.similarity_search("Icelandic capital", k=1)

The model loads on first use, so constructing KjarniEmbeddings is free.

Example

examples/rag_without_pytorch.ipynb builds a small retrieval pipeline and measures the install size, the vectors against sentence-transformers, and the speed.

License

MIT

langchain-kjarni

langchain-kjarni