llama-index-embeddings-kjarni
LlamaIndex embeddings with Kjarni: local models 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 llama-index-embeddings-kjarni
Usage
from llama_index.core import Settings, VectorStoreIndex, SimpleDirectoryReader
from llama_index.embeddings.kjarni import KjarniEmbedding
Settings.embed_model = KjarniEmbedding(model_name="minilm-l6-v2")
documents = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(documents)
nodes = index.as_retriever().retrieve("What does the contract say about renewal?")
| Setting | Default | |
|---|---|---|
model_name |
"minilm-l6-v2" |
Any Kjarni embedding model, e.g. mpnet-base-v2, bge-m3 (multilingual). kjarni model list shows all. |
device |
"cpu" |
"gpu" runs on Vulkan, Metal or DirectX 12. |
embed_batch_size |
32 |
Texts per call into the engine. |
query_prefix, text_prefix |
"" |
For models trained with prefixes, e.g. "query: " and "passage: " for E5. |
cache_dir |
Kjarni's cache | Where models are stored. |
The model loads on first use, so constructing KjarniEmbedding is free.
License
MIT
llama-index-embeddings-kjarni
1 maintainer
Metadata
1 maintainer
Release files for llama-index-embeddings-kjarni 0.1.0
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Release files / llama_index_embeddings_kjarni-0.1.0.tar.gz
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| Size | 4.8 kB |
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