langchain-turbopuffer 0.1.0

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langchain-turbopuffer

Use Turbopuffer as a vector store for LangChain.

Usage

poetry add git+https://github.com/skyzh/langchain-turbopuffer
# see example.py for usage

from langchain_turbopuffer import TurbopufferVectorStore

vectorstore = TurbopufferVectorStore(
    embedding=OllamaEmbeddings(model="mxbai-embed-large"),
    namespace="langchain-turbopuffer-test",
    api_key=os.getenv("TURBOPUFFER_API_KEY"),
)

Local Development

git clone https://github.com/skyzh/langchain-turbopuffer
cd langchain-turbopuffer
poetry env use 3.12
poetry install

ollama pull mxbai-embed-large llama3.2
ollama run llama3.2
export TURBOPUFFER_API_KEY=your_api_key
poetry run python example.py
poetry run python example.py --skip-load

In the example, you can ask questions like "What is prompt engineering?"

License

MIT

Note that the example.py is from langchain-risinglight based on several online tutorials (see the file header for more details).