langchain-infinispan

An integration package connecting Infinispan and LangChain.

Installation

pip install langchain-infinispan

Usage

from langchain_infinispan import InfinispanVectorStore
from langchain_openai import OpenAIEmbeddings

vector_store = InfinispanVectorStore(
    embedding=OpenAIEmbeddings(),
    cache_name="my_vectors",
    ispn_url="http://localhost:11222",
    ispn_user="admin",
    ispn_password="password",
)

# Add documents
vector_store.add_texts(
    ["Hello world", "Infinispan is fast"],
    metadatas=[{"source": "greeting"}, {"source": "fact"}],
)

# Search
results = vector_store.similarity_search("hello", k=2)

With Bearer token authentication

vector_store = InfinispanVectorStore(
    embedding=OpenAIEmbeddings(),
    cache_name="my_vectors",
    ispn_url="http://localhost:11222",
    ispn_bearer_token="my-token",
)

With pre-built client

from langchain_infinispan import InfinispanVectorStore, InfinispanClient

client = InfinispanClient(
    url="http://localhost:11222",
    username="admin",
    password="password",
)
vector_store = InfinispanVectorStore(
    embedding=OpenAIEmbeddings(),
    cache_name="my_vectors",
    client=client,
)

Distance strategies

Pass similarity=DistanceStrategy.<X> to choose the vector similarity function (default COSINE):

Relevance scores from similarity_search_with_relevance_scores are normalized to [0, 1] for all strategies.

Requirements

• Infinispan 15+ with vector search support
• Python 3.10+

Development

This project uses uv for dependency management.

cd libs/infinispan
make dev_install        # uv sync --all-groups
make test               # unit tests
make integration_tests  # requires a running Infinispan 15+ server

See CONTRIBUTING.md for full setup, prerequisites, and how to run each test suite.