langchain-fluxvector 0.1.1

LangChain integration for FluxVector — vector search with built-in embeddings

langchain-fluxvector

LangChain integration for FluxVector — vector search API with built-in embeddings.

Installation

pip install langchain-fluxvector

Usage

from langchain_fluxvector import FluxVectorStore

# FluxVector handles embeddings server-side — no OpenAI key needed
vs = FluxVectorStore(
    api_key="fv_live_...",
    collection="my-docs",
)

# Add documents
vs.add_texts([
    "FluxVector is a vector search API",
    "It supports 100+ languages",
    "Hybrid BM25 + vector search included",
])

# Search
results = vs.similarity_search("multilingual search", k=3)
for doc in results:
    print(doc.page_content, doc.metadata["score"])

Why FluxVector?

• No embedding model needed — FluxVector embeds text server-side
• Hybrid search — BM25 + vector with RRF fusion, out of the box
• 100+ languages — Multilingual-e5-large handles cross-lingual retrieval
• $29/mo for 1M vectors — Flat pricing, no per-query charges
• Self-host free — Same Docker image, your infrastructure

With LangChain Chains

from langchain_fluxvector import FluxVectorStore
from langchain_openai import ChatOpenAI
from langchain.chains import RetrievalQA

vs = FluxVectorStore(api_key="fv_live_...", collection="docs")
retriever = vs.as_retriever(search_kwargs={"k": 5})

qa = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(),
    retriever=retriever,
)
answer = qa.invoke("What languages does FluxVector support?")

Links

• FluxVector Docs
• FluxVector Console
• Python SDK