PYTHON PACKAGE · LLAMAINDEX VECTOR STORE

LlamaIndex retrieval on managed Apache Solr

OpensolrVectorStore + OpensolrEmbedding: server-side GPU embeddings, native hybrid query mode, and standard metadata filters — no local model, no vector database to run.

$ pip install llama-index-opensolr

Quickstart

Standard LlamaIndex interfaces — swap in Opensolr and your index, retriever, and query engine work unchanged.

from llama_index.core import VectorStoreIndex, StorageContext, Document
from llama_index.vector_stores.opensolr import OpensolrVectorStore
from llama_index.embeddings.opensolr import OpensolrEmbedding

store = OpensolrVectorStore(index_name="mysite__dense",
                           email="you@example.com", api_key="...",
                           create_if_missing=True)
embed_model = OpensolrEmbedding(email="you@example.com", api_key="...",
                                index_name="mysite__dense")

index = VectorStoreIndex.from_documents(
    [Document(text="Hybrid search fuses BM25 with vector similarity")],
    storage_context=StorageContext.from_defaults(vector_store=store),
    embed_model=embed_model,
)
retriever = index.as_retriever(similarity_top_k=5)

What you get

The pieces most vector stores in the directory make you assemble yourself.

Server-side embeddings

1024-dim multilingual E5, computed on Opensolr's GPU infrastructure at both index and query time. No OpenAI key, no sentence-transformers install.

True HYBRID query mode

VectorStoreQueryMode.HYBRID with tunable alpha fuses BM25 and kNN scores per document via Opensolr's native {!hybrid} Solr parser — not client-side score juggling.

Standard filters, real Solr

MetadataFilters (EQ, NE, IN, NIN, ranges) map to Solr fq. And every index is plain Apache Solr underneath — facets, highlighting, the whole /select API.

Also available for: LangChain · MCP / AI Agents · Haystack · Laravel

Build your RAG pipeline on managed Solr

Free 15-day trial, no credit card — the included AI quota covers the whole quickstart.