LlamaIndex integration with Infino vector store

pip install llama-index-vector-stores-infino

import infino
from llama_index.core import Document, StorageContext, VectorStoreIndex
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.vector_stores.infino import InfinoVectorStore

connection = infino.connect("./data")
store = InfinoVectorStore(connection, table_name="docs", dim=1536)

storage_context = StorageContext.from_defaults(vector_store=store)
index = VectorStoreIndex.from_documents(
    [Document(text="Infino runs search on object storage.")],
    storage_context=storage_context,
    embed_model=OpenAIEmbedding(),
)

retriever = index.as_retriever(similarity_top_k=4)

import infino

# Local (embedded): Parquet under a directory, no server.
connection = infino.connect("./data")

# Object storage (embedded): Parquet in your bucket, queried in place.
connection = infino.connect(
    "s3://my-bucket/rag", storage_options={"aws_region": "us-east-1"}
)

# Hosted Infino Cloud: sign up at https://platform.infino.ws for an API key.
connection = infino.connect(
    "https://api.platform.infino.ws/<database>", api_key="..."
)

index.as_retriever(similarity_top_k=10)                                            # vector (default)
index.as_retriever(similarity_top_k=10, vector_store_query_mode="text_search")     # BM25
index.as_retriever(similarity_top_k=10, vector_store_query_mode="hybrid")          # hybrid (RRF)
index.as_retriever(similarity_top_k=10, vector_store_query_mode="mmr",
                   vector_store_kwargs={"embed_model": embed_model})               # MMR