Infino

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SQL, full-text, and vector search over your data on object storage — one engine, no server to run.

Infino keeps your data in Apache Parquet on object storage (local disk, Amazon S3, or any S3-compatible store) and runs SQL, full-text (BM25), and vector search over it from a single system. Each file is a valid Parquet file with BM25 and vector indexes embedded directly inside it; a table composes many such files with snapshot-isolated reads, append-only writes, and atomic commits. It runs in your process — there is no daemon, no cluster, and no managed service to operate.

Use it for RAG, agent memory, hybrid search, and semantic search: an embedded vector database, full-text (BM25) search engine, and SQL query engine in one library.

Installation

pip install infino

Or with uv:

uv add infino            # add to a uv-managed project
uv pip install infino    # install into the active environment

Requires Python 3.9 or newer. pyarrow is installed as a dependency; pandas is optional and used only if you pass DataFrames.

Quickstart

import infino
import pyarrow as pa

# Connect to a catalog. Use a local path or an S3 URI for durable storage;
# "memory://" is ephemeral and handy for tests.
db = infino.connect("./data")

# Tiny stand-in for your embedding model so this runs as-is — a 16-dim
# one-hot by topic. Real embeddings are dense and higher-dimensional.
def embed(topic):
    v = [0.0] * 16
    v[topic] = 1.0
    return v

# Declare a schema and which columns to index. An "_id" column is added
# automatically — you don't define it. A vector column's dim must be in
# [16, 4096]; here we use 16, the floor.
schema = pa.schema([
    pa.field("source", pa.large_utf8(), nullable=False),
    pa.field("body", pa.large_utf8(), nullable=False),
    pa.field("embedding", pa.list_(pa.float32(), 16), nullable=False),
])
docs = db.create_table(
    "docs", schema, infino.IndexSpec().fts("body").vector("embedding", 16, "cosine")
)

# Append rows. One append is one atomic commit.
docs.append([
    {"source": "help-center", "body": "To cancel a subscription, open Settings then Billing.", "embedding": embed(0)},
    {"source": "help-center", "body": "Refunds return to the original payment method.",         "embedding": embed(0)},
    {"source": "blog",        "body": "Enable dark mode under Settings then Appearance.",        "embedding": embed(1)},
])

# Retrieve context to ground an agent's next answer — keyword, vector,
# hybrid (BM25 + vector fused in one pass), or SQL. Each returns a pyarrow.Table.
keyword  = docs.bm25_search("body", "cancel subscription", k=5)                           # BM25
semantic = docs.vector_search("embedding", embed(0), k=5)                                 # vector kNN
hybrid   = docs.hybrid_search("body", "cancel subscription", "embedding", embed(0), k=5)  # fused
billing  = db.query_sql("SELECT body FROM docs WHERE source = 'help-center'")             # SQL filter

Documentation

Full docs, guides, and the API reference live at infino.ai/docs:

Building from source

The bindings are built with maturin and require a Rust toolchain.

python3 -m venv .venv && source .venv/bin/activate
pip install maturin pytest pyarrow
maturin develop          # compile the extension and install it into the venv
pytest tests/

License

Apache-2.0.

Project links

Data verified by PyPI on Aug 25, 2026
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Credits

Author: Infino

GitHub Statistics

Data verified by PyPI on Aug 25, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

Apache Software License (Apache-2.0)

Requires

Python >=3.9

Provides Extra

test

Tags

search vector-search vector-database full-text-search bm25 semantic-search hybrid-search rag embeddings retrieval information-retrieval agent-memory sql parquet object-storage s3 azure-blob-storage gcs

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