langchain-serenedb

A LangChain vector store integration for SereneDB

SereneDB speaks the PostgreSQL wire protocol, so this package connects with psycopg3. It maps the integration onto SereneDB's native capabilities:

Installation

pip install langchain-serenedb

Requires Python 3.10+.

Quickstart (engine + table)

from langchain_serenedb import SereneDBEngine, IVFIndex

engine = SereneDBEngine.from_connection_string(
    "host=127.0.0.1 port=7890 user=postgres dbname=postgres"
)

# Table only (vector search falls back to an exact scan until an index is built):
engine.init_vectorstore_table(table_name="my_docs", vector_size=768)

# Or create the table and its IVF ANN index in one call, so vector search is
# accelerated from the start:
engine.init_vectorstore_table("my_docs", 768, vector_index=IVFIndex())
#   ...or the combined full-text + vector index for hybrid search:
#   engine.init_vectorstore_table("my_docs", 768, hybrid_search_config=HybridSearchConfig())

# ... after writing rows, publish them to the inverted index:
engine.refresh_table("my_docs")

Tip: for a large bulk load, SereneDB trains better IVF clusters if you create the index after loading (store.apply_vector_index(IVFIndex())); creating it up front with the table is the convenient choice for incremental workloads.

Contributing

Building, testing, linting, and running the suite (locally or in Docker Compose) are covered in CONTRIBUTING.md.

License

MIT