jammi-client
Pure-Python gRPC client for a remote Jammi engine — the deploy half of the develop→deploy journey.
jammi-ai runs an embedded engine in-process and bundles this client for its
remote targets. jammi-client is the lean variant: a universal
py3-none-any wheel that links no candle/ML stack — just grpcio + protobuf
pyarrow— for production where the engine runs behind a server.
One front door
import jammi_client
db = jammi_client.connect("https://engine.example.com")
db.add_source("patents", url="s3://corpus/patents.parquet", format="parquet")
db.generate_embeddings(source="patents", model="local:tiny_bert",
columns=["abstract"], key="id", modality="text")
q = db.encode_query(model="local:tiny_bert", query="quantum computing")
hits = db.search("patents", query=q, k=5) # -> pyarrow.Table
connect(target) is the Python mirror of the Rust Jammi::open(Target):
transport is config, not a code path.
Authenticated channels
A bearer-protected endpoint is reached by attaching credentials to the channel — the bearer rides the channel (attached once at connect), not threaded through every call:
from jammi_client import connect, BearerCredentials
db = connect("https://engine.example.com", credentials=BearerCredentials(token))
The same works on a plaintext grpc:// target for local development. The
channel-level bearer covers the typed gRPC verbs; db.sql() (the Flight SQL
lane) does not yet carry it — tracked at
issue #96.
| target | transport |
|---|---|
https://host / grpcs://host:8081 |
secure remote |
http://host / grpc://host:8081 |
plaintext remote |
file:///data |
local engine — not in this build; raises NoEmbeddedEngineError pointing at pip install jammi-ai |
Scaling local→remote is an env flip (connect(os.environ["JAMMI_TARGET"]))
with no code change. Productionising from the embed wheel to this lean client is
a one-line import swap (import jammi_ai → import jammi_client), connect
unchanged.
Generated from the canonical proto
The wire stubs under jammi_client/_generated/ are generated from the same
jammi.v1 proto the engine, npm client, and embed wheel speak — one source, no
parallel schema. Regenerate after a proto change:
pip install -e '.[dev]'
make generate
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