polign — Python client for polign_db
A thin Python client for polign with two interchangeable transports:
- HTTP (
polign.Client) — zero dependencies, pure stdlib. Talks JSON to the server's HTTP listener (default:23000). - gRPC (
polign.GrpcClient) — install with the[grpc]extra. Wire-compatible with the Go client, against the gRPC listener (default:23001).
Both expose the same seven operations with identical semantics: put,
put_many, get, get_many, list, delete, search.
Install
Not yet published to PyPI — install from a checkout of the repo:
pip install ./sdk/python # HTTP client, no dependencies
pip install './sdk/python[grpc]' # + gRPC transport (grpcio, protobuf)
(From inside this directory: pip install .; add -e for an editable
development install — needs pip ≥ 21.3.)
Quick start
from polign import Client
client = Client("http://localhost:23000")
# Upsert. Collections are auto-created on first put, inferring their
# dimension from the vector. Values accept lists or numpy arrays.
client.put("docs", "doc-1", embedding, metadata={"title": "Cats", "url": "/cats"})
# Nearest-neighbour search (distance: smaller = closer)
for hit in client.search("docs", values=query_embedding, k=10):
print(hit.id, hit.distance, hit.metadata)
Swap in gRPC by changing two lines — the rest of the code is identical:
from polign import GrpcClient
client = GrpcClient("localhost:23001")
Operations
client.put("docs", "doc-1", values, metadata={"k": "v"}) # upsert, returns id
client.put_many("docs", [Vector(id="a", values=va), Vector(id="b", values=vb)])
# batch upsert, one request
v = client.get("docs", "doc-1") # Vector(id, values, metadata)
vs = client.get_many("docs", ["a", "b"]) # batch read, byte-exact values:
# never a compressed reconstruction
# (get may return one on a cold-
# flushed collection); unknown ids
# omitted, request order kept
page = client.list("docs", limit=100, offset=0) # page.vectors, page.total
client.delete("docs", "doc-1") # True; False if absent (no error)
hits = client.search("docs", values=q, k=10) # [Hit(id, distance, score, metadata)]
Search options
from polign import Fusion
client.search(
"docs",
values=q, # vector leg (either values or text required)
k=10,
ef=64, # HNSW beam width override (0 = server default)
filter={"lang": "en"}, # metadata predicate (see below)
text="quick brown fox", # BM25 leg (needs a segment index server-side)
fusion=Fusion(method="linear", alpha=0.6), # hybrid fusion; default RRF
cold=True, nprobe=8, # serve from object-store segments
)
text alone runs a pure BM25 search; values + text runs hybrid search
fused server-side. hit.score is the BM25/fused relevance (larger = better)
and is 0.0 on a pure vector search.
filter takes the same dict language on both transports (see
docs/FILTERING.md in the main repo): bare values are equality
(ANDed across keys); per-key operator objects ($eq, $ne, $in, $gt,
$gte, $lt, $lte, $exists) and the composers $and/$or/$not
express richer predicates:
filter={
"tenant": "acme",
"score": {"$gte": 0.5},
"$or": [{"lang": "en"}, {"lang": {"$exists": False}}],
}
Auth and tenants
client = Client(
"https://db.example.com:23000",
api_key="plgn_<key_id>_<secret>", # sent as Authorization: Bearer
tenant="acme/search/prod", # org/project/namespace
)
Servers started without -auth-stores need no credentials. With TLS enabled
server-side, use an https:// URL (HTTP) or pass
credentials=grpc.ssl_channel_credentials() (gRPC).
Errors
All errors subclass polign.PolignError:
| Exception | HTTP | gRPC |
|---|---|---|
InvalidArgumentError |
400 | INVALID_ARGUMENT |
AuthenticationError |
401 | UNAUTHENTICATED |
PermissionDeniedError |
403 | PERMISSION_DENIED |
NotFoundError |
404 | NOT_FOUND |
NotOwnerError |
421 | FAILED_PRECONDITION |
RateLimitError |
429 | RESOURCE_EXHAUSTED |
ServerError |
5xx | INTERNAL |
ConnectionError |
— | UNAVAILABLE |
NotOwnerError.owner names the owning node in fleet mode — reconnect there
and retry.
Notes & caveats
Mirrors the Go client's caveats (see docs/CLIENT.md in the main repo):
- Embed documents and queries with the same model — distances are only meaningful within one embedding space.
- Auto-created collections use the server's default metric (L2) and hybrid IVF index; metric and index tuning are not yet exposed over the wire.
- Bulk loads should use
put_many— one request per batch instead of one per vector. The server validates the whole batch up front (id, non-empty values, uniform dimension, at most 5000 vectors per batch: an invalid batch applies nothing); on a rarer mid-batch failure earlier vectors remain applied, and since puts are idempotent upserts you simply retry the batch. Chunk larger loads into batches of 5000. - Metadata is
str -> stronly; filter scalars (numbers, booleans) compare against the stored string by their literal form ("0.5","true").
Development
cd sdk/python
pip install -e .[grpc] pytest
pytest tests/test_unit.py # stub-server unit tests
pytest tests/test_integration.py # builds & boots the real server (needs Go)
Regenerate the vendored gRPC stubs after changing proto/vectordb.proto
(from the repo root):
python -m grpc_tools.protoc -I proto \
--python_out=sdk/python/polign/_pb \
--grpc_python_out=sdk/python/polign/_pb \
--pyi_out=sdk/python/polign/_pb \
proto/vectordb.proto
sed -i '' 's/^import vectordb_pb2 as/from . import vectordb_pb2 as/' \
sdk/python/polign/_pb/vectordb_pb2_grpc.py
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