trustgraph/trustgraph-flow/trustgraph/storage/doc_embeddings/qdrant/write.py
2025-04-20 09:15:12 +01:00

101 lines
2.8 KiB
Python

"""
Accepts entity/vector pairs and writes them to a Qdrant store.
"""
from qdrant_client import QdrantClient
from qdrant_client.models import PointStruct
from qdrant_client.models import Distance, VectorParams
import uuid
from .... base import DocumentEmbeddingsStoreService
default_ident = "de-write"
default_store_uri = 'http://localhost:6333'
class Processor(DocumentEmbeddingsStoreService):
def __init__(self, **params):
store_uri = params.get("store_uri", default_store_uri)
api_key = params.get("api_key", None)
super(Processor, self).__init__(
**params | {
"store_uri": store_uri,
"api_key": api_key,
}
)
self.last_collection = None
self.qdrant = QdrantClient(url=store_uri, api_key=api_key)
async def store_document_embeddings(self, message):
for emb in message.chunks:
chunk = emb.chunk.decode("utf-8")
if chunk == "": return
for vec in emb.vectors:
dim = len(vec)
collection = (
"d_" + message.metadata.user + "_" +
message.metadata.collection + "_" +
str(dim)
)
if collection != self.last_collection:
if not self.client.collection_exists(collection):
try:
self.client.create_collection(
collection_name=collection,
vectors_config=VectorParams(
size=dim, distance=Distance.COSINE
),
)
except Exception as e:
print("Qdrant collection creation failed")
raise e
self.last_collection = collection
self.client.upsert(
collection_name=collection,
points=[
PointStruct(
id=str(uuid.uuid4()),
vector=vec,
payload={
"doc": chunk,
}
)
]
)
@staticmethod
def add_args(parser):
DocumentEmbeddingsStoreService.add_args(parser)
parser.add_argument(
'-t', '--store-uri',
default=default_store_uri,
help=f'Qdrant URI (default: {default_store_uri})'
)
parser.add_argument(
'-k', '--api-key',
default=None,
help=f'Qdrant API key (default: None)'
)
def run():
Processor.launch(default_ident, __doc__)