apunkt/trustgraph
1
0
Fork 0
mirror of https://github.com/trustgraph-ai/trustgraph.git synced 2026-07-17 01:01:03 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions 1

Fix GE load

Browse source
This commit is contained in:
Cyber MacGeddon 2025-01-13 14:37:33 +00:00
parent f0c86a58cb
commit 9225ff7c79
3 changed files with 22 additions and 7 deletions
Download patch file Download diff file Expand all files Collapse all files

9
trustgraph-cli/scripts/tg-load-kg-core
View file

@ -51,8 +51,13 @@ async def load_ge(running, queue, url):
"user": msg["m"]["u"], "user": msg["m"]["u"],
"collection": msg["m"]["c"], "collection": msg["m"]["c"],
}, },
"vectors": msg["v"], "entities": [
"entity": msg["e"], {
"entity": ent["e"],
"vectors": ent["v"],
}
for ent in msg["e"]
],
} }
try: try:

9
trustgraph-cli/scripts/tg-save-kg-core
View file

@ -57,8 +57,13 @@ async def fetch_ge(running, queue, user, collection, url):
"u": data["metadata"]["user"], "u": data["metadata"]["user"],
"c": data["metadata"]["collection"], "c": data["metadata"]["collection"],
}, },
"v": data["vectors"], "e": [
"e": data["entity"], {
"e": ent["entity"],
"v": ent["vectors"],
}
for ent in data["entities"]
]
} }
]) ])
if msg.type == aiohttp.WSMsgType.ERROR: if msg.type == aiohttp.WSMsgType.ERROR:

11
trustgraph-flow/trustgraph/gateway/graph_embeddings_load.py
View file

@ -5,7 +5,7 @@ import uuid
from aiohttp import WSMsgType from aiohttp import WSMsgType
from .. schema import Metadata from .. schema import Metadata
from .. schema import GraphEmbeddings from .. schema import GraphEmbeddings, EntityEmbeddings
from .. schema import graph_embeddings_store_queue from .. schema import graph_embeddings_store_queue
from . publisher import Publisher from . publisher import Publisher
@ -50,8 +50,13 @@ class GraphEmbeddingsLoadEndpoint(SocketEndpoint):
user=data["metadata"]["user"], user=data["metadata"]["user"],
collection=data["metadata"]["collection"], collection=data["metadata"]["collection"],
), ),
entity=to_value(data["entity"]), entities=[
vectors=data["vectors"], EntityEmbeddings(
entity=to_value(ent["entity"]),
vectors=ent["vectors"],
)
for ent in data["entities"]
]
) )
self.publisher.send(None, elt) self.publisher.send(None, elt)