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Embeddings API scores (#671)

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- Put scores in all responses
- Remove unused 'middle' vector layer. Vector of texts -> vector of (vector embedding)
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cybermaggedon 2026-03-09 10:53:44 +00:00 • committed by GitHub
parent 4fa7cc7d7c
commit f2ae0e8623
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65 changed files with 1339 additions and 1292 deletions
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12
trustgraph-base/trustgraph/api/async_socket_client.py
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@ -282,12 +282,12 @@ class AsyncSocketFlowInstance:
async def graph_embeddings_query(self, text: str, user: str, collection: str, limit: int = 10, **kwargs):
"""Query graph embeddings for semantic search"""
# First convert text to embeddings vectors
# First convert text to embedding vector
emb_result = await self.embeddings(texts=[text])
vectors = emb_result.get("vectors", [[]])[0]
vector = emb_result.get("vectors", [[]])[0]
request = {
"vectors": vectors,
"vector": vector,
"user": user,
"collection": collection,
"limit": limit
@ -352,12 +352,12 @@ class AsyncSocketFlowInstance:
limit: int = 10, **kwargs
):
"""Query row embeddings for semantic search on structured data"""
# First convert text to embeddings vectors
# First convert text to embedding vector
emb_result = await self.embeddings(texts=[text])
vectors = emb_result.get("vectors", [[]])[0]
vector = emb_result.get("vectors", [[]])[0]
request = {
"vectors": vectors,
"vector": vector,
"schema_name": schema_name,
"user": user,
"collection": collection,