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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
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commit f2ae0e8623
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13
tests/integration/test_rag_streaming_protocol.py
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@ -9,6 +9,7 @@ import pytest
from unittest.mock import AsyncMock, MagicMock, call
from trustgraph.retrieval.graph_rag.graph_rag import GraphRag
from trustgraph.retrieval.document_rag.document_rag import DocumentRag
from trustgraph.schema import EntityMatch, ChunkMatch, Term, IRI
class TestGraphRagStreamingProtocol:
@ -25,7 +26,10 @@ class TestGraphRagStreamingProtocol:
def mock_graph_embeddings_client(self):
"""Mock graph embeddings client"""
client = AsyncMock()
client.query.return_value = ["entity1", "entity2"]
client.query.return_value = [
EntityMatch(entity=Term(type=IRI, iri="entity1"), score=0.95),
EntityMatch(entity=Term(type=IRI, iri="entity2"), score=0.90)
]
return client
@pytest.fixture
@ -202,9 +206,12 @@ class TestDocumentRagStreamingProtocol:
@pytest.fixture
def mock_doc_embeddings_client(self):
"""Mock document embeddings client that returns chunk IDs"""
"""Mock document embeddings client that returns chunk matches"""
client = AsyncMock()
client.query.return_value = ["doc/c1", "doc/c2"]
client.query.return_value = [
ChunkMatch(chunk_id="doc/c1", score=0.95),
ChunkMatch(chunk_id="doc/c2", score=0.90)
]
return client
@pytest.fixture