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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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57
tests/unit/test_retrieval/test_document_rag.py
View file

@ -175,9 +175,14 @@ class TestQuery:
test_vectors = [[0.1, 0.2, 0.3]]
mock_embeddings_client.embed.return_value = [test_vectors]
# Mock document embeddings returns chunk_ids
test_chunk_ids = ["doc/c1", "doc/c2"]
mock_doc_embeddings_client.query.return_value = test_chunk_ids
# Mock document embeddings returns ChunkMatch objects
mock_match1 = MagicMock()
mock_match1.chunk_id = "doc/c1"
mock_match1.score = 0.95
mock_match2 = MagicMock()
mock_match2.chunk_id = "doc/c2"
mock_match2.score = 0.85
mock_doc_embeddings_client.query.return_value = [mock_match1, mock_match2]
# Initialize Query
query = Query(
@ -195,9 +200,9 @@ class TestQuery:
# Verify embeddings client was called (now expects list)
mock_embeddings_client.embed.assert_called_once_with([test_query])
# Verify doc embeddings client was called correctly (with extracted vectors)
# Verify doc embeddings client was called correctly (with extracted vector)
mock_doc_embeddings_client.query.assert_called_once_with(
test_vectors,
vector=test_vectors,
limit=15,
user="test_user",
collection="test_collection"
@ -218,11 +223,16 @@ class TestQuery:
# Mock embeddings and document embeddings responses
# New batch format: [[[vectors]]] - get_vector extracts [0]
test_vectors = [[0.1, 0.2, 0.3]]
test_chunk_ids = ["doc/c3", "doc/c4"]
mock_match1 = MagicMock()
mock_match1.chunk_id = "doc/c3"
mock_match1.score = 0.9
mock_match2 = MagicMock()
mock_match2.chunk_id = "doc/c4"
mock_match2.score = 0.8
expected_response = "This is the document RAG response"
mock_embeddings_client.embed.return_value = [test_vectors]
mock_doc_embeddings_client.query.return_value = test_chunk_ids
mock_doc_embeddings_client.query.return_value = [mock_match1, mock_match2]
mock_prompt_client.document_prompt.return_value = expected_response
# Initialize DocumentRag
@ -245,9 +255,9 @@ class TestQuery:
# Verify embeddings client was called (now expects list)
mock_embeddings_client.embed.assert_called_once_with(["test query"])
# Verify doc embeddings client was called (with extracted vectors)
# Verify doc embeddings client was called (with extracted vector)
mock_doc_embeddings_client.query.assert_called_once_with(
test_vectors,
vector=test_vectors,
limit=10,
user="test_user",
collection="test_collection"
@ -275,7 +285,10 @@ class TestQuery:
# Mock responses (batch format)
mock_embeddings_client.embed.return_value = [[[0.1, 0.2]]]
mock_doc_embeddings_client.query.return_value = ["doc/c5"]
mock_match = MagicMock()
mock_match.chunk_id = "doc/c5"
mock_match.score = 0.9
mock_doc_embeddings_client.query.return_value = [mock_match]
mock_prompt_client.document_prompt.return_value = "Default response"
# Initialize DocumentRag
@ -289,9 +302,9 @@ class TestQuery:
# Call DocumentRag.query with minimal parameters
result = await document_rag.query("simple query")
# Verify default parameters were used (vectors extracted from batch)
# Verify default parameters were used (vector extracted from batch)
mock_doc_embeddings_client.query.assert_called_once_with(
[[0.1, 0.2]],
vector=[[0.1, 0.2]],
limit=20, # Default doc_limit
user="trustgraph", # Default user
collection="default" # Default collection
@ -316,7 +329,10 @@ class TestQuery:
# Mock responses (batch format)
mock_embeddings_client.embed.return_value = [[[0.7, 0.8]]]
mock_doc_embeddings_client.query.return_value = ["doc/c6"]
mock_match = MagicMock()
mock_match.chunk_id = "doc/c6"
mock_match.score = 0.88
mock_doc_embeddings_client.query.return_value = [mock_match]
# Initialize Query with verbose=True
query = Query(
@ -347,7 +363,10 @@ class TestQuery:
# Mock responses (batch format)
mock_embeddings_client.embed.return_value = [[[0.3, 0.4]]]
mock_doc_embeddings_client.query.return_value = ["doc/c7"]
mock_match = MagicMock()
mock_match.chunk_id = "doc/c7"
mock_match.score = 0.92
mock_doc_embeddings_client.query.return_value = [mock_match]
mock_prompt_client.document_prompt.return_value = "Verbose RAG response"
# Initialize DocumentRag with verbose=True
@ -487,7 +506,13 @@ class TestQuery:
final_response = "Machine learning is a field of AI that enables computers to learn and improve from experience without being explicitly programmed."
mock_embeddings_client.embed.return_value = [query_vectors]
mock_doc_embeddings_client.query.return_value = retrieved_chunk_ids
mock_matches = []
for chunk_id in retrieved_chunk_ids:
mock_match = MagicMock()
mock_match.chunk_id = chunk_id
mock_match.score = 0.9
mock_matches.append(mock_match)
mock_doc_embeddings_client.query.return_value = mock_matches
mock_prompt_client.document_prompt.return_value = final_response
# Initialize DocumentRag
@ -511,7 +536,7 @@ class TestQuery:
mock_embeddings_client.embed.assert_called_once_with([query_text])
mock_doc_embeddings_client.query.assert_called_once_with(
query_vectors,
vector=query_vectors,
limit=25,
user="research_user",
collection="ml_knowledge"

16
tests/unit/test_retrieval/test_graph_rag.py
View file

@ -193,12 +193,20 @@ class TestQuery:
test_vectors = [[0.1, 0.2, 0.3]]
mock_embeddings_client.embed.return_value = [test_vectors]
# Mock entity objects that have string representation
# Mock EntityMatch objects with entity that has string representation
mock_entity1 = MagicMock()
mock_entity1.__str__ = MagicMock(return_value="entity1")
mock_match1 = MagicMock()
mock_match1.entity = mock_entity1
mock_match1.score = 0.95
mock_entity2 = MagicMock()
mock_entity2.__str__ = MagicMock(return_value="entity2")
mock_graph_embeddings_client.query.return_value = [mock_entity1, mock_entity2]
mock_match2 = MagicMock()
mock_match2.entity = mock_entity2
mock_match2.score = 0.85
mock_graph_embeddings_client.query.return_value = [mock_match1, mock_match2]
# Initialize Query
query = Query(
@ -216,9 +224,9 @@ class TestQuery:
# Verify embeddings client was called (now expects list)
mock_embeddings_client.embed.assert_called_once_with([test_query])
# Verify graph embeddings client was called correctly (with extracted vectors)
# Verify graph embeddings client was called correctly (with extracted vector)
mock_graph_embeddings_client.query.assert_called_once_with(
vectors=test_vectors,
vector=test_vectors,
limit=25,
user="test_user",
collection="test_collection"