trustgraph/tests/unit/test_retrieval/test_document_rag_service.py
cybermaggedon 14e49d83c7
Expose LLM token usage across all service layers (#782)
Expose LLM token usage (in_token, out_token, model) across all
service layers

Propagate token counts from LLM services through the prompt,
text-completion, graph-RAG, document-RAG, and agent orchestrator
pipelines to the API gateway and Python SDK. All fields are Optional
— None means "not available", distinguishing from a real zero count.

Key changes:

- Schema: Add in_token/out_token/model to TextCompletionResponse,
  PromptResponse, GraphRagResponse, DocumentRagResponse,
  AgentResponse

- TextCompletionClient: New TextCompletionResult return type. Split
  into text_completion() (non-streaming) and
  text_completion_stream() (streaming with per-chunk handler
  callback)

- PromptClient: New PromptResult with response_type
  (text/json/jsonl), typed fields (text/object/objects), and token
  usage. All callers updated.

- RAG services: Accumulate token usage across all prompt calls
  (extract-concepts, edge-scoring, edge-reasoning,
  synthesis). Non-streaming path sends single combined response
  instead of chunk + end_of_session.

- Agent orchestrator: UsageTracker accumulates tokens across
  meta-router, pattern prompt calls, and react reasoning. Attached
  to end_of_dialog.

- Translators: Encode token fields when not None (is not None, not truthy)

- Python SDK: RAG and text-completion methods return
  TextCompletionResult (non-streaming) or RAGChunk/AgentAnswer with
  token fields (streaming)

- CLI: --show-usage flag on tg-invoke-llm, tg-invoke-prompt,
  tg-invoke-graph-rag, tg-invoke-document-rag, tg-invoke-agent
2026-04-13 14:38:34 +01:00

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Python

"""
Unit test for DocumentRAG service parameter passing fix.
Tests that user and collection parameters from the message are correctly
passed to the DocumentRag.query() method.
"""
import pytest
from unittest.mock import MagicMock, AsyncMock, patch, ANY
from trustgraph.retrieval.document_rag.rag import Processor
from trustgraph.schema import DocumentRagQuery, DocumentRagResponse
class TestDocumentRagService:
"""Test DocumentRAG service parameter passing"""
@patch('trustgraph.retrieval.document_rag.rag.DocumentRag')
@pytest.mark.asyncio
async def test_user_and_collection_parameters_passed_to_query(self, mock_document_rag_class):
"""
Test that user and collection from message are passed to DocumentRag.query().
This is a regression test for the bug where user/collection parameters
were ignored, causing wrong collection names like 'd_trustgraph_default_384'
instead of 'd_my_user_test_coll_1_384'.
"""
# Setup processor
processor = Processor(
taskgroup=MagicMock(),
id="test-processor",
doc_limit=10
)
# Setup mock DocumentRag instance
mock_rag_instance = AsyncMock()
mock_document_rag_class.return_value = mock_rag_instance
mock_rag_instance.query.return_value = ("test response", {"in_token": None, "out_token": None, "model": None})
# Setup message with custom user/collection
msg = MagicMock()
msg.value.return_value = DocumentRagQuery(
query="test query",
user="my_user", # Custom user (not default "trustgraph")
collection="test_coll_1", # Custom collection (not default "default")
doc_limit=5
)
msg.properties.return_value = {"id": "test-id"}
# Setup flow mock
consumer = MagicMock()
flow = MagicMock()
# Mock flow to return AsyncMock for clients and response producer
mock_producer = AsyncMock()
def flow_router(service_name):
if service_name == "response":
return mock_producer
return AsyncMock() # embeddings, doc-embeddings, prompt clients
flow.side_effect = flow_router
# Execute
await processor.on_request(msg, consumer, flow)
# Verify: DocumentRag.query was called with correct parameters
mock_rag_instance.query.assert_called_once_with(
"test query",
user="my_user", # Must be from message, not hardcoded default
collection="test_coll_1", # Must be from message, not hardcoded default
doc_limit=5,
explain_callback=ANY, # Explainability callback is always passed
save_answer_callback=ANY, # Librarian save callback is always passed
)
# Verify response was sent
mock_producer.send.assert_called_once()
sent_response = mock_producer.send.call_args[0][0]
assert isinstance(sent_response, DocumentRagResponse)
assert sent_response.response == "test response"
assert sent_response.error is None
@patch('trustgraph.retrieval.document_rag.rag.DocumentRag')
@pytest.mark.asyncio
async def test_non_streaming_mode_sets_end_of_stream_true(self, mock_document_rag_class):
"""
Test that non-streaming mode sets end_of_stream=True in response.
This is a regression test for the bug where non-streaming responses
didn't set end_of_stream, causing clients to hang waiting for more data.
"""
# Setup processor
processor = Processor(
taskgroup=MagicMock(),
id="test-processor",
doc_limit=10
)
# Setup mock DocumentRag instance
mock_rag_instance = AsyncMock()
mock_document_rag_class.return_value = mock_rag_instance
mock_rag_instance.query.return_value = ("A document about cats.", {"in_token": None, "out_token": None, "model": None})
# Setup message with non-streaming request
msg = MagicMock()
msg.value.return_value = DocumentRagQuery(
query="What is a cat?",
user="trustgraph",
collection="default",
doc_limit=10,
streaming=False # Non-streaming mode
)
msg.properties.return_value = {"id": "test-id"}
# Setup flow mock
consumer = MagicMock()
flow = MagicMock()
mock_producer = AsyncMock()
def flow_router(service_name):
if service_name == "response":
return mock_producer
return AsyncMock()
flow.side_effect = flow_router
# Execute
await processor.on_request(msg, consumer, flow)
# Verify: response was sent with end_of_stream=True
mock_producer.send.assert_called_once()
sent_response = mock_producer.send.call_args[0][0]
assert isinstance(sent_response, DocumentRagResponse)
assert sent_response.response == "A document about cats."
assert sent_response.end_of_stream is True, "Non-streaming response must have end_of_stream=True"
assert sent_response.end_of_session is True
assert sent_response.error is None