mirror of
https://github.com/trustgraph-ai/trustgraph.git
synced 2026-04-26 00:46:22 +02:00
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
This commit is contained in:
cybermaggedon
GitHub
parent
67cfa80836
commit
14e49d83c7
60 changed files with 1252 additions and 577 deletions
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@ -6,6 +6,7 @@ import pytest
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from unittest.mock import MagicMock, AsyncMock
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from trustgraph.retrieval.document_rag.document_rag import DocumentRag, Query
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from trustgraph.base import PromptResult
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# Sample chunk content mapping for tests
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@ -132,7 +133,7 @@ class TestQuery:
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mock_rag.prompt_client = mock_prompt_client
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# Mock the prompt response with concept lines
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mock_prompt_client.prompt.return_value = "machine learning\nartificial intelligence\ndata patterns"
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mock_prompt_client.prompt.return_value = PromptResult(response_type="text", text="machine learning\nartificial intelligence\ndata patterns")
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query = Query(
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rag=mock_rag,
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@ -157,7 +158,7 @@ class TestQuery:
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mock_rag.prompt_client = mock_prompt_client
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# Mock empty response
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mock_prompt_client.prompt.return_value = ""
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mock_prompt_client.prompt.return_value = PromptResult(response_type="text", text="")
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query = Query(
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rag=mock_rag,
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@ -258,7 +259,7 @@ class TestQuery:
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mock_doc_embeddings_client = AsyncMock()
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# Mock concept extraction
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mock_prompt_client.prompt.return_value = "test concept"
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mock_prompt_client.prompt.return_value = PromptResult(response_type="text", text="test concept")
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# Mock embeddings - one vector per concept
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test_vectors = [[0.1, 0.2, 0.3]]
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@ -273,7 +274,7 @@ class TestQuery:
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expected_response = "This is the document RAG response"
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mock_doc_embeddings_client.query.return_value = [mock_match1, mock_match2]
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mock_prompt_client.document_prompt.return_value = expected_response
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mock_prompt_client.document_prompt.return_value = PromptResult(response_type="text", text=expected_response)
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document_rag = DocumentRag(
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prompt_client=mock_prompt_client,
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@ -315,7 +316,8 @@ class TestQuery:
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assert "Relevant document content" in docs
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assert "Another document" in docs
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assert result == expected_response
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result_text, usage = result
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assert result_text == expected_response
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@pytest.mark.asyncio
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async def test_document_rag_query_with_defaults(self, mock_fetch_chunk):
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@ -325,7 +327,7 @@ class TestQuery:
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mock_doc_embeddings_client = AsyncMock()
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# Mock concept extraction fallback (empty → raw query)
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mock_prompt_client.prompt.return_value = ""
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mock_prompt_client.prompt.return_value = PromptResult(response_type="text", text="")
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# Mock responses
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mock_embeddings_client.embed.return_value = [[[0.1, 0.2]]]
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@ -333,7 +335,7 @@ class TestQuery:
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mock_match.chunk_id = "doc/c5"
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mock_match.score = 0.9
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mock_doc_embeddings_client.query.return_value = [mock_match]
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mock_prompt_client.document_prompt.return_value = "Default response"
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mock_prompt_client.document_prompt.return_value = PromptResult(response_type="text", text="Default response")
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document_rag = DocumentRag(
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prompt_client=mock_prompt_client,
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@ -352,7 +354,8 @@ class TestQuery:
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collection="default" # Default collection
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)
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assert result == "Default response"
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result_text, usage = result
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assert result_text == "Default response"
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@pytest.mark.asyncio
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async def test_get_docs_with_verbose_output(self):
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@ -401,7 +404,7 @@ class TestQuery:
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mock_doc_embeddings_client = AsyncMock()
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# Mock concept extraction
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mock_prompt_client.prompt.return_value = "verbose query test"
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mock_prompt_client.prompt.return_value = PromptResult(response_type="text", text="verbose query test")
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# Mock responses
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mock_embeddings_client.embed.return_value = [[[0.3, 0.4]]]
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@ -409,7 +412,7 @@ class TestQuery:
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mock_match.chunk_id = "doc/c7"
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mock_match.score = 0.92
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mock_doc_embeddings_client.query.return_value = [mock_match]
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mock_prompt_client.document_prompt.return_value = "Verbose RAG response"
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mock_prompt_client.document_prompt.return_value = PromptResult(response_type="text", text="Verbose RAG response")
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document_rag = DocumentRag(
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prompt_client=mock_prompt_client,
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@ -428,7 +431,8 @@ class TestQuery:
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assert call_args.kwargs["query"] == "verbose query test"
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assert "Verbose doc content" in call_args.kwargs["documents"]
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assert result == "Verbose RAG response"
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result_text, usage = result
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assert result_text == "Verbose RAG response"
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@pytest.mark.asyncio
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async def test_get_docs_with_empty_results(self):
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@ -469,11 +473,11 @@ class TestQuery:
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mock_doc_embeddings_client = AsyncMock()
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# Mock concept extraction
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mock_prompt_client.prompt.return_value = "query with no matching docs"
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mock_prompt_client.prompt.return_value = PromptResult(response_type="text", text="query with no matching docs")
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mock_embeddings_client.embed.return_value = [[[0.5, 0.6]]]
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mock_doc_embeddings_client.query.return_value = []
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mock_prompt_client.document_prompt.return_value = "No documents found response"
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mock_prompt_client.document_prompt.return_value = PromptResult(response_type="text", text="No documents found response")
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document_rag = DocumentRag(
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prompt_client=mock_prompt_client,
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@ -490,7 +494,8 @@ class TestQuery:
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documents=[]
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)
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assert result == "No documents found response"
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result_text, usage = result
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assert result_text == "No documents found response"
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@pytest.mark.asyncio
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async def test_get_vectors_with_verbose(self):
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@ -525,7 +530,7 @@ class TestQuery:
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final_response = "Machine learning is a field of AI that enables computers to learn and improve from experience without being explicitly programmed."
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# Mock concept extraction
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mock_prompt_client.prompt.return_value = "machine learning\nartificial intelligence"
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mock_prompt_client.prompt.return_value = PromptResult(response_type="text", text="machine learning\nartificial intelligence")
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# Mock embeddings - one vector per concept
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query_vectors = [[0.1, 0.2, 0.3, 0.4, 0.5], [0.6, 0.7, 0.8, 0.9, 1.0]]
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@ -541,7 +546,7 @@ class TestQuery:
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MagicMock(chunk_id="doc/ml3", score=0.82),
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]
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mock_doc_embeddings_client.query.side_effect = [mock_matches_1, mock_matches_2]
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mock_prompt_client.document_prompt.return_value = final_response
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mock_prompt_client.document_prompt.return_value = PromptResult(response_type="text", text=final_response)
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document_rag = DocumentRag(
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prompt_client=mock_prompt_client,
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@ -584,7 +589,8 @@ class TestQuery:
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assert "Common ML techniques include supervised and unsupervised learning..." in docs
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assert len(docs) == 3 # doc/ml2 deduplicated
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assert result == final_response
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result_text, usage = result
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assert result_text == final_response
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@pytest.mark.asyncio
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async def test_get_docs_deduplicates_across_concepts(self):
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@ -12,6 +12,7 @@ from unittest.mock import AsyncMock
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from dataclasses import dataclass
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from trustgraph.retrieval.document_rag.document_rag import DocumentRag
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from trustgraph.base import PromptResult
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from trustgraph.provenance.namespaces import (
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RDF_TYPE, PROV_ENTITY, PROV_WAS_DERIVED_FROM,
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@ -89,8 +90,8 @@ def build_mock_clients():
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# 1. Concept extraction
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async def mock_prompt(template_id, variables=None, **kwargs):
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if template_id == "extract-concepts":
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return "return policy\nrefund"
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return ""
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return PromptResult(response_type="text", text="return policy\nrefund")
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return PromptResult(response_type="text", text="")
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prompt_client.prompt.side_effect = mock_prompt
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@ -113,8 +114,9 @@ def build_mock_clients():
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fetch_chunk.side_effect = mock_fetch
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# 5. Synthesis
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prompt_client.document_prompt.return_value = (
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"Items can be returned within 30 days for a full refund."
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prompt_client.document_prompt.return_value = PromptResult(
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response_type="text",
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text="Items can be returned within 30 days for a full refund.",
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)
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return prompt_client, embeddings_client, doc_embeddings_client, fetch_chunk
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@ -340,12 +342,12 @@ class TestDocumentRagQueryProvenance:
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clients = build_mock_clients()
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rag = DocumentRag(*clients)
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result = await rag.query(
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result_text, usage = await rag.query(
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query="What is the return policy?",
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explain_callback=AsyncMock(),
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)
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assert result == "Items can be returned within 30 days for a full refund."
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assert result_text == "Items can be returned within 30 days for a full refund."
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@pytest.mark.asyncio
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async def test_no_explain_callback_still_works(self):
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@ -353,8 +355,8 @@ class TestDocumentRagQueryProvenance:
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clients = build_mock_clients()
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rag = DocumentRag(*clients)
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result = await rag.query(query="What is the return policy?")
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assert result == "Items can be returned within 30 days for a full refund."
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result_text, usage = await rag.query(query="What is the return policy?")
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assert result_text == "Items can be returned within 30 days for a full refund."
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@pytest.mark.asyncio
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async def test_all_triples_in_retrieval_graph(self):
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@ -34,7 +34,7 @@ class TestDocumentRagService:
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# Setup mock DocumentRag instance
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mock_rag_instance = AsyncMock()
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mock_document_rag_class.return_value = mock_rag_instance
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mock_rag_instance.query.return_value = "test response"
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mock_rag_instance.query.return_value = ("test response", {"in_token": None, "out_token": None, "model": None})
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# Setup message with custom user/collection
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msg = MagicMock()
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@ -97,7 +97,7 @@ class TestDocumentRagService:
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# Setup mock DocumentRag instance
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mock_rag_instance = AsyncMock()
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mock_document_rag_class.return_value = mock_rag_instance
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mock_rag_instance.query.return_value = "A document about cats."
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mock_rag_instance.query.return_value = ("A document about cats.", {"in_token": None, "out_token": None, "model": None})
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# Setup message with non-streaming request
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msg = MagicMock()
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@ -130,4 +130,5 @@ class TestDocumentRagService:
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assert isinstance(sent_response, DocumentRagResponse)
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assert sent_response.response == "A document about cats."
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assert sent_response.end_of_stream is True, "Non-streaming response must have end_of_stream=True"
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assert sent_response.end_of_session is True
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assert sent_response.error is None
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@ -7,6 +7,7 @@ import unittest.mock
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from unittest.mock import MagicMock, AsyncMock
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from trustgraph.retrieval.graph_rag.graph_rag import GraphRag, Query
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from trustgraph.base import PromptResult
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class TestGraphRag:
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@ -172,7 +173,7 @@ class TestQuery:
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mock_prompt_client = AsyncMock()
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mock_rag.prompt_client = mock_prompt_client
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mock_prompt_client.prompt.return_value = "machine learning\nneural networks\n"
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mock_prompt_client.prompt.return_value = PromptResult(response_type="text", text="machine learning\nneural networks\n")
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query = Query(
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rag=mock_rag,
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@ -196,7 +197,7 @@ class TestQuery:
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mock_prompt_client = AsyncMock()
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mock_rag.prompt_client = mock_prompt_client
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mock_prompt_client.prompt.return_value = ""
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mock_prompt_client.prompt.return_value = PromptResult(response_type="text", text="")
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query = Query(
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rag=mock_rag,
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@ -220,7 +221,7 @@ class TestQuery:
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mock_rag.graph_embeddings_client = mock_graph_embeddings_client
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# extract_concepts returns empty -> falls back to [query]
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mock_prompt_client.prompt.return_value = ""
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mock_prompt_client.prompt.return_value = PromptResult(response_type="text", text="")
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# embed returns one vector set for the single concept
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test_vectors = [[0.1, 0.2, 0.3]]
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@ -565,14 +566,14 @@ class TestQuery:
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# Mock prompt responses for the multi-step process
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async def mock_prompt(prompt_name, variables=None, streaming=False, chunk_callback=None):
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if prompt_name == "extract-concepts":
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return "" # Falls back to raw query
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return PromptResult(response_type="text", text="")
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elif prompt_name == "kg-edge-scoring":
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return json.dumps({"id": test_edge_id, "score": 0.9})
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return PromptResult(response_type="jsonl", objects=[{"id": test_edge_id, "score": 0.9}])
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elif prompt_name == "kg-edge-reasoning":
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return json.dumps({"id": test_edge_id, "reasoning": "relevant"})
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return PromptResult(response_type="jsonl", objects=[{"id": test_edge_id, "reasoning": "relevant"}])
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elif prompt_name == "kg-synthesis":
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return expected_response
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return ""
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return PromptResult(response_type="text", text=expected_response)
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return PromptResult(response_type="text", text="")
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mock_prompt_client.prompt = mock_prompt
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@ -607,7 +608,8 @@ class TestQuery:
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explain_callback=collect_provenance
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)
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assert response == expected_response
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response_text, usage = response
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assert response_text == expected_response
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# 5 events: question, grounding, exploration, focus, synthesis
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assert len(provenance_events) == 5
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@ -13,6 +13,7 @@ from dataclasses import dataclass
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from trustgraph.retrieval.graph_rag.graph_rag import GraphRag, edge_id
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from trustgraph.schema import Triple as SchemaTriple, Term, IRI, LITERAL
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from trustgraph.base import PromptResult
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from trustgraph.provenance.namespaces import (
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RDF_TYPE, PROV_ENTITY, PROV_WAS_DERIVED_FROM,
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@ -136,24 +137,36 @@ def build_mock_clients():
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async def mock_prompt(template_id, variables=None, **kwargs):
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if template_id == "extract-concepts":
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return prompt_responses["extract-concepts"]
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return PromptResult(
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response_type="text",
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text=prompt_responses["extract-concepts"],
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)
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elif template_id == "kg-edge-scoring":
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# Score all edges highly, using the IDs that GraphRag computed
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edges = variables.get("knowledge", [])
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return [
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{"id": e["id"], "score": 10 - i}
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for i, e in enumerate(edges)
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]
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return PromptResult(
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response_type="jsonl",
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objects=[
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{"id": e["id"], "score": 10 - i}
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for i, e in enumerate(edges)
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],
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)
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elif template_id == "kg-edge-reasoning":
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# Provide reasoning for each edge
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edges = variables.get("knowledge", [])
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return [
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{"id": e["id"], "reasoning": f"Relevant edge {i}"}
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for i, e in enumerate(edges)
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]
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return PromptResult(
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response_type="jsonl",
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objects=[
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{"id": e["id"], "reasoning": f"Relevant edge {i}"}
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for i, e in enumerate(edges)
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],
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)
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elif template_id == "kg-synthesis":
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return synthesis_answer
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return ""
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return PromptResult(
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response_type="text",
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text=synthesis_answer,
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)
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return PromptResult(response_type="text", text="")
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prompt_client.prompt.side_effect = mock_prompt
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@ -413,13 +426,13 @@ class TestGraphRagQueryProvenance:
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async def explain_callback(triples, explain_id):
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events.append({"triples": triples, "explain_id": explain_id})
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result = await rag.query(
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result_text, usage = await rag.query(
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query="What is quantum computing?",
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explain_callback=explain_callback,
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edge_score_limit=0,
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)
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assert result == "Quantum computing applies physics principles to computation."
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assert result_text == "Quantum computing applies physics principles to computation."
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@pytest.mark.asyncio
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async def test_parent_uri_links_question_to_parent(self):
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@ -450,12 +463,12 @@ class TestGraphRagQueryProvenance:
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clients = build_mock_clients()
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rag = GraphRag(*clients)
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result = await rag.query(
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result_text, usage = await rag.query(
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query="What is quantum computing?",
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edge_score_limit=0,
|
||||
)
|
||||
|
||||
assert result == "Quantum computing applies physics principles to computation."
|
||||
assert result_text == "Quantum computing applies physics principles to computation."
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_all_triples_in_retrieval_graph(self):
|
||||
|
|
|
|||
|
|
@ -44,7 +44,7 @@ class TestGraphRagService:
|
|||
await explain_callback([], "urn:trustgraph:prov:retrieval:test")
|
||||
await explain_callback([], "urn:trustgraph:prov:selection:test")
|
||||
await explain_callback([], "urn:trustgraph:prov:answer:test")
|
||||
return "A small domesticated mammal."
|
||||
return "A small domesticated mammal.", {"in_token": None, "out_token": None, "model": None}
|
||||
|
||||
mock_rag_instance.query.side_effect = mock_query
|
||||
|
||||
|
|
@ -79,8 +79,8 @@ class TestGraphRagService:
|
|||
# Execute
|
||||
await processor.on_request(msg, consumer, flow)
|
||||
|
||||
# Verify: 6 messages sent (4 provenance + 1 chunk + 1 end_of_session)
|
||||
assert mock_response_producer.send.call_count == 6
|
||||
# Verify: 5 messages sent (4 provenance + 1 combined chunk with end_of_session)
|
||||
assert mock_response_producer.send.call_count == 5
|
||||
|
||||
# First 4 messages are explain (emitted in real-time during query)
|
||||
for i in range(4):
|
||||
|
|
@ -88,17 +88,12 @@ class TestGraphRagService:
|
|||
assert prov_msg.message_type == "explain"
|
||||
assert prov_msg.explain_id is not None
|
||||
|
||||
# 5th message is chunk with response
|
||||
# 5th message is chunk with response and end_of_session
|
||||
chunk_msg = mock_response_producer.send.call_args_list[4][0][0]
|
||||
assert chunk_msg.message_type == "chunk"
|
||||
assert chunk_msg.response == "A small domesticated mammal."
|
||||
assert chunk_msg.end_of_stream is True
|
||||
|
||||
# 6th message is empty chunk with end_of_session=True
|
||||
close_msg = mock_response_producer.send.call_args_list[5][0][0]
|
||||
assert close_msg.message_type == "chunk"
|
||||
assert close_msg.response == ""
|
||||
assert close_msg.end_of_session is True
|
||||
assert chunk_msg.end_of_session is True
|
||||
|
||||
# Verify provenance triples were sent to provenance queue
|
||||
assert mock_provenance_producer.send.call_count == 4
|
||||
|
|
@ -187,7 +182,7 @@ class TestGraphRagService:
|
|||
|
||||
async def mock_query(**kwargs):
|
||||
# Don't call explain_callback
|
||||
return "Response text"
|
||||
return "Response text", {"in_token": None, "out_token": None, "model": None}
|
||||
|
||||
mock_rag_instance.query.side_effect = mock_query
|
||||
|
||||
|
|
@ -218,17 +213,12 @@ class TestGraphRagService:
|
|||
# Execute
|
||||
await processor.on_request(msg, consumer, flow)
|
||||
|
||||
# Verify: 2 messages (chunk + empty chunk to close)
|
||||
assert mock_response_producer.send.call_count == 2
|
||||
# Verify: 1 combined message (chunk with end_of_session)
|
||||
assert mock_response_producer.send.call_count == 1
|
||||
|
||||
# First is the response chunk
|
||||
# Single message has response and end_of_session
|
||||
chunk_msg = mock_response_producer.send.call_args_list[0][0][0]
|
||||
assert chunk_msg.message_type == "chunk"
|
||||
assert chunk_msg.response == "Response text"
|
||||
assert chunk_msg.end_of_stream is True
|
||||
|
||||
# Second is empty chunk to close session
|
||||
close_msg = mock_response_producer.send.call_args_list[1][0][0]
|
||||
assert close_msg.message_type == "chunk"
|
||||
assert close_msg.response == ""
|
||||
assert close_msg.end_of_session is True
|
||||
assert chunk_msg.end_of_session is True
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue