trustgraph/trustgraph-cli/trustgraph/cli/invoke_document_rag.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

270 lines
7.6 KiB
Python

"""
Uses the DocumentRAG service to answer a question
"""
import argparse
import os
import sys
from trustgraph.api import (
Api,
ExplainabilityClient,
RAGChunk,
ProvenanceEvent,
Question,
Grounding,
Exploration,
Synthesis,
)
default_url = os.getenv("TRUSTGRAPH_URL", 'http://localhost:8088/')
default_token = os.getenv("TRUSTGRAPH_TOKEN", None)
default_user = 'trustgraph'
default_collection = 'default'
default_doc_limit = 10
def question_explainable(
url, flow_id, question_text, user, collection, doc_limit, token=None, debug=False
):
"""Execute document RAG with explainability - shows provenance events inline."""
api = Api(url=url, token=token)
socket = api.socket()
flow = socket.flow(flow_id)
explain_client = ExplainabilityClient(flow, retry_delay=0.2, max_retries=10)
try:
# Stream DocumentRAG with explainability - process events as they arrive
for item in flow.document_rag_explain(
query=question_text,
user=user,
collection=collection,
doc_limit=doc_limit,
):
if isinstance(item, RAGChunk):
# Print response content
print(item.content, end="", flush=True)
elif isinstance(item, ProvenanceEvent):
# Use inline entity if available, otherwise fetch from graph
prov_id = item.explain_id
explain_graph = item.explain_graph or "urn:graph:retrieval"
entity = item.entity
if entity is None:
entity = explain_client.fetch_entity(
prov_id,
graph=explain_graph,
user=user,
collection=collection
)
if entity is None:
if debug:
print(f"\n [warning] Could not fetch entity: {prov_id}", file=sys.stderr)
continue
# Display based on entity type
if isinstance(entity, Question):
print(f"\n [question] {prov_id}", file=sys.stderr)
if entity.query:
print(f" Query: {entity.query}", file=sys.stderr)
if entity.timestamp:
print(f" Time: {entity.timestamp}", file=sys.stderr)
elif isinstance(entity, Grounding):
print(f"\n [grounding] {prov_id}", file=sys.stderr)
if entity.concepts:
for concept in entity.concepts:
print(f" Concept: {concept}", file=sys.stderr)
elif isinstance(entity, Exploration):
print(f"\n [exploration] {prov_id}", file=sys.stderr)
if entity.chunk_count:
print(f" Chunks retrieved: {entity.chunk_count}", file=sys.stderr)
elif isinstance(entity, Synthesis):
print(f"\n [synthesis] {prov_id}", file=sys.stderr)
if entity.document:
print(f" Document: {entity.document}", file=sys.stderr)
else:
if debug:
print(f"\n [unknown] {prov_id} (type: {entity.entity_type})", file=sys.stderr)
print() # Final newline
finally:
socket.close()
def question(
url, flow_id, question_text, user, collection, doc_limit,
streaming=True, token=None, explainable=False, debug=False,
show_usage=False
):
# Explainable mode uses the API to capture and process provenance events
if explainable:
question_explainable(
url=url,
flow_id=flow_id,
question_text=question_text,
user=user,
collection=collection,
doc_limit=doc_limit,
token=token,
debug=debug
)
return
# Create API client
api = Api(url=url, token=token)
if streaming:
# Use socket client for streaming
socket = api.socket()
flow = socket.flow(flow_id)
try:
response = flow.document_rag(
query=question_text,
user=user,
collection=collection,
doc_limit=doc_limit,
streaming=True
)
# Stream output
last_chunk = None
for chunk in response:
print(chunk.content, end="", flush=True)
last_chunk = chunk
print() # Final newline
if show_usage and last_chunk:
print(
f"Input tokens: {last_chunk.in_token} "
f"Output tokens: {last_chunk.out_token} "
f"Model: {last_chunk.model}",
file=sys.stderr,
)
finally:
socket.close()
else:
# Use REST API for non-streaming
flow = api.flow().id(flow_id)
result = flow.document_rag(
query=question_text,
user=user,
collection=collection,
doc_limit=doc_limit,
)
print(result.text)
if show_usage:
print(
f"Input tokens: {result.in_token} "
f"Output tokens: {result.out_token} "
f"Model: {result.model}",
file=sys.stderr,
)
def main():
parser = argparse.ArgumentParser(
prog='tg-invoke-document-rag',
description=__doc__,
)
parser.add_argument(
'-u', '--url',
default=default_url,
help=f'API URL (default: {default_url})',
)
parser.add_argument(
'-t', '--token',
default=default_token,
help='Authentication token (default: $TRUSTGRAPH_TOKEN)',
)
parser.add_argument(
'-f', '--flow-id',
default="default",
help=f'Flow ID (default: default)'
)
parser.add_argument(
'-q', '--question',
required=True,
help=f'Question to answer',
)
parser.add_argument(
'-U', '--user',
default=default_user,
help=f'User ID (default: {default_user})'
)
parser.add_argument(
'-C', '--collection',
default=default_collection,
help=f'Collection ID (default: {default_collection})'
)
parser.add_argument(
'-d', '--doc-limit',
type=int,
default=default_doc_limit,
help=f'Document limit (default: {default_doc_limit})'
)
parser.add_argument(
'--no-streaming',
action='store_true',
help='Disable streaming (use non-streaming mode)'
)
parser.add_argument(
'-x', '--explainable',
action='store_true',
help='Show provenance events: Question, Exploration, Synthesis (implies streaming)'
)
parser.add_argument(
'--debug',
action='store_true',
help='Show debug output for troubleshooting'
)
parser.add_argument(
'--show-usage',
action='store_true',
help='Show token usage and model on stderr'
)
args = parser.parse_args()
try:
question(
url=args.url,
flow_id=args.flow_id,
question_text=args.question,
user=args.user,
collection=args.collection,
doc_limit=args.doc_limit,
streaming=not args.no_streaming,
token=args.token,
explainable=args.explainable,
debug=args.debug,
show_usage=args.show_usage,
)
except Exception as e:
print("Exception:", e, flush=True)
if __name__ == "__main__":
main()