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

trustgraph-cli/trustgraph/cli/invoke_graph_rag.py

@@ -753,7 +753,7 @@ def question(
         url, flow_id, question, user, collection, entity_limit, triple_limit,
         max_subgraph_size, max_path_length, edge_score_limit=50,
         edge_limit=25, streaming=True, token=None,
-        explainable=False, debug=False
+        explainable=False, debug=False, show_usage=False
 ):
 
     # Explainable mode uses the API to capture and process provenance events
@@ -798,16 +798,26 @@ def question(
             )
 
             # Stream output
+            last_chunk = None
             for chunk in response:
-                print(chunk, end="", flush=True)
+                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)
-        resp = flow.graph_rag(
+        result = flow.graph_rag(
             query=question,
             user=user,
             collection=collection,
@@ -818,7 +828,15 @@ def question(
             edge_score_limit=edge_score_limit,
             edge_limit=edge_limit,
         )
-        print(resp)
+        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():
 
@@ -923,6 +941,12 @@ def main():
         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:
@@ -943,6 +967,7 @@ def main():
             token=args.token,
             explainable=args.explainable,
             debug=args.debug,
+            show_usage=args.show_usage,
         )
 
     except Exception as e: