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