mirror of
https://github.com/trustgraph-ai/trustgraph.git
synced 2026-06-13 08:45:13 +02:00
Introduces `workspace` as the isolation boundary for config, flows,
library, and knowledge data. Removes `user` as a schema-level field
throughout the code, API specs, and tests; workspace provides the
same separation more cleanly at the trusted flow.workspace layer
rather than through client-supplied message fields.
Design
------
- IAM tech spec (docs/tech-specs/iam.md) documents current state,
proposed auth/access model, and migration direction.
- Data ownership model (docs/tech-specs/data-ownership-model.md)
captures the workspace/collection/flow hierarchy.
Schema + messaging
------------------
- Drop `user` field from AgentRequest/Step, GraphRagQuery,
DocumentRagQuery, Triples/Graph/Document/Row EmbeddingsRequest,
Sparql/Rows/Structured QueryRequest, ToolServiceRequest.
- Keep collection/workspace routing via flow.workspace at the
service layer.
- Translators updated to not serialise/deserialise user.
API specs
---------
- OpenAPI schemas and path examples cleaned of user fields.
- Websocket async-api messages updated.
- Removed the unused parameters/User.yaml.
Services + base
---------------
- Librarian, collection manager, knowledge, config: all operations
scoped by workspace. Config client API takes workspace as first
positional arg.
- `flow.workspace` set at flow start time by the infrastructure;
no longer pass-through from clients.
- Tool service drops user-personalisation passthrough.
CLI + SDK
---------
- tg-init-workspace and workspace-aware import/export.
- All tg-* commands drop user args; accept --workspace.
- Python API/SDK (flow, socket_client, async_*, explainability,
library) drop user kwargs from every method signature.
MCP server
----------
- All tool endpoints drop user parameters; socket_manager no longer
keyed per user.
Flow service
------------
- Closure-based topic cleanup on flow stop: only delete topics
whose blueprint template was parameterised AND no remaining
live flow (across all workspaces) still resolves to that topic.
Three scopes fall out naturally from template analysis:
* {id} -> per-flow, deleted on stop
* {blueprint} -> per-blueprint, kept while any flow of the
same blueprint exists
* {workspace} -> per-workspace, kept while any flow in the
workspace exists
* literal -> global, never deleted (e.g. tg.request.librarian)
Fixes a bug where stopping a flow silently destroyed the global
librarian exchange, wedging all library operations until manual
restart.
RabbitMQ backend
----------------
- heartbeat=60, blocked_connection_timeout=300. Catches silently
dead connections (broker restart, orphaned channels, network
partitions) within ~2 heartbeat windows, so the consumer
reconnects and re-binds its queue rather than sitting forever
on a zombie connection.
Tests
-----
- Full test refresh: unit, integration, contract, provenance.
- Dropped user-field assertions and constructor kwargs across
~100 test files.
- Renamed user-collection isolation tests to workspace-collection.
244 lines
6.5 KiB
Python
244 lines
6.5 KiB
Python
"""
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Execute a SPARQL query against the TrustGraph knowledge graph.
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"""
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import argparse
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import os
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import json
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import sys
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from trustgraph.api import Api
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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_workspace = os.getenv("TRUSTGRAPH_WORKSPACE", "default")
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default_collection = 'default'
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def _term_cell(val):
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"""Extract display string from a wire-format term."""
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if val is None:
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return ""
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t = val.get("t", "")
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if t == "i":
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return val.get("i", "")
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elif t == "l":
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return val.get("v", "")
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return val.get("v", val.get("i", ""))
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def _term_str(val):
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"""Convert a wire-format term to a Turtle-style display string."""
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if val is None:
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return "?"
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t = val.get("t", "")
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if t == "i":
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return f"<{val.get('i', '')}>"
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elif t == "l":
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v = val.get("v", "")
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dt = val.get("d", "")
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lang = val.get("l", "")
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if lang:
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return f'"{v}"@{lang}'
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elif dt:
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return f'"{v}"^^<{dt}>'
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return f'"{v}"'
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return str(val)
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def sparql_query(url, token, flow_id, query, collection, limit,
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batch_size, output_format, workspace="default"):
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socket = Api(url=url, token=token, workspace=workspace).socket()
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flow = socket.flow(flow_id)
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variables = None
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all_rows = []
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try:
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for response in flow.sparql_query_stream(
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query=query,
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collection=collection,
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limit=limit,
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batch_size=batch_size,
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):
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if "error" in response:
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err = response["error"]
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msg = err.get("message", err) if isinstance(err, dict) else err
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raise RuntimeError(msg)
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query_type = response.get("query-type", "select")
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# ASK queries - just print and return
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if query_type == "ask":
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print("true" if response.get("ask-result") else "false")
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return
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# CONSTRUCT/DESCRIBE - print triples
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if query_type in ("construct", "describe"):
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triples = response.get("triples", [])
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if not triples:
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print("No triples.")
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elif output_format == "json":
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print(json.dumps(triples, indent=2))
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else:
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for t in triples:
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s = _term_str(t.get("s"))
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p = _term_str(t.get("p"))
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o = _term_str(t.get("o"))
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print(f"{s} {p} {o} .")
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return
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# SELECT - accumulate bindings across batches
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if variables is None:
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variables = response.get("variables", [])
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bindings = response.get("bindings", [])
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for binding in bindings:
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values = binding.get("values", [])
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all_rows.append([_term_cell(v) for v in values])
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# Output SELECT results
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if variables is None:
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print("No results.")
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return
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if not all_rows:
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print("No results.")
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return
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if output_format == "json":
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rows = []
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for row in all_rows:
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rows.append({
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var: cell for var, cell in zip(variables, row)
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})
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print(json.dumps(rows, indent=2))
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else:
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# Table format
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col_widths = [len(v) for v in variables]
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for row in all_rows:
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for i, cell in enumerate(row):
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if i < len(col_widths):
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col_widths[i] = max(col_widths[i], len(cell))
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header = " | ".join(
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v.ljust(col_widths[i]) for i, v in enumerate(variables)
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)
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separator = "-+-".join("-" * w for w in col_widths)
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print(header)
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print(separator)
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for row in all_rows:
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line = " | ".join(
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cell.ljust(col_widths[i]) if i < len(col_widths) else cell
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for i, cell in enumerate(row)
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)
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print(line)
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finally:
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socket.close()
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def main():
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parser = argparse.ArgumentParser(
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prog='tg-invoke-sparql-query',
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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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'-w', '--workspace',
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default=default_workspace,
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help=f'Workspace (default: {default_workspace})',
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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='Flow ID (default: default)',
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)
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parser.add_argument(
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'-q', '--query',
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help='SPARQL query string',
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)
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parser.add_argument(
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'-i', '--input',
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help='Read SPARQL query from file (use - for stdin)',
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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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'-l', '--limit',
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type=int,
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default=10000,
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help='Result limit (default: 10000)',
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)
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parser.add_argument(
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'-b', '--batch-size',
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type=int,
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default=20,
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help='Streaming batch size (default: 20)',
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)
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parser.add_argument(
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'--format',
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choices=['table', 'json'],
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default='table',
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help='Output format (default: table)',
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)
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args = parser.parse_args()
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# Get query from argument or file
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query = args.query
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if not query and args.input:
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if args.input == '-':
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query = sys.stdin.read()
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else:
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with open(args.input) as f:
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query = f.read()
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if not query:
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parser.error("Either -q/--query or -i/--input is required")
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try:
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sparql_query(
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url=args.url,
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token=args.token,
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flow_id=args.flow_id,
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query=query,
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collection=args.collection,
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limit=args.limit,
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batch_size=args.batch_size,
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output_format=args.format,
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workspace=args.workspace,
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)
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except Exception as e:
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print(f"Exception: {e}", flush=True, file=sys.stderr)
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sys.exit(1)
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if __name__ == "__main__":
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main()
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