mirror of
https://github.com/trustgraph-ai/trustgraph.git
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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.
145 lines
No EOL
4.1 KiB
Python
145 lines
No EOL
4.1 KiB
Python
"""
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Connects to the graph query service and dumps all graph edges.
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Uses streaming mode for lower time-to-first-result and reduced memory overhead.
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Named graphs:
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- Default graph (empty): Core knowledge facts
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- urn:graph:source: Extraction provenance (document/chunk sources)
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- urn:graph:retrieval: Query-time explainability (question, exploration, focus, synthesis)
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"""
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import argparse
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import os
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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_collection = 'default'
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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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# Named graph constants for convenience
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GRAPH_DEFAULT = ""
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GRAPH_SOURCE = "urn:graph:source"
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GRAPH_RETRIEVAL = "urn:graph:retrieval"
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def show_graph(url, flow_id, collection, limit, batch_size, graph=None, show_graph_column=False, token=None, workspace="default"):
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socket = Api(url, token=token, workspace=workspace).socket()
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flow = socket.flow(flow_id)
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try:
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for batch in flow.triples_query_stream(
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collection=collection,
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s=None, p=None, o=None,
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g=graph, # Filter by named graph (None = all graphs)
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limit=limit,
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batch_size=batch_size,
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):
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for triple in batch:
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s = triple.get("s", {})
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p = triple.get("p", {})
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o = triple.get("o", {})
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g = triple.get("g") # Named graph (None = default graph)
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# Format terms for display
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s_str = s.get("v", s.get("i", str(s)))
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p_str = p.get("v", p.get("i", str(p)))
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o_str = o.get("v", o.get("i", str(o)))
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if show_graph_column:
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g_str = g if g else "(default)"
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print(f"[{g_str}]", s_str, p_str, o_str)
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else:
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print(s_str, p_str, o_str)
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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-show-graph',
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description=__doc__,
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)
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parser.add_argument(
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'-u', '--api-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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'-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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'-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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'-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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'-l', '--limit',
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type=int,
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default=10000,
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help='Maximum number of triples to return (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='Triples per streaming batch (default: 20)',
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)
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parser.add_argument(
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'-g', '--graph',
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default=None,
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help='Filter by named graph (e.g., urn:graph:source, urn:graph:retrieval). Use "" for default graph only.',
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)
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parser.add_argument(
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'--show-graph',
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action='store_true',
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help='Show graph column in output',
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)
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args = parser.parse_args()
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# Handle empty string for default graph filter
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graph = args.graph
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if graph == '""' or graph == "''":
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graph = "" # Filter to default graph only
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try:
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show_graph(
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url = args.api_url,
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flow_id = args.flow_id,
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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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graph = graph,
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show_graph_column = args.show_graph,
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token = args.token,
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workspace=args.workspace,
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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() |