mirror of
https://github.com/trustgraph-ai/trustgraph.git
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d35473f7f7
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.
74 lines
1.8 KiB
Python
74 lines
1.8 KiB
Python
import asyncio
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import uuid
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import logging
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from aiohttp import WSMsgType
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from ... schema import Metadata
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from ... schema import ExtractedObject
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from ... base import Publisher
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from . serialize import to_subgraph
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# Module logger
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logger = logging.getLogger(__name__)
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class RowsImport:
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def __init__(
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self, ws, running, backend, queue
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):
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self.ws = ws
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self.running = running
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self.publisher = Publisher(
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backend, topic = queue, schema = ExtractedObject
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)
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async def start(self):
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await self.publisher.start()
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async def destroy(self):
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# Step 1: Stop accepting new messages
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self.running.stop()
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# Step 2: Wait for publisher to drain its queue
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logger.info("Draining publisher queue...")
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await self.publisher.stop()
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# Step 3: Close websocket only after queue is drained
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if self.ws:
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await self.ws.close()
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async def receive(self, msg):
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data = msg.json()
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# Handle both single object and array of objects for backward compatibility
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values_data = data["values"]
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if not isinstance(values_data, list):
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# Single object - wrap in array
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values_data = [values_data]
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elt = ExtractedObject(
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metadata=Metadata(
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id=data["metadata"]["id"],
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collection=data["metadata"]["collection"],
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),
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schema_name=data["schema_name"],
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values=values_data,
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confidence=data.get("confidence", 1.0),
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source_span=data.get("source_span", ""),
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)
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await self.publisher.send(None, elt)
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async def run(self):
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while self.running.get():
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await asyncio.sleep(0.5)
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if self.ws:
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await self.ws.close()
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self.ws = None
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