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.
65 lines
1.8 KiB
Python
65 lines
1.8 KiB
Python
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import asyncio
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import uuid
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import logging
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from . librarian import LibrarianRequestor
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# Module logger
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logger = logging.getLogger(__name__)
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class DocumentStreamExport:
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def __init__(self, backend):
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self.backend = backend
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async def process(self, data, error, ok, request):
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workspace = request.query.get("workspace", "default")
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document_id = request.query.get("document-id")
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chunk_size = int(request.query.get("chunk-size", 1024 * 1024))
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if not document_id:
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return await error("Missing required parameter: document-id")
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response = await ok()
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lr = LibrarianRequestor(
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backend=self.backend,
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consumer="api-gateway-doc-stream-" + str(uuid.uuid4()),
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subscriber="api-gateway-doc-stream-" + str(uuid.uuid4()),
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)
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try:
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await lr.start()
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async def responder(resp, fin):
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if "content" in resp:
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content = resp["content"]
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# Content is base64 encoded, write as-is for client to decode
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# Or decode here and write raw bytes
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import base64
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chunk_data = base64.b64decode(content)
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await response.write(chunk_data)
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await lr.process(
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{
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"operation": "stream-document",
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"workspace": workspace,
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"document-id": document_id,
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"chunk-size": chunk_size,
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},
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responder
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)
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except Exception as e:
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logger.error(f"Document stream exception: {e}", exc_info=True)
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finally:
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await lr.stop()
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await response.write_eof()
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return response
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