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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.
43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
from dataclasses import dataclass, field
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from ..core.primitives import Error, Term, Triple
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from ..core.topic import queue
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############################################################################
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# SPARQL query
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@dataclass
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class SparqlBinding:
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"""A single row of SPARQL SELECT results.
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Values are ordered to match the variables list in SparqlQueryResponse.
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"""
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values: list[Term | None] = field(default_factory=list)
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@dataclass
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class SparqlQueryRequest:
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collection: str = ""
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query: str = "" # SPARQL query string
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limit: int = 10000 # Safety limit on results
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streaming: bool = False # Enable streaming mode
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batch_size: int = 20 # Bindings per batch in streaming mode
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@dataclass
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class SparqlQueryResponse:
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error: Error | None = None
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query_type: str = "" # "select", "ask", "construct", "describe"
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# For SELECT queries
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variables: list[str] = field(default_factory=list)
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bindings: list[SparqlBinding] = field(default_factory=list)
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# For ASK queries
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ask_result: bool = False
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# For CONSTRUCT/DESCRIBE queries
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triples: list[Triple] = field(default_factory=list)
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is_final: bool = True # False for intermediate batches in streaming
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sparql_query_request_queue = queue('sparql-query', cls='request')
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sparql_query_response_queue = queue('sparql-query', cls='response')
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