mirror of
https://github.com/trustgraph-ai/trustgraph.git
synced 2026-05-15 18:35:15 +02:00
feat: workspace-based multi-tenancy, replacing user as tenancy axis (#840)
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.
This commit is contained in:
cybermaggedon
GitHub
parent
9332089b3d
commit
d35473f7f7
377 changed files with 6868 additions and 5785 deletions
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@ -49,26 +49,26 @@ async def app_lifespan(server: FastMCP, websocket_url: str = "ws://api-gateway:8
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logging.info("Shutdown complete")
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async def get_socket_manager(ctx, user):
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async def get_socket_manager(ctx):
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lifespan_context = ctx.request_context.lifespan_context
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sockets = lifespan_context.sockets
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websocket_url = lifespan_context.websocket_url
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gateway_token = lifespan_context.gateway_token
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if user in sockets:
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if "default" in sockets:
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logging.info("Return existing socket manager")
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return sockets[user]
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return sockets["default"]
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logging.info(f"Opening socket to {websocket_url}...")
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# Create manager with empty pending requests
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manager = WebSocketManager(websocket_url, token=gateway_token)
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# Start reader task with the proper manager
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await manager.start()
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sockets[user] = manager
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sockets["default"] = manager
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logging.info("Return new socket manager")
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return manager
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@ -372,7 +372,6 @@ class McpServer:
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async def graph_rag(
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self,
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question: str,
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user: str | None = None,
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collection: str | None = None,
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entity_limit: int | None = None,
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triple_limit: int | None = None,
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@ -391,7 +390,6 @@ class McpServer:
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Args:
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question: The question or query to answer using the knowledge graph.
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The system will find relevant entities and relationships to inform the response.
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user: User identifier for access control and personalization (default: "trustgraph").
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collection: Knowledge collection to query (default: "default").
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Different collections may contain domain-specific knowledge.
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entity_limit: Maximum number of entities to retrieve during graph traversal.
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@ -414,7 +412,6 @@ class McpServer:
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- Perform research queries across connected information
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"""
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if user is None: user = "trustgraph"
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if collection is None: collection = "default"
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if flow_id is None: flow_id = "default"
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@ -423,7 +420,7 @@ class McpServer:
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logging.info("GraphRAG request made via websocket")
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manager = await get_socket_manager(ctx, user)
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manager = await get_socket_manager(ctx)
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await ctx.session.send_log_message(
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level="info",
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@ -437,7 +434,6 @@ class McpServer:
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"query": question
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}
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if user: request_data["user"] = user
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if collection: request_data["collection"] = collection
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if entity_limit: request_data["entity_limit"] = entity_limit
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if triple_limit: request_data["triple_limit"] = triple_limit
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@ -466,7 +462,6 @@ class McpServer:
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async def agent(
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self,
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question: str,
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user: str | None = None,
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collection: str | None = None,
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flow_id: str | None = None,
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ctx: Context = None,
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@ -481,7 +476,6 @@ class McpServer:
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Args:
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question: The question or task for the agent to solve. Can be complex
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queries requiring multiple steps, analysis, or tool usage.
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user: User identifier for personalization and access control (default: "trustgraph").
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collection: Knowledge collection the agent can access (default: "default").
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Determines what information and tools are available.
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flow_id: Agent workflow to use (default: "default"). Different flows
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@ -501,7 +495,6 @@ class McpServer:
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through log messages, so you can follow its reasoning steps.
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"""
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if user is None: user = "trustgraph"
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if collection is None: collection = "default"
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if flow_id is None: flow_id = "default"
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@ -510,7 +503,7 @@ class McpServer:
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logging.info("Agent request made via websocket")
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manager = await get_socket_manager(ctx, user)
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manager = await get_socket_manager(ctx)
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await ctx.session.send_log_message(
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level="info",
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@ -524,7 +517,6 @@ class McpServer:
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"question": question
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}
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if user: request_data["user"] = user
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if collection: request_data["collection"] = collection
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gen = manager.request("agent", request_data, flow_id)
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@ -1143,23 +1135,18 @@ class McpServer:
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async def get_knowledge_cores(
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self,
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user: str | None = None,
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ctx: Context = None,
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) -> KnowledgeCoresResponse:
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"""
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List all available knowledge graph cores for a user.
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List all available knowledge graph cores in the current workspace.
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Knowledge cores are packaged collections of structured knowledge that can
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be loaded into the system for querying and reasoning. They contain entities,
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relationships, and facts organized as knowledge graphs.
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Args:
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user: User identifier to list cores for (default: "trustgraph").
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Different users may have access to different knowledge cores.
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Returns:
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KnowledgeCoresResponse containing a list of available knowledge core IDs.
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Use this for:
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- Discovering available knowledge collections
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- Understanding what knowledge domains are accessible
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@ -1167,14 +1154,12 @@ class McpServer:
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- Managing knowledge resources
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"""
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if user is None: user = "trustgraph"
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if ctx is None:
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raise RuntimeError("No context provided")
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logging.info("Get knowledge cores request made via websocket")
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manager = await get_socket_manager(ctx, user)
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manager = await get_socket_manager(ctx)
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await ctx.session.send_log_message(
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level="info",
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@ -1185,7 +1170,6 @@ class McpServer:
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request_data = {
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"operation": "list-kg-cores",
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"user": user
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}
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gen = manager.request("knowledge", request_data, None)
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@ -1199,40 +1183,35 @@ class McpServer:
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async def delete_kg_core(
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self,
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core_id: str,
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user: str | None = None,
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ctx: Context = None,
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) -> DeleteKgCoreResponse:
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"""
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Permanently delete a knowledge graph core.
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This operation removes a knowledge core from storage. Use with caution
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as this action cannot be undone.
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Args:
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core_id: Unique identifier of the knowledge core to delete.
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user: User identifier (default: "trustgraph"). Only cores owned
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by this user can be deleted.
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Returns:
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DeleteKgCoreResponse confirming the deletion.
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Use this for:
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- Cleaning up obsolete knowledge cores
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- Removing test or experimental data
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- Managing storage space
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- Maintaining organized knowledge collections
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Warning: This permanently deletes the knowledge core and all its data.
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"""
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if user is None: user = "trustgraph"
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if ctx is None:
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raise RuntimeError("No context provided")
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logging.info("Delete KG core request made via websocket")
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manager = await get_socket_manager(ctx, user)
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manager = await get_socket_manager(ctx)
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await ctx.session.send_log_message(
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level="info",
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@ -1244,7 +1223,6 @@ class McpServer:
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request_data = {
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"operation": "delete-kg-core",
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"id": core_id,
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"user": user
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}
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gen = manager.request("knowledge", request_data, None)
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@ -1258,27 +1236,25 @@ class McpServer:
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self,
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core_id: str,
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flow: str,
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user: str | None = None,
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collection: str | None = None,
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ctx: Context = None,
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) -> LoadKgCoreResponse:
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"""
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Load a knowledge graph core into the active system for querying.
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This operation makes a knowledge core available for GraphRAG queries,
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triple searches, and other knowledge-based operations.
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Args:
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core_id: Unique identifier of the knowledge core to load.
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flow: Processing flow to use for loading the core. Different flows
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may apply different processing, indexing, or optimization steps.
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user: User identifier (default: "trustgraph").
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collection: Target collection name (default: "default"). The loaded
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knowledge will be available under this collection name.
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Returns:
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LoadKgCoreResponse confirming the core has been loaded.
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Use this for:
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- Making knowledge cores available for queries
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- Switching between different knowledge domains
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@ -1286,7 +1262,6 @@ class McpServer:
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- Preparing knowledge for GraphRAG operations
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"""
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if user is None: user = "trustgraph"
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if collection is None: collection = "default"
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if ctx is None:
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@ -1294,7 +1269,7 @@ class McpServer:
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logging.info("Load KG core request made via websocket")
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manager = await get_socket_manager(ctx, user)
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manager = await get_socket_manager(ctx)
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await ctx.session.send_log_message(
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level="info",
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@ -1307,7 +1282,6 @@ class McpServer:
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"operation": "load-kg-core",
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"id": core_id,
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"flow": flow,
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"user": user,
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"collection": collection
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}
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@ -1321,42 +1295,38 @@ class McpServer:
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async def get_kg_core(
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self,
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core_id: str,
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user: str | None = None,
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ctx: Context = None,
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) -> GetKgCoreResponse:
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"""
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Download and retrieve the complete content of a knowledge graph core.
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This tool streams the entire content of a knowledge core, returning all
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entities, relationships, and metadata. Due to potentially large data sizes,
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the content is streamed in chunks.
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Args:
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core_id: Unique identifier of the knowledge core to retrieve.
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user: User identifier (default: "trustgraph").
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Returns:
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GetKgCoreResponse containing all chunks of the knowledge core data.
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Each chunk contains part of the knowledge graph structure.
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Use this for:
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- Examining knowledge core content and structure
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- Debugging knowledge graph data
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- Exporting knowledge for backup or analysis
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- Understanding the scope and quality of knowledge
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Note: Large knowledge cores may take significant time to download.
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Progress updates are provided through log messages during streaming.
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"""
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if user is None: user = "trustgraph"
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if ctx is None:
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raise RuntimeError("No context provided")
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logging.info("Get KG core request made via websocket")
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manager = await get_socket_manager(ctx, user)
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manager = await get_socket_manager(ctx)
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await ctx.session.send_log_message(
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level="info",
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@ -1368,7 +1338,6 @@ class McpServer:
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request_data = {
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"operation": "get-kg-core",
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"id": core_id,
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"user": user
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}
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# Collect all streaming responses
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@ -1713,27 +1682,22 @@ class McpServer:
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async def get_documents(
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self,
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user: str | None = None,
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ctx: Context = None,
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) -> DocumentsResponse:
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"""
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List all documents stored in the TrustGraph document library.
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This tool returns metadata for all documents that have been uploaded
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to the system, including their processing status and properties.
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Args:
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user: User identifier to list documents for (default: "trustgraph").
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Only documents owned by this user will be returned.
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Returns:
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DocumentsResponse containing metadata for each document including:
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- Document ID and title
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- Upload timestamp and user
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- Upload timestamp
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- MIME type and size information
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- Tags and custom metadata
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- Processing status
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Use this for:
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- Browsing available documents
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- Managing document collections
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@ -1741,14 +1705,12 @@ class McpServer:
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- Auditing document storage
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"""
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if user is None: user = "trustgraph"
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if ctx is None:
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raise RuntimeError("No context provided")
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logging.info("Get documents request made via websocket")
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manager = await get_socket_manager(ctx, user)
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manager = await get_socket_manager(ctx)
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await ctx.session.send_log_message(
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level="info",
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@ -1759,7 +1721,6 @@ class McpServer:
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request_data = {
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"operation": "list-documents",
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"user": user
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}
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gen = manager.request("librarian", request_data, None)
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@ -1772,26 +1733,21 @@ class McpServer:
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async def get_processing(
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self,
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user: str | None = None,
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ctx: Context = None,
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) -> ProcessingResponse:
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"""
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List all documents currently in the processing queue.
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This tool shows documents that are being processed or waiting to be
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processed, along with their processing status and configuration.
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Args:
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user: User identifier (default: "trustgraph"). Only processing
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jobs for this user will be returned.
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Returns:
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ProcessingResponse containing processing metadata including:
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- Processing job ID and document ID
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- Processing flow and status
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- Target collection and user
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- Target collection
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- Timestamp and progress information
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Use this for:
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- Monitoring document processing progress
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- Debugging processing issues
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@ -1799,14 +1755,12 @@ class McpServer:
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- Understanding system workload
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"""
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if user is None: user = "trustgraph"
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if ctx is None:
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raise RuntimeError("No context provided")
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logging.info("Get processing request made via websocket")
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manager = await get_socket_manager(ctx, user)
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manager = await get_socket_manager(ctx)
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await ctx.session.send_log_message(
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level="info",
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@ -1817,7 +1771,6 @@ class McpServer:
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request_data = {
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"operation": "list-processing",
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"user": user
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}
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gen = manager.request("librarian", request_data, None)
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@ -1837,16 +1790,15 @@ class McpServer:
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title: str = "",
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comments: str = "",
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tags: List[str] | None = None,
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user: str | None = None,
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ctx: Context = None,
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) -> LoadDocumentResponse:
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"""
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Upload a document to the TrustGraph document library.
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This tool stores documents with rich metadata for later processing,
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search, and knowledge extraction. Documents can be text files, PDFs,
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or other supported formats.
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Args:
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document: The document content as a string. For binary files,
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this should be base64-encoded content.
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@ -1856,11 +1808,10 @@ class McpServer:
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title: Human-readable title for the document.
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comments: Optional description or notes about the document.
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tags: List of tags for categorizing and finding the document.
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||||
user: User identifier (default: "trustgraph").
|
||||
|
||||
|
||||
Returns:
|
||||
LoadDocumentResponse confirming the document has been stored.
|
||||
|
||||
|
||||
Use this for:
|
||||
- Adding new documents to the knowledge base
|
||||
- Storing reference materials and data sources
|
||||
|
|
@ -1868,7 +1819,6 @@ class McpServer:
|
|||
- Importing external content for analysis
|
||||
"""
|
||||
|
||||
if user is None: user = "trustgraph"
|
||||
if tags is None: tags = []
|
||||
|
||||
if ctx is None:
|
||||
|
|
@ -1876,7 +1826,7 @@ class McpServer:
|
|||
|
||||
logging.info("Load document request made via websocket")
|
||||
|
||||
manager = await get_socket_manager(ctx, user)
|
||||
manager = await get_socket_manager(ctx)
|
||||
|
||||
await ctx.session.send_log_message(
|
||||
level="info",
|
||||
|
|
@ -1897,7 +1847,6 @@ class McpServer:
|
|||
"title": title,
|
||||
"comments": comments,
|
||||
"metadata": metadata,
|
||||
"user": user,
|
||||
"tags": tags
|
||||
},
|
||||
"content": document
|
||||
|
|
@ -1913,40 +1862,35 @@ class McpServer:
|
|||
async def remove_document(
|
||||
self,
|
||||
document_id: str,
|
||||
user: str | None = None,
|
||||
ctx: Context = None,
|
||||
) -> RemoveDocumentResponse:
|
||||
"""
|
||||
Permanently remove a document from the library.
|
||||
|
||||
|
||||
This operation deletes a document and all its associated metadata.
|
||||
Use with caution as this action cannot be undone.
|
||||
|
||||
|
||||
Args:
|
||||
document_id: Unique identifier of the document to remove.
|
||||
user: User identifier (default: "trustgraph"). Only documents
|
||||
owned by this user can be removed.
|
||||
|
||||
|
||||
Returns:
|
||||
RemoveDocumentResponse confirming the document has been deleted.
|
||||
|
||||
|
||||
Use this for:
|
||||
- Cleaning up obsolete or incorrect documents
|
||||
- Managing storage space
|
||||
- Removing sensitive or inappropriate content
|
||||
- Maintaining organized document collections
|
||||
|
||||
|
||||
Warning: This permanently deletes the document and all its metadata.
|
||||
"""
|
||||
|
||||
if user is None: user = "trustgraph"
|
||||
|
||||
if ctx is None:
|
||||
raise RuntimeError("No context provided")
|
||||
|
||||
logging.info("Remove document request made via websocket")
|
||||
|
||||
manager = await get_socket_manager(ctx, user)
|
||||
manager = await get_socket_manager(ctx)
|
||||
|
||||
await ctx.session.send_log_message(
|
||||
level="info",
|
||||
|
|
@ -1958,7 +1902,6 @@ class McpServer:
|
|||
request_data = {
|
||||
"operation": "remove-document",
|
||||
"document-id": document_id,
|
||||
"user": user
|
||||
}
|
||||
|
||||
gen = manager.request("librarian", request_data, None)
|
||||
|
|
@ -1973,42 +1916,39 @@ class McpServer:
|
|||
processing_id: str,
|
||||
document_id: str,
|
||||
flow: str,
|
||||
user: str | None = None,
|
||||
collection: str | None = None,
|
||||
tags: List[str] | None = None,
|
||||
ctx: Context = None,
|
||||
) -> AddProcessingResponse:
|
||||
"""
|
||||
Queue a document for processing through a specific workflow.
|
||||
|
||||
|
||||
This tool adds a document to the processing queue where it will be
|
||||
processed by the specified flow to extract knowledge, create embeddings,
|
||||
or perform other analysis operations.
|
||||
|
||||
|
||||
Args:
|
||||
processing_id: Unique identifier for this processing job.
|
||||
document_id: ID of the document to process (must exist in library).
|
||||
flow: Processing flow to use. Different flows perform different
|
||||
types of analysis (e.g., knowledge extraction, summarization).
|
||||
user: User identifier (default: "trustgraph").
|
||||
collection: Target collection for processed knowledge (default: "default").
|
||||
Results will be stored under this collection name.
|
||||
tags: Optional tags for categorizing this processing job.
|
||||
|
||||
|
||||
Returns:
|
||||
AddProcessingResponse confirming the document has been queued.
|
||||
|
||||
|
||||
Use this for:
|
||||
- Processing uploaded documents into knowledge
|
||||
- Extracting entities and relationships from text
|
||||
- Creating searchable embeddings
|
||||
- Converting documents into structured knowledge
|
||||
|
||||
|
||||
Note: Processing may take time depending on document size and flow complexity.
|
||||
Use get_processing to monitor progress.
|
||||
"""
|
||||
|
||||
if user is None: user = "trustgraph"
|
||||
if collection is None: collection = "default"
|
||||
if tags is None: tags = []
|
||||
|
||||
|
|
@ -2017,7 +1957,7 @@ class McpServer:
|
|||
|
||||
logging.info("Add processing request made via websocket")
|
||||
|
||||
manager = await get_socket_manager(ctx, user)
|
||||
manager = await get_socket_manager(ctx)
|
||||
|
||||
await ctx.session.send_log_message(
|
||||
level="info",
|
||||
|
|
@ -2036,7 +1976,6 @@ class McpServer:
|
|||
"document-id": document_id,
|
||||
"time": timestamp,
|
||||
"flow": flow,
|
||||
"user": user,
|
||||
"collection": collection,
|
||||
"tags": tags
|
||||
}
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue