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.

tests/integration/test_kg_extract_store_integration.py

@@ -97,7 +97,6 @@ class TestKnowledgeGraphPipelineIntegration:
         return Chunk(
             metadata=Metadata(
                 id="doc-123",
-                user="test_user",
                 collection="test_collection",
             ),
             chunk=b"Machine Learning is a subset of Artificial Intelligence. Neural Networks are used in Machine Learning to process complex patterns."
@@ -247,7 +246,6 @@ class TestKnowledgeGraphPipelineIntegration:
         # Arrange
         metadata = Metadata(
             id="test-doc",
-            user="test_user",
             collection="test_collection",
         )
 
@@ -305,7 +303,6 @@ class TestKnowledgeGraphPipelineIntegration:
         # Arrange
         metadata = Metadata(
             id="test-doc",
-            user="test_user",
             collection="test_collection",
         )
 
@@ -375,7 +372,6 @@ class TestKnowledgeGraphPipelineIntegration:
         sample_triples = Triples(
             metadata=Metadata(
                 id="test-doc",
-                user="test_user",
                 collection="test_collection",
             ),
             triples=[
@@ -390,11 +386,14 @@ class TestKnowledgeGraphPipelineIntegration:
         mock_msg = MagicMock()
         mock_msg.value.return_value = sample_triples
 
+        mock_flow = MagicMock()
+        mock_flow.workspace = "test_workspace"
+
         # Act
-        await processor.on_triples(mock_msg, None, None)
+        await processor.on_triples(mock_msg, None, mock_flow)
 
         # Assert
-        mock_cassandra_store.add_triples.assert_called_once_with(sample_triples)
+        mock_cassandra_store.add_triples.assert_called_once_with("test_workspace", sample_triples)
 
     @pytest.mark.asyncio
     async def test_knowledge_store_graph_embeddings_storage(self, mock_cassandra_store):
@@ -407,7 +406,6 @@ class TestKnowledgeGraphPipelineIntegration:
         sample_embeddings = GraphEmbeddings(
             metadata=Metadata(
                 id="test-doc",
-                user="test_user",
                 collection="test_collection",
             ),
             entities=[
@@ -421,11 +419,14 @@ class TestKnowledgeGraphPipelineIntegration:
         mock_msg = MagicMock()
         mock_msg.value.return_value = sample_embeddings
 
+        mock_flow = MagicMock()
+        mock_flow.workspace = "test_workspace"
+
         # Act
-        await processor.on_graph_embeddings(mock_msg, None, None)
+        await processor.on_graph_embeddings(mock_msg, None, mock_flow)
 
         # Assert
-        mock_cassandra_store.add_graph_embeddings.assert_called_once_with(sample_embeddings)
+        mock_cassandra_store.add_graph_embeddings.assert_called_once_with("test_workspace", sample_embeddings)
 
     @pytest.mark.asyncio
     async def test_end_to_end_pipeline_coordination(self, definitions_processor, relationships_processor, 
@@ -553,7 +554,7 @@ class TestKnowledgeGraphPipelineIntegration:
         )
         
         sample_chunk = Chunk(
-            metadata=Metadata(id="test", user="user", collection="collection"),
+            metadata=Metadata(id="test", collection="collection"),
             chunk=b"Test chunk"
         )
         
@@ -580,7 +581,7 @@ class TestKnowledgeGraphPipelineIntegration:
         # Arrange
         large_chunk_batch = [
             Chunk(
-                metadata=Metadata(id=f"doc-{i}", user="user", collection="collection"),
+                metadata=Metadata(id=f"doc-{i}", collection="collection"),
                 chunk=f"Document {i} contains machine learning and AI content.".encode("utf-8")
             )
             for i in range(100)  # Large batch
@@ -617,7 +618,6 @@ class TestKnowledgeGraphPipelineIntegration:
         # Arrange
         original_metadata = Metadata(
             id="test-doc-123",
-            user="test_user",
             collection="test_collection",
         )
 
@@ -646,9 +646,7 @@ class TestKnowledgeGraphPipelineIntegration:
         entity_contexts_call = entity_contexts_producer.send.call_args[0][0]
         
         assert triples_call.metadata.id == "test-doc-123"
-        assert triples_call.metadata.user == "test_user"
         assert triples_call.metadata.collection == "test_collection"
         
         assert entity_contexts_call.metadata.id == "test-doc-123"
-        assert entity_contexts_call.metadata.user == "test_user"
         assert entity_contexts_call.metadata.collection == "test_collection"