trustgraph/trustgraph-cli/trustgraph/cli/invoke_llm.py

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"""
Invokes the text completion service by specifying an LLM system prompt
and user prompt. Both arguments are required.
"""
import argparse
import os
from trustgraph.api import Api
default_url = os.getenv("TRUSTGRAPH_URL", 'http://localhost:8088/')
default_token = os.getenv("TRUSTGRAPH_TOKEN", None)
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.
2026-04-21 23:23:01 +01:00
default_workspace = os.getenv("TRUSTGRAPH_WORKSPACE", "default")
Expose LLM token usage across all service layers (#782) Expose LLM token usage (in_token, out_token, model) across all service layers Propagate token counts from LLM services through the prompt, text-completion, graph-RAG, document-RAG, and agent orchestrator pipelines to the API gateway and Python SDK. All fields are Optional — None means "not available", distinguishing from a real zero count. Key changes: - Schema: Add in_token/out_token/model to TextCompletionResponse, PromptResponse, GraphRagResponse, DocumentRagResponse, AgentResponse - TextCompletionClient: New TextCompletionResult return type. Split into text_completion() (non-streaming) and text_completion_stream() (streaming with per-chunk handler callback) - PromptClient: New PromptResult with response_type (text/json/jsonl), typed fields (text/object/objects), and token usage. All callers updated. - RAG services: Accumulate token usage across all prompt calls (extract-concepts, edge-scoring, edge-reasoning, synthesis). Non-streaming path sends single combined response instead of chunk + end_of_session. - Agent orchestrator: UsageTracker accumulates tokens across meta-router, pattern prompt calls, and react reasoning. Attached to end_of_dialog. - Translators: Encode token fields when not None (is not None, not truthy) - Python SDK: RAG and text-completion methods return TextCompletionResult (non-streaming) or RAGChunk/AgentAnswer with token fields (streaming) - CLI: --show-usage flag on tg-invoke-llm, tg-invoke-prompt, tg-invoke-graph-rag, tg-invoke-document-rag, tg-invoke-agent
2026-04-13 14:38:34 +01:00
def query(url, flow_id, system, prompt, streaming=True, token=None,
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.
2026-04-21 23:23:01 +01:00
show_usage=False, workspace="default"):
# Create API client
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.
2026-04-21 23:23:01 +01:00
api = Api(url=url, token=token, workspace=workspace)
socket = api.socket()
flow = socket.flow(flow_id)
try:
# Call text completion
response = flow.text_completion(
system=system,
prompt=prompt,
streaming=streaming
)
if streaming:
Expose LLM token usage across all service layers (#782) Expose LLM token usage (in_token, out_token, model) across all service layers Propagate token counts from LLM services through the prompt, text-completion, graph-RAG, document-RAG, and agent orchestrator pipelines to the API gateway and Python SDK. All fields are Optional — None means "not available", distinguishing from a real zero count. Key changes: - Schema: Add in_token/out_token/model to TextCompletionResponse, PromptResponse, GraphRagResponse, DocumentRagResponse, AgentResponse - TextCompletionClient: New TextCompletionResult return type. Split into text_completion() (non-streaming) and text_completion_stream() (streaming with per-chunk handler callback) - PromptClient: New PromptResult with response_type (text/json/jsonl), typed fields (text/object/objects), and token usage. All callers updated. - RAG services: Accumulate token usage across all prompt calls (extract-concepts, edge-scoring, edge-reasoning, synthesis). Non-streaming path sends single combined response instead of chunk + end_of_session. - Agent orchestrator: UsageTracker accumulates tokens across meta-router, pattern prompt calls, and react reasoning. Attached to end_of_dialog. - Translators: Encode token fields when not None (is not None, not truthy) - Python SDK: RAG and text-completion methods return TextCompletionResult (non-streaming) or RAGChunk/AgentAnswer with token fields (streaming) - CLI: --show-usage flag on tg-invoke-llm, tg-invoke-prompt, tg-invoke-graph-rag, tg-invoke-document-rag, tg-invoke-agent
2026-04-13 14:38:34 +01:00
last_chunk = None
for chunk in response:
Expose LLM token usage across all service layers (#782) Expose LLM token usage (in_token, out_token, model) across all service layers Propagate token counts from LLM services through the prompt, text-completion, graph-RAG, document-RAG, and agent orchestrator pipelines to the API gateway and Python SDK. All fields are Optional — None means "not available", distinguishing from a real zero count. Key changes: - Schema: Add in_token/out_token/model to TextCompletionResponse, PromptResponse, GraphRagResponse, DocumentRagResponse, AgentResponse - TextCompletionClient: New TextCompletionResult return type. Split into text_completion() (non-streaming) and text_completion_stream() (streaming with per-chunk handler callback) - PromptClient: New PromptResult with response_type (text/json/jsonl), typed fields (text/object/objects), and token usage. All callers updated. - RAG services: Accumulate token usage across all prompt calls (extract-concepts, edge-scoring, edge-reasoning, synthesis). Non-streaming path sends single combined response instead of chunk + end_of_session. - Agent orchestrator: UsageTracker accumulates tokens across meta-router, pattern prompt calls, and react reasoning. Attached to end_of_dialog. - Translators: Encode token fields when not None (is not None, not truthy) - Python SDK: RAG and text-completion methods return TextCompletionResult (non-streaming) or RAGChunk/AgentAnswer with token fields (streaming) - CLI: --show-usage flag on tg-invoke-llm, tg-invoke-prompt, tg-invoke-graph-rag, tg-invoke-document-rag, tg-invoke-agent
2026-04-13 14:38:34 +01:00
print(chunk.content, end="", flush=True)
last_chunk = chunk
print()
Expose LLM token usage across all service layers (#782) Expose LLM token usage (in_token, out_token, model) across all service layers Propagate token counts from LLM services through the prompt, text-completion, graph-RAG, document-RAG, and agent orchestrator pipelines to the API gateway and Python SDK. All fields are Optional — None means "not available", distinguishing from a real zero count. Key changes: - Schema: Add in_token/out_token/model to TextCompletionResponse, PromptResponse, GraphRagResponse, DocumentRagResponse, AgentResponse - TextCompletionClient: New TextCompletionResult return type. Split into text_completion() (non-streaming) and text_completion_stream() (streaming with per-chunk handler callback) - PromptClient: New PromptResult with response_type (text/json/jsonl), typed fields (text/object/objects), and token usage. All callers updated. - RAG services: Accumulate token usage across all prompt calls (extract-concepts, edge-scoring, edge-reasoning, synthesis). Non-streaming path sends single combined response instead of chunk + end_of_session. - Agent orchestrator: UsageTracker accumulates tokens across meta-router, pattern prompt calls, and react reasoning. Attached to end_of_dialog. - Translators: Encode token fields when not None (is not None, not truthy) - Python SDK: RAG and text-completion methods return TextCompletionResult (non-streaming) or RAGChunk/AgentAnswer with token fields (streaming) - CLI: --show-usage flag on tg-invoke-llm, tg-invoke-prompt, tg-invoke-graph-rag, tg-invoke-document-rag, tg-invoke-agent
2026-04-13 14:38:34 +01:00
if show_usage and last_chunk:
print(
f"Input tokens: {last_chunk.in_token} "
f"Output tokens: {last_chunk.out_token} "
f"Model: {last_chunk.model}",
file=__import__('sys').stderr,
)
else:
Expose LLM token usage across all service layers (#782) Expose LLM token usage (in_token, out_token, model) across all service layers Propagate token counts from LLM services through the prompt, text-completion, graph-RAG, document-RAG, and agent orchestrator pipelines to the API gateway and Python SDK. All fields are Optional — None means "not available", distinguishing from a real zero count. Key changes: - Schema: Add in_token/out_token/model to TextCompletionResponse, PromptResponse, GraphRagResponse, DocumentRagResponse, AgentResponse - TextCompletionClient: New TextCompletionResult return type. Split into text_completion() (non-streaming) and text_completion_stream() (streaming with per-chunk handler callback) - PromptClient: New PromptResult with response_type (text/json/jsonl), typed fields (text/object/objects), and token usage. All callers updated. - RAG services: Accumulate token usage across all prompt calls (extract-concepts, edge-scoring, edge-reasoning, synthesis). Non-streaming path sends single combined response instead of chunk + end_of_session. - Agent orchestrator: UsageTracker accumulates tokens across meta-router, pattern prompt calls, and react reasoning. Attached to end_of_dialog. - Translators: Encode token fields when not None (is not None, not truthy) - Python SDK: RAG and text-completion methods return TextCompletionResult (non-streaming) or RAGChunk/AgentAnswer with token fields (streaming) - CLI: --show-usage flag on tg-invoke-llm, tg-invoke-prompt, tg-invoke-graph-rag, tg-invoke-document-rag, tg-invoke-agent
2026-04-13 14:38:34 +01:00
print(response.text)
if show_usage:
print(
f"Input tokens: {response.in_token} "
f"Output tokens: {response.out_token} "
f"Model: {response.model}",
file=__import__('sys').stderr,
)
finally:
# Clean up socket connection
socket.close()
def main():
parser = argparse.ArgumentParser(
prog='tg-invoke-llm',
description=__doc__,
)
parser.add_argument(
'-u', '--url',
default=default_url,
help=f'API URL (default: {default_url})',
)
parser.add_argument(
'-t', '--token',
default=default_token,
help='Authentication token (default: $TRUSTGRAPH_TOKEN)',
)
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.
2026-04-21 23:23:01 +01:00
parser.add_argument(
'-w', '--workspace',
default=default_workspace,
help=f'Workspace (default: {default_workspace})',
)
parser.add_argument(
'system',
nargs=1,
help='LLM system prompt e.g. You are a helpful assistant',
)
parser.add_argument(
'prompt',
nargs=1,
help='LLM prompt e.g. What is 2 + 2?',
)
parser.add_argument(
'-f', '--flow-id',
default="default",
help=f'Flow ID (default: default)'
)
parser.add_argument(
'--no-streaming',
action='store_true',
help='Disable streaming (default: streaming enabled)'
)
Expose LLM token usage across all service layers (#782) Expose LLM token usage (in_token, out_token, model) across all service layers Propagate token counts from LLM services through the prompt, text-completion, graph-RAG, document-RAG, and agent orchestrator pipelines to the API gateway and Python SDK. All fields are Optional — None means "not available", distinguishing from a real zero count. Key changes: - Schema: Add in_token/out_token/model to TextCompletionResponse, PromptResponse, GraphRagResponse, DocumentRagResponse, AgentResponse - TextCompletionClient: New TextCompletionResult return type. Split into text_completion() (non-streaming) and text_completion_stream() (streaming with per-chunk handler callback) - PromptClient: New PromptResult with response_type (text/json/jsonl), typed fields (text/object/objects), and token usage. All callers updated. - RAG services: Accumulate token usage across all prompt calls (extract-concepts, edge-scoring, edge-reasoning, synthesis). Non-streaming path sends single combined response instead of chunk + end_of_session. - Agent orchestrator: UsageTracker accumulates tokens across meta-router, pattern prompt calls, and react reasoning. Attached to end_of_dialog. - Translators: Encode token fields when not None (is not None, not truthy) - Python SDK: RAG and text-completion methods return TextCompletionResult (non-streaming) or RAGChunk/AgentAnswer with token fields (streaming) - CLI: --show-usage flag on tg-invoke-llm, tg-invoke-prompt, tg-invoke-graph-rag, tg-invoke-document-rag, tg-invoke-agent
2026-04-13 14:38:34 +01:00
parser.add_argument(
'--show-usage',
action='store_true',
help='Show token usage and model on stderr'
)
args = parser.parse_args()
try:
query(
url=args.url,
flow_id=args.flow_id,
system=args.system[0],
prompt=args.prompt[0],
streaming=not args.no_streaming,
token=args.token,
Expose LLM token usage across all service layers (#782) Expose LLM token usage (in_token, out_token, model) across all service layers Propagate token counts from LLM services through the prompt, text-completion, graph-RAG, document-RAG, and agent orchestrator pipelines to the API gateway and Python SDK. All fields are Optional — None means "not available", distinguishing from a real zero count. Key changes: - Schema: Add in_token/out_token/model to TextCompletionResponse, PromptResponse, GraphRagResponse, DocumentRagResponse, AgentResponse - TextCompletionClient: New TextCompletionResult return type. Split into text_completion() (non-streaming) and text_completion_stream() (streaming with per-chunk handler callback) - PromptClient: New PromptResult with response_type (text/json/jsonl), typed fields (text/object/objects), and token usage. All callers updated. - RAG services: Accumulate token usage across all prompt calls (extract-concepts, edge-scoring, edge-reasoning, synthesis). Non-streaming path sends single combined response instead of chunk + end_of_session. - Agent orchestrator: UsageTracker accumulates tokens across meta-router, pattern prompt calls, and react reasoning. Attached to end_of_dialog. - Translators: Encode token fields when not None (is not None, not truthy) - Python SDK: RAG and text-completion methods return TextCompletionResult (non-streaming) or RAGChunk/AgentAnswer with token fields (streaming) - CLI: --show-usage flag on tg-invoke-llm, tg-invoke-prompt, tg-invoke-graph-rag, tg-invoke-document-rag, tg-invoke-agent
2026-04-13 14:38:34 +01:00
show_usage=args.show_usage,
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.
2026-04-21 23:23:01 +01:00
workspace=args.workspace,
)
except Exception as e:
print("Exception:", e, flush=True)
if __name__ == "__main__":
main()