feat: Add conversation variable persistence layer - #26
Conversation
… factory to pass the ConversationVariableUpdater factory (the only non-VariablePool dependency), plus a unit test to verify the injection path. - `api/core/workflow/nodes/variable_assigner/v2/node.py` adds a kw-only `conv_var_updater_factory` dependency (defaulting to `conversation_variable_updater_factory`) and stores it for use in `_run`. - `api/core/workflow/nodes/node_factory.py` now injects the factory when creating VariableAssigner v2 nodes. - `api/tests/unit_tests/core/workflow/nodes/variable_assigner/v2/test_variable_assigner_v2.py` adds a test asserting the factory is injected. Tests not run. Next steps (optional): 1) `make lint` 2) `make type-check` 3) `uv run --project api --dev dev/pytest/pytest_unit_tests.sh`
…ructor args. - `api/core/workflow/nodes/node_factory.py` now directly instantiates `VariableAssignerNode` with the injected dependency, and uses a direct call for all other nodes. No tests run.
Add a new command for GraphEngine to update a group of variables. This command takes a group of variable selectors and new values. When the engine receives the command, it will update the corresponding variable in the variable pool. If it does not exist, it will add it; if it does, it will overwrite it. Both behaviors should be treated the same and do not need to be distinguished.
…be-kanban 0941477f) Create a new persistence layer for the Graph Engine. This layer receives a ConversationVariableUpdater upon initialization, which is used to persist the received ConversationVariables to the database. It can retrieve the currently processing ConversationId from the engine's variable pool. It captures the successful execution event of each node and determines whether the type of this node is VariableAssigner(v1 and v2). If so, it retrieves the variable name and value that need to be updated from the node's outputs. This layer is only used in the Advanced Chat. It should be placed outside of Core.Workflow package.
…rs/conversation_variable_persist_layer.py` to satisfy SIM118 - chore(lint): run `make lint` (passes; warnings about missing RECORD during venv package uninstall) - chore(type-check): run `make type-check` (fails: 1275 errors for missing type stubs like `opentelemetry`, `click`, `sqlalchemy`, `flask`, `pydantic`, `pydantic_settings`)
…tType validation and casting - test(graph-engine): update VariableUpdate usages to include value_type in command tests
… drop common_helpers usage - refactor(variable-assigner-v2): inline updated variable payload and drop common_helpers usage Tests not run.
…n and remove value type validation - test(graph-engine): update UpdateVariablesCommand tests to pass concrete Variable instances - fix(graph-engine): align VariableUpdate values with selector before adding to VariablePool Tests not run.
…e handling for v1/v2 process_data - refactor(app-layer): read updated variables from process_data in conversation variable persistence layer - test(app-layer): adapt persistence layer tests to use common_helpers updated-variable payloads Tests not run.
…nce reads from process_data
…fter venv changes) - chore(type-check): run `make type-check` (fails: 1275 missing type stubs across dependencies) Details: - `make lint` fails with `ModuleNotFoundError: No module named 'dotenv_linter.cli'`. - `make type-check` fails with missing stubs for `opentelemetry`, `click`, `sqlalchemy`, `flask`, `pydantic`, `pydantic_settings`, etc.
…ableUnion and remove value type validation" This reverts commit 5ebc87a.
…h SegmentType validation and casting" This reverts commit 3edd525.
This reverts commit 67007f6.
…y out of core.workflow into `api/services/conversation_variable_updater.py` - refactor(app): update advanced chat app runner and conversation service to import the new updater factory Tests not run.
…-linter module missing) - chore(type-check): run `make type-check` (fails: 1275 missing type stubs) Details: - `make lint` reports: `No matches for ignored import core.workflow.nodes.variable_assigner.common.impl -> extensions.ext_database` and ends with `ModuleNotFoundError: No module named 'dotenv_linter.cli'`. - `make type-check` fails with missing type stubs for `opentelemetry`, `click`, `sqlalchemy`, `flask`, `pydantic`, `pydantic_settings`, etc.
…impl import in `api/.importlinter`
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augment review |
🤖 Augment PR SummarySummary: This PR introduces a graph-engine persistence layer for conversation variables, moving database writes out of Variable Assigner node execution. Changes:
Technical Notes: Persistence is driven by 🤖 Was this summary useful? React with 👍 or 👎 |
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| conv_var_updater.flush() | ||
| updated_variables = [common_helpers.variable_to_processed_data(assigned_variable_selector, updated_variable)] | ||
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| updated_variables = [common_helpers.variable_to_processed_data(assigned_variable_selector, original_variable)] |
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updated_variables is built from original_variable, so UpdatedVariable.new_value will reflect the pre-update value rather than what was written to the pool (updated_variable). This can break downstream consumers that rely on process_data to represent the post-write state.
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| self._pending_updates.append((conversation_id, variable)) | ||
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| def flush(self) -> None: | ||
| for conversation_id, variable in self._pending_updates: |
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flush() iterates _pending_updates but never clears it, so subsequent flush() calls will reapply prior updates and the list can grow across graph events. This is especially risky because the new persistence layer calls flush() per updated variable, which can cause repeated writes of earlier updates.
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Benchmark PR from qodo-benchmark#167