Using this argument, you can get more customization since you can do
`langgraph build` or directly `docker build` your image and then re-use
the `langgraph up --image my-image` and have it also spin up redis &
postgres for you.
Easier then writing your own compose file
- It now executes the same pregel algo as when the graph is executed
(without running any user code in nodes or conditional edges) to
discover all the edges
- This means we now support drawing the graph for all Pregel instances,
not just StateGraph
- This is done in preparation for new edge/node type coming in separate
PR
- Known changes
- custom labels on conditional edges to END are no longer displayed
- It now executes the same pregel algo as when the graph is executed (without running any user code in nodes or conditional edges) to discover all the edges
- This means we now support drawing the graph for all Pregel instances, not just StateGraph
If the state schema uses validators, skip the model construct
optimization.
For context, pydantic state can be significantly slower to run than
typed dict and dataclass states due to the full recursive validation.
We have some optimizations to reduce the impact of this (using cached
validators with model_construct), but this doesn't handle things like
field_validator.
We prefer correctness over performance, obviously.
Resolves: https://github.com/langchain-ai/langgraph/issues/4074
Signed-off-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>
The configuration expects the key "fields", not "text_fields": I had
failed to update across all implementations in the original PR
Thank you to Vincent Min for the fix!
---------
Co-authored-by: Vincent Min <93780551+VMinB12@users.noreply.github.com>
