- standardize on using model IDs, no more aliases - makes future
maintenance easier
- use latest models in docstrings to highlight support
- remove remaining sonnet 3-7 usage due to deprecation
Depends on #33751
- use latest models in examples to highlight support
- standardize on using IDs in examples - no more aliases to improve
determinism in future tests
- bump lock
- in integration tests, fix stale casettes and use `MODEL_NAME`
uniformly where possible
- add case for default max tokens for sonnet-4-5 (was missing)
While working on ToolRuntime in TS I discovered that Python still uses
`thread_model_call_count` and `run_model_call_count` in ToolNode tests
which afaik we removed.
Moving all `ToolNode` related improvements back to LangGraph and
importing them in LC!
pairing w/ https://github.com/langchain-ai/langgraph/pull/6321
this fixes a couple of things:
1. `InjectedState`, store etc will continue to work as expected no
matter where the import is from
2. `ToolRuntime` is now usable w/in langgraph, woohoo!
* attach the latest `AIMessage` to all `StructuredOutputError`s so that
relevant middleware can use as desired
* raise `StructuredOutputError` from `ProviderStrategy` logic in case of
failed parsing (so that we can retry from middleware)
* added a test suite w/ example custom middleware that retries for tool
+ provider strategy
Long term, we could add our own opinionated structured output retry
middleware, but this at least unblocks folks who want to use custom
retry logic in the short term :)
```py
class StructuredOutputRetryMiddleware(AgentMiddleware):
"""Retries model calls when structured output parsing fails."""
def __init__(self, max_retries: int) -> None:
self.max_retries = max_retries
def wrap_model_call(
self, request: ModelRequest, handler: Callable[[ModelRequest], ModelResponse]
) -> ModelResponse:
for attempt in range(self.max_retries + 1):
try:
return handler(request)
except StructuredOutputError as exc:
if attempt == self.max_retries:
raise
ai_content = exc.ai_message.content
error_message = (
f"Your previous response was:\n{ai_content}\n\n"
f"Error: {exc}. Please try again with a valid response."
)
request.messages.append(HumanMessage(content=error_message))
```
if "url" in annotation: in Line 15 , already ensures "url" is key in
annotation , so no need to check again to set "url" key in out object
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
The LLM shouldn't be seeing parameters it cannot control in the
ToolMessage error it gets when it invokes a tool with incorrect args.
This fixes the behavior within langchain to address immediate issue.
We may want to change the behavior in langchain_core as well to prevent
validation of injected arguments. But this would be done in a separate
change
