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https://github.com/langchain-ai/langgraph.git
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Add debug logging
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@@ -39,7 +39,7 @@ Check `examples` for more examples.
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- [x] Test different input and output types (str, str sequence)
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- [x] Add tests for Stream, UniqueInbox
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- [ ] Add tests for subscribe_to_each().join()
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- [ ] Add optional debug logging
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- [x] Add optional debug logging
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- [ ] Implement checkpointing
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- [ ] Save checkpoints at end of each step
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- [ ] Load checkpoint at start of invocation
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@@ -110,8 +110,10 @@ draft_revise_loop = Pregel(
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},
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# output will be a dict with keys "draft" and "notes"
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output=["draft", "notes"],
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# input can be a dict with any of the channels as keys
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input=None,
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# input will be a dict with a single key, "question"
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input=["question"],
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# debug logging
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debug=True,
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)
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# run
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@@ -10,6 +10,8 @@ from langchain.callbacks.manager import (
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AsyncCallbackManagerForChainRun,
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CallbackManagerForChainRun,
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)
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from langchain.globals import get_debug
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from langchain.utils.input import get_bolded_text, get_colored_text
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from langchain.pydantic_v1 import BaseModel, create_model
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from langchain.schema.runnable import (
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Runnable,
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@@ -48,6 +50,8 @@ class Pregel(RunnableSerializable[dict[str, Any] | Any, dict[str, Any] | Any]):
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step_timeout: Optional[float] = None
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debug: bool
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class Config:
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arbitrary_types_allowed = True
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@@ -58,6 +62,7 @@ class Pregel(RunnableSerializable[dict[str, Any] | Any, dict[str, Any] | Any]):
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output: str | Sequence[str],
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input: str | Sequence[str],
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step_timeout: Optional[float] = None,
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debug: Optional[bool] = None,
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) -> None:
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chains_flat: list[PregelInvoke | PregelBatch] = []
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for chain in chains:
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@@ -74,6 +79,7 @@ class Pregel(RunnableSerializable[dict[str, Any] | Any, dict[str, Any] | Any]):
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output=output,
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input=input,
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step_timeout=step_timeout,
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debug=debug if debug is not None else get_debug(),
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)
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@property
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@@ -181,6 +187,18 @@ class Pregel(RunnableSerializable[dict[str, Any] | Any, dict[str, Any] | Any]):
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# channels are guaranteed to be immutable for the duration of the step,
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# with channel updates applied only at the transition between steps
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for step in range(config["recursion_limit"]):
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if self.debug:
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from pprint import pformat
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n_tasks = len(next_tasks)
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print(
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f"{get_colored_text('[pregel/step]', color='blue')} "
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+ get_bolded_text(
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f"Starting step {step} with {n_tasks} task{'s' if n_tasks > 1 else ''}. Current values:\n"
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)
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+ pformat({k: read(k) for k in channels})
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)
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# collect all writes to channels, without applying them yet
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pending_writes = deque[tuple[str, Any]]()
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@@ -281,6 +299,18 @@ class Pregel(RunnableSerializable[dict[str, Any] | Any, dict[str, Any] | Any]):
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# channels are guaranteed to be immutable for the duration of the step,
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# channel updates being applied only at the transition between steps
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for step in range(config["recursion_limit"]):
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if self.debug:
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from pprint import pformat
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n_tasks = len(next_tasks)
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print(
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f"{get_colored_text('[pregel/step]', color='blue')} "
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+ get_bolded_text(
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f"Starting step {step} with {n_tasks} task{'s' if n_tasks > 1 else ''}. Current values:\n"
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)
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+ pformat({k: read(k) for k in channels})
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)
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# collect all writes to channels, without applying them yet
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pending_writes = deque[tuple[str, Any]]()
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