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doc: clean doc word description. (#30895)

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Signed-off-by: zhanluxianshen <zhanluxianshen@163.com>
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docs/docs/changes/changelog/core.mdx
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@ -6,5 +6,5 @@
- `BaseChatModel` methods `__call__`, `call_as_llm`, `predict`, `predict_messages`. Will be removed in 0.2.0. Use `BaseChatModel.invoke` instead.
- `BaseChatModel` methods `apredict`, `apredict_messages`. Will be removed in 0.2.0. Use `BaseChatModel.ainvoke` instead.
- `BaseLLM` methods `__call__, `predict`, `predict_messages`. Will be removed in 0.2.0. Use `BaseLLM.invoke` instead.
- `BaseLLM` methods `__call__`, `predict`, `predict_messages`. Will be removed in 0.2.0. Use `BaseLLM.invoke` instead.
- `BaseLLM` methods `apredict`, `apredict_messages`. Will be removed in 0.2.0. Use `BaseLLM.ainvoke` instead.

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docs/docs/concepts/runnables.mdx
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@ -126,7 +126,7 @@ Please see the [Configurable Runnables](#configurable-runnables) section for mor
LangChain will automatically try to infer the input and output types of a Runnable based on available information.
Currently, this inference does not work well for more complex Runnables that are built using [LCEL](/docs/concepts/lcel) composition, and the inferred input and / or output types may be incorrect. In these cases, we recommend that users override the inferred input and output types using the `with_types` method ([API Reference](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.with_types
).
)).
## RunnableConfig
@ -194,7 +194,7 @@ In Python 3.11 and above, this works out of the box, and you do not need to do a
In Python 3.9 and 3.10, if you are using **async code**, you need to manually pass the `RunnableConfig` through to the `Runnable` when invoking it.
This is due to a limitation in [asyncio's tasks](https://docs.python.org/3/library/asyncio-task.html#asyncio.create_task) in Python 3.9 and 3.10 which did
not accept a `context` argument).
not accept a `context` argument.
Propagating the `RunnableConfig` manually is done like so:

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docs/docs/how_to/index.mdx
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@ -351,7 +351,7 @@ LangSmith allows you to closely trace, monitor and evaluate your LLM application
It seamlessly integrates with LangChain and LangGraph, and you can use it to inspect and debug individual steps of your chains and agents as you build.
LangSmith documentation is hosted on a separate site.
You can peruse [LangSmith how-to guides here](https://docs.smith.langchain.com/how_to_guides/), but we'll highlight a few sections that are particularly
You can peruse [LangSmith how-to guides here](https://docs.smith.langchain.com/), but we'll highlight a few sections that are particularly
relevant to LangChain below:
### Evaluation

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docs/docs/tutorials/index.mdx
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@ -38,7 +38,7 @@ LangSmith allows you to closely trace, monitor and evaluate your LLM application
It seamlessly integrates with LangChain, and you can use it to inspect and debug individual steps of your chains as you build.
LangSmith documentation is hosted on a separate site.
You can peruse [LangSmith tutorials here](https://docs.smith.langchain.com/tutorials/).
You can peruse [LangSmith tutorials here](https://docs.smith.langchain.com/).
### Evaluation