上一篇我们讲到了 RunnableSequence invoke 方法。这个方法里调用的 config_with_context 方法比较难理解。所以在讲解 invoke 方法之前，我们先来了解一下 langchain 中的 Context 和 RunnableConfig。
1. RunnableConfig
RunnableConfig 的定义很简单:
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class RunnableConfig(TypedDict, total=False):
"""Configuration for a Runnable."""
tags: list[str]
"""
Tags for this call and any sub-calls (eg. a Chain calling an LLM).
You can use these to filter calls.
"""
metadata: dict[str, Any]
"""
Metadata for this call and any sub-calls (eg. a Chain calling an LLM).
Keys should be strings, values should be JSON-serializable.
"""
callbacks: Callbacks
"""
Callbacks for this call and any sub-calls (eg. a Chain calling an LLM).
Tags are passed to all callbacks, metadata is passed to handle*Start callbacks.
"""
run_name: str
"""
Name for the tracer run for this call. Defaults to the name of the class.
"""
max_concurrency: Optional[int]
"""
Maximum number of parallel calls to make. If not provided, defaults to
ThreadPoolExecutor's default.
"""
recursion_limit: int
"""
Maximum number of times a call can recurse. If not provided, defaults to 25.
"""
configurable: dict[str, Any]
"""
Runtime values for attributes previously made configurable on this Runnable,
or sub-Runnables, through .configurable_fields() or .configurable_alternatives().
Check .output_schema() for a description of the attributes that have been made
configurable.
"""
run_id: Optional[uuid.UUID]
"""
Unique identifier for the tracer run for this call. If not provided, a new UUID
will be generated.
"""
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这里面大多数是元数据和控制参数，其中最重要的是 configurable 属性。通过注释可以看到，configurable 与 Runnable 的 configurable_fields、configurable_alternatives 方法有关。
