Serialize a "not implemented" object.
Check if the given class is a subclass of Pydantic BaseModel.
Check if the given class is a subclass of any of the following:
pydantic.BaseModel in Pydantic 2.xpydantic.v1.BaseModel in Pydantic 2.xCheck if a string is a valid UUID.
Convert the data of a node to a string.
Convert the data of a node to a JSON-serializable format.
Runnable that can be serialized to JSON.
Protocol for objects that can be converted to a string.
Dictionary of labels for nodes and edges in a graph.
Edge in a graph.
Node in a graph.
Branch in a graph.
Enum for different curve styles supported by Mermaid.
Schema for Hexadecimal color codes for different node types.
Enum for different draw methods supported by Mermaid.
Graph of nodes and edges.
A unit of work that can be invoked, batched, streamed, transformed and composed.
invoke/ainvoke: Transforms a single input into an output.batch/abatch: Efficiently transforms multiple inputs into outputs.stream/astream: Streams output from a single input as it's produced.astream_log: Streams output and selected intermediate results from an
input.Built-in optimizations:
Batch: By default, batch runs invoke() in parallel using a thread pool executor. Override to optimize batching.
Async: Methods with 'a' prefix are asynchronous. By default, they execute
the sync counterpart using asyncio's thread pool.
Override for native async.
All methods accept an optional config argument, which can be used to configure execution, add tags and metadata for tracing and debugging etc.
Runnables expose schematic information about their input, output and config via
the input_schema property, the output_schema property and config_schema
method.
Runnable objects can be composed together to create chains in a declarative way.
Any chain constructed this way will automatically have sync, async, batch, and streaming support.
The main composition primitives are RunnableSequence and RunnableParallel.
RunnableSequence invokes a series of runnables sequentially, with
one Runnable's output serving as the next's input. Construct using
the | operator or by passing a list of runnables to RunnableSequence.
RunnableParallel invokes runnables concurrently, providing the same input
to each. Construct it using a dict literal within a sequence or by passing a
dict to RunnableParallel.
For example,
from langchain_core.runnables import RunnableLambda
# A RunnableSequence constructed using the `|` operator
sequence = RunnableLambda(lambda x: x + 1) | RunnableLambda(lambda x: x * 2)
sequence.invoke(1) # 4
sequence.batch([1, 2, 3]) # [4, 6, 8]
# A sequence that contains a RunnableParallel constructed using a dict literal
sequence = RunnableLambda(lambda x: x + 1) | {
"mul_2": RunnableLambda(lambda x: x * 2),
"mul_5": RunnableLambda(lambda x: x * 5),
}
sequence.invoke(1) # {'mul_2': 4, 'mul_5': 10}All Runnables expose additional methods that can be used to modify their
behavior (e.g., add a retry policy, add lifecycle listeners, make them
configurable, etc.).
These methods will work on any Runnable, including Runnable chains
constructed by composing other Runnables.
See the individual methods for details.
For example,
from langchain_core.runnables import RunnableLambda
import random
def add_one(x: int) -> int:
return x + 1
def buggy_double(y: int) -> int:
"""Buggy code that will fail 70% of the time"""
if random.random() > 0.3:
print('This code failed, and will probably be retried!') # noqa: T201
raise ValueError('Triggered buggy code')
return y * 2
sequence = (
RunnableLambda(add_one) |
RunnableLambda(buggy_double).with_retry( # Retry on failure
stop_after_attempt=10,
wait_exponential_jitter=False
)
)
print(sequence.input_schema.model_json_schema()) # Show inferred input schema
print(sequence.output_schema.model_json_schema()) # Show inferred output schema
print(sequence.invoke(2)) # invoke the sequence (note the retry above!!)As the chains get longer, it can be useful to be able to see intermediate results to debug and trace the chain.
You can set the global debug flag to True to enable debug output for all chains:
from langchain_core.globals import set_debug
set_debug(True)Alternatively, you can pass existing or custom callbacks to any given chain:
from langchain_core.tracers import ConsoleCallbackHandler
chain.invoke(..., config={"callbacks": [ConsoleCallbackHandler()]})For a UI (and much more) checkout LangSmith.
A unit of work that can be invoked, batched, streamed, transformed and composed.
invoke/ainvoke: Transforms a single input into an output.batch/abatch: Efficiently transforms multiple inputs into outputs.stream/astream: Streams output from a single input as it's produced.astream_log: Streams output and selected intermediate results from an
input.Built-in optimizations:
Batch: By default, batch runs invoke() in parallel using a thread pool executor. Override to optimize batching.
Async: Methods with 'a' prefix are asynchronous. By default, they execute
the sync counterpart using asyncio's thread pool.
Override for native async.
All methods accept an optional config argument, which can be used to configure execution, add tags and metadata for tracing and debugging etc.
Runnables expose schematic information about their input, output and config via
the input_schema property, the output_schema property and config_schema
method.
Runnable objects can be composed together to create chains in a declarative way.
Any chain constructed this way will automatically have sync, async, batch, and streaming support.
The main composition primitives are RunnableSequence and RunnableParallel.
RunnableSequence invokes a series of runnables sequentially, with
one Runnable's output serving as the next's input. Construct using
the | operator or by passing a list of runnables to RunnableSequence.
RunnableParallel invokes runnables concurrently, providing the same input
to each. Construct it using a dict literal within a sequence or by passing a
dict to RunnableParallel.
For example,
from langchain_core.runnables import RunnableLambda
# A RunnableSequence constructed using the `|` operator
sequence = RunnableLambda(lambda x: x + 1) | RunnableLambda(lambda x: x * 2)
sequence.invoke(1) # 4
sequence.batch([1, 2, 3]) # [4, 6, 8]
# A sequence that contains a RunnableParallel constructed using a dict literal
sequence = RunnableLambda(lambda x: x + 1) | {
"mul_2": RunnableLambda(lambda x: x * 2),
"mul_5": RunnableLambda(lambda x: x * 5),
}
sequence.invoke(1) # {'mul_2': 4, 'mul_5': 10}All Runnables expose additional methods that can be used to modify their
behavior (e.g., add a retry policy, add lifecycle listeners, make them
configurable, etc.).
These methods will work on any Runnable, including Runnable chains
constructed by composing other Runnables.
See the individual methods for details.
For example,
from langchain_core.runnables import RunnableLambda
import random
def add_one(x: int) -> int:
return x + 1
def buggy_double(y: int) -> int:
"""Buggy code that will fail 70% of the time"""
if random.random() > 0.3:
print('This code failed, and will probably be retried!') # noqa: T201
raise ValueError('Triggered buggy code')
return y * 2
sequence = (
RunnableLambda(add_one) |
RunnableLambda(buggy_double).with_retry( # Retry on failure
stop_after_attempt=10,
wait_exponential_jitter=False
)
)
print(sequence.input_schema.model_json_schema()) # Show inferred input schema
print(sequence.output_schema.model_json_schema()) # Show inferred output schema
print(sequence.invoke(2)) # invoke the sequence (note the retry above!!)As the chains get longer, it can be useful to be able to see intermediate results to debug and trace the chain.
You can set the global debug flag to True to enable debug output for all chains:
from langchain_core.globals import set_debug
set_debug(True)Alternatively, you can pass existing or custom callbacks to any given chain:
from langchain_core.tracers import ConsoleCallbackHandler
chain.invoke(..., config={"callbacks": [ConsoleCallbackHandler()]})For a UI (and much more) checkout LangSmith.