How to filter messages
In more complex chains and agents we might track state with a list ofmessages. This list can start to accumulate messages from multiple different models, speakers, sub-chains, etc., and we may only want to pass subsets of this full list of messages to each model call in the chain/agent.
Thefilter_messages utility makes it easy to filter messages by type, id, or name.
Basic usage
from langchain_core.messagesimport(
AIMessage,
HumanMessage,
SystemMessage,
filter_messages,
)
messages=[
SystemMessage("you are a good assistant",id="1"),
HumanMessage("example input",id="2", name="example_user"),
AIMessage("example output",id="3", name="example_assistant"),
HumanMessage("real input",id="4", name="bob"),
AIMessage("real output",id="5", name="alice"),
]
filter_messages(messages, include_types="human")
[HumanMessage(content='example input', name='example_user', id='2'),
HumanMessage(content='real input', name='bob', id='4')]
filter_messages(messages, exclude_names=["example_user","example_assistant"])
[SystemMessage(content='you are a good assistant', id='1'),
HumanMessage(content='real input', name='bob', id='4'),
AIMessage(content='real output', name='alice', id='5')]
filter_messages(messages, include_types=[HumanMessage, AIMessage], exclude_ids=["3"])
[HumanMessage(content='example input', name='example_user', id='2'),
HumanMessage(content='real input', name='bob', id='4'),
AIMessage(content='real output', name='alice', id='5')]
Chaining
filter_messages can be used in an imperatively (like above) or declaratively, making it easy to compose with other components in a chain:
%pip install-qU langchain-anthropic
from langchain_anthropicimport ChatAnthropic
llm= ChatAnthropic(model="claude-3-sonnet-20240229", temperature=0)
# Notice we don't pass in messages. This creates
# a RunnableLambda that takes messages as input
filter_= filter_messages(exclude_names=["example_user","example_assistant"])
chain= filter_| llm
chain.invoke(messages)
AIMessage(content=[], response_metadata={'id': 'msg_01Wz7gBHahAwkZ1KCBNtXmwA', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 3}}, id='run-b5d8a3fe-004f-4502-a071-a6c025031827-0', usage_metadata={'input_tokens': 16, 'output_tokens': 3, 'total_tokens': 19})
Looking at the LangSmith trace we can see that before the messages are passed to the model they are filtered:https://smith.langchain.com/public/f808a724-e072-438e-9991-657cc9e7e253/r
Looking at just the filter_, we can see that it's a Runnable object that can be invoked like all Runnables:
filter_.invoke(messages)
[HumanMessage(content='real input', name='bob', id='4'),
AIMessage(content='real output', name='alice', id='5')]
API reference
For a complete description of all arguments head to the API reference:https://python.langchain.com/api_reference/core/messages/langchain_core.messages.utils.filter_messages.html