How to run custom functions
This guide assumes familiarity with the following concepts:
You can use arbitrary functions as Runnables. This is useful for formatting or when you need functionality not provided by other LangChain components, and custom functions used as Runnables are called RunnableLambdas.
Note that all inputs to these functions need to be a SINGLE argument. If you have a function that accepts multiple arguments, you should write a wrapper that accepts a single dict input and unpacks it into multiple argument.
This guide will cover:
- How to explicitly create a runnable from a custom function using the
RunnableLambdaconstructor and the convenience@chaindecorator - Coercion of custom functions into runnables when used in chains
- How to accept and use run metadata in your custom function
- How to stream with custom functions by having them return generators
Using the constructor
Below, we explicitly wrap our custom logic using the RunnableLambda constructor:
%pip install -qU langchain langchain_openai
import os
from getpass import getpass
os.environ["OPENAI_API_KEY"] = getpass()
from operator import itemgetter
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableLambda
from langchain_openai import ChatOpenAI
def length_function(text):
return len(text)
def _multiple_length_function(text1, text2):
return len(text1) * len(text2)
def multiple_length_function(_dict):
return _multiple_length_function(_dict["text1"], _dict["text2"])
model = ChatOpenAI()
prompt = ChatPromptTemplate.from_template("what is {a} + {b}")
chain1 = prompt | model
chain = (
{
"a": itemgetter("foo") | RunnableLambda(length_function),
"b": {"text1": itemgetter("foo"), "text2": itemgetter("bar")}
| RunnableLambda(multiple_length_function),
}
| prompt
| model
)
chain.invoke({"foo": "bar", "bar": "gah"})
AIMessage(content='3 + 9 equals 12.', response_metadata={'token_usage': {'completion_tokens': 8, 'prompt_tokens': 14, 'total_tokens': 22}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-73728de3-e483-49e3-ad54-51bd9570e71a-0')
The convenience @chain decorator
You can also turn an arbitrary function into a chain by adding a @chain decorator. This is functionaly equivalent to wrapping the function in a RunnableLambda constructor as shown above. Here's an example:
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import chain
prompt1 = ChatPromptTemplate.from_template("Tell me a joke about {topic}")
prompt2 = ChatPromptTemplate.from_template("What is the subject of this joke: {joke}")
@chain
def custom_chain(text):
prompt_val1 = prompt1.invoke({"topic": text})
output1 = ChatOpenAI().invoke(prompt_val1)
parsed_output1 = StrOutputParser().invoke(output1)
chain2 = prompt2 | ChatOpenAI() | StrOutputParser()
return chain2.invoke({"joke": parsed_output1})
custom_chain.invoke("bears")
'The subject of the joke is the bear and his girlfriend.'
Above, the @chain decorator is used to convert custom_chain into a runnable, which we invoke with the .invoke() method.
If you are using a tracing with LangSmith, you should see a custom_chain trace in there, with the calls to OpenAI nested underneath.
Automatic coercion in chains
When using custom functions in chains with the pipe operator (|), you can omit the RunnableLambda or @chain constructor and rely on coercion. Here's a simple example with a function that takes the output from the model and returns the first five letters of it:
prompt = ChatPromptTemplate.from_template("tell me a story about {topic}")
model = ChatOpenAI()
chain_with_coerced_function = prompt | model | (lambda x: x.content[:5])
chain_with_coerced_function.invoke({"topic": "bears"})
'Once '
Note that we didn't need to wrap the custom function (lambda x: x.content[:5]) in a RunnableLambda constructor because the model on the left of the pipe operator is already a Runnable. The custom function is coerced into a runnable. See this section for more information.
Passing run metadata
Runnable lambdas can optionally accept a RunnableConfig parameter, which they can use to pass callbacks, tags, and other configuration information to nested runs.
import json
from langchain_core.runnables import RunnableConfig
def parse_or_fix(text: str, config: RunnableConfig):
fixing_chain = (
ChatPromptTemplate.from_template(
"Fix the following text:\n\n```text\n{input}\n```\nError: {error}"
" Don't narrate, just respond with the fixed data."
)
| model
| StrOutputParser()
)
for _ in range(3):
try:
return json.loads(text)
except Exception as e:
text = fixing_chain.invoke({"input": text, "error": e}, config)
return "Failed to parse"
from langchain_community.callbacks import get_openai_callback
with get_openai_callback() as cb:
output = RunnableLambda(parse_or_fix).invoke(
"{foo: bar}", {"tags": ["my-tag"], "callbacks": [cb]}
)
print(output)
print(cb)
{'foo': 'bar'}
Tokens Used: 62
Prompt Tokens: 56
Completion Tokens: 6
Successful Requests: 1
Total Cost (USD): $9.6e-05
from langchain_community.callbacks import get_openai_callback
with get_openai_callback() as cb:
output = RunnableLambda(parse_or_fix).invoke(
"{foo: bar}", {"tags": ["my-tag"], "callbacks": [cb]}
)
print(output)
print(cb)
{'foo': 'bar'}
Tokens Used: 62
Prompt Tokens: 56
Completion Tokens: 6
Successful Requests: 1
Total Cost (USD): $9.6e-05
Streaming
You can use generator functions (ie. functions that use the yield keyword, and behave like iterators) in a chain.
The signature of these generators should be Iterator[Input] -> Iterator[Output]. Or for async generators: AsyncIterator[Input] -> AsyncIterator[Output].
These are useful for:
- implementing a custom output parser
- modifying the output of a previous step, while preserving streaming capabilities
Here's an example of a custom output parser for comma-separated lists. First, we create a chain that generates such a list as text:
from typing import Iterator, List
prompt = ChatPromptTemplate.from_template(
"Write a comma-separated list of 5 animals similar to: {animal}. Do not include numbers"
)
str_chain = prompt | model | StrOutputParser()
for chunk in str_chain.stream({"animal": "bear"}):
print(chunk, end="", flush=True)
lion, tiger, wolf, gorilla, panda
Next, we define a custom function that will aggregate the currently streamed output and yield it when the model generates the next comma in the list:
# This is a custom parser that splits an iterator of llm tokens
# into a list of strings separated by commas
def split_into_list(input: Iterator[str]) -> Iterator[List[str]]:
# hold partial input until we get a comma
buffer = ""
for chunk in input:
# add current chunk to buffer
buffer += chunk
# while there are commas in the buffer
while "," in buffer:
# split buffer on comma
comma_index = buffer.index(",")
# yield everything before the comma
yield [buffer[:comma_index].strip()]
# save the rest for the next iteration
buffer = buffer[comma_index + 1 :]
# yield the last chunk
yield [buffer.strip()]
list_chain = str_chain | split_into_list
for chunk in list_chain.stream({"animal": "bear"}):
print(chunk, flush=True)
['lion']
['tiger']
['wolf']
['gorilla']
['raccoon']
Invoking it gives a full array of values:
list_chain.invoke({"animal": "bear"})
['lion', 'tiger', 'wolf', 'gorilla', 'raccoon']
Async version
If you are working in an async environment, here is an async version of the above example:
from typing import AsyncIterator
async def asplit_into_list(
input: AsyncIterator[str],
) -> AsyncIterator[List[str]]: # async def
buffer = ""
async for (
chunk
) in input: # `input` is a `async_generator` object, so use `async for`
buffer += chunk
while "," in buffer:
comma_index = buffer.index(",")
yield [buffer[:comma_index].strip()]
buffer = buffer[comma_index + 1 :]
yield [buffer.strip()]
list_chain = str_chain | asplit_into_list
async for chunk in list_chain.astream({"animal": "bear"}):
print(chunk, flush=True)
['lion']
['tiger']
['wolf']
['gorilla']
['panda']
await list_chain.ainvoke({"animal": "bear"})
['lion', 'tiger', 'wolf', 'gorilla', 'panda']
Next steps
Now you've learned a few different ways to use custom logic within your chains, and how to implement streaming.
To learn more, see the other how-to guides on runnables in this section.