Run custom functions | Документация для разработчиков

Run custom functions

Обновлено 24 мая 2024

You can use arbitrary functions in the pipeline.

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 input and unpacks it into multiple argument.

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"])

prompt = ChatPromptTemplate.from_template("what is {a} + {b}")
model = ChatOpenAI()

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.')

Accepting a Runnable Config

Runnable lambdas can optionally accept a RunnableConfig, which they can use to pass callbacks, tags, and other configuration information to nested runs.

from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnableConfig
import json

def parse_or_fix(text: str, config: RunnableConfig):
fixing_chain = (
"Fix the following text:\n\n```text\n{input}\n```\nError: {error}"
" Don't narrate, just respond with the fixed data."
| ChatOpenAI()
| StrOutputParser()
for _ in range(3):
return json.loads(text)
except Exception as e:
text = fixing_chain.invoke({"input": text, "error": e}, config)
return "Failed to parse"
from langchain.callbacks import get_openai_callback

with get_openai_callback() as cb:
output = RunnableLambda(parse_or_fix).invoke(
"{foo: bar}", {"tags": ["my-tag"], "callbacks": [cb]}
    {'foo': 'bar'}
Tokens Used: 65
Prompt Tokens: 56
Completion Tokens: 9
Successful Requests: 1
Total Cost (USD): $0.00010200000000000001
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