Different call methods

All classes inherited from Chain offer a few ways of running chain logic. The most direct one is by using __call__:

from langchain.chains.llm import LLMChain
from langchain.chat_models.openai import ChatOpenAI
from langchain_core.prompts import PromptTemplate

chat = ChatOpenAI(temperature=0)
prompt_template = "Tell me a {adjective} joke"
llm_chain = LLMChain(llm=chat, prompt=PromptTemplate.from_template(prompt_template))

llm_chain(inputs={"adjective": "corny"})

{'adjective': 'corny',
 'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}

By default, __call__ returns both the input and output key values. You can configure it to only return output key values by setting return_only_outputs to True.

llm_chain("corny", return_only_outputs=True)

{'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}

If the Chain only outputs one output key (i.e. only has one element in its output_keys), you can use run method. Note that run outputs a string instead of a dictionary.

# llm_chain only has one output key, so we can use run
llm_chain.output_keys

['text']

llm_chain.run({"adjective": "corny"})

'Why did the tomato turn red? Because it saw the salad dressing!'

In the case of one input key, you can input the string directly without specifying the input mapping.

# These two are equivalent
llm_chain.run({"adjective": "corny"})
llm_chain.run("corny")

# These two are also equivalent
llm_chain("corny")
llm_chain({"adjective": "corny"})

{'adjective': 'corny',
 'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}

Tips: You can easily integrate a Chain object as a Tool in your Agent via its run method. See an example here.