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LCEL makes it easy to build complex chains from basic components, and supports out of the box functionality such as streaming, parallelism, and logging.

Basic example: prompt + model + output parser​

The most basic and common use case is chaining a prompt template and a model together. To see how this works, let’s create a chain that takes a topic and generates a joke:

from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

prompt = ChatPromptTemplate.from_template("tell me a short joke about {topic}")
model = ChatOpenAI()
output_parser = StrOutputParser()

chain = prompt | model | output_parser

chain.invoke({"topic": "ice cream"})
"Why did the ice cream go to therapy?\n\nBecause it had too many toppings and couldn't find its cone-fidence!"

Notice this line of this code, where we piece together then different components into a single chain using LCEL:

chain = prompt | model | output_parser

The | symbol is similar to a unix pipe operator, which chains together the different components feeds the output from one component as input into the next component.

In this chain the user input is passed to the prompt template, then the prompt template output is passed to the model, then the model output is passed to the output parser. Let’s take a look at each component individually to really understand what’s going on.

1. Prompt​

prompt is a BasePromptTemplate, which means it takes in a dictionary of template variables and produces a PromptValue. A PromptValue is a wrapper around a completed prompt that can be passed to either an LLM (which takes a string as input) or ChatModel (which takes a sequence of messages as input). It can work with either language model type because it defines logic both for producing BaseMessages and for producing a string.

prompt_value = prompt.invoke({"topic": "ice cream"})
prompt_value
ChatPromptValue(messages=[HumanMessage(content='tell me a short joke about ice cream')])
prompt_value.to_messages()
[HumanMessage(content='tell me a short joke about ice cream')]
prompt_value.to_string()
'Human: tell me a short joke about ice cream'

2. Model​

The PromptValue is then passed to model. In this case our model is a ChatModel, meaning it will output a BaseMessage.

message = model.invoke(prompt_value)
message
AIMessage(content="Why did the ice cream go to therapy? \n\nBecause it had too many toppings and couldn't find its cone-fidence!")

If our model was an LLM, it would output a string.

from langchain.llms import OpenAI

llm = OpenAI(model="gpt-3.5-turbo-instruct")
llm.invoke(prompt_value)
'\n\nRobot: Why did the ice cream go to therapy? Because it had a rocky road.'

3. Output parser​

And lastly we pass our model output to the output_parser, which is a BaseOutputParser meaning it takes either a string or a BaseMessage as input. The StrOutputParser specifically simple converts any input into a string.

output_parser.invoke(message)
"Why did the ice cream go to therapy? \n\nBecause it had too many toppings and couldn't find its cone-fidence!"

4. Entire Pipeline​

To follow the steps along:

  1. We pass in user input on the desired topic as {"topic": "ice cream"}
  2. The prompt component takes the user input, which is then used to construct a PromptValue after using the topic to construct the prompt.
  3. The model component takes the generated prompt, and passes into the OpenAI LLM model for evaluation. The generated output from the model is a ChatMessage object.
  4. Finally, the output_parser component takes in a ChatMessage, and transforms this into a Python string, which is returned from the invoke method.

Note that if you’re curious about the output of any components, you can always test out a smaller version of the chain such as prompt or prompt | model to see the intermediate results:

input = {"topic": "ice cream"}

prompt.invoke(input)
# > ChatPromptValue(messages=[HumanMessage(content='tell me a short joke about ice cream')])

(prompt | model).invoke(input)
# > AIMessage(content="Why did the ice cream go to therapy?\nBecause it had too many toppings and couldn't cone-trol itself!")

RAG Search Example​

For our next example, we want to run a retrieval-augmented generation chain to add some context when responding to questions.

# Requires:
# pip install langchain docarray tiktoken

from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.prompts import ChatPromptTemplate
from langchain.vectorstores import DocArrayInMemorySearch
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnableParallel, RunnablePassthrough

vectorstore = DocArrayInMemorySearch.from_texts(
    ["harrison worked at kensho", "bears like to eat honey"],
    embedding=OpenAIEmbeddings(),
)
retriever = vectorstore.as_retriever()

template = """Answer the question based only on the following context:
{context}

Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)
model = ChatOpenAI()
output_parser = StrOutputParser()

setup_and_retrieval = RunnableParallel(
    {"context": retriever, "question": RunnablePassthrough()}
)
chain = setup_and_retrieval | prompt | model | output_parser

chain.invoke("where did harrison work?")

In this case, the composed chain is:

chain = setup_and_retrieval | prompt | model | output_parser

To explain this, we first can see that the prompt template above takes in context and question as values to be substituted in the prompt. Before building the prompt template, we want to retrieve relevant documents to the search and include them as part of the context.

As a preliminary step, we’ve setup the retriever using an in memory store, which can retrieve documents based on a query. This is a runnable component as well that can be chained together with other components, but you can also try to run it separately:

retriever.invoke("where did harrison work?")

We then use the RunnableParallel to prepare the expected inputs into the prompt by using the entries for the retrieved documents as well as the original user question, using the retriever for document search, and RunnablePassthrough to pass the user’s question:

setup_and_retrieval = RunnableParallel(
    {"context": retriever, "question": RunnablePassthrough()}
)

To review, the complete chain is:

setup_and_retrieval = RunnableParallel(
    {"context": retriever, "question": RunnablePassthrough()}
)
chain = setup_and_retrieval | prompt | model | output_parser

With the flow being:

  1. The first steps create a RunnableParallel object with two entries. The first entry, context will include the document results fetched by the retriever. The second entry, question will contain the user’s original question. To pass on the question, we use RunnablePassthrough to copy this entry.
  2. Feed the dictionary from the step above to the prompt component. It then takes the user input which is question as well as the retrieved document which is context to construct a prompt and output a PromptValue.
  3. The model component takes the generated prompt, and passes into the OpenAI LLM model for evaluation. The generated output from the model is a ChatMessage object.
  4. Finally, the output_parser component takes in a ChatMessage, and transforms this into a Python string, which is returned from the invoke method.

Next steps​

We recommend reading our Why use LCEL section next to see a side-by-side comparison of the code needed to produce common functionality with and without LCEL.