In this lesson, you will learn about chains and how to use them to create reusable components.
Chains allows you to combine language models with different data sources and third-party APIs.
Using LLMChain
The simplest chain is an LLMChain. An LLMChain combines a prompt template with an LLM and returns a response.
Previously, you created a program that used a prompt template and an LLM to generate a response about fruit.
Click to reveal the code for the program.
from langchain_openai import OpenAI
from langchain.prompts import PromptTemplate
llm = OpenAI(openai_api_key="sk-...")
template = PromptTemplate(template="""
You are a cockney fruit and vegetable seller.
Your role is to assist your customer with their fruit and vegetable needs.
Respond using cockney rhyming slang.
Tell me about the following fruit: {fruit}
""", input_variables=["fruit"])
response = llm.invoke(template.format(fruit="apple"))
print(response)You can combine this program into a chain and create a reusable component.
from langchain_openai import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
llm = OpenAI(openai_api_key="sk-...")
template = PromptTemplate.from_template("""
You are a cockney fruit and vegetable seller.
Your role is to assist your customer with their fruit and vegetable needs.
Respond using cockney rhyming slang.
Tell me about the following fruit: {fruit}
""")
llm_chain = LLMChain(
llm=llm,
prompt=template
)
response = llm_chain.invoke({"fruit": "apple"})
print(response)Note how the llm_chain.invoke method accepts the template parameters as a dictionary.
response = llm_chain.invoke({"fruit": "apple"})The response is a dictionary containing the template parameters and a text key containing the response from the LLM.
{'fruit': 'apple', 'text': 'Well, mate, apples and pears are what I sell...'}
Output Parsers
You can specify that a chain uses a specific output parser to parse the output of the LLM.
Setting the output_parser parameter to StrOutputParser on the LLMChain would ensure the response is a string.
from langchain.schema import StrOutputParser
llm_chain = LLMChain(
llm=llm,
prompt=template,
output_parser=StrOutputParser()
)You can change the prompt to instruct the LLM to return a specific output type. For example, return JSON by specifying Output JSON and give a format in the prompt:
template = PromptTemplate.from_template("""
You are a cockney fruit and vegetable seller.
Your role is to assist your customer with their fruit and vegetable needs.
Respond using cockney rhyming slang.
Output JSON as {{"description": "your response here"}}
Tell me about the following fruit: {fruit}
""")You can ensure Langchain parses the response as JSON by specifying SimpleJsonOutputParser as the output_parser:
from langchain.output_parsers.json import SimpleJsonOutputParser
llm_chain = LLMChain(
llm=llm,
prompt=template,
output_parser=SimpleJsonOutputParser()
)The benefits of using chains are:
-
Modularity: LangChain provides many modules that can be used to build language model applications. These modules can be used as stand-alones in simple applications and they can be combined for more complex use cases.
-
Customizability: Most LangChain applications allow you to configure the LLM and/or the prompt used, so knowing how to take advantage of this will be a big enabler.
-
Ease of Use: The components are designed to be easy to use, regardless of whether you are using the rest of the LangChain framework or not.
-
Standard Interface: LangChain provides a standard interface for chains, enabling developers to create sequences of calls that go beyond a single LLM call.
Check Your Understanding
Chains
True or False - Chains are used to create modular and reusable components.
-
✓ True
-
❏ False
Hint
Two benefits of using chains are that they are customizable and implement a standard interface.
Solution
The statement is True. You can use Chains to create modular and reusable components.
Lesson Summary
In this lesson, you learned about LLM chains and how they can group a prompt, LLM, and output parser.
In the next lesson, you will learn about chat models.