langchain/docs/modules/chains/getting_started.ipynb

Getting Started

In this tutorial, we will learn about creating simple chains in LangChain. We will learn how to create a chain, add components to it, and run it.

In this tutorial, we will cover:

• Using a simple LLM chain
• Creating sequential chains
• Creating a custom chain

Why do we need chains?

Chains allow us to combine multiple components together to create a single, coherent application. For example, we can create a chain that takes user input, formats it with a PromptTemplate, and then passes the formatted response to an LLM. We can build more complex chains by combining multiple chains together, or by combining chains with other components.

Quick start: Using LLMChain

The LLMChain is a simple chain that takes in a prompt template, formats it with the user input and returns the response from an LLM.

To use the LLMChain, first create a prompt template.

In [1]:

from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI

llm = OpenAI(temperature=0.9)
prompt = PromptTemplate(
    input_variables=["product"],
    template="What is a good name for a company that makes {product}?",
)

We can now create a very simple chain that will take user input, format the prompt with it, and then send it to the LLM.

In [2]:

from langchain.chains import LLMChain
chain = LLMChain(llm=llm, prompt=prompt)

# Run the chain only specifying the input variable.
print(chain.run("colorful socks"))

Colorful Toes Co.

If there are multiple variables, you can input them all at once using a dictionary.

In [3]:

prompt = PromptTemplate(
    input_variables=["company", "product"],
    template="What is a good name for {company} that makes {product}?",
)
chain = LLMChain(llm=llm, prompt=prompt)
print(chain.run({
    'company': "ABC Startup",
    'product': "colorful socks"
    }))

Socktopia Colourful Creations.

You can use a chat model in an LLMChain as well:

In [4]:

from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
)
human_message_prompt = HumanMessagePromptTemplate(
        prompt=PromptTemplate(
            template="What is a good name for a company that makes {product}?",
            input_variables=["product"],
        )
    )
chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt])
chat = ChatOpenAI(temperature=0.9)
chain = LLMChain(llm=chat, prompt=chat_prompt_template)
print(chain.run("colorful socks"))

Rainbow Socks Co.

Different ways of calling chains

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

In [5]:

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"})

Out [5]:

{'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.

In [6]:

llm_chain("corny", return_only_outputs=True)

Out [6]:

{'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.

In [7]:

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

Out [7]:

['text']

In [8]:

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

Out [8]:

'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.

In [9]:

# 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"})

Out [9]:

{'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.

Add memory to chains

Chain supports taking a BaseMemory object as its memory argument, allowing Chain object to persist data across multiple calls. In other words, it makes Chain a stateful object.

In [10]:

from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory

conversation = ConversationChain(
    llm=chat,
    memory=ConversationBufferMemory()
)

conversation.run("Answer briefly. What are the first 3 colors of a rainbow?")
# -> The first three colors of a rainbow are red, orange, and yellow.
conversation.run("And the next 4?")
# -> The next four colors of a rainbow are green, blue, indigo, and violet.

Out [10]:

'The next four colors of a rainbow are green, blue, indigo, and violet.'

Essentially, BaseMemory defines an interface of how langchain stores memory. It allows reading of stored data through load_memory_variables method and storing new data through save_context method. You can learn more about it in Memory section.

Debug Chain

It can be hard to debug Chain object solely from its output as most Chain objects involve a fair amount of input prompt preprocessing and LLM output post-processing. Setting verbose to True will print out some internal states of the Chain object while it is being ran.

In [11]:

conversation = ConversationChain(
    llm=chat,
    memory=ConversationBufferMemory(),
    verbose=True
)
conversation.run("What is ChatGPT?")

Out [11]:

> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

Current conversation:

Human: What is ChatGPT?
AI:

> Finished chain.

'ChatGPT is an AI language model developed by OpenAI. It is based on the GPT-3 architecture and is capable of generating human-like responses to text prompts. ChatGPT has been trained on a massive amount of text data and can understand and respond to a wide range of topics. It is often used for chatbots, virtual assistants, and other conversational AI applications.'

Combine chains with the SequentialChain

The next step after calling a language model is to make a series of calls to a language model. We can do this using sequential chains, which are chains that execute their links in a predefined order. Specifically, we will use the SimpleSequentialChain. This is the simplest type of a sequential chain, where each step has a single input/output, and the output of one step is the input to the next.

In this tutorial, our sequential chain will:

1. First, create a company name for a product. We will reuse the LLMChain we'd previously initialized to create this company name.
2. Then, create a catchphrase for the product. We will initialize a new LLMChain to create this catchphrase, as shown below.

In [12]:

second_prompt = PromptTemplate(
    input_variables=["company_name"],
    template="Write a catchphrase for the following company: {company_name}",
)
chain_two = LLMChain(llm=llm, prompt=second_prompt)

Now we can combine the two LLMChains, so that we can create a company name and a catchphrase in a single step.

In [13]:

from langchain.chains import SimpleSequentialChain
overall_chain = SimpleSequentialChain(chains=[chain, chain_two], verbose=True)

# Run the chain specifying only the input variable for the first chain.
catchphrase = overall_chain.run("colorful socks")
print(catchphrase)

> Entering new SimpleSequentialChain chain...
Rainbow Socks Co.


"Put a little rainbow in your step!"

> Finished chain.


"Put a little rainbow in your step!"

Create a custom chain with the Chain class

LangChain provides many chains out of the box, but sometimes you may want to create a custom chain for your specific use case. For this example, we will create a custom chain that concatenates the outputs of 2 LLMChains.

In order to create a custom chain:

1. Start by subclassing the Chain class,
2. Fill out the input_keys and output_keys properties,
3. Add the _call method that shows how to execute the chain.

These steps are demonstrated in the example below:

In [14]:

from langchain.chains import LLMChain
from langchain.chains.base import Chain

from typing import Dict, List


class ConcatenateChain(Chain):
    chain_1: LLMChain
    chain_2: LLMChain

    @property
    def input_keys(self) -> List[str]:
        # Union of the input keys of the two chains.
        all_input_vars = set(self.chain_1.input_keys).union(set(self.chain_2.input_keys))
        return list(all_input_vars)

    @property
    def output_keys(self) -> List[str]:
        return ['concat_output']

    def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
        output_1 = self.chain_1.run(inputs)
        output_2 = self.chain_2.run(inputs)
        return {'concat_output': output_1 + output_2}

Now, we can try running the chain that we called.

In [15]:

prompt_1 = PromptTemplate(
    input_variables=["product"],
    template="What is a good name for a company that makes {product}?",
)
chain_1 = LLMChain(llm=llm, prompt=prompt_1)

prompt_2 = PromptTemplate(
    input_variables=["product"],
    template="What is a good slogan for a company that makes {product}?",
)
chain_2 = LLMChain(llm=llm, prompt=prompt_2)

concat_chain = ConcatenateChain(chain_1=chain_1, chain_2=chain_2)
concat_output = concat_chain.run("colorful socks")
print(f"Concatenated output:\n{concat_output}")

Concatenated output:


Funky Footwear Company

"Brighten Up Your Day with Our Colorful Socks!"

That's it! For more details about how to do cool things with Chains, check out the how-to guide for chains.