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ishaan-jaff 2023-07-29 07:06:56 -07:00
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docs/snippets/modules/chains/get_started.mdx
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#### Using `LLMChain`
The `LLMChain` is most basic building block chain. It 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.
```python
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
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.
```python
from langchain.chains import LLMChain
chain = LLMChain(llm=llm, prompt=prompt)
# Run the chain only specifying the input variable.
print(chain.run("colorful socks"))
```
<CodeOutputBlock lang="python">
```
Colorful Toes Co.
```
</CodeOutputBlock>
If there are multiple variables, you can input them all at once using a dictionary.
```python
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"
}))
```
<CodeOutputBlock lang="python">
```
Socktopia Colourful Creations.
```
</CodeOutputBlock>
You can use a chat model in an `LLMChain` as well:
```python
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"))
```
<CodeOutputBlock lang="python">
```
Rainbow Socks Co.
```
</CodeOutputBlock>