forked from Archives/langchain
38 lines
1.3 KiB
Markdown
38 lines
1.3 KiB
Markdown
# LLM Chains
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Calling an LLM is a great first step, but it's just the beginning.
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Normally when you use an LLM in an application, you are not sending user input directly to the LLM.
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Instead, you are probably taking user input and constructing a prompt, and then sending that to the LLM.
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For example, in the previous example, the text we passed in was hardcoded to ask for a name for a company that made colorful socks.
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In this imaginary service, what we would want to do is take only the user input describing what the company does, and then format the prompt with that information.
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This is easy to do with LangChain!
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First lets define the prompt:
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```python
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from langchain.prompts import PromptTemplate
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prompt = PromptTemplate(
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input_variables=["product"],
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template="What is a good name for a company that makes {product}?",
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)
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```
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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:
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```python
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from langchain.chains import LLMChain
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chain = LLMChain(llm=llm, prompt=prompt)
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```
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Now we can run that chain only specifying the product!
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```python
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chain.run("colorful socks")
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```
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There we go! There's the first chain - an LLM Chain.
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This is one of the simpler types of chains, but understanding how it works will set you up well for working with more complex chains.
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