```python
from langchain import PromptTemplate, OpenAI, LLMChain

prompt_template = "What is a good name for a company that makes {product}?"

llm = OpenAI(temperature=0)
llm_chain = LLMChain(
    llm=llm,
    prompt=PromptTemplate.from_template(prompt_template)
)
llm_chain("colorful socks")
```

<CodeOutputBlock lang="python">

```
    {'product': 'colorful socks', 'text': '\n\nSocktastic!'}
```

</CodeOutputBlock>

## Additional ways of running LLM Chain

Aside from `__call__` and `run` methods shared by all `Chain` object, `LLMChain` offers a few more ways of calling the chain logic:

- `apply` allows you run the chain against a list of inputs:


```python
input_list = [
    {"product": "socks"},
    {"product": "computer"},
    {"product": "shoes"}
]

llm_chain.apply(input_list)
```

<CodeOutputBlock lang="python">

```
    [{'text': '\n\nSocktastic!'},
     {'text': '\n\nTechCore Solutions.'},
     {'text': '\n\nFootwear Factory.'}]
```

</CodeOutputBlock>

- `generate` is similar to `apply`, except it return an `LLMResult` instead of string. `LLMResult` often contains useful generation such as token usages and finish reason.


```python
llm_chain.generate(input_list)
```

<CodeOutputBlock lang="python">

```
    LLMResult(generations=[[Generation(text='\n\nSocktastic!', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nTechCore Solutions.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nFootwear Factory.', generation_info={'finish_reason': 'stop', 'logprobs': None})]], llm_output={'token_usage': {'prompt_tokens': 36, 'total_tokens': 55, 'completion_tokens': 19}, 'model_name': 'text-davinci-003'})
```

</CodeOutputBlock>

- `predict` is similar to `run` method except that the input keys are specified as keyword arguments instead of a Python dict.


```python
# Single input example
llm_chain.predict(product="colorful socks")
```

<CodeOutputBlock lang="python">

```
    '\n\nSocktastic!'
```

</CodeOutputBlock>


```python
# Multiple inputs example

template = """Tell me a {adjective} joke about {subject}."""
prompt = PromptTemplate(template=template, input_variables=["adjective", "subject"])
llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0))

llm_chain.predict(adjective="sad", subject="ducks")
```

<CodeOutputBlock lang="python">

```
    '\n\nQ: What did the duck say when his friend died?\nA: Quack, quack, goodbye.'
```

</CodeOutputBlock>

## Parsing the outputs

By default, `LLMChain` does not parse the output even if the underlying `prompt` object has an output parser. If you would like to apply that output parser on the LLM output, use `predict_and_parse` instead of `predict` and `apply_and_parse` instead of `apply`. 

With `predict`:


```python
from langchain.output_parsers import CommaSeparatedListOutputParser

output_parser = CommaSeparatedListOutputParser()
template = """List all the colors in a rainbow"""
prompt = PromptTemplate(template=template, input_variables=[], output_parser=output_parser)
llm_chain = LLMChain(prompt=prompt, llm=llm)

llm_chain.predict()
```

<CodeOutputBlock lang="python">

```
    '\n\nRed, orange, yellow, green, blue, indigo, violet'
```

</CodeOutputBlock>

With `predict_and_parser`:


```python
llm_chain.predict_and_parse()
```

<CodeOutputBlock lang="python">

```
    ['Red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet']
```

</CodeOutputBlock>

## Initialize from string

You can also construct an LLMChain from a string template directly.


```python
template = """Tell me a {adjective} joke about {subject}."""
llm_chain = LLMChain.from_string(llm=llm, template=template)
```


```python
llm_chain.predict(adjective="sad", subject="ducks")
```

<CodeOutputBlock lang="python">

```
    '\n\nQ: What did the duck say when his friend died?\nA: Quack, quack, goodbye.'
```

</CodeOutputBlock>
