How to use Output parser with LLM Chain

###Use of Output parser with LLM Chain

I want to use the sequential chain of two LLm chains. The first chain is coded as below. I want to get the output of this chain as a Python list of aspects.

```
# This is an LLMChain for Aspects Extraction.

examples =[ {
"review": '''Improve the communication updates to families. Improve consistency, with housekeeping it was supposed to be weekly but now it is every 2 or 3 weeks. There is no consistency in the staff either due to the high turnover rate. Improve the services in the dining room along with meal options.''',
"aspects": '''Relevant Aspects are communication on updates, housekeeping consistency, staff turnover, and dining services.'''
},
            
{"review": '''On paper they do, but my wife has not been brought to them. I have not had a meeting to set up a plan for her. No one wheels her to partake in the activities. They need somebody there that could take them to activities if they wanted. They should bring them to activities where other people will watch over them. The people that are in charge, like the head nurse and activities director, are good about getting ahold of and answering your questions. Once you get down to the next level, they are overwhelmed. They could use another set of eyes and hands. In the memory care area, a lot of people need care at the same time.''',
"aspects": '''Relevant Aspects are Staff Attitude, Care Plan Setup, Staff Involvement in Activities, Oversight during Activities, Memory Care Area'''}
]

#Configure a formatter that will format the few shot examples into a string. This formatter should be a PromptTemplate object.
prompt_template='''
Review: {review}
{aspects}
'''
example_prompt = PromptTemplate(input_variables=["review", "aspects"], template= prompt_template, output_parser=output_parser )

final_prompt = FewShotPromptTemplate(
    examples=examples, 
    example_prompt=example_prompt, 
    suffix="Review: {review}", 
    input_variables=["review"],
    prefix="We are extracting the aspects from the review given by the residents of nursing homes. Take the Review as input and extract the different aspects about the the staff, food, \
building, activities, management, cost of the the nuursing home."
)

output=aspect_extraction_chain.predict_and_parse(review="The community has popcorn days, church, birthday celebrations, holiday parties, therapy dogs, and so much more. My mother is very happy here, and she is kept active. They do a great job of keeping the elderly minds active and involved. The dining program is great as well. My mother tends to eat slow, but the dining program always lets my mother stay to finish her food. Any residents that want to practice religion, this is also offered here! More outings have been added, they just went to Walmart recently.")
```

The current result is a string like 
'Relevant Aspects are Activities, Elderly Minds Engagement, Dining Program, Religious Offerings, Outings.'

I want the result as: ['\nActivities', 'Elderly Minds Engagement', 'Dining Program', 'Religious Offerings', 'Outings.']


### Suggestion:

Kindly guide me on How to use langchain output parser for it.