Capturing all content from Output Parser / OpenAI Function Calling
Labels: name: area: embeddings, name: auto:question, name: auto:improvement

### System Info

Langchain Version: 0.0.207


### Who can help?

Is there a way to get the whole output with Output Parser or OpenAI function calling?

I have a simple prompt where I get the LLM to output responses to a set of questions, and I would like to get a structured response that separates the question number and the response to the question generated by the LLM.

I have been testing it out with Structured Output Parsers but when I use it, the answers I get are often a shortened version of the original answer. This depends on the description I put in the answer_schema below. I've tried a variety of descriptions to capture as much of the original response as possible, but it is always lacking a lot of detail.

```
question_number_schema = ResponseSchema(name = "question_number", 
                                        description = "Question Number, e.g. 1, 2, 3", type = "number")

answer_schema = ResponseSchema(name = "answer", 
                                        description = "Detailed Response")
response_schemas = [question_number_schema, answer_schema]
```

### Information

- [ ] The official example notebooks/scripts
- [ ] My own modified scripts

### Related Components

- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async

### Reproduction

For example, if my original output without the Structured Output Parser was:
```
'''
Question 3: Unsupervised fine-tuning involves updating a pre-existing language model using unstructured datasets such as research papers, articles, forums, or websites. This approach allows the language model to learn patterns and vocabularies within the data without the need for explicit human labeling.
Question 4: Supervised fine-tuning leverages labeled samples of data to train the language model. These labeled samples consist of input prompts paired with corresponding desired outputs, providing explicit guidance on the desired structure or behavior. This approach is used when specific outputs or classifications are required, such as text classification.
'''
```

I'm currently getting something like:
```
{"Question 3": "Unsupervised fine-tuning updates pre-existing language models using unstructured datasets.",
"Question 4": "Supervised fine-tuning trains language models with labeled samples."}
```

### Expected behavior

I'd like to get something like this:
```
{"Question 3": "Unsupervised fine-tuning involves updating a pre-existing language model using unstructured datasets such as research papers, articles, forums, or websites. This approach allows the language model to learn patterns and vocabularies within the data without the need for explicit human labeling.",
"Question 4": "Supervised fine-tuning leverages labeled samples of data to train the language model. These labeled samples consist of input prompts paired with corresponding desired outputs, providing explicit guidance on the desired structure or behavior. This approach is used when specific outputs or classifications are required, such as text classification."}
```

This isn't the exact example I'm using, but the idea is similar.