`Chatbots` use case example is not working
Labels: name: area: vector store, name: auto:bug, name: area: models

### System Info

Langchain version: 0.0.271
Platform: Ubuntu 20.04
Device: Nvidia-T4
Python version: 3.9.17


### Who can help?

@hwchase17 @agola11 


### Information

- [X] The official example notebooks/scripts
- [ ] My own modified scripts

### Related Components

- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [X] Callbacks/Tracing
- [ ] Async

### Reproduction

```
from typing import Any, Dict, List, Optional
from langchain.pydantic_v1 import Field, root_validator
from langchain.llms import VLLM
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.prompts import (
    ChatPromptTemplate,
    MessagesPlaceholder,
    SystemMessagePromptTemplate,
    HumanMessagePromptTemplate,
)
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory

class MyVLLM(VLLM):
    dtype: str = 'auto'
    vllm_kwargs: Dict[str, Any] = Field(default_factory=dict)
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that python package exists in environment."""

        try:
            from vllm import LLM as VLLModel
        except ImportError:
            raise ImportError(
                "Could not import vllm python package. "
                "Please install it with `pip install vllm`."
            )

        values["client"] = VLLModel(
            model=values["model"],
            tensor_parallel_size=values["tensor_parallel_size"],
            trust_remote_code=values["trust_remote_code"],
            dtype=values["dtype"],
            **values['vllm_kwargs']
        )

        return values

llm = MyVLLM(model="tiiuae/falcon-7b",
        trust_remote_code=True,  # mandatory for hf models
        max_new_tokens=128,
        top_k=10,
        top_p=0.95,
        temperature=0.8,
        dtype='float16',
        vllm_kwargs = {'gpu_memory_utilization': 0.98},
        callbacks=[StreamingStdOutCallbackHandler()]
)

# Prompt 
prompt = ChatPromptTemplate(
    messages=[
        SystemMessagePromptTemplate.from_template(
            "You are a nice chatbot having a conversation with a human."
        ),
        # The `variable_name` here is what must align with memory
        MessagesPlaceholder(variable_name="chat_history"),
        HumanMessagePromptTemplate.from_template("{question}")
    ]
)

# Notice that we `return_messages=True` to fit into the MessagesPlaceholder
# Notice that `"chat_history"` aligns with the MessagesPlaceholder name
memory = ConversationBufferMemory(memory_key="chat_history",return_messages=True)
conversation = LLMChain(
    llm=llm,
    prompt=prompt,
    verbose=True,
    memory=memory
)
```

### Expected behavior

I am following the `Chatbots` example [here](https://python.langchain.com/docs/use_cases/chatbots).
It's not working as expected. The responses returned are weird that not just a single LLM response is there but also some human responses. What is happening there?

![signal-2023-08-24-154224_002](https://github.com/langchain-ai/langchain/assets/140134804/02295cc8-11dc-42bb-8006-ff936befaa05)