The memory did not record the full input message, it only record the input parameter values
Labels: name: auto:bug

### System Info

Langchina:  0.0.221,
OS: WSL
Python 3.11

### Who can help?

The memory did not record the full input message, it only record the input parameter values

Below is a simple code to use the default ConversationBufferMemory.

++++++++++++++++++++++++++
llm = conn.connecting.get_model_azureopenai()
    mem = langchain.memory.ConversationBufferMemory()

    msg1 = langchain.prompts.HumanMessagePromptTemplate.from_template("Hi, I am {name}. How do you pronounce my name?")
    prompt1 = langchain.prompts.ChatPromptTemplate.from_messages([msg1])
    print("Input message=", prompt1.format(name="Cao"))

    chain1 = langchain.chains.LLMChain(
        llm=llm,
        prompt=prompt1,
        memory=mem,
    )

    resp1 = chain1.run({"name": "Cao"})
    print(resp1)
    print("+"*100)
    print(mem)

+++++++++++++++++++++++++++++++++++

But the real memory only recorded like below:
chat_memory=ChatMessageHistory(messages=[HumanMessage(content='Cao', additional_kwargs={}, example=False), AIMessage(content='Hello Cao! Your name is pronounced as "Ts-ow" with the "Ts" sound similar to the beginning of "tsunami" and "ow" rhyming with "cow."', additional_kwargs={}, example=False)]) output_key=None input_key=None return_messages=False human_prefix='Human' ai_prefix='AI' memory_key='history'


It should record the full input, not only the parameter itself:
Input message= Human: Hi, I am Cao. How do you pronounce my name?


### Information

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

### Related Components

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

### Reproduction


Below is a simple code to use the default ConversationBufferMemory.

++++++++++++++++++++++++++
llm = conn.connecting.get_model_azureopenai()
    mem = langchain.memory.ConversationBufferMemory()

    msg1 = langchain.prompts.HumanMessagePromptTemplate.from_template("Hi, I am {name}. How do you pronounce my name?")
    prompt1 = langchain.prompts.ChatPromptTemplate.from_messages([msg1])
    print("Input message=", prompt1.format(name="Cao"))

    chain1 = langchain.chains.LLMChain(
        llm=llm,
        prompt=prompt1,
        memory=mem,
    )

    resp1 = chain1.run({"name": "Cao"})
    print(resp1)
    print("+"*100)
    print(mem)

+++++++++++++++++++++++++++++++++++

But the real memory only recorded like below:
chat_memory=ChatMessageHistory(messages=[HumanMessage(content='Cao', additional_kwargs={}, example=False), AIMessage(content='Hello Cao! Your name is pronounced as "Ts-ow" with the "Ts" sound similar to the beginning of "tsunami" and "ow" rhyming with "cow."', additional_kwargs={}, example=False)]) output_key=None input_key=None return_messages=False human_prefix='Human' ai_prefix='AI' memory_key='history'


It should record the full input, not only the parameter itself:
Input message= Human: Hi, I am Cao. How do you pronounce my name?

### Expected behavior


It should record the full input, not only the parameter itself:
Input message= Human: Hi, I am Cao. How do you pronounce my name?