Prolonged execution times when using ConversationChain and ChatOpenAI
Labels: name: area: memory, name: auto:bug, name: area: models

### System Info

## Description:

### Context:
I'm using LangChain to develop an application that interacts with the gpt-3.5-turbo-16k model to handle long chains of up to 16384 tokens.

### Problem:
While the first message processes quickly **(specially if i have not previues context in the inputs)**, from the second message onward, I experience excessively long execution times, exceeding 5 minutes. On occasions, I receive timeout errors exceeding 10 minutes like the following:

`Retrying langchain.chat_models.openai.ChatOpenAI.completion_with_retry.<locals>._completion_with_retry in 1.0 seconds as it raised Timeout: Request timed out: HTTPSConnectionPool(host='api.openai.com', port=443): Read timed out. (read timeout=600).`

#### **It's worth noting that when using the OpenAI API directly with the same context and length, the response is almost immediate.**

### Relevant Code:
```
from langchain.chains.conversation.prompt import ENTITY_MEMORY_CONVERSATION_TEMPLATE
from langchain.chat_models import ChatOpenAI
from langchain.memory.entity import ConversationEntityMemory

def create_conversation_chain(inputs, num_msgs=3):
    """
    Creates the base instance for the conversation with the llm and the memory
    :param num_msgs: Number of messages to include in the memory buffer
    :return: The conversation chain instance
    """
    load_dotenv()

    llm = ChatOpenAI(
        temperature=0,
        model_name=MODEL,
        verbose=False,
    )
    memory = ConversationEntityMemory(
        llm=llm,
        k=num_msgs,
    )

    if inputs:
        for inp in inputs:
            memory.save_context(inp[0], inp[1])

    conversation = ConversationChain(
        llm=llm,
        memory=memory,
        prompt=ENTITY_MEMORY_CONVERSATION_TEMPLATE,
        verbose=True,
    )
    return conversation

conversation = create_conversation_chain(inputs=self.input_msgs_entries, num_msgs=num_msgs_to_include)
ans = self.conversation.predict(input=msg)
```
Feel free to send me questions about my code if you need to know something else, but essentialy that is what I have

### Additional Details:

1. Operating System: Windows 10
2. Python Version: 3.10
3. LangChain Version: 0.0.271
4. I've tried with the streaming=True parameter cause I saw that in other issue, but the results remain the same.

### Request:
Could you help me identify and address the cause of these prolonged execution times when using LangChain, especially compared to direct use of the OpenAI API?


Thank you very much for your help!! ^^

### Who can help?

@hwchase17 @agola11

### Information

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

### Related Components

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

### Reproduction

## Steps to Reproduce the Behavior:

1. Setup Environment:

> - Install LangChain version 0.0.271 on a Windows 10 (but i tryed it in ubuntu and same problems) machine with Python 3.10.
> - gpt-3.5-turbo-16k model.

2. Implement the Conversation Chain:

> - Utilize the create_conversation_chain function provided in the initial description.

3. Provide Context:
> - Use a context (inputs) that is long enough to approach the 6000 to 9000 (context + msg) tokens but i am also getting this time consuming responses with  lower bounds, I let you bellow one file with text (it is spanish text) that I use as an input to call gpt-3.5-turbo-16k.

4. Initialize and Predict:
> - Call the function:
> `conversation = create_conversation_chain(inputs=input_msgs_entries, num_msgs=num_msgs_to_include_in_buffer)`
> - Then, predict using:
>  `ans = conversation.predict(input=msg)`

5. Observe Delay:
> - Note that while the first message processes quickly **(specially if i have not previues context in the inputs)**, subsequent messages experience prolonged execution times, sometimes exceeding 10 minutes.
> - Occasionally, timeout errors might occur, indicating a failure in the request due to excessive waiting time.

6. Compare with Direct OpenAI API:
> - Directly send the same context and message to the OpenAI API, without using LangChain.
> - Observe that the response is almost immediate, highlighting the difference in performance. 

7. Test with Streaming:
> - Set the streaming=True parameter and observe that the prolonged execution times persist.


[test_random_conv_text.txt](https://github.com/langchain-ai/langchain/files/12437477/test_random_conv_text.txt)

### Expected behavior

## Expected Behavior:
When utilizing the create_conversation_chain function with the gpt-3.5-turbo-16k model to handle chains close to 16384 tokens:

1. **Consistent Performance:** The execution times for each message, regardless of it being the first or subsequent ones, should be relatively consistent and not show drastic differences.

2. **Reasonable Response Times:** Even for longer contexts approaching the model's token limit, the response time should be within a reasonable range, certainly not exceeding 10 minutes for a single prediction.
 
3. **No Timeout Errors:** The system should handle the requests efficiently, avoiding timeout errors, especially if the direct OpenAI API call with the same context responds almost immediately.

4. **Memory Efficiency:** The memory management system, especially when handling the context and inputs, should efficiently store and retrieve data without causing significant delays.