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I am using this code in order to maintain a conversation history. However, since I use Fla

@kapa.ai
I am using this code in order to maintain a conversation history. However, since I use Flask, python garbage collector frees up all values of variables after sending response to frontend. It means that values of memory and self.chat_engine are always wiped out after sending the response to the frontend. Do you have any suggestions to maintain memory and self.chat_engine considering client & servers work including Flask ?

Create a chat memory buffer for conversation history

memory = ChatMemoryBuffer.from_defaults(token_limit=3900)

# Create a chat engine
self.chat_engine = CondensePlusContextChatEngine.from_defaults(
index.as_retriever(),
memory=memory,
llm=llm,
context_prompt=(
"You are a chatbot, able to have normal interactions, as well as talk in a professional manner"
"about the attached document(s)."
"Here are the relevant documents for the context:\n"
"{context_str}"
"\nInstruction: Use the previous chat history, or the context above, to interact and help the user."
),
verbose=False,
)

return self.chat_engine
k
i
21 comments
Please stand by @ihshan :) as I search the knowledge base. I normally take about 20 seconds or so. :wave:
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In a stateless environment like Flask, maintaining state between requests can be challenging. However, there are several ways to persist data across requests in Flask:

  1. Flask Session: Flask provides a session object which is a dictionary that stores data across requests. When a client sends a request, Flask saves all the values stored in the session object and sends them to the client. When the client sends another request, Flask restores the session object with the values received from the client. However, Flask's session is not suitable for storing large objects like your chat_engine.
  1. Server-side Session: You can use server-side session libraries like Flask-Session. It allows you to specify different kinds of session interfaces. You can store the session data in a file, on a Redis server, or on other kinds of database.
  1. Database: You can use a database to store the state of your application. This could be a SQL database like PostgreSQL or MySQL, or a NoSQL database like MongoDB or Redis. You would save the state after each request and load it before processing a new request.
  1. Caching: You can use caching systems like Redis or Memcached to store your data. These systems keep your data in memory, so accessing it is very fast.
  1. Global Variables: In some cases, you might be able to store the data in global variables. However, this is generally not recommended because it can lead to problems if your application is running in multiple processes or threads, which is common in production environments.
In your case, you might want to consider using a combination of these methods. For example, you could use a database or server-side sessions to store the ChatMemoryBuffer and CondensePlusContextChatEngine objects. You would need to make sure that these objects can be serialized and deserialized correctly, which might require some additional work.

Please note that these are general suggestions and might need to be adapted to your specific use case. Also, remember that storing data across requests can have implications for the performance and scalability of your application, so it's important to choose the right method for your needs.

For more information, you can refer to the Flask documentation ().
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Do you have any other alternatives @kapa.ai assuming that I have a list of conversation history ?
Let me check the knowledge base for you. One moment! :books:
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Yes, if you have a list of conversation history, you can use it to recreate the state of your chat engine for each request. Here's how you can do it:

  1. Serialize the conversation history: After each interaction, you can serialize the conversation history (i.e., convert it to a format that can be stored or transmitted, like JSON) and send it back to the client.
  1. Store the conversation history on the client side: The client can store the conversation history in a cookie, local storage, or another client-side storage solution.
  1. Send the conversation history with each request: When the client sends a new request, it also sends the conversation history.
  1. Deserialize the conversation history: At the beginning of each request, you can deserialize the conversation history (i.e., convert it back into a Python object) and use it to recreate the state of your chat engine.
Here's an example of how you can implement this:

```python
from flask import request, jsonify
from llama_index.core.memory import ChatMemoryBuffer
from llama_index.core.chat_engine import CondensePlusContextChatEngine
from llama_index.core.message import ChatMessage
import json

@app.route('/chat', methods=['POST'])
def chat():
# Get the conversation history from the request
conversation_history_json = request.json.get('conversation_history')

# Deserialize the conversation history
conversation_history = [ChatMessage.from_dict(msg) for msg in json.loads(conversation_history_json)]

# Create a chat memory buffer and load the conversation history
memory = ChatMemoryBuffer.from_defaults(token_limit=3900)
memory.set(conversation_history)

Create a chat engine

chat_engine = CondensePlusContextChatEngine.from_defaults(
index.as_retriever(),
memory=memory,
llm=llm,
context_prompt=(
"You are a chatbot, able to have normal interactions, as well as talk"
" about the Kendrick and Drake beef."
"Here are the relevant documents for the context:\n"
"{context_str}"
"\nInstruction: Use the previous chat history, or the context above, to interact and help the user."
),
verbose=True,
)

# Process the chat message
message = request.json.get('message')
response = chat_engine.chat(message)

# Serialize the updated conversation history
conversation_history_json = json.dumps([msg.to_dict() for msg in memory.get_all()])

# Send the response and the updated conversation history
return jsonify({'response': str(response), 'conversation_history': conversation_history_json})
```

In this example, the client needs to send the conversation history with each request, and the server sends the updated conversation history with each response. The client is responsible for storing the conversation history between requests.

Please note that this is a simplified example and might need to be adapted to your specific use case. Also, remember that storing data on the client side can have security implications, so it's important to choose the right method for your needs.

For more information, you can refer to the Flask documentation () and the LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai I want to use point no.3 Send the conversation history with each request: When the client sends a new request, it also sends the conversation history.
Let me check the knowledge base for you. One moment! :books:
----------------------
Sure, you can definitely send the conversation history with each request. Here's a simplified example of how you can do this using Flask:

Plain Text
from flask import request, jsonify
from llama_index.core.memory import ChatMemoryBuffer
from llama_index.core.chat_engine import CondensePlusContextChatEngine
from llama_index.core.message import ChatMessage
import json

@app.route('/chat', methods=['POST'])
def chat():
    # Get the conversation history from the request
    conversation_history_json = request.json.get('conversation_history')

    # Deserialize the conversation history
    conversation_history = [ChatMessage.from_dict(msg) for msg in json.loads(conversation_history_json)]

    # Create a chat memory buffer and load the conversation history
    memory = ChatMemoryBuffer.from_defaults(token_limit=3900)
    memory.set(conversation_history)

    # Create a chat engine
    chat_engine = CondensePlusContextChatEngine.from_defaults(
        index.as_retriever(),
        memory=memory,
        llm=llm,
        context_prompt=(
            "You are a chatbot, able to have normal interactions, as well as talk"
            " about the Kendrick and Drake beef."
            "Here are the relevant documents for the context:\n"
            "{{context_str}}"
            "\nInstruction: Use the previous chat history, or the context above, to interact and help the user."
        ),
        verbose=True,
    )

    # Process the chat message
    message = request.json.get('message')
    response = chat_engine.chat(message)

    # Serialize the updated conversation history
    conversation_history_json = json.dumps([msg.to_dict() for msg in memory.get_all()])

    # Send the response and the updated conversation history
    return jsonify({'response': str(response), 'conversation_history': conversation_history_json})
In this example, the client needs to send the conversation history with each request, and the server sends the updated conversation history with each response. The client is responsible for storing the conversation history between requests.

Please note that this is a simplified example and might need to be adapted to your specific use case. Also, remember that storing data on the client side can have security implications, so it's important to choose the right method for your needs.

For more information, you can refer to the Flask documentation () and the LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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@kapa.ai How can I build ChatMessage object to be put in SimpleChatStore['chat_history'], considering that I have already a list of dictionary of conversation history that I have got from the frontend, such as
conversation_history = [
{'user': "How much is the cost to build a factory?"},
{'bot': "It is $500 to build a new factory"}
]

Also, in order to build ChatMessage, do I have to change the key with assistant instead of bot ?
Let me check the knowledge base for you. One moment! :books:
----------------------
Yes, you can build a ChatMessage object from your conversation history and store it in SimpleChatStore['chat_history']. The ChatMessage object requires a role and content. The role can be either "system", "user", or "assistant". In your case, you should replace "bot" with "assistant". Here's how you can do it:

Plain Text
from llama_index.core.message import ChatMessage
from llama_index.core.storage.chat_store.simple_chat_store import SimpleChatStore

# Your conversation history
conversation_history = [
    {'user': "How much is the cost to build a factory?"},
    {'bot': "It is $500 to build a new factory"}
]

# Convert the conversation history to a list of ChatMessage objects
chat_messages = []
for message in conversation_history:
    for role, content in message.items():
        if role == 'bot':
            role = 'assistant'
        chat_messages.append(ChatMessage(role=role, content=content))

# Create a SimpleChatStore and set the chat history
chat_store = SimpleChatStore()
chat_store.set_messages('chat_history', chat_messages)


In this example, we first convert the conversation history to a list of ChatMessage objects. We then create a SimpleChatStore and use the set_messages method to set the chat history.

Please note that this is a simplified example and might need to be adapted to your specific use case. Also, remember that storing data in a SimpleChatStore can have implications for the performance and scalability of your application, so it's important to choose the right method for your needs.

For more information, you can refer to the LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai Here is my code, can you please integrate that to my code

memory = ChatMemoryBuffer.from_defaults(token_limit=3900)

# Create a chat engine
self.chat_engine = CondensePlusContextChatEngine.from_defaults(
index.as_retriever(),
memory=memory,
llm=llm,
context_prompt=(
"You are a chatbot, able to have normal interactions, as well as talk in a professional manner"
"about the attached document(s)."
"Here are the relevant documents for the context:\n"
"{context_str}"
"\nInstruction: Use the previous chat history, or the context above, to interact and help the user."
),
verbose=False,
)

return self.chat_engine
Let me check the knowledge base for you. One moment! :books:
----------------------
Sure, I can help you integrate the conversation history into your code. Here's how you can do it:

Plain Text
from llama_index.core.message import ChatMessage
from llama_index.core.memory import ChatMemoryBuffer
from llama_index.core.chat_engine import CondensePlusContextChatEngine

# Your conversation history
conversation_history = [
    {'user': "How much is the cost to build a factory?"},
    {'bot': "It is $500 to build a new factory"}
]

# Convert the conversation history to a list of ChatMessage objects
chat_messages = []
for message in conversation_history:
    for role, content in message.items():
        if role == 'bot':
            role = 'assistant'
        chat_messages.append(ChatMessage(role=role, content=content))

# Create a chat memory buffer and load the conversation history
memory = ChatMemoryBuffer.from_defaults(token_limit=3900)
memory.set(chat_messages)

# Create a chat engine
self.chat_engine = CondensePlusContextChatEngine.from_defaults(
    index.as_retriever(),
    memory=memory,
    llm=llm,
    context_prompt=(
        "You are a chatbot, able to have normal interactions, as well as talk in a professional manner"
        "about the attached document(s)."
        "Here are the relevant documents for the context:\n"
        "{context_str}"
        "\nInstruction: Use the previous chat history, or the context above, to interact and help the user."
    ),
    verbose=False,
)

return self.chat_engine


In this example, we first convert the conversation history to a list of ChatMessage objects. We then create a ChatMemoryBuffer and use the set method to load the conversation history. The ChatMemoryBuffer is then passed to the CondensePlusContextChatEngine.
Please note that this is a simplified example and might need to be adapted to your specific use case. Also, remember that storing data in a ChatMemoryBuffer can have implications for the performance and scalability of your application, so it's important to choose the right method for your needs.

For more information, you can refer to the LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai
I imported this code
from llama_index.core.message import ChatMessage

But it says error
from llama_index.core.message import ChatMessage
ModuleNotFoundError: No module named 'llama_index.core.message'
Let me check the knowledge base for you. One moment! :books:
----------------------
The error message you're seeing typically means that the module you're trying to import doesn't exist. In this case, it seems like the ChatMessage class is not located in llama_index.core.message.

Based on the knowledge sources provided, the ChatMessage class is actually located in llama_index.core.llms. So, you should be able to import the ChatMessage class like this:

Plain Text
from llama_index.core.llms import ChatMessage


If you're still seeing an error after making this change, it could mean that the llama_index package is not installed or not installed correctly. You can install it using pip:

Plain Text
pip install llama-index


If you've already installed the llama_index package and are still seeing the error, it could be an issue with your Python environment. Make sure that you're using the correct Python interpreter and that the llama_index package is installed in the same environment.

For more information, you can refer to the LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
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