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消息 "Messages passed in must be of odd length" 的出现是因为 LlamaIndex 框架中的 react 聊天模式要求消息序列的长度必须是奇数。这是 react 模式的一个特定要求，而你的当前实现并未满足这个要求。
要解决这个问题，请确保传递给 react 聊天模式的消息序列长度是奇数。你可以通过在将消息传递给聊天引擎之前添加一个检查并根据需要调整消息长度来实现这一点。
以下是如何修改你的代码以确保消息长度为奇数的示例：

if prompt := st.chat_input("Your question"):
    add_to_message_history("user", prompt)

    # Ensure the message length is odd
    if len(prompt) % 2 == 0:
        prompt += " "  # Add a space to make the length odd

    # Display the new question immediately after it is entered
    with st.chat_message("user"):
        st.write(prompt)

    # If last message is not from assistant, generate a new response
    with st.chat_message("assistant"):
        response = st.session_state["chat_engine"].stream_chat(prompt)
        response_str = ""
        response_container = st.empty()
        for token in response.response_gen:
            response_str += token
            response_container.write(response_str)
        add_to_message_history("assistant", response.response)

    # Save the state of the generator
    st.session_state["response_gen"] = response.response_gen

这个修改确保在将消息传递给聊天引擎之前，消息长度是奇数，这应该可以防止出现 NotImplementedError 的情况。

• 要继续与 Dosu 交谈，请提及 @dosu。*

一些Huggingface模型对消息传递的工作方式非常严格。很难处理，需要更新类以强制消息长度为奇数。

我添加了一个方法来确保消息是奇数，但我仍然遇到了问题。似乎对于消息序列进行了奇数检查，而不是对消息本身进行检查。

我打印了'messages',它产生了一个包含两个ChatMessage的数组，一个来自系统，另一个来自用户。
[ChatMessage(role=<MessageRole.SYSTEM: 'system'>, content='You are designed to help with a variety of tasks, from answering questions to providing summaries to other types of analyses.

Tools

You have access to a wide variety of tools. You are responsible for using the tools in any sequence you deem appropriate to complete the task at
hand.
This may require breaking the task into subtasks and using different tools to complete each subtask.

You
have access to the following tools:

Tool Name: query_engine_tool
Tool Description: Useful for running a natural language query
against a knowledge base and get back a natural language response.

Tool Args: {"type": "object", "properties": {"input": {"title": "Input", "type": "string"}}, "required": ["input"]}

Output Format

Please answer in the same language as the question and use the following format:

\nThought: The current language of the user is: (user\'s language). I need to use a tool to help me answer the question.\nAction: tool name (one of query_engine_tool) if using a tool.\nAction Input: the input to the tool, in a JSON format representing the kwargs (e.g. {"input": "hello world", "num_beams": 5})\n

Please ALWAYS start with a Thought.

Please use a valid JSON format for the Action Input. Do NOT do this {'input': 'hello world', 'num_beams': 5}.

If this format is used, the user will respond in the following format:

\nObservation: tool response\n

You should keep repeating the above format till you have enough information to answer the question without using any more tools. At that point, you MUST respond in the one of the following two formats:

\nThought: I can answer without using any more tools. I\'ll use the user\'s language to answer\nAnswer: [your answer here (In the same language as the user\'s question)]\n

\nThought: I cannot answer the question with the provided tools.\nAnswer: [your answer here (In the same language as the user\'s question)]\n

Current Conversation

Below is the current conversation consisting of interleaving human and assistant messages.
', additional_kwargs={}), ChatMessage(role=<MessageRole.USER: 'user'>, content='who is paul? ', additional_kwargs={})]
可能在llama_index\llms\huggingface\base.py实现中存在问题。

NotImplementedError 消息为 "传入的消息必须为奇数长度" ,这是因为 LlamaIndex 框架中的 react 聊天模式要求消息序列的长度必须为奇数。这是 react 模式的一个特定要求，而在您当前的实现中并未满足。

要解决此问题，您需要确保传递给 chat 方法的消息序列长度为奇数。错误是由于消息序列(而非单个消息)的长度必须为奇数而引发的。

以下是如何修改代码以确保序列长度为奇数的方法：

1. 在调用 chat 方法之前，检查消息序列的长度。
2. 如果长度为偶数，则添加一个虚拟消息使其变为奇数。

以下是一个示例实现：

def 

from typing import List
from llama_index.legacy.core.llms.types import ChatMessage, MessageRole

def ensure_odd_length(messages: List[ChatMessage]) -> List[ChatMessage]:
if len(messages) % 2 == 0:
# Add a dummy message to make the length odd
messages.append(ChatMessage(role=MessageRole.SYSTEM, content="Dummy message"))
return messages

Example usage

messages = [
ChatMessage(role=MessageRole.SYSTEM, content="System message content"),
ChatMessage(role=MessageRole.USER, content="User message content")
] # This is even length

messages = ensure_odd_length(messages) # This will make it odd length

response = chat_engine.chat(messages)

():
    # 此函数 `ensure_odd_length` 检查消息的长度，如果长度为偶数，则添加一个虚拟消息，确保在将其传递给 `chat` 方法 [[1]](https://github.com/run-llama/llama_index/blob/main/llama-index-core/tests/memory/test_chat_summary_memory_buffer.py)[[2]](https://github.com/run-llama/llama_index/blob/main/llama-index-legacy/tests/llms/test_llama_utils.py) 之前，序列长度始终为奇数。
    pass

• 要继续与 Dosu 对话，请提及 @dosu。*

Implement ensure odd message and now I get another exception
File "C:\Users\lenovo\AppData\Local\pypoetry\Cache\virtualenvs\simplerag-GtP-BaND-py3.12\Lib\site-packages\llama_index\llms\huggingface\base.py", line 450, in chat_messages_to_conversational_kwargs
raise NotImplementedError(
NotImplementedError: Didn't handle when messages aren't ordered in alternating pairs of (<MessageRole.USER: 'user'>, <MessageRole.ASSISTANT: 'assistant'>).
I had to modify the below line in chat_memory_buffer.py as m.content was throwing an error.

I guess it's too much to handle to make this work.
@logan-markewich@dosu

要解决与消息顺序相关的 NotImplementedError 问题，您需要确保消息是按照交替的 <MessageRole.USER: 'user'> 和 <MessageRole.ASSISTANT: 'assistant'> 对进行排序的。您可以使用 merge_neighboring_same_role_messages 函数来帮助维护这种顺序。
以下是如何使用 merge_neighboring_same_role_messages 函数的方法：

from llama_index.legacy.core.llms.types import ChatMessage, MessageRole
from llama_index.legacy.llms.gemini_utils import merge_neighboring_same_role_messages

def ensure_alternating_pairs(messages: List[ChatMessage]) -> List[ChatMessage]:
    # Merge neighboring messages with the same role
    merged_messages = merge_neighboring_same_role_messages(messages)
    
    # Ensure the messages are in alternating pairs
    for i in range(0, len(merged_messages) - 1, 2):
        if merged_messages[i].role != MessageRole.USER or merged_messages[i + 1].role != MessageRole.ASSISTANT:
            raise ValueError("Messages are not in alternating pairs of USER and ASSISTANT.")
    
    return merged_messages

# Example usage
messages = [
    ChatMessage(role=MessageRole.SYSTEM, content="System message content"),
    ChatMessage(role=MessageRole.USER, content="User message content"),
    ChatMessage(role=MessageRole.ASSISTANT, content="Assistant message content")
]

messages = ensure_alternating_pairs(messages)

response = chat_engine.chat(messages)

此函数首先合并具有相同角色的相邻消息，然后检查以确保消息是按照交替的 USER 和 ASSISTANT 对进行排序的。如果消息顺序不正确，它会引发一个 ValueError 错误。
通过采用这种方法，您可以确保消息序列满足 chat_messages_to_conversational_kwargs 函数的要求，并避免出现 NotImplementedError1(https://github.com/run-llama/llama_index/blob/main/llama-index-core/llama_index/core/utilities/gemini_utils.py)2(https://github.com/run-llama/llama_index/blob/main/llama-index-legacy/llama_index/legacy/llms/gemini_utils.py) 错误。

• 要继续与 Dosu 对话，请提及 @dosu。*

在实现以下异常时，弹出了while循环。
文件 "C:\Users\lenovo\AppData\Local\pypoetry\Cache\virtualenvs\simplerag-GtP-BaND-py3.12\Lib\site-packagesllama_index\legacyllms\gemini_utils.py",第116行，in merge_neighboring_same_role_messages
merged_message = ChatMessage(
^^^^^^^^^^^^
文件 "C:\Users\lenovo\AppData\Local\pypoetry\Cachevirtualenvs\simplerag-GtP-BaND-py3.12\Lib\site-packages\pydantic\v1\main.py",第341行，in init
raise validation_error
pydantic.v1.error_wrappers.ValidationError:对于ChatMessage有1个验证错误
additional_kwargs
none不是允许的值(类型=type_error.none.not_allowed)
@logan-markewich@dosu

您遇到的 ValidationError 表示 ChatMessage 类中的 additional_kwargs 字段不能是 None 。这个字段必须是一个字典，即使它是空的。
要解决这个问题，确保如果没有提供 additional_kwargs 字段， additional_kwargs 字段始终被初始化为空字典。以下是如何修改 merge_neighboring_same_role_messages 函数以处理此问题的示例：

from typing import Sequence
from llama_index.legacy.core.llms.types import ChatMessage, MessageRole

def merge_neighboring_same_role_messages(
    messages: Sequence[ChatMessage],
) -> Sequence[ChatMessage]:
    # Gemini does not support multiple messages of the same role in a row, so we merge them
    merged_messages = []
    i = 0

    while i < len(messages):
        current_message = messages[i]
        # Initialize merged content with current message content
        merged_content = [current_message.content]

        # Check if the next message exists and has the same role
        while (
            i + 1 < len(messages)
            and messages[i + 1].role == current_message.role
        ):
            i += 1
            next_message = messages[i]
            merged_content.extend([next_message.content])

        # Ensure additional_kwargs is not None
        additional_kwargs = current_message.additional_kwargs or {}

        # Create a new ChatMessage with merged content
        merged_message = ChatMessage(
            role=current_message.role,
            content="\n".join([str(msg_content) for msg_content in merged_content]),
            additional_kwargs=additional_kwargs,
        )
        merged_messages.append(merged_message)
        i += 1

    return merged_messages

此修改确保 additional_kwargs 始终是一个字典，从而防止 ValidationError 的发生。

• 要继续与 Dosu 对话，请提及 @dosu。*