是的，有一种方法可以在基于用户角色的上下文聊天引擎中添加元数据过滤。ContextChatEngine 类允许使用 BaseNodePostprocessor 示例，这些示例可以根据元数据和用户角色进行自定义过滤节点。
下面是一个如何实现基于用户角色的自定义 BaseNodePostprocessor 以过滤节点的示例：

from llama_index.core.postprocessor.types import BaseNodePostprocessor
from llama_index.core.schema import NodeWithScore, QueryBundle

class RoleBasedNodePostprocessor(BaseNodePostprocessor):
    def __init__(self, user_role: str):
        self.user_role = user_role

    def postprocess_nodes(self, nodes: List[NodeWithScore], query_bundle: QueryBundle) -> List[NodeWithScore]:
        # Implement your filtering logic based on self.user_role
        filtered_nodes = [node for node in nodes if self._filter_node(node)]
        return filtered_nodes

    def _filter_node(self, node: NodeWithScore) -> bool:
        # Example filtering logic based on metadata and user role
        node_metadata = node.node.metadata
        if self.user_role == 'admin':
            return True  # Admins see all nodes
        elif self.user_role == 'user':
            return node_metadata.get('visibility', 'public') == 'public'
        else:
            return False  # Default to hiding nodes for unknown roles

# Usage in ContextChatEngine
node_postprocessors = [RoleBasedNodePostprocessor(user_role='user')]
context_chat_engine = ContextChatEngine(
    retriever=retriever,
    llm=llm,
    memory=memory,
    prefix_messages=prefix_messages,
    node_postprocessors=node_postprocessors
)

在这个例子中，RoleBasedNodePostprocessor 根据 user_role 属性过滤节点。您可以自定义 _filter_node 方法以根据元数据和用户角色 1(https://github.com/run-llama/llama_index/blob/main/llama-index-core/llama_index/core/chat_engine/context.py) 实现所需的特定过滤逻辑。

• 要继续与 Dosu 交谈，请提及 @dosu。*

Same way you filter a query engine
index.as_chat_engine(..., filters=filters)

过滤器只是被传递到检索器底层。

在上面的代码中，我可以直接将过滤器作为参数添加吗？

是的，您可以直接在初始化代码中为 ContextChatEngine 添加过滤器作为参数。您需要修改 __init__ 方法以包含 filters 参数并更新相关方法以使用此参数。
以下是如何修改代码以包含过滤器的方法：

1. 将 filters 参数添加到 __init__ 方法中。
2. 更新 _generate_context 和 _agenerate_context 方法以使用 filters 参数。

class ContextChatEngine(BaseChatEngine):
    """
Context Chat Engine.

Uses a retriever to retrieve a context, set the context in the system prompt,
and then uses an LLM to generate a response, for a fluid chat experience.
"""

    def __init__(
        self,
        retriever: BaseRetriever,
        llm: LLM,
        memory: BaseMemory,
        prefix_messages: List[ChatMessage],
        node_postprocessors: Optional[List[BaseNodePostprocessor]] = None,
        context_template: Optional[str] = None,
        callback_manager: Optional[CallbackManager] = None,
        filters: Optional[List[Any]] = None,  # Add filters parameter
    ) -> None:
        self._retriever = retriever
        self._llm = llm
        self._memory = memory
        self._prefix_messages = prefix_messages
        self._node_postprocessors = node_postprocessors or []
        self._context_template = context_template or DEFAULT_CONTEXT_TEMPLATE
        self._filters = filters or []  # Initialize filters

        self.callback_manager = callback_manager or CallbackManager([])
        for node_postprocessor in self._node_postprocessors:
            node_postprocessor.callback_manager = self.callback_manager

    def _generate_context(self, message: str) -> Tuple[str, List[NodeWithScore]]:
        """Generate context information from a message."""
        nodes = self._retriever.retrieve(message, filters=self._filters)  # Use filters
        for postprocessor in self._node_postprocessors:
            nodes = postprocessor.postprocess_nodes(
                nodes, query_bundle=QueryBundle(message)
            )

        context_str = "\n\n".join(
            [n.node.get_content(metadata_mode=MetadataMode.LLM).strip() for n in nodes]
        )

        return self._context_template.format(context_str=context_str), nodes

    async def _agenerate_context(self, message: str) -> Tuple[str, List[NodeWithScore]]:
        """Generate context information from a message."""
        nodes = await self._retriever.aretrieve(message, filters=self._filters)  # Use filters
        for postprocessor in self._node_postprocessors:
            nodes = postprocessor.postprocess_nodes(
                nodes, query_bundle=QueryBundle(message)
            )
        context_str = "\n\n".join(
            [n.node.get_content(metadata_mode=MetadataMode.LLM).strip() for n in nodes]
        )

        return self._context_template.format(context_str=context_str), nodes

此修改允许您在初始化 ContextChatEngine 时直接传递过滤器，并确保在生成上下文期间使用这些过滤器。

• 要继续与 Dosu 交谈，请提及 @dosu。*