[Question]: I want to make sure that the chatbot uses the retriever only when the question is related to ingested documents. about llama_index HOT 1 OPEN

class ContextChatEngine(BaseChatEngine):
    # ... other methods ...

    def _generate_context(self, message: str) -> Tuple[str, List[NodeWithScore]]:
        """Generate context information from a message."""
        if not self._is_related_to_ingested_documents(message):
            return "The question is not related to the ingested documents.", []

        nodes = self._retriever.retrieve(message)
        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."""
        if not self._is_related_to_ingested_documents(message):
            return "The question is not related to the ingested documents.", []

        nodes = await self._retriever.aretrieve(message)
        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

    def _is_related_to_ingested_documents(self, query_str: str) -> bool:
        # Implement your logic to determine if the query is related to ingested documents
        # For example, you could check for specific keywords or use a more complex NLP model
        return True  # Placeholder implementation