[Bug]: BM25 Retriever - Corpus uses default MetadataMode while reading content from nodes instead of MetadataMode.EMBED or user provided option about llama_index HOT 1 OPEN

To address the issue where the BM25 Retriever does not utilize MetadataMode.EMBED or a user-provided option for metadata handling, you can extend the BM25Retriever class to support metadata. This involves modifying the retriever to include an additional parameter for metadata handling and ensuring that both the BM25 and vector index retrievers handle metadata consistently.

Here's a concise approach to modify the BM25Retriever:

1. Extend BM25Retriever: Add a use_metadata boolean flag to indicate whether to use metadata.
2. Uniform Metadata Handling: Ensure the vector index retriever also supports metadata in a similar manner.
3. Metadata Preprocessing: Implement a step to combine text with metadata before indexing and retrieval.
4. Query Expansion: Incorporate metadata into queries for consistent retrieval.
5. Custom Retrieval Logic: Adjust the retrieval methods to consider metadata in scoring.
6. Hybrid Model Integration: Ensure metadata handling is consistent across both retrievers.

Example modification for BM25Retriever:

class BM25Retriever(BaseRetriever):
    def __init__(
        self,
        use_metadata: bool = False,  # New parameter
        # other parameters
    ) -> None:
        self._use_metadata = use_metadata
        # Initialization code
        self._corpus = [self._build_corpus_entry(node) for node in self._nodes]
        # Additional initialization

    def _build_corpus_entry(self, node: BaseNode) -> List[str]:
        content = self._tokenizer(node.get_content())
        if self._use_metadata and node.metadata:
            metadata_content = ' '.join([f"{k}:{v}" for k, v in node.metadata.items()])
            content += self._tokenizer(metadata_content)
        return content

This approach ensures that metadata is consistently handled across both the BM25 and vector index retrievers, improving the accuracy and relevance of search results in a hybrid retrieval model.

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