[Question]: How do I prioritize exact matches in my retriever? (BM25 or any other) about llama_index HOT 1 OPEN

1. You can experiment with the k1 parameter in BM25 by using the rank_bm25 library, which the BM25Retriever class from LlamaIndex relies on. Here is an example of how you can modify the BM25Retriever class to include the k1 parameter:

   from rank_bm25 import BM25Okapi
   
   class BM25Retriever(BaseRetriever):
       def __init__(
           self,
           nodes: List[BaseNode],
           tokenizer: Optional[Callable[[str], List[str]]],
           similarity_top_k: int = DEFAULT_SIMILARITY_TOP_K,
           k1: float = 1.5,  # Default value for k1
           callback_manager: Optional[CallbackManager] = None,
           objects: Optional[List[IndexNode]] = None,
           object_map: Optional[dict] = None,
           verbose: bool = False,
       ) -> None:
           try:
               from rank_bm25 import BM25Okapi
           except ImportError:
               raise ImportError("Please install rank_bm25: pip install rank-bm25")
   
           self._nodes = nodes
           self._tokenizer = tokenizer or tokenize_remove_stopwords
           self._similarity_top_k = similarity_top_k
           self._corpus = [self._tokenizer(node.get_content()) for node in self._nodes]
           self.bm25 = BM25Okapi(self._corpus, k1=k1)  # Set k1 parameter here
           super().__init__(
               callback_manager=callback_manager,
               object_map=object_map,
               objects=objects,
               verbose=verbose,
           )

2. For a retrieval engine that is more biased towards text matches rather than term frequency, you might consider using a vector-based retrieval method such as those provided by dense embedding models (e.g., Sentence Transformers). These models focus more on semantic similarity rather than term frequency. LlamaIndex supports vector-based retrieval methods, which can be more effective for capturing semantic meaning in text. You can explore using the VectorStoreIndex class in LlamaIndex for this purpose ^[1][2].

Additionally, you can use the MetadataFilters class to handle exact matches in VectorStoreIndex. Here is an example:

```python
from llama_index.legacy.vector_stores.types import VectorStoreQuery, VectorStoreQueryMode, MetadataFilters, MetadataFilter, FilterOperator

# Create exact match filters
filters = MetadataFilters(filters=[
    MetadataFilter(key="field_name", value="exact_value", operator=FilterOperator.EQ)
])

# Create a query with the TEXT_SEARCH mode to prioritize exact matches
query = VectorStoreQuery(
    query_str="your query",
    mode=VectorStoreQueryMode.TEXT_SEARCH,
    filters=filters
)

# Execute the query
result = vector_store.query(query)
```

This configuration ensures that the query prioritizes exact matches by using the TEXT_SEARCH mode and applying the exact match filters ^[3].

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