How to return similarity score from a langchain hybrid search retriever?

[lingvisa]

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Example Code

from langchain_milvus.retrievers import MilvusCollectionHybridSearchRetriever
retriever = MilvusCollectionHybridSearchRetriever(
        collection=collection,
        #rerank=WeightedRanker(0.5, 0.5),
        rerank=RRFRanker(),
        anns_fields=["dense_vector", "sparse_vector"],
        field_embeddings=[dense_embedding_func, sparse_embedding_func],
        field_search_params=[dense_search_params, sparse_search_params],
        top_k=top_k,
        text_field='text',
    )

Description

In is there a way for me return the similarity score?

docs = retriever.aget_relevant_documents(question)

This only return a list of docs, but without similarity scores. In contrast, this following function return similarity scores:

from langchain_milvus import Milvus
retriever = Milvus(
        dense_embedding_func,
        collection_name=collection_name,
        vector_field="dense_vector",
        consistency_level="Strong",
    )
retriever.asimilarity_search(question, k=top_k)

So my question is, for MilvusCollectionHybridSearchRetriever, can I also get similarity scores with returned documents?

System Info

langchain==0.2.15
langchain-aws==0.1.7
langchain-community==0.2.10
langchain-core==0.2.36
langchain-google-vertexai==1.0.8
langchain-milvus==0.1.4
langchain-openai==0.1.23
langchain-text-splitters==0.2.2

[dosubot[bot]]

To return similarity scores from a MilvusCollectionHybridSearchRetriever in LangChain, you can use the similarity_search_with_score method. Here is an example:

results = retriever.similarity_search_with_score(query="your_query", k=top_k)
for doc, score in results:
    print(f"* [SIM={score:.3f}] {doc.page_content} [{doc.metadata}]")

This will output the documents along with their similarity scores. Make sure you have set up your retriever as shown in the integration details:

sparse_search_params = {"metric_type": "IP"}
dense_search_params = {"metric_type": "IP", "params": {}}
retriever = MilvusCollectionHybridSearchRetriever(
    collection=collection,
    rerank=RRFRanker(),
    anns_fields=["dense_vector", "sparse_vector"],
    field_embeddings=[dense_embedding_func, sparse_embedding_func],
    field_search_params=[dense_search_params, sparse_search_params],
    top_k=top_k,
    text_field='text',
)

For more detailed documentation, refer to the API reference ^[1][2].

To continue talking to Dosu, mention @dosu.

[lingvisa]

@dosu, But if you look at its API, this retriever doesn't have the function 'similarity_search_with_score()'

[dosubot[bot]]

The MilvusCollectionHybridSearchRetriever does indeed have a method called similarity_search_with_score that returns both documents and their similarity scores. Here is the relevant method:

def similarity_search_with_score(
    self,
    query: str,
    k: int = 4,
    param: Optional[dict] = None,
    expr: Optional[str] = None,
    timeout: Optional[float] = None,
    **kwargs: Any,
) -> List[Tuple[Document, float]]:
    """Perform a search on a query string and return results with score.

    For more information about the search parameters, take a look at the pymilvus
    documentation found here:
    https://milvus.io/api-reference/pymilvus/v2.4.x/ORM/Collection/search.md

    Args:
        query (str): The text being searched.
        k (int, optional): The amount of results to return. Defaults to 4.
        param (dict): The search params for the specified index.
            Defaults to None.
        expr (str, optional): Filtering expression. Defaults to None.
        timeout (float, optional): How long to wait before timeout error.
            Defaults to None.
        kwargs: Collection.search() keyword arguments.

    Returns:
        List[Tuple[Document, float]]: Result doc and score.
    """
    if self.col is None:
        logger.debug("No existing collection to search.")
        return []

    # Embed the query text.
    embedding = self.embedding_func.embed_query(query)
    timeout = self.timeout or timeout
    res = self.similarity_search_with_score_by_vector(
        embedding=embedding, k=k, param=param, expr=expr, timeout=timeout, **kwargs
    )
    return res

You can use this method to retrieve documents along with their similarity scores:

results = retriever.similarity_search_with_score(query="your_query", k=top_k)
for doc, score in results:
    print(f"* [SIM={score:.3f}] {doc.page_content} [{doc.metadata}]")

This will output the documents along with their similarity scores ^[1].

To continue talking to Dosu, mention @dosu.

[lingvisa]

@dosu You are wrong. The function you mentioned is from this class, not from the hybrid search class.

retriever = Milvus(
        dense_embedding_func,
        collection_name=collection_name,
        vector_field="dense_vector",
        consistency_level="Strong",
    )

[lingvisa]

@dosu You are wrong. The function you mentioned is from this class, not from the hybrid search class.

retriever = Milvus(
dense_embedding_func,
collection_name=collection_name,
vector_field="dense_vector",
consistency_level="Strong",
)

[dosubot[bot]]

You are correct. The MilvusCollectionHybridSearchRetriever class does not have a similarity_search_with_score method. Instead, you can use the _get_relevant_documents method to retrieve documents, but it does not return similarity scores directly.

To achieve this, you can modify the _get_relevant_documents method to include similarity scores in the returned results. Here is an example of how you can do this:

from langchain_milvus.retrievers import MilvusCollectionHybridSearchRetriever

class CustomMilvusCollectionHybridSearchRetriever(MilvusCollectionHybridSearchRetriever):
    def _get_relevant_documents_with_scores(self, query: str, **kwargs: Any):
        requests = self._build_ann_search_requests(query)
        search_result = self.collection.hybrid_search(
            requests, self.rerank, limit=self.top_k, output_fields=self.output_fields
        )
        documents = self._process_search_result(search_result)
        scores = [result.score for result in search_result[0]]
        return list(zip(documents, scores))

# Usage
retriever = CustomMilvusCollectionHybridSearchRetriever(
    collection=collection,
    rerank=RRFRanker(),
    anns_fields=["dense_vector", "sparse_vector"],
    field_embeddings=[dense_embedding_func, sparse_embedding_func],
    field_search_params=[dense_search_params, sparse_search_params],
    top_k=top_k,
    text_field='text',
)

results = retriever._get_relevant_documents_with_scores(query="your_query")
for doc, score in results:
    print(f"* [SIM={score:.3f}] {doc.page_content} [{doc.metadata}]")

This custom retriever class will return documents along with their similarity scores.

To continue talking to Dosu, mention @dosu.