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DSPy-Qdrant

Qdrant powered custom retriever module for DSPy.

Installation

pip install dspy-qdrant

Usage

from dspy_qdrant import QdrantRM

The QdrantRM class enables semantic search by retrieving the top-k most relevant documents from a Qdrant collection using a sentence embedding model.

Parameters

Name Type Description Default
qdrant_collection_name str Name of the Qdrant collection used for retrieval. Required
qdrant_client QdrantClient An initialized instance of qdrant_client.QdrantClient. Required
k int Number of top documents to retrieve per query. 3
document_field str Field in the Qdrant payload that contains the raw document content. "document"
vectorizer BaseSentenceVectorizer Embedding model for vectorizing queries. Uses FastEmbedVectorizer if not provided. None
vector_name str Name of the vector field in Qdrant collection to use for search. Defaults to the first found. None

Use in a Module's forward() Function

import dspy

from qdrant_client import QdrantClient
from dspy_qdrant import QdrantRM

qdrant_client = QdrantClient()

class MyModule(dspy.Module):
    def __init__(self, num_passages: int = 5):
        super().__init__()
        self.num_passages = num_passages

    def forward(self, question: str):
        retrieve = QdrantRM(
            qdrant_collection_name="my_collection_name",
            qdrant_client=qdrant_client,
            k=self.num_passages
        )
        # Do something with results...

📚 See Also

Metadata

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