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QdrantRM

Qdrant is an open-source, high-performance vector search engine/database written in Rust. It can be used to retrieve semantically relevant passages to pass as context to your language model.

You can refer to the end-to-end example demonstrating the use of DSPy and Qdrant.

Setting Up QdrantRM​

QdrantRM can be instantiated with any custom vectorizer and configured to return any payload field.

  • qdrant_collection_name (str): The name of the Qdrant collection.
  • qdrant_client (QdrantClient): An instance of qdrant_client.QdrantClient.
  • k (int, optional): The default number of top passages to retrieve. Default: 3.
  • document_field (str, optional): The key in the Qdrant payload with the content. Default: "document".
  • vectorizer (BaseSentenceVectorizer, optional): An implementation sentence_vectorizer.BaseSentenceVectorizer. Default: sentence_vectorizer.FastEmbedVectorizer.
  • vector_name (str, optional): Name of the vector in the collection. Default: The first available vector name.

Under the Hood​

forward(self, query_or_queries: Union[str, list[str]], k: Optional[int] = None, filter: Optional[models.Filter] = None) -> dspy.Prediction​

Parameters:

  • query_or_queries (Union[str, List[str]]): The query or queries to search for.
  • k (Optional[int]): The number of top passages to retrieve. Defaults to self.k.
  • filter (Optional[qdrant_client.models.Filter]): "Only include points satisfying the filter conditions". Default: None.

Returns:

  • dspy.Prediction: Contains the retrieved passages, each represented as a dotdict with a long_text attribute.

Example Usage​

import os

from qdrant_client import QdrantClient

import dspy
from dsp.modules.sentence_vectorizer import OpenAIVectorizer
from dspy.retrieve.qdrant_rm import QdrantRM

os.environ["OPENAI_API_KEY"] = "<YOUR_OPENAI_API_KEY>"

client = QdrantClient(url="http://localhost:6333/")
vectorizer = OpenAIVectorizer(model="text-embedding-3-small")

qdrant_retriever = QdrantRM(
    qdrant_client=client,
    qdrant_collection_name="{collection_name}",
    vectorizer=vectorizer,
    document_field="text",
)

dspy.settings.configure(rm=qdrant_retriever)
retrieve = dspy.Retrieve()

retrieve("Some computer programs.")