Stanford DSPy

DSPy is the framework for solving advanced tasks with language models (LMs) and retrieval models (RMs). It unifies techniques for prompting and fine-tuning LMs — and approaches for reasoning, self-improvement, and augmentation with retrieval and tools.

  • Provides composable and declarative modules for instructing LMs in a familiar Pythonic syntax.

  • Introduces an automatic compiler that teaches LMs how to conduct the declarative steps in your program.

Qdrant can be used as a retrieval mechanism in the DSPy flow.


For the Qdrant retrieval integration, include dspy-ai with the qdrant extra:

pip install dspy-ai[qdrant]


We can configure DSPy settings to use the Qdrant retriever model like so:

import dspy
from dspy.retrieve.qdrant_rm import QdrantRM

from qdrant_client import QdrantClient

turbo = dspy.OpenAI(model="gpt-3.5-turbo")
qdrant_client = QdrantClient()  # Defaults to a local instance at http://localhost:6333/
qdrant_retriever_model = QdrantRM("collection-name", qdrant_client, k=3)

dspy.settings.configure(lm=turbo, rm=qdrant_retriever_model)

Using the retriever is pretty simple. The dspy.Retrieve(k) module will search for the top-k passages that match a given query.

retrieve = dspy.Retrieve(k=3)
question = "Some question about my data"
topK_passages = retrieve(question).passages

print(f"Top {retrieve.k} passages for question: {question} \n", "\n")

for idx, passage in enumerate(topK_passages):
    print(f"{idx+1}]", passage, "\n")

With Qdrant configured as the retriever for contexts, you can set up a DSPy module like so:

class RAG(dspy.Module):
    def __init__(self, num_passages=3):

        self.retrieve = dspy.Retrieve(k=num_passages)

    def forward(self, question):
        context = self.retrieve(question).passages

With the generic RAG blueprint now in place, you can add the many interactions offered by DSPy with context retrieval powered by Qdrant.

Next steps

Find DSPy usage docs and examples here.