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title, aliases
| title | aliases | |
|---|---|---|
| Stanford DSPy |
|
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.
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Provides composable and declarative modules for instructing LMs in a familiar Pythonic syntax.
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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.
Installation
For the Qdrant retrieval integration, include dspy-ai with the qdrant extra:
pip install dspy-ai[qdrant]
Usage
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):
super().__init__()
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
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Find DSPy usage docs and examples here.