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72 lines
2.2 KiB
Markdown
72 lines
2.2 KiB
Markdown
---
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title: Stanford DSPy
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weight: 1500
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aliases: [ ../integrations/dspy/ ]
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---
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# Stanford DSPy
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[DSPy](https://github.com/stanfordnlp/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.
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Qdrant can be used as a retrieval mechanism in the DSPy flow.
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## Installation
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For the Qdrant retrieval integration, include `dspy-ai` with the `qdrant` extra:
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```bash
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pip install dspy-ai[qdrant]
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```
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## Usage
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We can configure `DSPy` settings to use the Qdrant retriever model like so:
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```python
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import dspy
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from dspy.retrieve.qdrant_rm import QdrantRM
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from qdrant_client import QdrantClient
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turbo = dspy.OpenAI(model="gpt-3.5-turbo")
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qdrant_client = QdrantClient() # Defaults to a local instance at http://localhost:6333/
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qdrant_retriever_model = QdrantRM("collection-name", qdrant_client, k=3)
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dspy.settings.configure(lm=turbo, rm=qdrant_retriever_model)
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```
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Using the retriever is pretty simple. The `dspy.Retrieve(k)` module will search for the top-k passages that match a given query.
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```python
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retrieve = dspy.Retrieve(k=3)
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question = "Some question about my data"
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topK_passages = retrieve(question).passages
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print(f"Top {retrieve.k} passages for question: {question} \n", "\n")
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for idx, passage in enumerate(topK_passages):
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print(f"{idx+1}]", passage, "\n")
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```
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With Qdrant configured as the retriever for contexts, you can set up a DSPy module like so:
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```python
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class RAG(dspy.Module):
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def __init__(self, num_passages=3):
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super().__init__()
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self.retrieve = dspy.Retrieve(k=num_passages)
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...
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def forward(self, question):
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context = self.retrieve(question).passages
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...
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```
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With the generic RAG blueprint now in place, you can add the many interactions offered by DSPy with context retrieval powered by Qdrant.
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## Next steps
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Find DSPy usage docs and examples [here](https://github.com/stanfordnlp/dspy#4-documentation--tutorials).
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