DSPy

The sie-dspy package provides an embedder compatible with DSPy’s retrieval components. Use SIEEmbedder with dspy.retrievers.Embeddings for semantic search.

Installation

pip install sie-dspy

This installs sie-sdk and numpy as dependencies.

Start the Server

# Docker (recommended)
docker run -p 8080:8080 ghcr.io/superlinked/sie:latest

# Or with GPU
docker run --gpus all -p 8080:8080 ghcr.io/superlinked/sie:latest

Embedder

SIEEmbedder is callable and works directly with DSPy’s retrieval components.

from sie_dspy import SIEEmbedder

embedder = SIEEmbedder(
    base_url="http://localhost:8080",
    model="BAAI/bge-m3",
)

# Embed a single text
vector = embedder("What is machine learning?")
print(vector.shape)  # (1024,)

# Embed multiple texts
vectors = embedder(["First document", "Second document"])
print(vectors.shape)  # (2, 1024)

Configuration Options

Parameter	Type	Default	Description
base_url	str	http://localhost:8080	SIE server URL
model	str	BAAI/bge-m3	Model to use
gpu	str	None	Target GPU type for routing
options	dict	None	Model-specific options
timeout_s	float	180.0	Request timeout in seconds

Full Example

Build a complete DSPy RAG program using SIE embeddings:

import dspy
from sie_dspy import SIEEmbedder

# 1. Configure DSPy with your LLM
lm = dspy.LM("openai/gpt-4o-mini")
dspy.configure(lm=lm)

# 2. Create SIE embedder
embedder = SIEEmbedder(
    base_url="http://localhost:8080",
    model="BAAI/bge-m3",
)

# 3. Build retriever with your corpus
corpus = [
    "Machine learning is a branch of artificial intelligence.",
    "Neural networks are inspired by biological neurons.",
    "Deep learning uses multiple layers of neural networks.",
    "Python is popular for machine learning development.",
]

retriever = dspy.retrievers.Embeddings(
    corpus=corpus,
    embedder=embedder,
    k=2,
)

# 4. Define a RAG signature
class RAG(dspy.Signature):
    """Answer questions using retrieved context."""
    context: list[str] = dspy.InputField(desc="retrieved passages")
    question: str = dspy.InputField()
    answer: str = dspy.OutputField()

# 5. Build the RAG program
class RAGProgram(dspy.Module):
    def __init__(self):
        self.retriever = retriever
        self.generate = dspy.ChainOfThought(RAG)

    def forward(self, question: str):
        passages = self.retriever(question)
        return self.generate(context=passages, question=question)

# 6. Run the program
rag = RAGProgram()
result = rag("What is deep learning?")
print(result.answer)

What’s Next

• Encode Text - embedding API details
• Model Catalog - all supported embedding models