Dspy embedding retrieval

Skill OmidZamani/dspy-skills/skills/dspy-embedding-retrieval

Use for DSPy retrieval with dspy.Embedder, dspy.Embeddings, FAISS indexes, semantic search, and local or hosted embedding models.From its SKILL.md

Install

npx -y skills add OmidZamani/dspy-skills --skill dspy-embedding-retrieval

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

runs commandsInstructs the agent to run 1 command, including `pip install faiss-cpu`.

SKILL.md

2.8 KB, 626 tokens by cl100k_base, as published. Nobody here has run it

DSPy Embedding Retrieval

Goal

Build semantic retrieval over an application-owned text corpus with dspy.Embedder and dspy.Embeddings.

Basic Hosted Embedder

import dspy

corpus = [
    "DSPy programs are composed from modules.",
    "MIPROv2 optimizes instructions and demonstrations.",
    "RLM explores large contexts with a sandboxed REPL.",
]

embedder = dspy.Embedder("openai/text-embedding-3-small")
search = dspy.Embeddings(corpus=corpus, embedder=embedder, k=2)

result = search("Which optimizer tunes prompts?")
print(result.passages)
print(result.indices)

Use in RAG

class LocalRAG(dspy.Module):
    def __init__(self, retriever):
        super().__init__()
        self.retriever = retriever
        self.answer = dspy.ChainOfThought("context: list[str], question -> answer")

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

Custom Local Embeddings

Wrap any callable that accepts list[str] and returns a 2D numeric array:

from sentence_transformers import SentenceTransformer
import dspy

model = SentenceTransformer("sentence-transformers/static-retrieval-mrl-en-v1")
embedder = dspy.Embedder(model.encode)
search = dspy.Embeddings(corpus=corpus, embedder=embedder, k=5)

Scores, FAISS, and Persistence

Use dspy.EmbeddingsWithScores when downstream logic needs similarity thresholds or reranking.

For corpora at or above the brute_force_threshold default of 20_000, DSPy builds a FAISS index. Install FAISS first:

pip install faiss-cpu

Persist the index when embedding the corpus is expensive:

search.save("./retrieval-index")
loaded = dspy.Embeddings.from_saved("./retrieval-index", embedder=embedder)

Related Skills

Build a complete pipeline: dspy-rag-pipeline
Design typed context fields: dspy-signature-designer
Harden caches: dspy-production-deployment

Best Practices

Evaluate retrieval quality separately from answer quality.
Keep corpus chunking deterministic and versioned.
Persist expensive indexes.
Use EmbeddingsWithScores when debugging relevance.
Measure memory and latency before enabling FAISS for large corpora.

Official Documentation

Embedder API: https://dspy.ai/api/models/Embedder/
Embeddings API: https://dspy.ai/api/tools/Embeddings/

What ships with it: 1 file

408 B alongside SKILL.md, 1 of them executable

example.pyruns408 B