Retrieval Modules in DSPy

Guide the user through DSPy's retrieval modules for searching documents, computing embeddings, and building RAG (retrieval-augmented generation) pipelines.

Step 1: Gather context

Before building retrieval into a DSPy program, clarify:

1. What are you searching over? Your own documents, a knowledge base, an external corpus like Wikipedia?
2. How large is the corpus? A few hundred docs (in-memory FAISS works) vs. millions (need a dedicated vector store like Pinecone, Qdrant, or Chroma)?
3. Do you already have a search backend? If you have Elasticsearch, Pinecone, or another store, subclass dspy.Retrieve to wrap it. If not, use dspy.retrievers.Embeddings for a local solution.
4. Single-hop or multi-hop? Simple questions need one retrieval step. Compositional questions (e.g., "Where was the designer of the Eiffel Tower born?") need chained retrieval.

What retrieval modules are

DSPy provides retrieval modules that fetch relevant documents or passages given a query. These modules plug into DSPy programs just like dspy.Predict or dspy.ChainOfThought -- declare them in __init__, call them in forward(), and optimizers handle the rest.

There are four key components: