DSPy retrieval modules (dspy.Retrieve, dspy.ColBERTv2, dspy.Embedder, dspy.retrievers.Embeddings) for searching documents, computing embeddings, and building RAG pipelines. Use when you need to search over documents, build a RAG pipeline, connect DSPy to a vector database, compute embeddings for semantic search, set up ChromaDB or Pinecone with DSPy, or build knowledge-grounded question answering.
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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:
What are you searching over? Your own documents, a knowledge base, an external corpus like Wikipedia?
How large is the corpus? A few hundred docs (in-memory FAISS works) vs. millions (need a dedicated vector store li
[Description truncada. Veja o README completo no GitHub.]
