📄️ AzureAISearch
A retrieval module that utilizes Azure AI Search to retrieve top passages for a given query.
📄️ ChromadbRM
Adapted from documentation provided by https://github.com/animtel
📄️ ColBERTv2
Setting up the ColBERTv2 Client
📄️ DatabricksRM
Constructor
📄️ FaissRM
Constructor
📄️ LancedbRM
LanceDB is a developer-friendly, open source database for AI. From hyper scalable vector search and advanced retrieval for RAG, to streaming training data and interactive exploration of large scale AI datasets, LanceDB is the best foundation for your AI application.
📄️ MilvusRM
MilvusRM uses OpenAI's text-embedding-3-small embedding by default or any customized embedding function.
📄️ MyScaleRM
Constructor
📄️ Neo4jRM
Constructor
📄️ QdrantRM
Qdrant is an open-source, high-performance vector search engine/database written in Rust. It can be used to retrieve semantically relevant passages to pass as context to your language model.
📄️ RAGatouilleRM
Constructor
📄️ SnowflakeRM
Constructor
📄️ WatsonDiscoveryRM
Constructor
📄️ Weaviate Retrieval Model
Weaviate is an open-source vector database that can be used to retrieve relevant passages before passing it to the language model. Weaviate supports a variety of embedding models from OpenAI, Cohere, Google and more! Before building your DSPy program, you will need a Weaviate cluster running with data. You can follow this notebook as an example.
📄️ YouRM
Constructor
📄️ Creating Custom RM Client
DSPy provides support for various retrieval modules out of the box like ColBERTv2, AzureCognitiveSearch, Lancedb, Pinecone, Weaviate, etc. Unlike Language Model (LM) modules, creating a custom RM module is much more simple and flexible.