LangChain has many advanced retrieval methods to help address these challenges. (1) Multi representation indexing: Create a document representation (like a summary) that is well-suited for retrieval (read about this using the Multi Vector Retriever in a blog post from last week). (2) Query transformation: in this post, we'll review a few approaches to transform humans questions in order to improve retrieval. (3) Query construction: convert human question into a particular query syntax or language, which will be covered in a future post
DSPy provides composable and declarative modules for instructing LMs in a familiar Pythonic syntax. It upgrades "prompting techniques" like chain-of-thought and self-reflection from hand-adapted string manipulation tricks into truly modular generalized operations that learn to adapt to your task.
Unlock advanced customer segmentation techniques using LLMs, and improve your clustering models with advanced techniques