RAG & API Retrieval, Partitioning & Extraction
FlowMind aims to solve for hallucination by providing contextual reference data at inference; analogous to RAG. The API also seeks to retrieve, partition and extract relevant XML-like blocks. Blocks are again very much similar to chunks.
FlowMind is also challenged by the problems of selecting the top retrieved blocks/chunks and truncating blocks which are too long.
Embeddings are also used in FlowMind to search according to semantic similarity.
So FlowMind can be considered as JPMorganChase’s propriety RAG solution and obviously it meets their data privacy and governance requirements. What I find curious is that the market in general has settled on certain terminology and a shared understanding has been developed.
JPMorganChase breaks from these terms and introduces their own lexicon. However, FlowMind is very much comparable to RAG in general.
It is evident that through this implementation, JPMorganChase has full control over their stack on a very granular level. The process and flow Python functions created by FlowMind most probably fits into their current ecosystem.
MindFlow can also be leveraged by skilled users to generate flows based on a description which can be re-used.
The aim of FlowMind is to remedy hallucination in Large Language Models (LLMs) while ensuring there is no direct link between the LLM and proprietary code or data.
FlowMind creates flows or pipelines on the fly, a process the paper refers to as robotic process automation. There is a human-in-the-loop element, which can also be seen as a single dialog turn, allowing users to interact with and refine the generated workflows.
Application Programming Interfaces (APIs) are used for grounding the LLMs, serving as a contextual reference to guide their reasoning. This is followed by code generation, code execution, and ultimately delivering the final answer.
Stage 1: It begins by following a structured lecture plan (prompt template as seen above) to create a lecture prompt. This prompt educates the Large Language Model (LLM) about the context and APIs, preparing it to write code.
