This project is a retrieval augmented generation framework designed to build pipelines that connect unstructured data and knowledge graphs with large language models. It functions as a vector database orchestrator for indexing text and multimodal content, as well as a system for translating natural language queries into structured database commands.
The framework integrates a hybrid retrieval engine that combines dense vector search with sparse keyword matching to increase the precision of retrieved contexts. It further enhances reasoning and relationship mapping through a graph-augmented retrieval system.
The system includes a toolkit for measuring the quality of retrieval and generation processes using standardized metrics. It also provides mechanisms to enforce predefined schemas and patterns on model responses to ensure consistent output for downstream applications.
The project is implemented in Python.
