DataLang is a sophisticated yet simple tool that allows for the querying of databases through natural language. Users can set up their data sources, add data views, and interact with their data as if they were having a conversation. This eliminates the need for complex SQL queries, enabling users to obtain quick insights and responses using just plain language.

Graph_RAG is a Python-based framework designed to build and query knowledge graphs for retrieval-augmented generation (RAG). It supports ingestion of unstructured documents, automated extraction of entities and relationships using LLMs or NLP tools, and storage in graph databases such as Neo4j. With Graph_RAG, developers can construct connected knowledge graphs, execute semantic graph queries to identify relevant nodes and paths, and feed the retrieved context into LLM prompts. The framework provides modular pipelines, configurable components, and integration examples to facilitate end-to-end RAG applications, improving answer accuracy and interpretability through structured knowledge representation.