DataStax announced its retrieval augmented generation (RAG) solution, RAGStack, is now generally available powered by LlamaIndex as an open source framework, in addition to LangChain. DataStax RAGStack for LlamaIndex also supports an integration (currently in public preview) with LlamaIndex’s LlamaParse, which gives developers using Astra DB an API to parse and transform complex PDFs into vectors in minutes.
LlamaIndex is a framework for ingesting, indexing, and querying data for building generative AI applications and addresses the ingestion pipelines needed for enterprise-ready RAG. LlamaParse is LlamaIndex’s new offering that targets enterprise developers building RAG over complex PDFs; it enables clean extraction of tables by running recursive retrieval, promising more accurate parsing of the complex documents often found in business.
RAGStack with LlamaIndex offers a solution tailored to address the challenges encountered by enterprise developers in implementing RAG solutions. Benefits include a curated Python distribution available on PyPI for integration with Astra DB, DataStax Enterprise (DSE), and Apache Cassandra, and a live RAGStack test matrix and GenAI app templates.
Users can use LlamaIndex alone, or in combination with LangChain and their ecosystem including LangServe, LangChain Templates, and LangSmith.
