Generative AI is evolving fast, but one of the biggest challenges developers still face is getting models to work with the right information at the right time. Open source tools like Docling, which parses documents into machine-readable formats, and OpenSearch, a powerful search and analytics engine, are reshaping what’s possible when used together to ground AI responses in reliable context. By pairing advanced document understanding with state-of-the-art retrieval, these technologies help developers build AI systems that are not only accurate and trustworthy, but also adaptable to real-world data and scalable across domains.
Retrieval-Augmented Generation (RAG) has emerged as a standard pattern for improving large language models by supplementing them with external knowledge. Yet, two persistent challenges define the effectiveness of any RAG pipeline: whether documents are being faithfully converted into a form the model can understand, and whether the retrieval layer is surfacing the most relevant information for a given query. Subtle losses in translation can severely limit the usefulness of a RAG system. Similarly, weak retrieval strategies risk tainting the model output with noise instead of precise context. Addressing these two pain points is what separates robust RAG implementations from fragile ones.
Docling and OpenSearch work together to solve these problems. Docling delivers state-of-the-art accuracy in converting complex document formats—spanning text, tables, charts, and embedded graphics—into structured representations optimized for AI. Docling was created in IBM Research and open sourced in late 2024. It is widely recognized in the community as the top choice for document conversion in cases where accuracy is paramount. Docling also includes powerful vision-language models that harness the power of AI models for the specific task of understanding documents, including the new and popular GraniteDocling model. Docling is currently used by over 2,000 projects in GitHub, as well as major companies in domains such as technology and financial services.
OpenSearch strengthens the retrieval side of the pipeline. Beyond basic keyword matching, it offers vector search, semantic retrieval, and advanced ranking capabilities designed to sift through large, heterogeneous datasets and pinpoint the passages most relevant to a query. This ensures that language models are grounded in the right evidence, improving both the accuracy and explainability of their responses. OpenSearch is designed for scale, security, and extensibility—qualities that make it particularly attractive in production-grade AI systems.
Together, Docling and OpenSearch address the two most critical bottlenecks in RAG: faithful document ingestion and high-quality retrieval. By combining them, developers gain a stronger foundation for building domain-specific AI systems that can reliably answer questions, extract insights, and adapt to the complexities of real-world knowledge.
In practice, it is easy to get started using these technologies together in a RAG workflow. An example of how to use these to create an AI system that can answer questions from a technical report is available on the OpenSearch blog, here.
