RAG-Anything: All-in-One RAG Framework (2510.12323v1)

Abstract: Retrieval-Augmented Generation (RAG) has emerged as a fundamental paradigm for expanding LLMs beyond their static training limitations. However, a critical misalignment exists between current RAG capabilities and real-world information environments. Modern knowledge repositories are inherently multimodal, containing rich combinations of textual content, visual elements, structured tables, and mathematical expressions. Yet existing RAG frameworks are limited to textual content, creating fundamental gaps when processing multimodal documents. We present RAG-Anything, a unified framework that enables comprehensive knowledge retrieval across all modalities. Our approach reconceptualizes multimodal content as interconnected knowledge entities rather than isolated data types. The framework introduces dual-graph construction to capture both cross-modal relationships and textual semantics within a unified representation. We develop cross-modal hybrid retrieval that combines structural knowledge navigation with semantic matching. This enables effective reasoning over heterogeneous content where relevant evidence spans multiple modalities. RAG-Anything demonstrates superior performance on challenging multimodal benchmarks, achieving significant improvements over state-of-the-art methods. Performance gains become particularly pronounced on long documents where traditional approaches fail. Our framework establishes a new paradigm for multimodal knowledge access, eliminating the architectural fragmentation that constrains current systems. Our framework is open-sourced at: https://github.com/HKUDS/RAG-Anything.

• The paper introduces a dual-graph construction strategy that decomposes multimodal documents into atomic units to preserve structure and semantic context.
• It integrates a hybrid retrieval mechanism that combines structural navigation with semantic matching for comprehensive cross-modal evidence retrieval.
• The framework achieves superior performance on long-context, complex documents, highlighting its potential in applications such as finance and scientific research.

RAG-Anything: A Unified Framework for Multimodal Retrieval-Augmented Generation

Motivation and Problem Formulation

Retrieval-Augmented Generation (RAG) has become a central paradigm for extending the knowledge and reasoning capabilities of LLMs by enabling dynamic access to external information sources. However, the prevailing RAG frameworks are fundamentally text-centric, operating under the assumption that all knowledge is encoded as plain text. This assumption is misaligned with the multimodal nature of real-world information, where documents routinely contain a mixture of text, images, tables, and mathematical expressions. The inability of existing RAG systems to natively process and reason over such heterogeneous content leads to severe information loss, particularly in domains such as scientific research, finance, and medicine, where critical insights are often embedded in non-textual modalities.

RAG-Anything addresses three core technical challenges: (1) unified multimodal representation that preserves both intra- and inter-modal relationships, (2) structure-aware decomposition and parsing of complex document layouts, and (3) cross-modal retrieval mechanisms capable of reasoning over interconnected multimodal evidence. The framework is designed to eliminate the architectural fragmentation and modality-specific pipelines that have limited the scalability and generalizability of prior multimodal RAG systems.

Figure 1: Overview of the RAG-Anything universal RAG framework, illustrating the dual-graph construction, hybrid retrieval, and synthesis pipeline.

Dual-Graph Construction for Multimodal Knowledge Representation

The core innovation of RAG-Anything is its dual-graph construction strategy, which enables comprehensive and fine-grained modeling of multimodal documents. The process begins with the decomposition of each knowledge source into atomic content units, each tagged with its modality (text, image, table, equation, etc.) and extracted using specialized parsers to preserve semantic and structural context.

• Cross-Modal Knowledge Graph: Non-textual units (e.g., figures, tables) are transformed into structured graph entities. For each, two textual representations are generated: a detailed description for retrieval and an entity summary for graph construction. Context-aware processing ensures that each unit is grounded in its local document neighborhood. The resulting graph encodes intra-modal entities and their relationships, with explicit "belongs_to" edges linking fine-grained entities to their parent multimodal unit.
• Text-Based Knowledge Graph: Textual chunks are processed using standard NER and relation extraction pipelines to construct a complementary text-centric knowledge graph.
• Graph Fusion: The two graphs are merged via entity alignment, using entity names as primary keys, resulting in a unified knowledge graph that encodes both multimodal contextual relationships and fine-grained textual semantics.
• Dense Embedding Table: All graph entities, relations, and atomic content chunks are embedded into a unified vector space, enabling efficient cross-modal similarity search.

This dual-graph approach preserves modality-specific structure and enables robust cross-modal grounding, which is essential for accurate retrieval and reasoning in heterogeneous document collections.

Cross-Modal Hybrid Retrieval Mechanism

RAG-Anything introduces a hybrid retrieval architecture that combines structural knowledge navigation with semantic similarity matching:

• Structural Navigation: Queries are analyzed for modality cues, and exact entity matching is performed against the knowledge graph. Multi-hop neighborhood expansion retrieves related entities and relationships, capturing explicit cross-modal and intra-modal connections.
• Semantic Similarity Matching: Dense vector search is performed between the query embedding and all indexed components, surfacing semantically relevant content that may lack explicit structural links.
• Multi-Signal Fusion Scoring: Retrieved candidates from both pathways are unified and ranked using a fusion of structural importance, semantic similarity, and query-inferred modality preferences.

This hybrid mechanism ensures comprehensive coverage of relevant knowledge, balancing explicit structural relationships with implicit semantic connections, and is particularly effective for long-context and structurally complex documents.

Multimodal Synthesis and Response Generation

The synthesis stage constructs a structured textual context from the top-ranked retrieval candidates, concatenating entity summaries, relationship descriptions, and chunk contents with modality-aware delimiters. For visual artifacts, dereferencing is performed to recover the original content. The final response is generated by a vision-LLM (VLM) conditioned on the query, the assembled textual context, and the dereferenced visual content, enabling coherent, evidence-grounded, and visually informed answers.

Empirical Evaluation and Analysis

RAG-Anything is evaluated on DocBench and MMLongBench, two challenging multimodal DQA benchmarks with extensive, heterogeneous documents. The framework is compared against GPT-4o-mini, LightRAG, and MMGraphRAG. RAG-Anything consistently achieves the highest overall accuracy across domains and document types, with particularly strong gains on long-context documents and information-dense domains such as finance and research.

Figure 2: Performance evaluation across documents of varying lengths, showing RAG-Anything's increasing advantage as document length grows.

Ablation studies confirm that the primary performance gains derive from the dual-graph construction; chunk-only retrieval suffers significant drops, while removal of the reranker yields only marginal degradation. Case studies further demonstrate the framework's ability to resolve complex multi-panel visualizations and ambiguous tabular structures, outperforming baselines that lack explicit structural modeling.

Figure 3: Multi-panel figure interpretation case, where RAG-Anything correctly identifies the relevant panel and avoids confusion from adjacent panels.

Figure 4: Financial table navigation case, illustrating precise localization of the target cell amid similar entries.

Structure-Aware Reasoning in Multimodal Documents

RAG-Anything's explicit graph-based modeling of intra- and inter-modal relationships enables fine-grained reasoning that is unattainable with text-centric or modality-agnostic approaches. In visual reasoning tasks, the framework constructs graphs linking panels, axes, and captions, supporting accurate panel-level comparisons. In tabular navigation, row–column–unit graphs ensure precise cell selection and numeric grounding, even in the presence of repeated terminology or complex layouts.

Figure 5: Visual reasoning case, where RAG-Anything correctly interprets spatial relationships in a bar plot.

Figure 6: Tabular navigation case, demonstrating accurate extraction of the highest AUPRC value in a structurally ambiguous table.

Limitations and Failure Modes

Despite its advances, RAG-Anything exhibits two critical limitations: (1) a residual text-centric retrieval bias, leading to suboptimal performance when queries require visual information, and (2) rigidity in spatial processing, resulting in failures on documents with non-standard layouts or ambiguous table structures.

Figure 7: Cross-modal noise case, where all methods fail to retrieve the correct answer from the specified image.

Figure 8: Ambiguous table structure case, illustrating systematic failure to parse merged cells and unclear boundaries.

These failure modes highlight the need for further research into adaptive spatial reasoning and layout-aware parsing to enhance robustness in real-world multimodal document understanding.

Implications and Future Directions

RAG-Anything establishes a new foundation for multimodal RAG by demonstrating that unified, graph-based modeling of heterogeneous content is both feasible and effective. The dual-graph construction and hybrid retrieval mechanisms provide a scalable solution to the challenges of cross-modal reasoning and long-context retrieval. The strong empirical results, particularly on large and complex documents, suggest that future RAG systems should adopt similar unified representations to avoid the pitfalls of architectural fragmentation.

Theoretical implications include the necessity of explicit structural modeling for robust multimodal reasoning and the limitations of purely embedding-based or text-centric approaches. Practically, RAG-Anything's design principles can be extended to new modalities and domains, supporting applications in scientific literature analysis, financial document processing, and medical report understanding.

Future work should address the identified limitations by developing adaptive, layout-aware parsing and retrieval mechanisms, as well as more sophisticated cross-modal alignment strategies. Integrating visual processing capabilities that can dynamically adjust to document structure and query intent will be essential for achieving truly comprehensive multimodal intelligence.

Conclusion

RAG-Anything advances the state of the art in multimodal retrieval-augmented generation by introducing a unified, graph-based framework that models and retrieves knowledge across all major modalities. Its dual-graph construction and hybrid retrieval architecture enable precise, structure-aware reasoning in complex, long-context documents, yielding superior empirical performance and robust generalization. The framework's limitations point to critical directions for future research, particularly in adaptive spatial reasoning and cross-modal alignment, which are essential for the next generation of multimodal AI systems.