EA-GraphRAG is an efficient and adaptive retrieval framework that automatically routes queries between dense retrieval (DPR), graph-based retrieval, or their fusion based on the syntactic and semantic complexity of the query.

This framework implements a complexity-aware retrieval system that:

1. Analyzes query complexity using an MLP classifier trained on syntactic, semantic, and dependency-based features
2. Routes queries to appropriate retrieval methods:
   • Low complexity: Dense Passage Retrieval (DPR) using BGE embeddings
   • High complexity: Graph-based retrieval using entity-fact graph and Personalized PageRank
   • Medium complexity: Complexity-aware Reciprocal Rank Fusion (RRF) of both methods
3. Fuses results using linear-weighted RRF when both methods are used

Query → Feature Extraction → Complexity Classification → Retrieval Routing
                                                              ↓
                    ┌─────────────────────────────────────────┼─────────────────────────┐
                    ↓                                         ↓                         ↓
            Low Complexity                            Medium Complexity          High Complexity
                    ↓                                         ↓                         ↓
            Dense Retrieval                          Fusion (RRF)              Graph Retrieval
                    ↓                                         ↓                         ↓
            Retrieved Docs                          Retrieved Docs            Retrieved Docs

• Python 3.8+
• CUDA-capable GPU (recommended for embeddings and classifier)
• Java 8+ (for CoreNLP, optional but recommended for full feature extraction)

1. Clone or download this repository.

2. Install dependencies:

pip install -r requirements.txt

3. Download SpaCy model:

python -m spacy download en_core_web_md

4. Download Stanza resources:

python -c "import stanza; stanza.download('en')"

5. (Optional) Set up Stanford CoreNLP server for full syntactic feature extraction:

   • Download Stanford CoreNLP from https://stanfordnlp.github.io/CoreNLP/
   • Follow instructions to start the server

6. Prepare graph data (for graph retrieval, optional):

   • Extract OpenIE triples from corpus:

     python scripts/extract_openie.py \
         --corpus_path data/merged_4_corpus.json \
         --output_path data/openie/merged_4_openie.json

   • Build knowledge graph:

     python scripts/build_graph.py \
         --corpus_path data/merged_4_corpus.json \
         --openie_results_path data/openie/merged_4_openie.json \
         --output_graph_path data/graphs/merged_4_graph.pickle

python main.py \
    --classifier_path models/mlp_router_4k_optimized_best.pt \
    --dataset merged_4 \
    --data_file data/merged_4.json \
    --top_k 5 \
    --output_path output/merged_4_results.json

python main.py \
    --classifier_path models/mlp_router_4k_optimized_best.pt \
    --dataset merged_4 \
    --data_file data/merged_4.json \
    --corpus_path data/merged_4_corpus.json \
    --dense_retriever BAAI/bge-base-en-v1.5 \
    --graph_llm gpt-4o-mini \
    --graph_embedding BAAI/bge-base-en-v1.5 \
    --top_k 5 \
    --damping 0.5 \
    --use_corenlp \
    --device cuda \
    --output_path output/merged_4_results.json

• --classifier_path: Path to trained MLP classifier checkpoint (required)
• --dataset: Dataset name (required)
• --data_file: Path to input JSON file with queries (required)
• --corpus_path: Path to corpus JSON file (default: data/{dataset}_corpus.json)
• --dense_retriever: Dense retriever model name (default: BAAI/bge-base-en-v1.5)
• --graph_llm: LLM name for graph construction (default: gpt-4o-mini)
• --graph_embedding: Embedding model for graph (default: BAAI/bge-base-en-v1.5)
• --top_k: Number of documents to retrieve (default: 5)
• --damping: Damping factor for graph retrieval PPR (default: 0.5)
• --use_corenlp: Use CoreNLP for feature extraction (slower but more accurate)
• --device: Device for classifier (cpu or cuda, default: cpu)
• --output_path: Path to save results (default: output/{dataset}_results.json)

The input JSON file should contain a list of samples, each with a question or query field:

[
    {
        "id": "sample_1",
        "question": "What is the capital of France?",
        ...
    },
    {
        "id": "sample_2",
        "question": "Which actor starred in both Inception and The Dark Knight?",
        ...
    }
]

The output JSON file contains retrieval results for each query:

[
    {
        "sample_id": "sample_1",
        "query": "What is the capital of France?",
        "complexity_prob": 0.23,
        "complexity_level": "low",
        "method": "dense",
        "retrieved_documents": [...],
        "scores": [...],
        "fusion_weights": null,
        "top_k": 5
    },
    {
        "sample_id": "sample_2",
        "query": "Which actor starred in both Inception and The Dark Knight?",
        "complexity_prob": 0.65,
        "complexity_level": "medium",
        "method": "fusion",
        "retrieved_documents": [...],
        "scores": [...],
        "fusion_weights": [0.35, 0.65],
        "top_k": 5
    }
]

To train your own complexity classifier:

1. Prepare training data with labels indicating whether graph retrieval outperforms dense retrieval
2. Run the training script:

python scripts/train_classifier.py \
    --train_data data/train.json \
    --test_data data/test.json \
    --output_dir models/

See scripts/train_classifier.py for details.

EA-GraphRAG/
├── main.py                 # Main workflow script
├── src/
│   ├── classifier/        # Complexity classification module
│   │   ├── mlp_classifier.py
│   │   ├── feature_extractor.py
│   │   ├── syntactic_features.py
│   │   └── advanced_features.py
│   ├── retrieval/         # Retrieval modules
│   │   ├── dense_retriever.py
│   │   ├── graph_retriever.py
│   │   └── fusion.py
│   └── utils/             # Utility functions
├── data/                  # Data directory
├── models/                # Trained models
├── output/                # Output results
├── scripts/               # Training and utility scripts
├── requirements.txt       # Python dependencies
└── README.md             # This file

The classifier uses a comprehensive feature set:

• Length measures (MLS, MLT, MLC)
• Sentence complexity (C/S)
• Subordination (C/T, CT/T, DC/C, DC/T)
• Coordination (CP/C, CP/T, T/S)
• Phrasal sophistication (CN/C, CN/T, VP/T)

• Dependency distances (max, average, long-range)
• Relation types (subject-verb, object-verb, modifiers, coordination, subordination)
• Tree imbalance metrics

• Named entity counts and types
• Entity density
• Semantic role indicators
• Question type encoding

• Lexical diversity
• Content-to-function word ratios
• Information density
• Complexity markers (coordination, subordination, negation, passive voice)

• Uses BGE embeddings for semantic similarity
• Efficient for simple, factoid queries
• Fast retrieval with good performance on low-complexity queries

• Uses knowledge graph constructed from corpus (entity-fact graph)
• Performs Personalized PageRank (PPR) on the graph
• Effective for multi-hop and complex reasoning queries
• Adaptive PPR damping based on query complexity
• Does not use passage nodes in the graph (entity-fact only)

• Combines dense and graph retrieval results
• Uses linear weighting based on complexity probability
• Weights: dense_weight = 1 - complexity_prob, graph_weight = complexity_prob

If you use this framework in your research, please cite:

@inproceedings{ea_graphrag_2026,
  title={EA-GraphRAG: Efficient and Adaptive GraphRAG for Query Complexity-Aware Retrieval},
  author={Anonymous},
  booktitle={Proceedings of SIGIR 2026},
  year={2026}
}

This project is released under the MIT License. See LICENSE file for details.

• Stanford CoreNLP and Stanza for syntactic parsing
• SpaCy for dependency parsing and NER
• Sentence Transformers for dense embeddings
• python-igraph for graph processing

For questions or issues, please open an issue on the repository.