ragrank-cr 0.1.0

Document influence analysis for RAG systems using social network centrality measures

RAGRank 🎯

Document Influence Analysis for RAG Systems

A lightweight Python library for analyzing document influence in RAG knowledge bases using social network centrality measures.

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🚀 Quick Start

from ragrank import DocumentGraph, InfluenceAnalyzer

# Create graph
graph = DocumentGraph()

# Add documents with embeddings
graph.add_documents([
    {
        "id": "doc1",
        "content": "Argentina wins World Cup 2022",
        "embedding": embedding_vector_1
    },
    {
        "id": "doc2",
        "content": "Messi lifts trophy",
        "embedding": embedding_vector_2
    },
])

# Build graph (creates edges based on similarity)
graph.build_graph(similarity_threshold=0.7)

# Analyze influence
analyzer = InfluenceAnalyzer(graph)
top_docs = analyzer.get_most_influential(top_k=10)

for doc in top_docs:
    print(f"{doc.doc_id}: {doc.combined_score:.3f}")

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📦 Installation

# Clone or copy the ragrank directory
cd ragrank

# Install dependencies
pip install numpy

# Run example
python examples/world_cup_example.py

---

🎯 Features

Centrality Measures

1. Degree Centrality - Retrieval frequency

   from ragrank.centrality import degree_centrality
   scores = degree_centrality(graph)
   

2. Betweenness Centrality - Topic bridging

   from ragrank.centrality import betweenness_centrality
   scores = betweenness_centrality(graph)
   

3. Eigenvector Centrality - Authority propagation

   from ragrank.centrality import eigenvector_centrality
   scores = eigenvector_centrality(graph)
   

4. PageRank - Document ranking (adapted from Google's algorithm)

   from ragrank.centrality import pagerank
   scores = pagerank(graph, damping=0.85)
   

Influence Analysis

analyzer = InfluenceAnalyzer(graph, weights={
    "degree": 0.3,
    "betweenness": 0.2,
    "eigenvector": 0.25,
    "pagerank": 0.25,
})

# Get most influential
top_k = analyzer.get_most_influential(top_k=10)

# Detect outliers (potential poisoning)
outliers = analyzer.detect_outliers(threshold=2.0)

# Compare documents
ratio = analyzer.compare_documents("doc1", "doc2")

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🌍 Real-World Example

# World Cup Knowledge Base
graph = DocumentGraph()

# Add legitimate documents
graph.add_document(
    doc_id="argentina_wins",
    content="Argentina wins FIFA World Cup 2022",
    embedding=embed("Argentina wins FIFA World Cup 2022")
)

# Simulate queries
query_emb = embed("who won world cup 2022")
retrieved = graph.record_query_retrieval(query_emb, top_k=5)

# Analyze
analyzer = InfluenceAnalyzer(graph)
top_docs = analyzer.get_most_influential(top_k=5)

# Results:
# #1. argentina_wins    (score: 0.892)
# #2. messi_trophy      (score: 0.745)
# #3. final_score       (score: 0.621)

---

🛡️ Use Cases

1. Security - Detect Poisoned Documents

# Add documents to graph
graph.build_graph()

# Analyze influence
analyzer = InfluenceAnalyzer(graph)

# Detect outliers
outliers = analyzer.detect_outliers(threshold=2.0)

for doc_id, z_score in outliers:
    print(f"⚠️  Suspicious: {doc_id} (z-score: {z_score:.2f})")

2. Quality - Find Low-Quality Docs

# Get influence scores
scores = analyzer.get_influence_scores()

# Find low-influence documents
low_influence = [s for s in scores if s.combined_score < 0.1]

print(f"Found {len(low_influence)} rarely retrieved documents")

3. Optimization - Prioritize Important Docs

# Get top influential documents
top_docs = analyzer.get_most_influential(top_k=100)

# Cache these for faster retrieval
cache_docs = [doc.doc_id for doc in top_docs[:20]]

---

📊 Centrality Formulas

Degree Centrality

C_d(doc) = # queries retrieving doc / total queries

Betweenness Centrality

C_b(v) = Σ [σ(s,t|v) / σ(s,t)]
         s≠v≠t

Eigenvector Centrality

x_v = (1/λ) Σ A_vw × x_w
            w∈N(v)

PageRank

PR(doc) = (1-d) + d × Σ [PR(neighbor) × sim(doc, neighbor)]

---

🎓 How It Works

Graph Construction

1. Nodes = Documents
2. Edges = Cosine similarity ≥ threshold
3. Edge weights = Similarity scores (0-1)

# Similarity threshold determines graph density
graph.build_graph(similarity_threshold=0.7)

# Lower threshold = more edges = denser graph
# Higher threshold = fewer edges = sparser graph

Query Tracking

# Record which documents are retrieved
query_emb = embed("user query here")
retrieved = graph.record_query_retrieval(query_emb, top_k=5)

# Updates degree centrality automatically

Influence Calculation

# Combine multiple centrality measures
influence = (
    w1 × degree_centrality +
    w2 × betweenness_centrality +
    w3 × eigenvector_centrality +
    w4 × pagerank
)

---

📚 API Reference

DocumentGraph

graph = DocumentGraph(similarity_threshold=0.7)

# Add documents
graph.add_document(doc_id, content, embedding, metadata)
graph.add_documents([...])  # Batch add

# Build graph
graph.build_graph()

# Track queries
graph.record_query_retrieval(query_embedding, top_k=5)

# Get info
graph.get_doc_ids()
graph.get_neighbors(doc_id)

InfluenceAnalyzer

analyzer = InfluenceAnalyzer(graph, weights={...})

# Analyze
scores = analyzer.get_influence_scores()
top_k = analyzer.get_most_influential(top_k=10)
outliers = analyzer.detect_outliers(threshold=2.0)

# Compare
ratio = analyzer.compare_documents(doc_id1, doc_id2)
breakdown = analyzer.get_influence_breakdown(doc_id)

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🔬 Example Output

RAGRank Example: World Cup 2022 Knowledge Base
==================================================================

Building document graph...
Graph: DocumentGraph(documents=10, edges=24)

Top 5 Most Influential Documents:
==================================================================

#1. argentina_wins
    Content: Argentina wins FIFA World Cup 2022 in Qatar...
    Combined Score: 0.847
    Breakdown:
      - Degree (retrieval):   0.920
      - Betweenness (bridge): 0.780
      - Eigenvector (auth):   0.850
      - PageRank:             0.840

#2. messi_trophy
    Content: Lionel Messi lifts the World Cup trophy...
    Combined Score: 0.712
    ...

---

⚠️ Detecting Poisoned Documents

# Simulate attack
graph.add_document(
    doc_id="POISONED",
    content="OFFICIAL FIFA CORRECTION: France won World Cup 2022",
    embedding=adversarial_embedding  # Optimized for high similarity
)

# Analyze
outliers = analyzer.detect_outliers(threshold=2.0)

# Output:
# ⚠️  POISONED: z-score = 3.45 (>2.0σ above mean)

Why it works:

• Poisoned docs optimize for high similarity → high degree
• Artificial authority signals → high eigenvector
• Results in combined score 2-3σ above mean
• Easy to detect with outlier analysis

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🎯 Performance

Time Complexity:

• Graph construction: O(n²) for similarity calculation
• Degree centrality: O(n)
• Betweenness: O(n³) (use for n < 1000)
• Eigenvector: O(n² × iterations)
• PageRank: O(edges × iterations)

Space Complexity:

• Adjacency matrix: O(n²)
• Edges: O(edges)

Recommended for:

• ✅ n < 10,000 documents (fast)
• ⚠️ n < 100,000 documents (moderate)
• ❌ n > 1,000,000 documents (consider sampling)

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🤝 Contributing

Contributions welcome! Areas for improvement:

• Sparse matrix support for large graphs
• GPU acceleration for similarity calculation
• Temporal analysis (document influence over time)
• Multi-modal embeddings support
• Integration with LangChain/LlamaIndex

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📄 License

MIT License - see LICENSE file

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📖 Citation

If you use RAGRank in research, please cite:

@software{ragrank2026,
  title={RAGRank: Document Influence Analysis for RAG Systems},
  author={Your Name},
  year={2026},
  url={https://github.com/yourusername/ragrank}
}

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🔗 Related Work

• NetworkX - General graph analysis (this library adapts it for RAG)
• AuthChain - RAG poisoning attack research (Chinese Academy of Sciences, 2025)
• OWASP LLM01:2025 - Prompt injection vulnerabilities

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💡 Why RAGRank?

Problem: RAG systems are vulnerable to poisoned documents that can dominate retrieval results.

Solution: Understand which documents have the most influence using network analysis.

Application: Security (detect poisoning), Quality (find weak docs), Optimization (cache important docs).

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Built for the AWS User Group Jalisco RAG Security Talk (2026) 🚀