perf: bypass redundant NetworkX wrapper checks to optimize graph building loops (#798)

[suhaniiz]

📋 PR Checklist

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🔗 Related Issue

Closes #798

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📝 What does this PR do?

Optimizes performance inside the entity co-occurrence mapping loop within build_graph by removing redundant hash lookups.

Previously, the code invoked explicit wrapper methods (graph.has_node(...) and graph.has_edge(...)) sequentially before modifying attributes. This pattern forced NetworkX to execute multiple duplicate lookups across internal dictionary states on every element traversal.

This PR optimizes lookups by:

1. Migrating node tracking to check direct dictionary presence (entity.id in graph.nodes).
2. Streamlining edge discovery inside the tight $O(N^2)$ loop by evaluating entity adjacency directly within the pre-fetched source node mapping (right_id in graph[left_id]).

This significantly lowers lookup overhead when constructing high-density knowledge graphs from heavy multi-page payloads.

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🗂️ Type of Change

• 🐛 Bug fix
• ✨ New feature
• 🔧 Refactor / code cleanup
• 📝 Documentation update
• 🎨 UI / styling change
• ⚙️ CI / tooling / config change
• 🧪 Tests

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🧪 How was this tested?

• Tested the affected API endpoints manually
• Profiled data ingestion loops locally to ensure nodes and co-occurrence edges continue to map tracking metrics (mentions, weight, chunks, pages) accurately.

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📸 Screenshots (if UI change)

N/A (Performance optimization)

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⚠️ Anything to flag for reviewers?

No breaking architectural changes. The approach leans on standard, idiomatic Python dictionary lookup operations, remaining completely compatible with NetworkX's core internal graph dictionary structures.

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✅ Self-Review Checklist

• My branch is based on dev, not main
• I have not added any secrets / API keys
• I have not modified main branch or any HuggingFace deployment config
• My code follows the existing style (no unnecessary formatting changes)
• I have updated relevant docs / comments if needed

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