GraphForge: Local Document-to-Knowledge-Graph Intelligence System
Traditional retrieval pipelines answer questions from isolated text chunks but struggle with cross-document relationships, entity disambiguation, and multi-hop reasoning. This creates blind spots when users need to understand how people, organizations, events, and concepts are connected across many files. The challenge is to move beyond flat semantic search toward a structure-aware system that can represent and query relationships directly.
This project demonstrates how unstructured document collections can be transformed into a navigable intelligence layer.
- Converts static files into a connected knowledge graph that captures entities and relationships.
- Improves explainability by showing relationship paths, not only generated answers.
- Enables faster investigative workflows for analysts, researchers, and technical teams.
- Provides a foundation for later GraphRAG reasoning workflows such as hypothesis validation and impact tracing.
- Showcases advanced AI engineering skills beyond standard vector-only RAG, making it strong portfolio evidence for graph-native AI architecture.
- Independent researchers analyzing reports, notes, and reference documents.
- Startup teams building internal intelligence tooling without cloud dependencies.
- Data and AI engineers exploring GraphRAG patterns in a practical local environment.
- Product and strategy analysts who need relationship-centric insights from mixed document sets.
- Local document ingestion pipeline for PDFs, DOCX, TXT, and Markdown files.
- Entity extraction pipeline to detect people, organizations, locations, products, and domain concepts.
- Relationship extraction pipeline to infer edges between entities from document context.
- Knowledge graph construction and updates from newly ingested files.
- Entity explorer UI to inspect node profiles, source evidence, and neighboring connections.
- Relationship query interface for questions like "How is Entity A connected to Entity B?".
- Provenance tracking that links nodes/edges back to source documents and passages.
- Hybrid retrieval layer that can combine graph traversal signals with semantic context retrieval.
- Fully local deployment with no paid APIs required.
- Graph-aware reasoning agent that executes multi-step traversal plans before answer synthesis.
- Temporal relationship modeling (when a relationship started, changed, or ended).
- Confidence scoring and edge validation workflows.
- Human-in-the-loop graph curation interface for merging duplicates and correcting links.
- Domain ontology templates for faster adaptation to legal, finance, biotech, and cyber datasets.
- Incremental re-indexing and background processing queues for larger corpora.
- Visual path explanations that show why the system surfaced a specific relationship chain.
- Role-based access controls and project-level graph partitioning for team use.
- Not a production-scale distributed graph platform in MVP.
- Not a replacement for enterprise governance, security, or compliance tooling.
- Not a generic chatbot product; the focus is relationship intelligence.
- Not dependent on external paid LLM APIs.
- Not optimized for real-time streaming ingestion in the first release.
Local-first and cost-free stack focused on practical reproducibility.
- Frontend: React + Vite for interactive graph exploration and query workflows.
- Backend API: FastAPI for ingestion orchestration, extraction services, and query endpoints.
- NLP and Extraction: spaCy + optional local transformer models for entity and relation extraction.
- Graph Store: Neo4j Community Edition (local) for property graph modeling and Cypher queries.
- Embeddings and Retrieval: SentenceTransformers running locally for semantic signals.
- Local LLM Runtime (optional): Ollama for on-device inference in later reasoning stages.
- Task Execution: Celery or lightweight background workers for ingestion and graph build jobs.
- Storage: Local filesystem + SQLite/PostgreSQL (local) for metadata and job states.
- Visualization: Cytoscape.js or React Flow for graph interaction in the UI.
frontend/- UI for document upload, entity exploration, and relationship querying.backend/- API services, orchestration logic, and query endpoints.pipelines/- document parsing, entity extraction, relation extraction, and graph builders.graph/- graph schema definitions, Cypher templates, and graph utility services.retrieval/- semantic retrieval and graph-augmented retrieval components.models/- local model configuration, prompts, and runtime adapters.data/- local raw uploads, processed artifacts, and caches (gitignored where appropriate).tests/- unit and integration tests for ingestion, extraction, and query flows.docs/- architecture notes, decision records, and workflow diagrams.
- Extraction quality risk: noisy entity and relationship extraction can reduce graph reliability.
- Entity resolution risk: duplicates and ambiguous names require robust normalization and merge strategies.
- Graph drift risk: repeated ingestion may introduce conflicting edges without confidence-aware updates.
- Performance tradeoff: local-only constraints require careful batching and indexing for acceptable query latency.
- Explainability requirement: every relationship and answer should expose provenance to preserve trust.
- Model footprint: local NLP/LLM choices must balance quality with memory and hardware limits.
- Schema evolution: graph schema should remain flexible enough for new entity/edge types without frequent rewrites.
- UX complexity: graph exploration can overwhelm users if filtering, grouping, and path constraints are weak.
GraphRAG Intelligence Platform is an advanced, local-first AI system that transforms uploaded documents into a queryable knowledge graph. The project highlights end-to-end skills across NLP pipelines, graph data modeling, retrieval architecture, backend engineering, and interactive frontend design. It demonstrates a clear progression beyond standard RAG by emphasizing entity-centric structure, multi-hop relationship discovery, and explainable intelligence workflows suitable for real analytical use cases.