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arshad234567 / Hybrid-Healthcare-Retrieval-System-with-Qdrant-QQL-Neo4j-and-GLiNER2

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I Build a Healthcare retrieval system using GLiNER2, Neo4j, Qdrant, and QQL to convert doctor–patient conversations into structured medical intelligence. Implemented healthcare entity and relation extraction, constructed a knowledge graph, and enabled hybrid semantic and graph-based retrieval for efficient healthcare data search and analysis.

python neo4j reactjs entity-extraction knowledge-graph-embeddings fastapi huggingface qdrant-vector-database graph-rag gliner2
  • Updated Jul 12, 2026
  • Jupyter Notebook

ikinikar / prompt-based-map-routing

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An end-to-end prompt-based navigation task for the Pioneer Valley region: synthetic data, a custom evaluation suite, and several methods helping map the problem's Pareto front

graph-algorithms named-entity-recognition monte-carlo-simulation search-algorithm nlp-machine-learning nlp-keywords-extraction synsets a-star-algorithm nlp-datasets osmnx sentence-transformers constrained-decoding cross-attention bertscore llms ollama neurosymbolic-ai gliner-medium llama3 gliner2
  • Updated Jul 4, 2026
  • Jupyter Notebook

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