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Developed by Coder Coder | 🚀 Find more projects at CodinzHub Projects
An advanced Streamlit engine demonstrating how Knowledge Graph-based Retrieval-Augmented Generation (RAG) resolves multi-hop reasoning challenges with source provenance tracking.
Standard semantic vector search matches text chunks based on keyword/semantic similarity, but fails at:
CodinzHub GraphRAG addresses these challenges by:
ollama pull llama3.2
docker run -d \
--name neo4j \
-p 7474:7474 -p 7687:7687 \
-e NEO4J_AUTH=neo4j/password \
neo4j:latest
cd codinzhub-skills/codinzhub-rag/codinzhub-graph-rag
pip install -r requirements.txt
streamlit run codinzhub-graph-rag.py
flowchart TD
Doc[Raw Text/PDF Document] -->|Entity Parsing| LLM(Local Ollama Model)
LLM -->|Extract JSON Nodes & Edges| Parser[Structure Parser]
Parser -->|Add GraphNode| DB[(Neo4j Database)]
Parser -->|Add GraphEdge| DB
flowchart LR
Query[User Question] -->|Text Index Match| Search[Semantic Search]
Search -->|Multi-Hop Path Traversal| Expand[Depth-2 Connections]
Expand -->|Formatted Context Mapping| Gen[Attributed Generator]
Gen -->|Provenace Attributions| Out[Answer + Citations]
CloudEngine is an automated deployment framework designed by CodinzHub Labs. It uses
generative configuration templates to provision server clusters. The system was
architected by Coder Coder, who led the development team in San Francisco.
CloudEngine (TECHNOLOGY)CodinzHub Labs (ORGANIZATION)Coder Coder (PERSON)Coder Coder --[ARCHITECTED]--> CloudEngineCloudEngine --[DESIGNED_BY]--> CodinzHub Labsbolt://127.0.0.1:7687neo4jllama3.2Download the complete source code for this project. Extract the ZIP file and follow the instructions in the README to run it locally.
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