Multi-Modal Graph RAG
01©2025 — 2026AI & ML / Knowledge Graph / Vector DB / FastAPI
Key Highlights
★ 40% Query Performance Optimization
★ Multi-Hop Entity Traversal
★ Distributed Embedding Pipelines
PythonFastAPIPandasNumPyNeo4jPineconeGemini APILangChain
Architected an enterprise-grade Retrieval-Augmented Generation (RAG) platform combining vector search with knowledge graph databases (Neo4j) for deep context-aware intelligence, optimizing query retrieval performance by 40%.
( Architecture & Implementation )
Standard vector search fails when queries require multi-hop relational reasoning across complex document corpora. By combining Neo4j graph nodes with Pinecone high-dimensional embeddings, this platform ingests heterogeneous documents, builds dynamic semantic entity graphs, and executes hybrid graph-vector traversals for grounded LLM synthesis.
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Credits :
M Madhukar (Architecture & Implementation)Graph RAG & Context Intelligence (Domain)