Pro Track6.0 Hours•Level: Intermediate•Updated: October 2026

Production GraphRAG & Hybrid Retrieval Systems

Move beyond naive cosine similarity. Architect hybrid search with Reciprocal Rank Fusion, implement Microsoft GraphRAG with community detection, and build multi-vector visual retrieval with ColPali.

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Curriculum Outcomes & Engineering Deliverables

Combine sparse BM25 and dense embeddings via Reciprocal Rank Fusion (RRF)

Build dynamic GraphRAG pipelines extracting entities, relationships, and communities

Deploy cross-encoder rerankers (Cohere, BGE) to filter out semantic hallucinations

Implement multi-vector late-interaction retrieval for tables and complex PDFs

Included Engineering Deliverables

Hybrid Reciprocal Rank Fusion (RRF) search engine

Knowledge Graph entity-relation extraction pipeline (Neo4j)

ColPali multi-vector late-interaction PDF visual retrieval engine

Cross-encoder reranking microservice with latency profiling

Curriculum Modules

3 Modules • 5 Lessons

Module 012 Lessons

Hybrid Retrieval & Reciprocal Rank Fusion

Solving the blind spots of vector embeddings with lexical-semantic fusion.

The RAG Bottleneck & Dense vs. Sparse Representation

Interactive • 26 min

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Cross-Encoder Reranking & Context Compression

Deep Dive • 38 min

Pro

Module 022 Lessons

GraphRAG & Relational Knowledge

Hierarchical entity graphs for answering holistic, multi-hop questions.

Extracting Knowledge Graphs with Structured JSON Schemas

Code Lab • 40 min

Pro

Community Detection & Hierarchical Graph Summarization

Architecture • 44 min

Pro

Module 031 Lessons

Multimodal Document Retrieval

Visual RAG with ColPali for rich charts, tables, and blueprints.

ColPali: Late-Interaction Multi-Vector Patch Retrieval

Interactive • 40 min

Pro

Hardware & GPU Compute

Baseline GPUNVIDIA RTX 3080 or T4 / A10G on Cloud

VRAM Requirement12GB - 16GB VRAM

Recommended Configuration1x NVIDIA A10G (24GB) or RunPod Serverless

Prerequisites & Tech Stack

Stack Components

Neo4jQdrantColPaliBGE-RerankerPydanticNetworkX

Knowledge Prerequisites

LLM Foundations
Python async
Basic knowledge of vector embeddings