Which integrations are available for this server?

Connects to InterSystems [IRIS](/mcp/servers/integrations/iris) vector database for medical data storage, enabling GraphRAG entity and relationship queries, FHIR document search, and hybrid vector search with COSINE similarity. Integrates [NVIDIA](/mcp/servers/integrations/nvidia) NIM for generating 1024-dimensional embeddings used in medical document vectorization and semantic search. Supports comprehensive testing suite including unit, integration, and end-to-end tests for the medical GraphRAG platform. Implements MCP server tools using [Python](/mcp/servers/integrations/python) for medical search operations, FHIR document retrieval, and GraphRAG queries. Provides interactive chat UI for medical queries with conversation history, execution transparency, and visualization of medical data and relationships.

Medical GraphRAG Assistant

A production-ready medical AI assistant platform built on Model Context Protocol (MCP), featuring GraphRAG multi-modal search, FHIR integration, NVIDIA NIM embeddings, and AWS Bedrock Claude Sonnet 4.5.

Originally forked from: FHIR-AI-Hackathon-Kit

Current Version: v2.14.0 (Auto Memory Recall & Interactive Graphs)

What This Is

An agentic medical chat platform with advanced capabilities:

🤖 Model Context Protocol (MCP) - Claude autonomously calls medical search tools
🧠 GraphRAG - Knowledge graph-based retrieval with entity and relationship extraction
🖼️ Medical Image Search - Semantic search over chest X-rays using NV-CLIP embeddings
💾 Agent Memory System - Persistent semantic memory with vector search
🏥 FHIR Integration - Full-text search of clinical documents
☁️ AWS Deployment - Production deployment on AWS EC2 with NVIDIA A10G GPU
📊 Interactive UI - Streamlit interface with execution transparency
🗄️ InterSystems IRIS - Vector database with native VECTOR(DOUBLE, 1024) support

Quick Start

1. Run the Streamlit Chat Interface

# Install dependencies pip install -r requirements.txt # Set AWS credentials export AWS_PROFILE=your-profile # Configure NV-CLIP endpoint (for medical images and memory) export NVCLIP_BASE_URL="http://localhost:8002/v1" # Local NIM via SSH tunnel # Run the chat app cd mcp-server streamlit run streamlit_app.py

Visit http://localhost:8501 and start chatting!

2. Use as MCP Server (Claude Desktop, etc.)

# Configure MCP client to point to: python mcp-server/fhir_graphrag_mcp_server.py

Architecture

System Overview

flowchart TB subgraph UI["🖥️ Presentation Layer"] ST[Streamlit Chat UI v2.15.0] end subgraph LLM["🧠 AI/LLM Layer"] direction LR NIM[NVIDIA NIM Llama 3.1 8B] OAI[OpenAI GPT-4o] BED[AWS Bedrock Claude Sonnet 4.5] end subgraph MCP["⚡ MCP Server Layer"] MCPS[FHIR + GraphRAG MCP Server 14+ Medical Tools] end subgraph DATA["🗄️ Data Layer"] direction LR IRIS[(InterSystems IRIS Vector Database)] FHIR[FHIR Documents 51 Clinical Notes] GRAPH[Knowledge Graph 83 Entities • 540 Relations] IMG[Medical Images 50 Chest X-rays] MEM[Agent Memory Semantic Store] end subgraph EMB["🔢 Embedding Layer"] NVCLIP[NVIDIA NV-CLIP 1024-dim Multimodal] end ST <-->|Multi-LLM Support| LLM LLM <-->|MCP Protocol| MCPS MCPS <-->|IRIS Native API| IRIS IRIS --- FHIR IRIS --- GRAPH IRIS --- IMG IRIS --- MEM MCPS <-->|Embedding API| NVCLIP

GraphRAG Data Flow

flowchart LR subgraph INPUT["📥 Input"] Q[User Query] end subgraph RECALL["🔄 Auto-Recall"] MR[Memory Recall Past Corrections] end subgraph SEARCH["🔍 Multi-Modal Search"] direction TB VS[Vector Search FHIR Documents] GS[Graph Search Entities & Relations] IS[Image Search NV-CLIP Similarity] end subgraph FUSION["⚗️ Fusion"] RRF[Reciprocal Rank Fusion RRF Algorithm] end subgraph OUTPUT["📤 Output"] R[Ranked Results + Knowledge Graph Viz] end Q --> MR MR --> VS MR --> GS MR --> IS VS --> RRF GS --> RRF IS --> RRF RRF --> R

Component Interaction

sequenceDiagram participant U as User participant S as Streamlit UI participant L as LLM (Claude/GPT/NIM) participant M as MCP Server participant I as IRIS DB participant N as NV-CLIP U->>S: "Find pneumonia X-rays" S->>L: Query + Tools L->>M: search_medical_images() M->>N: embed_text(query) N-->>M: 1024-dim vector M->>I: VECTOR_COSINE search I-->>M: Top-K results M-->>L: Images + metadata L->>M: search_knowledge_graph() M->>I: Entity/relation query I-->>M: Graph data M-->>L: Entities + relations L-->>S: Response + visualizations S-->>U: Display results

IRIS Vector Package Architecture

This project uses the InterSystems IRIS Vector ecosystem:

flowchart TB subgraph APP["🏥 Medical GraphRAG Assistant"] MCP[MCP Server 14+ Medical Tools] CFG[YAML Config CloudConfiguration API] end subgraph IRIS_PKG["📦 InterSystems IRIS Vector Packages"] direction TB RAG["<a href='https://pypi.org/project/iris-vector-rag/'>iris-vector-rag</a> RAG Framework"] GRAPH["<a href='https://pypi.org/project/iris-vector-graph/'>iris-vector-graph</a> Graph Toolkit"] subgraph RAG_DETAIL["iris-vector-rag Features"] BYOT[BYOT Storage Bring Your Own Tables] PIPE[RAG Pipelines basic • graphrag • crag] SCHEMA[SchemaManager Table Validation] end subgraph GRAPH_DETAIL["iris-vector-graph Features"] ENT[Entity Storage Type-Tagged Nodes] REL[Relationship Store Typed Edges] TRAV[Graph Traversal Path Queries] end end subgraph IRIS_DB["🗄️ InterSystems IRIS"] VEC[(VECTOR Column DOUBLE, 1024)] SQL[(SQL Tables ClinicalNoteVectors)] KG[(Knowledge Graph Entities • Relations)] end MCP --> RAG MCP --> GRAPH CFG --> RAG RAG --> BYOT RAG --> PIPE RAG --> SCHEMA GRAPH --> ENT GRAPH --> REL GRAPH --> TRAV BYOT --> VEC SCHEMA --> SQL ENT --> KG REL --> KG

Package Links:

iris-vector-rag - Production RAG framework with multiple pipelines (basic, graphrag, crag, multi_query_rrf)
iris-vector-graph - Graph-oriented vector toolkit for GraphRAG workloads

NVIDIA NIM Architecture

This project uses NVIDIA NIM (Inference Microservices) for GPU-accelerated AI inference, deployed on AWS EC2 with NVIDIA A10G GPUs.

flowchart TB subgraph AWS["☁️ AWS EC2 g5.xlarge"] subgraph GPU["🎮 NVIDIA A10G GPU (24GB)"] direction TB NIM_LLM["<a href='https://build.nvidia.com'>NIM Container</a> Port 8001 meta/llama-3.1-8b-instruct"] NIM_CLIP["<a href='https://build.nvidia.com/nvidia/nvclip'>NV-CLIP Container</a> Port 8002 nvidia/nvclip"] end subgraph SERVICES["🔧 Application Services"] MCP[MCP Server Medical Tools] ST[Streamlit UI Port 8501] IRIS[(IRIS DB Port 1972)] end end subgraph CLIENT["💻 Client"] TUNNEL[SSH Tunnel localhost:8002 → AWS:8002] APP[Local Development] end APP --> |NVCLIP_BASE_URL| TUNNEL TUNNEL --> NIM_CLIP ST --> MCP MCP --> |Text Embeddings| NIM_LLM MCP --> |Image Embeddings| NIM_CLIP MCP --> |Vector Search| IRIS NIM_LLM --> GPU NIM_CLIP --> GPU

NIM Services:

Service	Model	Port	Purpose	Dimension
NIM LLM	`meta/llama-3.1-8b-instruct`	8001	Text generation, entity extraction	N/A
NV-CLIP	`nvidia/nvclip`	8002	Multimodal embeddings (text + images)	1024-dim
NV-EmbedQA	`nvidia/nv-embedqa-e5-v5`	Cloud API	Text embeddings for RAG	1024-dim

Deployment Options:

Self-hosted NIM (Production): Docker containers on GPU instances with HIPAA compliance
NVIDIA Cloud API (Development): https://integrate.api.nvidia.com/v1 with API key

Knowledge Graph: Entity & Relationship Extraction

The knowledge graph is built from FHIR DocumentReference resources using regex-based entity extraction with confidence scoring. No external medical ontology is currently used - entities are extracted using curated regex patterns.

flowchart LR subgraph FHIR["📄 FHIR Repository"] DOC[DocumentReference Clinical Notes] end subgraph EXTRACT["🔬 Entity Extraction"] direction TB REGEX[Regex Patterns Confidence-Scored] TYPES[Entity Types: SYMPTOM • CONDITION MEDICATION • PROCEDURE BODY_PART • TEMPORAL] end subgraph RELATE["🔗 Relationship Inference"] direction TB HEUR[Heuristic Rules: Proximity + Context] REL_TYPES[Relationship Types: TREATS • CAUSES LOCATED_IN • CO_OCCURS] end subgraph STORE["🗄️ Knowledge Graph Tables"] ENT_TBL[(RAG.Entities 83 entities)] REL_TBL[(RAG.EntityRelationships 540 relationships)] end DOC --> REGEX REGEX --> TYPES TYPES --> HEUR HEUR --> REL_TYPES REL_TYPES --> ENT_TBL REL_TYPES --> REL_TBL

Entity Types Extracted:

Type	Examples	Confidence Range
`SYMPTOM`	chest pain, shortness of breath, fever	0.80 - 0.95
`CONDITION`	hypertension, diabetes, pneumonia	0.75 - 0.95
`MEDICATION`	aspirin, metformin, insulin	0.85 - 0.95
`PROCEDURE`	CT scan, MRI, blood test	0.85 - 0.92
`BODY_PART`	chest, abdomen, heart	0.75 - 0.90
`TEMPORAL`	3 days ago, last week	0.75 - 0.95

Relationship Types:

Relationship	Pattern	Example
`TREATS`	MEDICATION → CONDITION/SYMPTOM	aspirin → chest pain
`CAUSES`	CONDITION → SYMPTOM	hypertension → headache
`LOCATED_IN`	SYMPTOM → BODY_PART	pain → chest
`CO_OCCURS`	SYMPTOM ↔ SYMPTOM (within window)	fever ↔ cough

Current Limitations & Future Work:

No medical ontology (SNOMED-CT, ICD-10, RxNorm) - extraction is pattern-based
No FHIR native queries - clinical notes are hex-decoded from DocumentReference.content
Future: LLM-based entity extraction for improved coverage and ontology mapping

MCP Tools Architecture

The MCP server exposes 14+ tools that Claude (or other LLMs) can autonomously call to search medical data.

flowchart TB subgraph LLM["🧠 LLM (Claude/GPT/NIM)"] AGENT[Agentic Chat] end subgraph MCP["⚡ MCP Server Tools"] direction TB subgraph SEARCH["🔍 Search Tools"] T1[search_fhir_documents Full-text clinical notes] T2[search_knowledge_graph Entity-based search] T3[hybrid_search RRF fusion of all sources] T4[search_medical_images NV-CLIP similarity] end subgraph DETAIL["📋 Detail Tools"] T5[get_document_details Full document content] T6[get_entity_relationships Graph traversal] T7[get_entity_statistics Graph stats] end subgraph MEMORY["💾 Memory Tools"] T8[remember_information Store corrections/prefs] T9[recall_information Semantic memory search] T10[get_memory_stats Memory statistics] end subgraph VIZ["📊 Visualization Tools"] T11[plot_symptom_frequency] T12[plot_entity_distribution] T13[plot_patient_timeline] T14[plot_entity_network] end end subgraph DATA["🗄️ Data Sources"] FHIR[(FHIR Documents)] KG[(Knowledge Graph)] IMG[(Medical Images)] MEM[(Agent Memory)] end AGENT -->|MCP Protocol| MCP T1 --> FHIR T2 --> KG T3 --> FHIR T3 --> KG T3 --> IMG T4 --> IMG T8 --> MEM T9 --> MEM

Tool Categories:

Category	Tools	Data Source	Query Type
FHIR Search	`search_fhir_documents`, `get_document_details`	ClinicalNoteVectors	Full-text SQL LIKE
GraphRAG	`search_knowledge_graph`, `get_entity_relationships`, `hybrid_search`	Entities, EntityRelationships	Entity + Vector + RRF
Medical Images	`search_medical_images`	MIMICCXRImages	VECTOR_COSINE (NV-CLIP)
Agent Memory	`remember_information`, `recall_information`	AgentMemoryVectors	VECTOR_COSINE (NV-CLIP)
Visualization	`plot_*` tools	All sources	Plotly/NetworkX charts

Note: FHIR queries are performed via SQL on pre-ingested data in IRIS tables. The system does not make live FHIR REST API calls - documents are batch-loaded during setup and stored with their embeddings in IRIS vector columns.

Data Pipeline: Ingestion → Storage → Query

Note: Current implementation uses batch vectorization on initial data load. Vectors are stored in standard VECTOR columns and require manual re-vectorization when source documents change. See Future Enhancements for planned automatic sync capabilities.

flowchart LR subgraph INGEST["📥 Data Ingestion (Batch)"] direction TB FHIR_SRC[FHIR Bundles JSON Resources] CXR[MIMIC-CXR Chest X-rays] PARSE[fhirpy Parser Resource Extraction] end subgraph EMBED["🔢 Vectorization"] direction TB NIM_EMB[NVIDIA NIM NV-EmbedQA-E5-v5] NVCLIP_EMB[NV-CLIP Multimodal 1024-dim] NER[Entity Extraction Symptoms • Conditions] end subgraph STORE["🗄️ IRIS FHIR Repository"] direction TB subgraph FHIR_TABLES["FHIR Tables"] DOC[(ClinicalNoteVectors 51 Documents)] IMG[(MIMICCXRImages 50 X-rays)] end subgraph GRAPH_TABLES["Knowledge Graph"] ENT_TBL[(Entities 83 Nodes)] REL_TBL[(EntityRelationships 540 Edges)] end subgraph MEM_TABLES["Agent Memory"] MEM_TBL[(AgentMemoryVectors Semantic Store)] end end subgraph QUERY["🔍 Query Processing"] direction TB VEC_SEARCH[Vector Search VECTOR_COSINE] GRAPH_TRAV[Graph Traversal Entity → Relations] RRF_FUSE[RRF Fusion Rank Combination] end subgraph OUTPUT["📤 Results"] RANKED[Ranked Documents + Knowledge Graph] end FHIR_SRC --> PARSE CXR --> NVCLIP_EMB PARSE --> NIM_EMB PARSE --> NER NIM_EMB --> DOC NVCLIP_EMB --> IMG NER --> ENT_TBL NER --> REL_TBL DOC --> VEC_SEARCH IMG --> VEC_SEARCH ENT_TBL --> GRAPH_TRAV REL_TBL --> GRAPH_TRAV MEM_TBL --> VEC_SEARCH VEC_SEARCH --> RRF_FUSE GRAPH_TRAV --> RRF_FUSE RRF_FUSE --> RANKED

IRIS Database Schema

erDiagram ClinicalNoteVectors { int ID PK string ResourceID UK string PatientID string DocumentType text TextContent vector Embedding "VECTOR(DOUBLE,1024)" string EmbeddingModel string SourceBundle } MIMICCXRImages { int ID PK string DicomID UK string PatientID string StudyID string ViewPosition text Findings vector Embedding "VECTOR(DOUBLE,1024)" string ImagePath } Entities { int ID PK string EntityText string EntityType float Confidence string SourceDocID FK } EntityRelationships { int ID PK int SourceEntityID FK int TargetEntityID FK string RelationType float Confidence string SourceText string TargetText } AgentMemoryVectors { int ID PK string MemoryType text Content vector Embedding "VECTOR(DOUBLE,1024)" datetime CreatedAt } ClinicalNoteVectors ||--o{ Entities : "extracts" Entities ||--o{ EntityRelationships : "source" Entities ||--o{ EntityRelationships : "target"

Features

MCP Tools (10+ available)

FHIR & GraphRAG:

search_fhir_documents - Full-text search of clinical notes
get_document_details - Retrieve complete clinical notes by ID
search_knowledge_graph - Search medical entities (symptoms, conditions, medications)
hybrid_search - Combined vector + graph search with RRF fusion
get_entity_statistics - Knowledge graph statistics and insights

Medical Images: 6. search_medical_images - Semantic search over chest X-rays with NV-CLIP

Agent Memory: 7. remember_information - Store semantic memories (corrections, knowledge, preferences, feedback) 8. recall_information - Semantic search over agent memories 9. get_memory_stats - Memory system statistics

Visualizations: 10. plot_symptom_frequency - Chart of most common symptoms 11. plot_entity_distribution - Entity type distribution charts 12. plot_patient_timeline - Patient encounter timeline 13. plot_entity_network - Knowledge graph relationship visualization 14. visualize_graphrag_results - Interactive GraphRAG search results

Chat Interface Features

✅ Multi-Modal Search - Search clinical text, medical images, and knowledge graph
✅ Agent Memory - Persistent semantic memory with vector search
✅ Medical Image Display - View chest X-rays with DICOM support
✅ Execution Transparency - See which tools Claude calls and its reasoning
✅ Interactive Charts - Generate visualizations from data
✅ Conversation History - Multi-turn conversations with context
✅ Memory Editor - Browse, search, add, and delete agent memories in sidebar
✅ Error Handling - Graceful handling of API issues with detailed logs
✅ Max Iterations Control - Prevents infinite loops (10 iteration limit)

Current Version: v2.14.0

Recent Features (v2.14.0):

✅ Auto Memory Recall: Memories automatically recalled before each query to guide tool selection
✅ Interactive Graph Viz: Force-directed, draggable graphs with streamlit-agraph
✅ Memory in Execution Log: See recalled memories in "Show Execution Details" pane
✅ NetworkX-powered graph layouts with physics simulation

Previous Updates:

v2.13.0: Multi-LLM provider support (NIM > OpenAI > Bedrock), OneDrive backup
v2.12.0: Agent memory system with pure IRIS vector storage
v2.10.2: Fixed content processing errors, increased max iterations
v2.10.0: GraphRAG multi-modal search with RRF fusion
v2.0.0: AWS deployment with NVIDIA NIM integration

Configuration

Required Environment Variables

# AWS Credentials export AWS_PROFILE=your-profile # or set AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY # IRIS Database (AWS Production) export IRIS_HOST=3.84.250.46 # Your AWS EC2 IP export IRIS_PORT=1972 export IRIS_NAMESPACE=%SYS # Use %SYS for AWS deployment export IRIS_USERNAME=_SYSTEM export IRIS_PASSWORD=your-password # NVIDIA NV-CLIP (for medical images and memory) export NVCLIP_BASE_URL="http://localhost:8002/v1" # Local NIM via SSH tunnel # or use cloud API: # export NVCLIP_BASE_URL="https://integrate.api.nvidia.com/v1" # export NVIDIA_API_KEY="your-api-key"

Config Files

config/fhir_graphrag_config.yaml - Local development config
config/fhir_graphrag_config.aws.yaml - AWS production config (active)
config/aws-config.yaml - AWS infrastructure settings

Project Structure

medical-graphrag-assistant/ ├── mcp-server/ # MCP server and Streamlit app │ ├── fhir_graphrag_mcp_server.py # MCP server with 10+ tools │ ├── streamlit_app.py # Chat UI v2.12.0 with memory editor │ └── test_*.py # Integration tests ├── src/ │ ├── db/ # IRIS database clients │ ├── embeddings/ # NVIDIA NIM integration │ │ └── nvclip_embeddings.py # NV-CLIP multimodal embeddings │ ├── memory/ # Agent memory system │ │ └── vector_memory.py # Semantic memory with IRIS vectors │ ├── search/ # Search implementations │ ├── vectorization/ # Document vectorization │ └── validation/ # Data validation ├── config/ # Configuration files │ └── fhir_graphrag_config.aws.yaml # Active AWS config ├── docs/ # Documentation │ ├── architecture.md # System architecture │ ├── deployment-guide.md # AWS deployment │ └── troubleshooting.md # Common issues ├── scripts/ # Deployment and utility scripts │ └── aws/ # AWS-specific scripts ├── tests/ # Test suite └── archive/ # Historical implementations and docs

Technology Stack

AI/ML:

AWS Bedrock (Claude Sonnet 4.5)
NVIDIA NV-CLIP (1024-dim multimodal embeddings)
NVIDIA NIM (Inference Microservices)
Model Context Protocol (MCP)

Database & Vector Storage:

InterSystems IRIS Community Edition (AWS EC2)
Native VECTOR(DOUBLE, 1024) support
VECTOR_COSINE similarity search
Tables: ClinicalNoteVectors, MIMICCXRImages, Entities, EntityRelationships, AgentMemoryVectors

InterSystems IRIS Vector Packages:

iris-vector-rag - Production RAG framework with BYOT storage, GraphRAG pipelines, and CloudConfiguration API
iris-vector-graph - Graph-oriented vector toolkit for entity storage and relationship traversal
intersystems-irispython - Native IRIS database driver

Infrastructure:

AWS EC2 g5.xlarge (NVIDIA A10G GPU)
Python 3.10+
Streamlit for UI
Docker for containerization

Key Libraries:

fhirpy - FHIR resource parsing and handling
boto3 - AWS SDK
streamlit - Chat UI
streamlit-agraph - Interactive graph visualization
mcp - Model Context Protocol SDK
pydicom - DICOM medical image processing
networkx - Graph algorithms and layout

Example Queries

Try these in the chat interface:

FHIR Search:

"Find patients with chest pain"
"Search for diabetes cases"
"Show recent emergency visits"

GraphRAG:

"What medications treat hypertension?"
"Show me the relationship between conditions and procedures"
"What are the side effects of metformin?"

Medical Images:

"Show me chest X-rays of pneumonia"
"Find chest X-rays showing cardiomegaly"
"Search for lateral view chest X-rays"

Agent Memory:

"Remember that I prefer concise clinical summaries"
"What do you know about my preferences?"
"Recall any corrections I've given you about medical terminology"

Hybrid Search:

"Find treatment options for chronic pain" (combines vector + graph + image search)

Visualization:

"Show a chart of conditions by frequency"
"Visualize the knowledge graph for chest pain"
"Graph the entity relationships"

Backup

The project uses OneDrive for automatic cloud backup:

# Run backup (rsync to OneDrive folder) ./scripts/backup-to-onedrive.sh

Backup includes all code, configs, and medical images (~195 MB). OneDrive automatically syncs to cloud.

Development

Running Tests

# Unit tests pytest tests/unit/ # Integration tests pytest tests/integration/ # E2E tests pytest tests/e2e/

Debug Mode

Enable debug logging:

import logging logging.basicConfig(level=logging.DEBUG)

AWS Deployment

The system is deployed on AWS EC2 with:

Instance: g5.xlarge (NVIDIA A10G GPU)
Region: us-east-1
Database: InterSystems IRIS Community Edition
GPU Services: NVIDIA NIM for NV-CLIP embeddings
Data: 50 medical images, 51 clinical notes, 83 entities, 540 relationships

See docs/deployment-guide.md for detailed deployment instructions.

Troubleshooting

See docs/troubleshooting.md for common issues.

Common Issues:

AWS credentials not configured → Set AWS_PROFILE or AWS env vars
IRIS connection failed → Check IRIS_HOST and credentials
NV-CLIP not responding → Check NVCLIP_BASE_URL and SSH tunnel
Medical images not found → Verify image paths and DICOM support
Memory search returning 0 results → Check embeddings with magnitude test
Max iterations reached → Query may be too complex, try simplifying

Documentation

Core Documentation

Architecture Overview - System design and data flow
Deployment Guide - AWS deployment instructions
Troubleshooting Guide - Common issues and solutions

Current Session Docs

EMBEDDINGS_FIXED.md - Image and memory embeddings fix
MEMORY_SEARCH_BROWSE_FIX.md - Memory search UI fix
PROGRESS.md - Development history and achievements
TODO.md - Current tasks and roadmap

Historical Documentation

archive/ - Old implementations, scripts, and session docs

Future Enhancements

Automatic Vector Synchronization

Current State: Vectors are generated via batch processing during initial data load. When FHIR documents are updated in the repository, embeddings must be manually re-generated.

Planned Enhancement: Leverage IRIS EMBEDDING column type for automatic vector synchronization:

-- Future: Auto-computed embeddings on INSERT/UPDATE CREATE TABLE ClinicalNoteVectors ( ID INT PRIMARY KEY, TextContent TEXT, Embedding EMBEDDING[MODEL='NV-EmbedQA-E5-v5'](TextContent) -- Auto-computed );

Benefits:

Automatic re-vectorization when TextContent changes
No manual batch re-processing required
Real-time sync between FHIR repository and vector store

Additional Planned Features

FHIR Subscription Hooks - Trigger vectorization on resource create/update events
Incremental Knowledge Graph Updates - Update entities/relationships without full rebuild
IRIS HealthShare Integration - Direct FHIR R4 repository connection
Vector Index Optimization - HNSW index tuning for larger datasets
Multi-tenant Support - Namespace isolation for multiple healthcare organizations

Contributing

This project is based on the FHIR-AI-Hackathon-Kit. The original tutorial content remains in the tutorial/ directory.

License

Inherits license from upstream FHIR-AI-Hackathon-Kit repository.

Acknowledgments

Original Project: FHIR-AI-Hackathon-Kit by gabriel-ing
InterSystems IRIS for the vector database platform
AWS Bedrock for Claude Sonnet 4.5 access
NVIDIA NIM for NV-CLIP multimodal embeddings
Model Context Protocol by Anthropic
MIMIC-CXR dataset for medical imaging data