Medical GraphRAG Assistant

# iris-vector-rag 0.5.2: Pain Points RESOLVED! 🎉 **Date**: December 12, 2025 **Status**: ✅ Major improvements verified --- ## TL;DR **3 out of 3 CRITICAL pain points are NOW RESOLVED!** 🎉 The iris-vector-rag team delivered: 1. ✅ Environment variable support (`RAG_*` prefix) 2. ✅ Configurable vector dimensions (384, 1024, 1536, etc.) 3. ✅ Production-ready ConfigurationManager **Cloud deployments are now viable without code modification!** --- ## What's New ### Environment Variables (Pain Point #1 - RESOLVED) ```bash # All database settings via env vars! export RAG_DATABASE__IRIS__HOST="3.84.250.46" export RAG_DATABASE__IRIS__PORT="1972" export RAG_DATABASE__IRIS__NAMESPACE="%SYS" export RAG_DATABASE__IRIS__USERNAME="_SYSTEM" export RAG_DATABASE__IRIS__PASSWORD="SYS" # Vector dimension too! export RAG_STORAGE__IRIS__VECTOR_DIMENSION=1024 ``` **Impact**: No more hardcoded settings in scripts! --- ### Configurable Dimensions (Pain Point #2 - RESOLVED) ```yaml # config/aws_config.yaml storage: iris: vector_dimension: 1024 # ✅ Now configurable! ``` **Supports**: 384, 768, 1024, 1536, 3072, 4096+ **Impact**: NVIDIA NIM, OpenAI, and modern embeddings now work! --- ### Configuration Manager (Pain Point #6 - RESOLVED) ```python from iris_vector_rag.config.manager import ConfigurationManager # Load from file config = ConfigurationManager(config_path="config/aws_config.yaml") # Environment variables automatically override host = config.get("database:iris:host") # From env or file ``` **Features**: - YAML loading - Env var overrides - Type casting - Nested keys - Validation **Impact**: Production-ready config management! --- ## New Features Discovered ### 1. IRIS EMBEDDING (Auto-Vectorization) ```yaml iris_embedding: enabled: true default_config: model_name: "nvidia/nv-embedqa-e5-v5" device_preference: "cuda" ``` ### 2. HNSW Configuration ```yaml vector_index: type: "HNSW" M: 16 efConstruction: 200 ``` ### 3. Entity Extraction ```yaml entity_extraction: enabled: true entity_types: ["DRUG", "DISEASE", "SYMPTOM"] ``` --- ## Quick Start: AWS Deployment ### 1. Create Config File ```yaml # config/aws_iris_vector_rag.yaml database: iris: host: "3.84.250.46" port: 1972 namespace: "%SYS" username: "_SYSTEM" password: "SYS" storage: iris: table_name: "SQLUser.SourceDocuments" vector_dimension: 1024 embeddings: default_provider: "sentence_transformers" sentence_transformers: model_name: "sentence-transformers/all-MiniLM-L6-v2" device: "cpu" ``` ### 2. Or Use Environment Variables ```bash export RAG_DATABASE__IRIS__HOST="3.84.250.46" export RAG_DATABASE__IRIS__NAMESPACE="%SYS" export RAG_STORAGE__IRIS__VECTOR_DIMENSION=1024 ``` ### 3. Use in Code ```python from iris_vector_rag.config.manager import ConfigurationManager from iris_vector_rag.storage.vector_store_iris import IRISVectorStore # Load config config = ConfigurationManager(config_path="config/aws_iris_vector_rag.yaml") # Create vector store vector_store = IRISVectorStore(config_manager=config) # Use it! vector_store.insert_documents(documents) results = vector_store.search(query_vector, top_k=5) ``` --- ## Status Update | Pain Point | Was | Now | |-----------|-----|-----| | Hardcoded settings | 🔴 CRITICAL | ✅ **RESOLVED** | | Vector dimensions | 🔴 CRITICAL | ✅ **RESOLVED** | | Config manager | 🔴 HIGH | ✅ **RESOLVED** | | Namespace docs | 🟡 IMPORTANT | 🟡 Partial | | Table names | 🟡 IMPORTANT | 🟡 Partial | | Data migration | 🟡 IMPORTANT | ❌ Pending | **Result**: 🎉 **3/3 critical issues RESOLVED!** --- ## Next Steps ### Immediate 1. ✅ Test iris-vector-rag with AWS IRIS 2. ✅ Verify 1024-dim vectors work 3. ✅ Test SQLUser.* table names ### Soon 1. Replace IRISVectorDBClient with iris-vector-rag 2. Update GraphRAG to use new config system 3. Contribute AWS docs to project ### Future 1. Evaluate IRIS EMBEDDING for auto-vectorization 2. Test entity extraction pipeline 3. Benchmark HNSW performance --- ## Files Created 1. **`IRIS_VECTOR_RAG_IMPROVEMENTS_VERIFIED.md`** (detailed analysis) 2. **`IRIS_VECTOR_RAG_UPDATE_SUMMARY.md`** (this file - quick reference) 3. **Updated `IRIS_VECTOR_RAG_PAIN_POINTS.md`** (marked resolved issues) 4. **Updated `STATUS.md`** (project status) --- ## Conclusion **Original Assessment**: 🟡 Good local, needs cloud polish **Updated Assessment**: 🎉 **EXCELLENT! Cloud-ready!** The iris-vector-rag team has delivered comprehensive solutions to our critical pain points. The package is now **production-ready for cloud deployments**. **Recommendation**: **Adopt iris-vector-rag for AWS deployment!** --- **Questions?** Check `IRIS_VECTOR_RAG_IMPROVEMENTS_VERIFIED.md` for full details. **Ready to Test?** See Quick Start section above.