Initial Release

Initial Release

Made update for path issue in Docker

Changed the runtime Docker volume layout so that only data/out is bind-mounted. Nothing is precomputed, all work stays in the timed run.

Found some more optimizations. Using zgrep now, using csv.reader, some other small changes. Down to 378 meaningful code characters.

Added wrong about link

Initial Release

Added some optimizations for RunScript

Using docker mounts, moved file paths out of Python execution, bringing total RunScript.mac character count down to 452 characters, 433 of which are "meaningful code" (assuming "meaningful code" means "everything after ROUTINE RunScript")

Initial Release

Initial Release

add AI support

Initial Release

Release Notes: iris-vector-rag v0.11.4 (OEX 1.0.2)

This release represents a complete rewrite and production hardening of the
framework since the initial OEX listing (1.0.1).

Package renamed

Previously listed on OEX as intersystems-iris-rag (rag-templates on PyPI).
Now published as iris-vector-rag on PyPI with a matching OEX module name.
Importable as iris_vector_rag. The iris_rag/ shim preserves legacy imports.

Six pipeline strategies, unified interface

All pipelines share one factory call and one response shape — answer,
retrieved_documents, contexts, sources, metadata — swappable with a
single create_pipeline(type, ...) call:

• BasicRAG — dense vector retrieval
• BasicRAGReranking — retrieval + cross-encoder reranking
• CRAG — Corrective RAG with relevance-gated web fallback
• HybridGraphRAG — GraphRAG + vector hybrid with entity extraction
• MultiQueryRRF — multi-query generation fused via Reciprocal Rank Fusion
• ColBERT/PyLate (PLAID) — late-interaction ColBERT with in-DB PLAID index

ObjectScript SDK (RAG.SDK.*)

Five ObjectScript classes callable directly from IRIS without touching Python:

• RAG.SDK.Pipeline — run any pipeline strategy by name
• RAG.SDK.Search — BM25, IVFFlat, and vector search paths
• RAG.SDK.Schema — table initialization, schema status, pip install hook
• RAG.SDK.Bridge — overlay support and default table configuration
• RAG.SDK.Evaluate — RAGAS evaluation from ObjectScript

Connection layer overhauled

• All IRIS connections route through get_iris_connection() — one path,
  no duplicates.
• Auto-detects embedded runtime via iris.runtime.get().state — skips TCP
  entirely when running inside IRIS or when IRISINSTALLDIR is set.
• Lazy import iris.dbapi in connection_pool.py — no ImportError at
  import time when iris is absent.
• Unified via iris-embedded-python-wrapper — handles embedded-kernel,
  embedded-local, and native-remote backends transparently.

attach_existing_corpus

Zero-copy bridge: point any pipeline at tables already in IRIS without
re-ingesting data.

Validation layer

Pre-flight checks before any pipeline runs: required tables exist, embeddings
are ≥95% non-NULL, IRIS VECTOR format valid. auto_setup=True creates missing
tables and embeddings on first use.

RAGAS evaluation

Side-by-side pipeline comparison with faithfulness, context precision, and
context recall. Uses real PMC biomedical documents — no synthetic data.

REST API and MCP server

• FastAPI REST API ([api] extra) with Redis-backed sessions.
• MCP server ([mcp] extra) — all pipelines exposed as MCP tools, usable
  from Claude and other MCP clients.

iris-vector-graph integration

GraphRAG, ColBERT/PLAID, BM25, IVFFlat, and shortestPath delegate to
iris-vector-graph — IRIS-native graph and vector operations without leaving
the database.

IPM/ZPM install

zpm install iris-vector-rag now also runs
pip install iris-vector-rag==0.11.4 via RAG.SDK.Schema.Install().

CI/CD

• GitHub Actions CI on Python 3.11 and 3.12.
• Release workflow: tests → build → PyPI (OIDC trusted publishing) →
  GitHub Release with changelog notes attached.

First release on OEX!

Initial Release

try WebApplication again

Initial Release

module version bump

one more bugfix with folder path

bugfix with WebApplication

Added IPM

Initial Release

Initial Release

Initial Release

Moved main functionality out of a class and into the RunScript routine. Some code golf optimizations to bring this down to 491 characters in total, 441 of which are Python code.

Initial Release

Fixed issue with web setup script

Optimized RunScript for speed by replacing the%SYSTEM.WorkMgr multi-process design with a single embedded-Python
call into a GIL-releasing OpenMP C kernel.

Added

• App.PEXSetup.cls to configure %Java Server for remote jgw and register PEX components
• Runtime PEX setup via pex-runtime.script and docker-iris-start.sh on IRIS container start
• Telegram bot credentials via .env (TELEGRAM_BOT_TOKEN, TELEGRAM_BOT_USERNAME)
• .env.example template for local configuration
• Published Docker images on Docker Hub: jackpto/iris-crypto-tracker-iris and jackpto/iris-crypto-tracker-jgw (tags 1.2.0, latest)

Changed

• Java Gateway runtime upgraded from Java 8 to Temurin 11 (Cloudflare/CoinPaprika TLS compatibility)
• IRIS JDBC/utils JARs aligned with IRIS 2026 (intersystems-jdbc-3.10.5, intersystems-utils-4.2.2)
• CryptoProduction uses %gatewayName=%Java Server instead of host/port settings
• CryptoService HTTP client: explicit User-Agent, Accept header, timeouts, and HTTP status handling
• Docker Compose: IRIS hostname iris, depends_on: jgw, jgw loads .env
• Base Java build image switched to eclipse-temurin:8-jdk

Fixed

• PEX / Java Gateway connection to jgw:55555 (NOTOPEN, gateway name mismatch)
• CoinPaprika API calls failing with SSLHandshakeException / blocked Java 8 User-Agent
• Missing OnTearDown() and invalid super.OnMessage() / super.OnProcessInput() calls in PEX Java classes
• Installer namespace/path for /opt/irisapp/
• Empty CSV header not flushed on first create

Security

• Removed hardcoded Telegram bot token from source; secrets stay in .env (gitignored)