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Orgforge

v1.1.1 Feature

This release adds 3 notable features for engineering teams evaluating rollout.

Published 4mo Productivity & Wikis
✓ No known CVEs patched
Read the diff → Tool health → What is this tool? →

✓ No known CVEs patched in this version

Topics

corpus-generator deterministic-ai enterprise-data enterprise-datasets synthetic-dataset-generation

Summary

AI summary

Expanded string truncation limits to 80 characters and added LLM-driven sentiment drift with crew‑AI rewriting.

Full changelog

[v1.1.1] — 2026-03-19

Changed

  • String Truncation Limits (src/, eval/): Standardized and expanded summary truncation limits from 40–60 characters to 80 characters across JIRA titles, Slack interactions, incident root causes, and PR titles to prevent critical context loss in logs and RAG retrieval.
  • RAG Embedding Logic (src/memory.py): Enhanced OllamaEmbedder to support asymmetric retrieval. The system now prepends specific instruction prefixes for search_query and search_document to improve embedding quality for models like Stella and MXBAI.
  • Causal Threading Fixes (eval/eval_harness.py): Refined _design_doc_threads to ensure design documents are only included in evaluation chains if they possess a valid causal_chain fact, preventing broken threads in the evaluation harness.
  • Codebase Formatting: Applied consistent linting and multi-line dictionary wrapping across eval/eval_harness.py and src/insider_threat.py to match project style guidelines.

Added

  • LLM-Driven Sentiment Drift (src/insider_threat.py): Replaced static text templating with a CrewAI-powered rewriting task. Disgruntled or malicious actors now use a worker_llm to authentically rewrite Slack messages, with negativity intensity scaling based on the days since the threat "onset."
  • Enhanced Vector Search Filtering (src/memory.py):
    • Added type_exclude support to recall(), allowing the RAG pipeline to explicitly ignore specific artifact types (e.g., hiding persona_skill from general queries).
    • Implemented a "causal floor" using the since parameter to allow bounded timestamp filtering ($gte and $lte) within MongoDB vector searches.
  • Comprehensive Memory Testing (tests/test_memory.py): Introduced a massive expansion of the test suite (20+ new tests) covering:
    • Ollama instruction prefix validation.
    • Mutually exclusive filter guards in recall().
    • Upsert logic for artifacts to prevent vector index duplication.
    • Event-type skip lists to reduce embedding noise for high-volume, low-signal events like standard Slack messages.

Full Changelog: https://github.com/aeriesec/orgforge/compare/v0.5.0...v1.1.1

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