Release 1.3.1

README.md

@@ -1,5 +1,5 @@
 <p align="center">
-  <img src="https://github.com/mohsin1218/Long-Trainer/blob/master/assets/longtrainer.png?raw=true" alt="LongTrainer Logo" width="320">
+  <img src="https://github.com/ENDEVSOLS/Long-Trainer/blob/master/assets/longtrainer.png?raw=true" alt="LongTrainer Logo">
 </p>
 
 <h1 align="center">LongTrainer 1.3.1 — Production-Ready RAG Framework</h1>
@@ -28,22 +28,23 @@
   <a href="https://github.com/ENDEVSOLS/Long-Trainer/blob/master/LICENSE">
     <img src="https://img.shields.io/github/license/ENDEVSOLS/Long-Trainer" alt="License">
   </a>
-
+  <a href="https://opencollective.com/longtrainer">
+    <img src="https://img.shields.io/opencollective/all/longtrainer?label=sponsors" alt="Open Collective">
+  </a>
 </p>
 
 <p align="center">
   <a href="https://endevsols.github.io/Long-Trainer/">Documentation</a> •
   <a href="#quick-start-">Quick Start</a> •
   <a href="#features-">Features</a> •
-  <a href="#migration-from-034">Migration from 0.3.4</a> 
+  <a href="#migration-from-034">Migration from 0.3.4</a> •
+  <a href="#support-the-project-">Sponsor</a>
 </p>
 
 ---
 
+> **"5 lines to production RAG"** — Multi-tenant bots, streaming, tools, and persistent memory.
 
-> [!TIP]
-> This package is part of the **[EnDevSols Long-Suite](https://github.com/ENDEVSOLS/Long-Suite)**—the definitive lifecycle for production RAG. Join our **[Central Community Hub](https://github.com/ENDEVSOLS/Long-Suite/discussions)** for architecture advice and support.
-> 
 ---
 
 ## What is LongTrainer?
@@ -111,7 +112,7 @@ brew install libmagic poppler tesseract qpdf libreoffice pandoc
 ### 🎬 Complete Workflow Demo
 
 <p align="center">
-  <img src="https://github.com/mohsin1218/Long-Trainer/blob/master/assets/longtrainer-demo.gif?raw=true" alt="LongTrainer Demo" width="700">
+  <img src="https://github.com/ENDEVSOLS/Long-Trainer/blob/master/assets/longtrainer-demo.gif?raw=true" alt="LongTrainer Demo" width="700">
 </p>
 
 > Full RAG workflow: initialize → create bot → ingest documents → vector search → Q&A — all with live progress tracking. Run it yourself: `python demos/longtrainer_demo.py`

demos/longtrainer.tape

@@ -0,0 +1,30 @@
+Output assets/longtrainer-demo.gif
+Require python
+
+Set Shell "bash"
+Set FontSize 14
+Set Width 1200
+Set Height 850
+Set Padding 30
+Set Theme "Catppuccin Mocha"
+
+Hide
+Type "source .venv/bin/activate"
+Enter
+Type "export OPENAI_API_KEY=$OPENAI_API_KEY"
+Enter
+Type "export VECTORSTORE=qdrant"
+Enter
+Type "export LLM_MODEL=gpt-5.2"
+Enter
+Type "export EMBEDDING_MODEL=text-embedding-3-small"
+Enter
+Type "clear"
+Enter
+Show
+
+Sleep 1s
+Type "python demos/longtrainer_demo.py"
+Enter
+
+Sleep 120s

demos/longtrainer_demo.py

@@ -129,7 +129,7 @@ def show_qa_panel(question, answer):
 # Build vectorstore kwargs
 vs_kwargs = {}
 if VECTORSTORE == "qdrant":
-    vs_kwargs = {"location": ":memory:"}
+    vs_kwargs = {"path": "qdrant_db"}
 
 # Show configuration
 config = {