Add summary documentation for issue creation

Added ISSUE_CREATION_SUMMARY.md with overview of all created templates,
automation script usage instructions, and implementation recommendations.

Co-authored-by: messkan <40677488+messkan@users.noreply.github.com>

Author: Copilot

ISSUE_CREATION_SUMMARY.md

@@ -0,0 +1,161 @@
+# Summary: GitHub Issues Prepared for v1.0.0 Release
+
+## What Was Created
+
+This PR adds comprehensive GitHub issue templates and documentation for tracking the v1.0.0 roadmap items from README.md.
+
+### 📁 Files Created
+
+#### Issue Templates (`.github/ISSUE_TEMPLATE/`)
+1. **bug_report.md** - Template for bug reports
+2. **feature_request.md** - Template for feature requests  
+3. **config.yml** - Issue template configuration
+4. **v1_advanced_strategies.md** - Advanced chunking strategies issue template
+5. **v1_benchmarking.md** - Benchmarking mode issue template
+6. **v1_mlflow.md** - MLFlow integration issue template
+7. **v1_performance.md** - Performance optimization issue template
+8. **v1_vector_stores.md** - Vector store connectors issue template
+
+#### Documentation
+- **.github/VERSION_1.0.0_ISSUES.md** (15KB) - Comprehensive documentation with detailed specifications for all v1.0.0 features
+- **.github/README.md** (4.5KB) - Instructions for creating issues
+
+#### Automation
+- **.github/create_v1_issues.sh** (9.7KB, executable) - Bash script to automatically create all 5 v1.0.0 issues via GitHub CLI
+
+## 🎯 The 5 v1.0.0 Roadmap Issues
+
+Based on README.md lines 53-58, the following issues are ready to be created:
+
+### 1. Advanced Chunking Strategies
+**Labels:** `enhancement`, `v1.0.0`, `chunking`
+
+Implements two new strategies:
+- **Hierarchical chunking** - Multi-level chunks with parent-child relationships
+- **Semantic similarity-based splitting** - Intelligent boundaries using embeddings
+
+**Key features:**
+- New CLI options: `--strategy hierarchical`, `--strategy semantic`
+- Integration with sentence-transformers
+- Compatible with existing `--use-tiktoken` flag
+
+### 2. Vector Store Export Connectors
+**Labels:** `enhancement`, `v1.0.0`, `integration`
+
+Direct integration with popular vector databases:
+- **Pinecone** - Cloud-native vector database
+- **Weaviate** - Open-source vector search engine
+- **Chroma** - Open-source embedding database
+
+**Key features:**
+- New `export` subcommand
+- Automatic embedding generation
+- Batch upload optimization
+- Metadata attachment (source file, chunk ID, strategy)
+
+### 3. Benchmarking Mode
+**Labels:** `enhancement`, `v1.0.0`, `evaluation`
+
+Automated testing and recommendations:
+- Tests multiple strategy/parameter combinations
+- Measures quality metrics (recall, precision, F1) and performance
+- Provides top 3 recommendations with justification
+- Beautiful CLI output with tables
+
+**Key features:**
+- New `benchmark` subcommand
+- Smart recommendation algorithm (weighted scoring)
+- Export results as JSON/CSV/HTML
+
+### 4. MLFlow Integration
+**Labels:** `enhancement`, `v1.0.0`, `mlops`
+
+Experiment tracking and reproducibility:
+- Log all chunking parameters and results
+- Track metrics across runs
+- Store chunks and reports as artifacts
+- Compare strategies over time
+
+**Key features:**
+- `--mlflow-tracking` flag
+- Automatic parameter/metric logging
+- Integration with MLFlow UI
+
+### 5. Performance Optimization
+**Labels:** `enhancement`, `v1.0.0`, `performance`
+
+Parallel processing for large datasets:
+- Multi-threaded file processing
+- Configurable worker count
+- Memory-efficient batch processing
+- Graceful error handling
+
+**Key features:**
+- `--workers N` flag to control parallelism
+- 3-5x speedup for medium datasets (10-100 files)
+- Progress bar with parallel execution
+- Near-linear scaling for large datasets
+
+## 📋 How to Use
+
+### Option 1: Automated Creation (Recommended)
+
+```bash
+cd .github
+./create_v1_issues.sh
+```
+
+**Requirements:**
+- GitHub CLI installed: https://cli.github.com/
+- Authenticated: `gh auth login`
+
+This will:
+1. Create a v1.0.0 milestone
+2. Create all 5 issues with proper labels and milestone assignment
+3. Provide a summary
+
+### Option 2: Manual Creation
+
+Go to https://github.com/messkan/rag-chunk/issues/new/choose and select one of the v1.0.0 templates.
+
+### Option 3: GitHub CLI (Individual)
+
+```bash
+gh issue create \
+  --repo messkan/rag-chunk \
+  --title "[v1.0.0] Feature Title" \
+  --body-file .github/ISSUE_TEMPLATE/v1_<feature>.md \
+  --label "enhancement,v1.0.0,<category>" \
+  --milestone "v1.0.0"
+```
+
+## 🗺️ Implementation Order Recommendation
+
+From `.github/VERSION_1.0.0_ISSUES.md`:
+
+1. **Performance Optimization** (Issue 5) - Foundation for larger datasets
+2. **Benchmarking Mode** (Issue 3) - Helps validate other features  
+3. **Advanced Strategies** (Issue 1) - Core functionality expansion
+4. **MLFlow Integration** (Issue 4) - Experiment tracking
+5. **Vector Store Connectors** (Issue 2) - External integrations
+
+## 📚 Additional Resources
+
+- **Detailed Specifications**: See `.github/VERSION_1.0.0_ISSUES.md`
+- **Current Roadmap**: See `README.md` lines 26-58
+- **Issue Instructions**: See `.github/README.md`
+
+## ✅ Next Steps
+
+1. Review the created templates and documentation
+2. Run `.github/create_v1_issues.sh` to create all issues
+3. (Optional) Create a GitHub project board to track progress
+4. Start implementing features in the recommended order
+
+## 🎉 Benefits
+
+- **Clear tracking** - Each roadmap item has a dedicated issue
+- **Comprehensive specs** - Detailed implementation guidance included
+- **Easy creation** - Automated script saves time
+- **Professional templates** - Bug reports and feature requests standardized
+- **Community ready** - Templates help contributors submit quality issues