docs: Added comprehensive documentation for packaging RAG systems with KitOps

docs/src/docs/integrations/Packing RAG Systems with KitOps.md

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+# Packaging RAG Systems
+
+## Overview
+
+RAG (Retrieval-Augmented Generation) systems combine language models with knowledge retrieval. This guide shows how to package RAG components—embedding models, vector databases, LLMs, and code—into a single ModelKit.
+
+**What you'll package:**
+- Embedding models for vectorization
+- Vector databases (ChromaDB, Pinecone, FAISS, etc.)
+- Language models
+- Application code and configs
+
+---
+
+## Prerequisites
+
+- Python 3.8+
+- KitOps CLI ([installation](https://kitops.ml/docs/cli/installation.html))
+- OCI registry access (Docker Hub, Jozu Hub, GHCR, etc.)
+
+---
+
+## Project Structure
+
+```
+rag-project/
+├── Kitfile
+├── models/
+│   ├── embeddings/
+│   └── llm/
+├── vectordb/
+├── datasets/
+├── code/
+│   ├── ingest.py
+│   ├── query.py
+│   └── requirements.txt
+└── configs/
+```
+
+---
+
+## Instructions
+
+### 1. Download Models
+
+```bash
+# Embedding model
+python3 -c "
+from sentence_transformers import SentenceTransformer
+model = SentenceTransformer('all-MiniLM-L6-v2')
+model.save('models/embeddings/all-MiniLM-L6-v2')
+"
+
+# LLM (quantized)
+huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.2-GGUF \
+  mistral-7b-instruct-v0.2.Q4_K_M.gguf \
+  --local-dir models/llm/mistral-7b
+```
+
+### 2. Build Vector Database
+
+```python
+# code/ingest.py
+import chromadb
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer('../models/embeddings/all-MiniLM-L6-v2')
+client = chromadb.PersistentClient(path="../vectordb/chroma_db")
+collection = client.get_or_create_collection("knowledge_base")
+
+# Load documents, generate embeddings, store
+# ... your logic here ...
+```
+
+Run this before packing to populate the vector database.
+
+### 3. Create Kitfile
+
+```yaml
+manifestVersion: 1.0
+
+package:
+  name: my-rag-system
+  version: 1.0.0
+
+model:
+  - name: embedding-model
+    path: ./models/embeddings/all-MiniLM-L6-v2
+    framework: sentence-transformers
+
+  - name: llm-model
+    path: ./models/llm/mistral-7b
+    framework: llama-cpp
+
+code:
+  - path: ./code
+
+datasets:
+  - name: knowledge-base
+    path: ./datasets/docs
+
+vectordb:
+  - name: chroma-db
+    path: ./vectordb/chroma_db
+
+configs:
+  - path: ./configs/retrieval_config.yaml
+```
+
+### 4. Pack and Push
+
+```bash
+# Pack
+kit pack . -t myregistry.com/username/my-rag:1.0.0
+
+# Push to any OCI registry
+kit push myregistry.com/username/my-rag:1.0.0          # Docker Hub
+kit push jozu.ml/username/my-rag:1.0.0                 # Jozu Hub
+kit push ghcr.io/username/my-rag:1.0.0                 # GitHub
+```
+
+### 5. Deploy
+
+```bash
+# Pull
+kit pull myregistry.com/username/my-rag:1.0.0
+
+# Unpack
+kit unpack myregistry.com/username/my-rag:1.0.0 -d ./deploy
+
+# Run
+cd deploy/code
+pip install -r requirements.txt
+python app.py
+```
+
+---
+
+## Example Query Pipeline
+
+```python
+# code/query.py
+import chromadb
+from sentence_transformers import SentenceTransformer
+from llama_cpp import Llama
+
+class RAGPipeline:
+    def __init__(self):
+        self.embedding_model = SentenceTransformer('../models/embeddings/all-MiniLM-L6-v2')
+        client = chromadb.PersistentClient(path="../vectordb/chroma_db")
+        self.collection = client.get_collection("knowledge_base")
+        self.llm = Llama(model_path="../models/llm/mistral-7b/mistral-7b-instruct-v0.2.Q4_K_M.gguf")
+
+    def query(self, question, top_k=5):
+        # Get embeddings and search
+        query_embedding = self.embedding_model.encode([question])
+        results = self.collection.query(query_embeddings=query_embedding.tolist(), n_results=top_k)
+
+        # Generate with context
+        context = "\n\n".join(results['documents'][0])
+        prompt = f"Context:\n{context}\n\nQuestion: {question}\n\nAnswer:"
+        response = self.llm(prompt, max_tokens=512)
+
+        return response['choices'][0]['text']
+```
+
+---
+
+## Tips
+
+- Use quantized models (GGUF, ONNX) to reduce size
+- Build vector indexes before packing
+- Version your datasets with your models
+- Tag by environment: `prod`, `staging`, `dev`
+
+- Troubleshooting
+IssueFixSlow uploadsUse quantized models; compress databasesAuth failuresRun kit login <registry>Missing filesCheck paths in Kitfile match your directories
+
+---
+