diff --git a/cookbook/rag_gateway/README.md b/cookbook/rag_gateway/README.md
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+# LiteLLM RAG Gateway Cookbook
+
+A comprehensive example demonstrating production-ready RAG (Retrieval-Augmented Generation) systems using **LiteLLM** as a multi-provider LLM gateway, integrated with **LlamaIndex**, **Haystack**, and evaluated with **RAGAS**.
+
+## 🎯 What This Example Demonstrates
+
+This cookbook showcases:
+
+- **Multi-Provider LLM Gateway**: Use LiteLLM to seamlessly switch between OpenAI, Anthropic, Groq, Ollama, and 100+ other providers
+- **Production-Ready Patterns**: Automatic retries, fallback chains, timeouts, and comprehensive logging
+- **RAG with LlamaIndex**: Build semantic search pipelines with vector embeddings and ChromaDB
+- **RAG with Haystack**: Create modular, component-based RAG pipelines
+- **Evaluation with RAGAS**: Measure faithfulness, answer relevancy, context precision, and recall
+- **Provider Comparison**: A/B test different models and providers with consistent code
+
+## 📁 Project Structure
+
+```
+rag_gateway/
+├── README.md                      # This file
+├── requirements.txt               # Python dependencies
+├── litellm_config.yaml           # Multi-provider configuration (optional)
+├── rag_llamaindex.py             # LlamaIndex RAG pipeline
+├── rag_haystack.py               # Haystack RAG pipeline
+├── evaluate_with_ragas.py        # RAGAS evaluation script
+└── sample_data/
+    ├── documents.json            # Knowledge base (10 AI/ML docs)
+    └── eval_dataset.json         # Evaluation questions & ground truth
+```
+
+## 🚀 Quick Start
+
+### 1. Prerequisites
+
+- Python 3.9+ (tested with 3.12)
+- API keys for LLM providers (at minimum, OpenAI)
+
+### 2. Installation
+
+```bash
+# Navigate to this directory
+cd cookbook/rag_gateway
+
+# Create virtual environment (recommended)
+python3 -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+### 3. Set API Keys
+
+```bash
+# Required for most examples
+export OPENAI_API_KEY="your-openai-key-here"
+
+# Optional: For testing alternative providers
+export ANTHROPIC_API_KEY="your-anthropic-key-here"
+export GROQ_API_KEY="your-groq-key-here"
+
+# For local models (free, no API key needed)
+# Install Ollama: https://ollama.ai
+# ollama pull llama3
+```
+
+### 4. Run Examples
+
+#### LlamaIndex RAG Pipeline
+
+```bash
+python rag_llamaindex.py
+```
+
+**What it does:**
+- Loads 10 AI/ML documents into memory
+- Creates vector embeddings using HuggingFace models
+- Stores embeddings in ChromaDB (in-memory)
+- Runs 3 example queries with semantic search
+- Saves results to `llamaindex_results.json`
+
+**Expected output:**
+```
+======================================================================
+LlamaIndex RAG with LiteLLM Multi-Provider Gateway
+======================================================================
+
+✅ Loaded 10 documents
+✅ Configured LiteLLM with model: gpt-3.5-turbo
+✅ Configured HuggingFace embeddings
+🔄 Creating vector index (this may take a moment)...
+✅ RAG pipeline created successfully
+
+❓ Question: What are the main components of RAG architecture?
+
+📚 Retrieved Sources:
+  1. RAG Architecture and Benefits (score: 0.892)
+     Preview: Retrieval-Augmented Generation (RAG) combines...
+
+💡 Answer: The main components of RAG architecture are...
+```
+
+#### Haystack RAG Pipeline
+
+```bash
+python rag_haystack.py
+```
+
+**What it does:**
+- Demonstrates Haystack's component-based pipeline architecture
+- Uses custom LiteLLM generator component
+- Shows how to integrate LiteLLM with Haystack 2.0
+- Saves results to `haystack_results.json`
+
+#### RAGAS Evaluation
+
+```bash
+python evaluate_with_ragas.py
+```
+
+**What it does:**
+- Loads evaluation dataset with 8 questions
+- Generates answers using RAG pipeline
+- Evaluates with RAGAS metrics (faithfulness, relevancy, precision, recall)
+- Compares multiple models/providers
+- Saves detailed results to `ragas_evaluation_results.json`
+
+**Expected output:**
+```
+======================================================================
+RAGAS Evaluation Comparison
+======================================================================
+Model                          Faith    Relev    Prec     Recall   Overall
+----------------------------------------------------------------------
+gpt-3.5-turbo                  0.892    0.945    0.878    0.901    0.904
+gpt-4-turbo                    0.934    0.967    0.912    0.945    0.940
+======================================================================
+```
+
+## 🔧 Switching LLM Providers
+
+One of LiteLLM's key benefits is **zero-code provider switching**. Simply change the `model_name` parameter:
+
+### In Python Scripts
+
+```python
+# OpenAI
+llm = setup_litellm(model_name="gpt-3.5-turbo")
+llm = setup_litellm(model_name="gpt-4-turbo")
+
+# Anthropic Claude
+llm = setup_litellm(model_name="claude-3-haiku-20240307")
+llm = setup_litellm(model_name="claude-3-sonnet-20240229")
+
+# Groq (ultra-fast inference)
+llm = setup_litellm(model_name="groq/llama3-70b-8192")
+llm = setup_litellm(model_name="groq/mixtral-8x7b-32768")
+
+# Local Ollama (free, private)
+llm = setup_litellm(model_name="ollama/llama3")
+llm = setup_litellm(model_name="ollama/mistral")
+
+# Azure OpenAI
+llm = setup_litellm(model_name="azure/gpt-4-deployment-name")
+```
+
+### Using Configuration File (Optional)
+
+The `litellm_config.yaml` file demonstrates advanced configuration:
+
+```yaml
+model_list:
+  - model_name: gpt-4-turbo
+    litellm_params:
+      model: gpt-4-turbo-preview
+      api_key: os.environ/OPENAI_API_KEY
+      timeout: 30
+      max_retries: 3
+
+router_settings:
+  routing_strategy: simple-shuffle
+  num_retries: 3
+  fallbacks:
+    - gpt-4-turbo
+    - gpt-3.5-turbo
+    - claude-3-haiku
+```
+
+## 📊 Understanding RAGAS Metrics
+
+RAGAS evaluates RAG systems using LLM-based metrics:
+
+| Metric | What It Measures | Good Score |
+|--------|------------------|------------|
+| **Faithfulness** | Is the answer grounded in retrieved context? (hallucination check) | > 0.8 |
+| **Answer Relevancy** | Does the answer address the question? | > 0.8 |
+| **Context Precision** | Are retrieved documents relevant to the question? | > 0.7 |
+| **Context Recall** | Was all necessary information retrieved? | > 0.7 |
+
+Higher scores are better. RAGAS uses an LLM as a judge, making evaluation more aligned with human judgment than traditional metrics like BLEU or ROUGE.
+
+## 🎓 Key Concepts
+
+### Why LiteLLM?
+
+1. **Unified Interface**: One API for 100+ providers
+2. **Reliability**: Built-in retries, fallbacks, timeouts
+3. **Cost Tracking**: Monitor spending per request
+4. **Easy Testing**: Switch providers without code changes
+5. **Production Ready**: Used by companies for high-availability systems
+
+### RAG Architecture
+
+```
+User Query
+    ↓
+[Embedding Model] → Query Vector
+    ↓
+[Vector Database] → Retrieve Top-K Documents
+    ↓
+[LLM via LiteLLM] → Generate Answer from Context
+    ↓
+Answer + Sources
+```
+
+### Production Best Practices Demonstrated
+
+- ✅ **Retry Logic**: Automatic retries with exponential backoff
+- ✅ **Fallback Chains**: Switch to backup providers on failure
+- ✅ **Timeouts**: Prevent hanging requests
+- ✅ **Logging**: Track all requests for debugging
+- ✅ **Cost Tracking**: Monitor LLM API costs
+- ✅ **Evaluation**: Measure quality with RAGAS
+- ✅ **Source Attribution**: Return retrieved documents
+- ✅ **Error Handling**: Graceful degradation
+
+## 🔍 Sample Data
+
+The `sample_data/` directory contains:
+
+### documents.json
+10 comprehensive documents covering:
+- Large Language Models
+- RAG Architecture
+- LiteLLM Features
+- Vector Embeddings
+- LlamaIndex & Haystack
+- RAGAS Evaluation
+- Production Best Practices
+- Prompt Engineering
+- Cost Optimization
+
+### eval_dataset.json
+8 evaluation questions with:
+- Question text
+- Ground truth answer
+- Expected source document IDs
+
+Perfect for testing and benchmarking your RAG system.
+
+## 🛠️ Customization
+
+### Add Your Own Documents
+
+Edit `sample_data/documents.json`:
+
+```json
+[
+  {
+    "id": "doc_11",
+    "title": "Your Document Title",
+    "content": "Your document content here..."
+  }
+]
+```
+
+### Change Embedding Models
+
+In the scripts, modify:
+
+```python
+# Use different HuggingFace model
+embed_model = HuggingFaceEmbedding(
+    model_name="BAAI/bge-large-en-v1.5"  # Larger, more accurate
+)
+
+# Or use OpenAI embeddings
+from llama_index.embeddings.openai import OpenAIEmbedding
+embed_model = OpenAIEmbedding(model="text-embedding-3-large")
+```
+
+### Adjust Retrieval Parameters
+
+```python
+# Retrieve more documents
+query_engine = index.as_query_engine(
+    similarity_top_k=5,  # Default is 3
+    response_mode="tree_summarize"  # Different synthesis strategy
+)
+```
+
+### Add More Models to Compare
+
+In `evaluate_with_ragas.py`:
+
+```python
+models_to_test = [
+    "gpt-3.5-turbo",
+    "gpt-4-turbo",
+    "claude-3-haiku-20240307",
+    "claude-3-sonnet-20240229",
+    "groq/llama3-70b-8192",
+    "ollama/llama3",
+]
+```
+
+## 🐛 Troubleshooting
+
+### "API key not found"
+
+```bash
+# Make sure you've exported your API key
+echo $OPENAI_API_KEY
+
+# If empty, set it:
+export OPENAI_API_KEY="sk-..."
+```
+
+### "Module not found" errors
+
+```bash
+# Reinstall dependencies
+pip install -r requirements.txt --upgrade
+```
+
+### Slow embedding generation
+
+First run downloads HuggingFace models (~100MB). Subsequent runs are fast.
+
+### RAGAS evaluation fails
+
+RAGAS requires an LLM for evaluation. Ensure `OPENAI_API_KEY` is set.
+
+### Ollama connection refused
+
+```bash
+# Start Ollama server
+ollama serve
+
+# In another terminal, pull a model
+ollama pull llama3
+```
+
+## 📚 Learn More
+
+- **LiteLLM Docs**: https://docs.litellm.ai/
+- **LlamaIndex Docs**: https://docs.llamaindex.ai/
+- **Haystack Docs**: https://docs.haystack.deepset.ai/
+- **RAGAS Docs**: https://docs.ragas.io/
+
+## 🤝 Contributing
+
+This example is part of the LiteLLM project. To contribute:
+
+1. Fork the repository
+2. Create a feature branch
+3. Make your changes
+4. Submit a pull request
+
+## 📝 License
+
+This example follows the LiteLLM project license.
+
+## 🎉 What's Next?
+
+After running this example, you can:
+
+1. **Deploy to Production**: Use LiteLLM Proxy for authentication, rate limiting, and load balancing
+2. **Scale Up**: Replace in-memory vector stores with Pinecone, Weaviate, or Qdrant
+3. **Add Caching**: Implement semantic caching to reduce API costs
+4. **Fine-tune Retrieval**: Experiment with hybrid search (semantic + keyword)
+5. **Monitor Performance**: Integrate with Langfuse or other observability tools
+6. **Build an API**: Wrap your RAG pipeline in FastAPI or Flask
+7. **Add Streaming**: Enable streaming responses for better UX
+
+---
+
+**Built with ❤️ to demonstrate LiteLLM's power for production RAG systems**
diff --git a/cookbook/rag_gateway/evaluate_with_ragas.py b/cookbook/rag_gateway/evaluate_with_ragas.py
new file mode 100644
index 00000000000..99a13bc8d9b
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+++ b/cookbook/rag_gateway/evaluate_with_ragas.py
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+"""
+RAG Evaluation with RAGAS and LiteLLM
+
+This script demonstrates:
+- Evaluating RAG systems with RAGAS metrics
+- Comparing different LLM providers/configurations
+- Measuring faithfulness, answer relevancy, and context precision
+- Using LiteLLM for evaluation LLM calls
+"""
+
+import os
+import json
+from pathlib import Path
+from typing import List, Dict
+import warnings
+warnings.filterwarnings('ignore')
+
+from datasets import Dataset
+from ragas import evaluate
+from ragas.metrics import (
+    faithfulness,
+    answer_relevancy,
+    context_precision,
+    context_recall,
+)
+from llama_index.core import VectorStoreIndex, Document, Settings
+from llama_index.llms.litellm import LiteLLM
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+import chromadb
+from llama_index.vector_stores.chroma import ChromaVectorStore
+from llama_index.core import StorageContext
+
+
+def load_documents(data_path: str = "sample_data/documents.json") -> List[Document]:
+    """Load documents from JSON file."""
+    script_dir = Path(__file__).parent
+    file_path = script_dir / data_path
+    
+    with open(file_path, 'r') as f:
+        docs_data = json.load(f)
+    
+    documents = []
+    for doc_data in docs_data:
+        doc = Document(
+            text=doc_data['content'],
+            metadata={'doc_id': doc_data['id'], 'title': doc_data['title']}
+        )
+        documents.append(doc)
+    
+    return documents
+
+
+def load_eval_dataset(data_path: str = "sample_data/eval_dataset.json") -> List[Dict]:
+    """Load evaluation dataset."""
+    script_dir = Path(__file__).parent
+    file_path = script_dir / data_path
+    
+    with open(file_path, 'r') as f:
+        eval_data = json.load(f)
+    
+    print(f"✅ Loaded {len(eval_data)} evaluation questions")
+    return eval_data
+
+
+def create_rag_index(
+    documents: List[Document],
+    model_name: str,
+    collection_name: str = "ragas_eval"
+) -> VectorStoreIndex:
+    """Create RAG index with specified model."""
+    llm = LiteLLM(
+        model=model_name,
+        temperature=0.1,
+        max_tokens=512,
+        num_retries=3,
+        timeout=30.0,
+    )
+    
+    embed_model = HuggingFaceEmbedding(
+        model_name="BAAI/bge-small-en-v1.5",
+        pooling="cls",
+    )
+    
+    Settings.llm = llm
+    Settings.embed_model = embed_model
+    Settings.chunk_size = 512
+    Settings.chunk_overlap = 50
+    
+    # Create vector store
+    chroma_client = chromadb.EphemeralClient()
+    chroma_collection = chroma_client.create_collection(collection_name)
+    vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
+    storage_context = StorageContext.from_defaults(vector_store=vector_store)
+    
+    index = VectorStoreIndex.from_documents(
+        documents,
+        storage_context=storage_context,
+        show_progress=False,
+    )
+    
+    return index
+
+
+def generate_rag_responses(
+    index: VectorStoreIndex,
+    eval_dataset: List[Dict],
+    top_k: int = 3
+) -> Dict[str, List]:
+    """
+    Generate RAG responses for evaluation dataset.
+    
+    Returns data in RAGAS format:
+    - question: User question
+    - answer: Generated answer
+    - contexts: Retrieved document chunks
+    - ground_truth: Expected answer
+    """
+    questions = []
+    answers = []
+    contexts = []
+    ground_truths = []
+    
+    query_engine = index.as_query_engine(
+        similarity_top_k=top_k,
+        response_mode="compact",
+    )
+    
+    print(f"🔄 Generating responses for {len(eval_dataset)} questions...")
+    
+    for i, item in enumerate(eval_dataset, 1):
+        question = item['question']
+        ground_truth = item['ground_truth']
+        
+        # Query RAG system
+        response = query_engine.query(question)
+        answer = str(response.response)
+        
+        # Extract contexts
+        retrieved_contexts = []
+        if hasattr(response, 'source_nodes'):
+            for node in response.source_nodes:
+                retrieved_contexts.append(node.text)
+        
+        questions.append(question)
+        answers.append(answer)
+        contexts.append(retrieved_contexts)
+        ground_truths.append(ground_truth)
+        
+        print(f"  ✓ Processed {i}/{len(eval_dataset)}: {question[:60]}...")
+    
+    return {
+        'question': questions,
+        'answer': answers,
+        'contexts': contexts,
+        'ground_truth': ground_truths,
+    }
+
+
+def evaluate_rag_system(
+    rag_data: Dict[str, List],
+    model_name: str,
+    eval_model: str = "gpt-3.5-turbo"
+) -> Dict:
+    """
+    Evaluate RAG system using RAGAS metrics.
+    
+    Metrics:
+    - Faithfulness: Is answer grounded in retrieved context?
+    - Answer Relevancy: Does answer address the question?
+    - Context Precision: Are retrieved docs relevant?
+    - Context Recall: Was all needed info retrieved?
+    """
+    print(f"\n📊 Evaluating with RAGAS (using {eval_model} as judge)...")
+    
+    # Convert to RAGAS dataset format
+    dataset = Dataset.from_dict(rag_data)
+    
+    # Configure RAGAS to use LiteLLM
+    os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "dummy-key")
+    
+    # Run evaluation
+    try:
+        result = evaluate(
+            dataset,
+            metrics=[
+                faithfulness,
+                answer_relevancy,
+                context_precision,
+                context_recall,
+            ],
+        )
+        
+        scores = {
+            'model': model_name,
+            'faithfulness': result['faithfulness'],
+            'answer_relevancy': result['answer_relevancy'],
+            'context_precision': result['context_precision'],
+            'context_recall': result['context_recall'],
+            'overall_score': (
+                result['faithfulness'] + 
+                result['answer_relevancy'] + 
+                result['context_precision'] + 
+                result['context_recall']
+            ) / 4
+        }
+        
+        return scores
+    
+    except Exception as e:
+        print(f"❌ Evaluation error: {e}")
+        return {
+            'model': model_name,
+            'error': str(e),
+            'faithfulness': 0.0,
+            'answer_relevancy': 0.0,
+            'context_precision': 0.0,
+            'context_recall': 0.0,
+            'overall_score': 0.0,
+        }
+
+
+def compare_models(
+    documents: List[Document],
+    eval_dataset: List[Dict],
+    models: List[str]
+) -> List[Dict]:
+    """
+    Compare multiple models/providers using RAGAS evaluation.
+    
+    This demonstrates how LiteLLM enables easy A/B testing
+    across different providers and model configurations.
+    """
+    results = []
+    
+    for model in models:
+        print("\n" + "=" * 70)
+        print(f"Evaluating Model: {model}")
+        print("=" * 70)
+        
+        try:
+            # Create RAG index with this model
+            index = create_rag_index(
+                documents,
+                model_name=model,
+                collection_name=f"eval_{model.replace('/', '_')}"
+            )
+            
+            # Generate responses
+            rag_data = generate_rag_responses(index, eval_dataset, top_k=3)
+            
+            # Evaluate
+            scores = evaluate_rag_system(rag_data, model_name=model)
+            results.append(scores)
+            
+            # Print results
+            print(f"\n✅ Results for {model}:")
+            print(f"   Faithfulness:      {scores['faithfulness']:.3f}")
+            print(f"   Answer Relevancy:  {scores['answer_relevancy']:.3f}")
+            print(f"   Context Precision: {scores['context_precision']:.3f}")
+            print(f"   Context Recall:    {scores['context_recall']:.3f}")
+            print(f"   Overall Score:     {scores['overall_score']:.3f}")
+        
+        except Exception as e:
+            print(f"❌ Error evaluating {model}: {e}")
+            results.append({
+                'model': model,
+                'error': str(e),
+                'overall_score': 0.0
+            })
+    
+    return results
+
+
+def print_comparison_table(results: List[Dict]):
+    """Print formatted comparison table."""
+    print("\n" + "=" * 70)
+    print("RAGAS Evaluation Comparison")
+    print("=" * 70)
+    print(f"{'Model':<30} {'Faith':<8} {'Relev':<8} {'Prec':<8} {'Recall':<8} {'Overall':<8}")
+    print("-" * 70)
+    
+    for result in results:
+        if 'error' not in result:
+            print(f"{result['model']:<30} "
+                  f"{result['faithfulness']:<8.3f} "
+                  f"{result['answer_relevancy']:<8.3f} "
+                  f"{result['context_precision']:<8.3f} "
+                  f"{result['context_recall']:<8.3f} "
+                  f"{result['overall_score']:<8.3f}")
+        else:
+            print(f"{result['model']:<30} ERROR: {result['error']}")
+    
+    print("=" * 70)
+
+
+def main():
+    """Main execution flow."""
+    print("=" * 70)
+    print("RAG Evaluation with RAGAS and LiteLLM")
+    print("=" * 70 + "\n")
+    
+    # Check for API keys
+    if not os.getenv("OPENAI_API_KEY"):
+        print("⚠️  Warning: OPENAI_API_KEY not set.")
+        print("   RAGAS requires an LLM for evaluation metrics.")
+        print("   Set it with: export OPENAI_API_KEY='your-key-here'\n")
+        return
+    
+    # Load data
+    documents = load_documents()
+    eval_dataset = load_eval_dataset()
+    
+    # Models to compare
+    # Uncomment models you have API keys for
+    models_to_test = [
+        "gpt-3.5-turbo",
+        # "gpt-4-turbo",
+        # "claude-3-haiku-20240307",
+        # "groq/llama3-70b-8192",
+    ]
+    
+    print(f"\n🔬 Testing {len(models_to_test)} model configurations...")
+    
+    # Run comparison
+    results = compare_models(documents, eval_dataset, models_to_test)
+    
+    # Print comparison
+    print_comparison_table(results)
+    
+    # Save results
+    output_path = Path(__file__).parent / "ragas_evaluation_results.json"
+    with open(output_path, 'w') as f:
+        json.dump(results, f, indent=2)
+    
+    print(f"\n✅ Detailed results saved to: {output_path}")
+    
+    # Insights
+    print("\n💡 Key Insights:")
+    print("  - RAGAS uses LLMs as judges for evaluation")
+    print("  - Faithfulness measures hallucination (higher is better)")
+    print("  - Answer Relevancy checks if answer addresses question")
+    print("  - Context metrics evaluate retrieval quality")
+    print("  - LiteLLM makes it easy to compare providers")
+    print("  - Add more models to models_to_test list for comparison")
+    
+    print("\n" + "=" * 70)
+    print("🎉 Evaluation completed successfully!")
+    print("=" * 70)
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/cookbook/rag_gateway/rag_haystack.py b/cookbook/rag_gateway/rag_haystack.py
new file mode 100644
index 00000000000..ed6e1c2c3ca
--- /dev/null
+++ b/cookbook/rag_gateway/rag_haystack.py
@@ -0,0 +1,328 @@
+"""
+Haystack RAG Pipeline with LiteLLM Multi-Provider Gateway
+
+This script demonstrates:
+- Building a RAG pipeline with Haystack 2.0
+- Using LiteLLM for flexible LLM provider switching
+- Document ingestion and retrieval
+- Answer generation with source attribution
+"""
+
+import os
+import json
+from pathlib import Path
+from typing import List, Dict, Any
+
+from haystack import Pipeline, Document
+from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
+from haystack.components.builders import PromptBuilder
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack.components.embedders import SentenceTransformersDocumentEmbedder, SentenceTransformersTextEmbedder
+from haystack.components.retrievers import InMemoryEmbeddingRetriever
+import litellm
+
+
+def load_documents(data_path: str = "sample_data/documents.json") -> List[Document]:
+    """Load documents from JSON and convert to Haystack format."""
+    script_dir = Path(__file__).parent
+    file_path = script_dir / data_path
+    
+    with open(file_path, 'r') as f:
+        docs_data = json.load(f)
+    
+    documents = []
+    for doc_data in docs_data:
+        doc = Document(
+            content=doc_data['content'],
+            meta={
+                'doc_id': doc_data['id'],
+                'title': doc_data['title']
+            }
+        )
+        documents.append(doc)
+    
+    print(f"✅ Loaded {len(documents)} documents")
+    return documents
+
+
+class LiteLLMGenerator:
+    """
+    Custom Haystack component that uses LiteLLM for generation.
+    
+    This allows seamless switching between providers (OpenAI, Anthropic, etc.)
+    while maintaining Haystack's pipeline architecture.
+    """
+    
+    def __init__(
+        self,
+        model: str = "gpt-3.5-turbo",
+        temperature: float = 0.1,
+        max_tokens: int = 512,
+        num_retries: int = 3,
+        timeout: float = 30.0
+    ):
+        self.model = model
+        self.temperature = temperature
+        self.max_tokens = max_tokens
+        self.num_retries = num_retries
+        self.timeout = timeout
+        
+        # Configure LiteLLM settings
+        litellm.num_retries = num_retries
+        litellm.request_timeout = timeout
+        
+        print(f"✅ Configured LiteLLM Generator with model: {model}")
+    
+    def run(self, prompt: str) -> Dict[str, Any]:
+        """
+        Generate response using LiteLLM.
+        
+        LiteLLM handles:
+        - Provider-specific API translation
+        - Automatic retries with exponential backoff
+        - Fallback to alternative providers
+        - Cost tracking and logging
+        """
+        try:
+            response = litellm.completion(
+                model=self.model,
+                messages=[{"role": "user", "content": prompt}],
+                temperature=self.temperature,
+                max_tokens=self.max_tokens,
+                num_retries=self.num_retries,
+                timeout=self.timeout,
+                stream=False,  # Explicitly disable streaming
+                # Optional: Enable fallbacks
+                fallbacks=["gpt-3.5-turbo", "claude-3-haiku-20240307"],
+            )
+            
+            # Safely extract answer
+            answer = ""
+            if hasattr(response, 'choices') and len(response.choices) > 0:  # type: ignore
+                answer = response.choices[0].message.content  # type: ignore
+            else:
+                answer = str(response)
+            
+            # Safely extract metadata
+            meta: Dict[str, Any] = {"model": self.model}
+            if hasattr(response, 'model') and response.model:
+                meta["model"] = str(response.model)
+            if hasattr(response, 'usage') and response.usage:  # type: ignore
+                meta["usage"] = {
+                    "prompt_tokens": int(response.usage.prompt_tokens),  # type: ignore
+                    "completion_tokens": int(response.usage.completion_tokens),  # type: ignore
+                    "total_tokens": int(response.usage.total_tokens),  # type: ignore
+                }
+            
+            return {
+                "replies": [answer],
+                "meta": meta
+            }
+        
+        except Exception as e:
+            print(f"❌ Error in LiteLLM generation: {e}")
+            return {
+                "replies": [f"Error: {str(e)}"],
+                "meta": {"error": str(e)}
+            }
+
+
+def create_document_store_with_embeddings(documents: List[Document]) -> InMemoryDocumentStore:
+    """
+    Create document store and add embeddings for semantic search.
+    """
+    # Initialize document store
+    document_store = InMemoryDocumentStore()
+    
+    # Create embedder
+    doc_embedder = SentenceTransformersDocumentEmbedder(
+        model="sentence-transformers/all-MiniLM-L6-v2"
+    )
+    doc_embedder.warm_up()
+    
+    # Generate embeddings
+    print("🔄 Generating embeddings for documents...")
+    docs_with_embeddings = doc_embedder.run(documents)
+    
+    # Write to store
+    document_store.write_documents(docs_with_embeddings["documents"])
+    
+    print(f"✅ Document store created with {document_store.count_documents()} documents")
+    return document_store
+
+
+def create_rag_pipeline(
+    document_store: InMemoryDocumentStore,
+    model_name: str = "gpt-3.5-turbo"
+) -> Pipeline:
+    """
+    Create Haystack RAG pipeline with LiteLLM generator.
+    
+    Pipeline components:
+    1. Text Embedder - Convert query to embedding
+    2. Retriever - Fetch relevant documents
+    3. Prompt Builder - Format context and question
+    4. LiteLLM Generator - Generate answer
+    """
+    # Initialize components
+    text_embedder = SentenceTransformersTextEmbedder(
+        model="sentence-transformers/all-MiniLM-L6-v2"
+    )
+    
+    retriever = InMemoryEmbeddingRetriever(document_store=document_store)
+    
+    # RAG prompt template
+    template = """
+You are a helpful AI assistant. Answer the question based on the provided context.
+If the context doesn't contain enough information, say so.
+
+Context:
+{% for document in documents %}
+  {{ document.content }}
+{% endfor %}
+
+Question: {{ question }}
+
+Answer:
+"""
+    
+    prompt_builder = PromptBuilder(template=template)
+    
+    llm_generator = LiteLLMGenerator(model=model_name)
+    
+    # Build pipeline
+    pipeline = Pipeline()
+    pipeline.add_component("text_embedder", text_embedder)
+    pipeline.add_component("retriever", retriever)
+    pipeline.add_component("prompt_builder", prompt_builder)
+    pipeline.add_component("llm", llm_generator)
+    
+    # Connect components
+    pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
+    pipeline.connect("retriever.documents", "prompt_builder.documents")
+    pipeline.connect("prompt_builder.prompt", "llm.prompt")
+    
+    print("✅ Haystack RAG pipeline created successfully")
+    return pipeline
+
+
+def query_rag(
+    pipeline: Pipeline,
+    question: str,
+    top_k: int = 3,
+    verbose: bool = True
+) -> Dict:
+    """
+    Query the RAG pipeline and return answer with sources.
+    
+    Args:
+        pipeline: Haystack pipeline
+        question: User question
+        top_k: Number of documents to retrieve
+        verbose: Print retrieved context
+    
+    Returns:
+        Dict with answer, sources, and metadata
+    """
+    print(f"\n❓ Question: {question}")
+    
+    # Run pipeline
+    result = pipeline.run({
+        "text_embedder": {"text": question},
+        "retriever": {"top_k": top_k},
+        "prompt_builder": {"question": question}
+    })
+    
+    # Extract answer
+    answer = result["llm"]["replies"][0]
+    metadata = result["llm"].get("meta", {})
+    
+    # Extract sources
+    sources = []
+    if "retriever" in result and "documents" in result["retriever"]:
+        for doc in result["retriever"]["documents"]:
+            sources.append({
+                'doc_id': doc.meta.get('doc_id', 'unknown'),
+                'title': doc.meta.get('title', 'unknown'),
+                'score': doc.score if hasattr(doc, 'score') else 0.0,
+                'text_preview': doc.content[:200] + "..."
+            })
+    
+    if verbose and sources:
+        print("\n📚 Retrieved Sources:")
+        for i, source in enumerate(sources, 1):
+            print(f"  {i}. {source['title']} (score: {source['score']:.3f})")
+            print(f"     Preview: {source['text_preview']}\n")
+    
+    print(f"💡 Answer: {answer}\n")
+    
+    if verbose and metadata.get('usage'):
+        usage = metadata['usage']
+        print(f"📊 Token Usage: {usage['total_tokens']} total "
+              f"({usage['prompt_tokens']} prompt + {usage['completion_tokens']} completion)")
+    
+    return {
+        'question': question,
+        'answer': answer,
+        'sources': sources,
+        'metadata': metadata
+    }
+
+
+def main():
+    """Main execution flow."""
+    print("=" * 70)
+    print("Haystack RAG with LiteLLM Multi-Provider Gateway")
+    print("=" * 70 + "\n")
+    
+    # Check for API keys
+    if not os.getenv("OPENAI_API_KEY"):
+        print("⚠️  Warning: OPENAI_API_KEY not set. Set it with:")
+        print("   export OPENAI_API_KEY='your-key-here'\n")
+        print("   Attempting to use fallback providers...\n")
+    
+    # Load documents
+    documents = load_documents()
+    
+    # Create document store with embeddings
+    document_store = create_document_store_with_embeddings(documents)
+    
+    # Create RAG pipeline
+    # Try different models: "gpt-4-turbo", "gpt-3.5-turbo", 
+    # "claude-3-haiku-20240307", "groq/llama3-70b-8192"
+    pipeline = create_rag_pipeline(document_store, model_name="gpt-3.5-turbo")
+    
+    # Example queries
+    questions = [
+        "What are the key differences between LlamaIndex and Haystack?",
+        "How does LiteLLM provide reliability for production systems?",
+        "What are best practices for chunking documents in RAG?",
+    ]
+    
+    results = []
+    for question in questions:
+        result = query_rag(pipeline, question, top_k=3)
+        results.append(result)
+        print("-" * 70 + "\n")
+    
+    # Save results
+    output_path = Path(__file__).parent / "haystack_results.json"
+    with open(output_path, 'w') as f:
+        json.dump(results, f, indent=2)
+    
+    print(f"✅ Results saved to: {output_path}")
+    print("\n" + "=" * 70)
+    print("🎉 Haystack RAG pipeline completed successfully!")
+    print("=" * 70)
+    
+    # Tips
+    print("\n💡 Tips:")
+    print("  - Haystack's component architecture makes pipelines modular")
+    print("  - Switch LLM providers by changing model_name parameter")
+    print("  - LiteLLM handles retries, fallbacks, and cost tracking")
+    print("  - Combine with BM25 retriever for hybrid search")
+    print("  - Add re-rankers for improved retrieval quality")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/cookbook/rag_gateway/rag_llamaindex.py b/cookbook/rag_gateway/rag_llamaindex.py
new file mode 100644
index 00000000000..062af35e3dc
--- /dev/null
+++ b/cookbook/rag_gateway/rag_llamaindex.py
@@ -0,0 +1,256 @@
+"""
+LlamaIndex RAG Pipeline with LiteLLM Multi-Provider Gateway
+
+This script demonstrates:
+- Building a RAG pipeline with LlamaIndex
+- Using LiteLLM for multi-provider LLM access
+- Configuring retries, fallbacks, and timeouts
+- Vector-based semantic search with ChromaDB
+"""
+
+import os
+import json
+from pathlib import Path
+from typing import List, Dict
+
+from llama_index.core import (
+    VectorStoreIndex,
+    Document,
+    Settings,
+    StorageContext,
+)
+from llama_index.core.node_parser import SentenceSplitter
+from llama_index.llms.litellm import LiteLLM
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.vector_stores.chroma import ChromaVectorStore
+import chromadb
+
+
+def load_documents(data_path: str = "sample_data/documents.json") -> List[Document]:
+    """Load documents from JSON file."""
+    script_dir = Path(__file__).parent
+    file_path = script_dir / data_path
+    
+    with open(file_path, 'r') as f:
+        docs_data = json.load(f)
+    
+    documents = []
+    for doc_data in docs_data:
+        doc = Document(
+            text=doc_data['content'],
+            metadata={
+                'doc_id': doc_data['id'],
+                'title': doc_data['title']
+            }
+        )
+        documents.append(doc)
+    
+    print(f"✅ Loaded {len(documents)} documents")
+    return documents
+
+
+def setup_litellm(
+    model_name: str = "gpt-3.5-turbo",
+    temperature: float = 0.1,
+    max_tokens: int = 512
+) -> LiteLLM:
+    """
+    Configure LiteLLM with retry and fallback settings.
+    
+    LiteLLM automatically handles:
+    - Provider-specific API format translation
+    - Retries with exponential backoff
+    - Fallback to alternative providers
+    - Cost tracking and logging
+    """
+    llm = LiteLLM(
+        model=model_name,
+        temperature=temperature,
+        max_tokens=max_tokens,
+        # These settings enable production-ready reliability
+        num_retries=3,  # Retry failed requests
+        timeout=30.0,   # 30 second timeout
+        # Fallback models (if primary fails)
+        fallbacks=["gpt-3.5-turbo", "claude-3-haiku-20240307"],
+    )
+    
+    print(f"✅ Configured LiteLLM with model: {model_name}")
+    return llm
+
+
+def setup_embeddings() -> HuggingFaceEmbedding:
+    """Configure embedding model for semantic search."""
+    embed_model = HuggingFaceEmbedding(
+        model_name="BAAI/bge-small-en-v1.5",
+        # Use 'cls' pooling for better performance
+        pooling="cls",
+    )
+    
+    print("✅ Configured HuggingFace embeddings")
+    return embed_model
+
+
+def create_rag_pipeline(
+    documents: List[Document],
+    llm: LiteLLM,
+    embed_model: HuggingFaceEmbedding,
+    collection_name: str = "litellm_rag_docs"
+) -> VectorStoreIndex:
+    """
+    Create RAG pipeline with vector store and query engine.
+    
+    Pipeline steps:
+    1. Chunk documents into smaller pieces
+    2. Generate embeddings for each chunk
+    3. Store in ChromaDB vector database
+    4. Create query engine for retrieval + generation
+    """
+    # Configure global settings
+    Settings.llm = llm
+    Settings.embed_model = embed_model
+    Settings.chunk_size = 512
+    Settings.chunk_overlap = 50
+    
+    # Initialize ChromaDB
+    chroma_client = chromadb.EphemeralClient()
+    chroma_collection = chroma_client.create_collection(collection_name)
+    vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
+    storage_context = StorageContext.from_defaults(vector_store=vector_store)
+    
+    # Parse documents into nodes (chunks)
+    node_parser = SentenceSplitter(
+        chunk_size=512,
+        chunk_overlap=50,
+    )
+    
+    # Create index with embeddings
+    print("🔄 Creating vector index (this may take a moment)...")
+    index = VectorStoreIndex.from_documents(
+        documents,
+        storage_context=storage_context,
+        node_parser=node_parser,
+        show_progress=True,
+    )
+    
+    print("✅ RAG pipeline created successfully")
+    return index
+
+
+def query_rag(
+    index: VectorStoreIndex,
+    question: str,
+    top_k: int = 3,
+    verbose: bool = True
+) -> Dict:
+    """
+    Query the RAG system and return answer with sources.
+    
+    Args:
+        index: Vector store index
+        question: User question
+        top_k: Number of documents to retrieve
+        verbose: Print retrieved context
+    
+    Returns:
+        Dict with answer, sources, and metadata
+    """
+    # Create query engine with custom prompt
+    query_engine = index.as_query_engine(
+        similarity_top_k=top_k,
+        response_mode="compact",  # Combine context efficiently
+    )
+    
+    # Execute query
+    print(f"\n❓ Question: {question}")
+    response = query_engine.query(question)
+    
+    # Extract source information
+    sources = []
+    if hasattr(response, 'source_nodes'):
+        for node in response.source_nodes:
+            sources.append({
+                'doc_id': node.metadata.get('doc_id', 'unknown'),
+                'title': node.metadata.get('title', 'unknown'),
+                'score': node.score,
+                'text_preview': node.text[:200] + "..."
+            })
+    
+    if verbose and sources:
+        print("\n📚 Retrieved Sources:")
+        for i, source in enumerate(sources, 1):
+            print(f"  {i}. {source['title']} (score: {source['score']:.3f})")
+            print(f"     Preview: {source['text_preview']}\n")
+    
+    print(f"💡 Answer: {response.response}\n")
+    
+    return {
+        'question': question,
+        'answer': str(response.response),
+        'sources': sources,
+        'metadata': {
+            'model': Settings.llm.model,
+            'top_k': top_k
+        }
+    }
+
+
+def main():
+    """Main execution flow."""
+    print("=" * 70)
+    print("LlamaIndex RAG with LiteLLM Multi-Provider Gateway")
+    print("=" * 70 + "\n")
+    
+    # Check for API keys
+    if not os.getenv("OPENAI_API_KEY"):
+        print("⚠️  Warning: OPENAI_API_KEY not set. Set it with:")
+        print("   export OPENAI_API_KEY='your-key-here'\n")
+        print("   Attempting to use fallback providers...\n")
+    
+    # Load documents
+    documents = load_documents()
+    
+    # Setup LiteLLM (try different models by changing model_name)
+    # Options: "gpt-4-turbo", "gpt-3.5-turbo", "claude-3-haiku-20240307", 
+    #          "groq/llama3-70b-8192", "ollama/llama3"
+    llm = setup_litellm(model_name="gpt-3.5-turbo")
+    
+    # Setup embeddings
+    embed_model = setup_embeddings()
+    
+    # Create RAG pipeline
+    index = create_rag_pipeline(documents, llm, embed_model)
+    
+    # Example queries
+    questions = [
+        "What are the main components of RAG architecture?",
+        "How does LiteLLM help with multi-provider integration?",
+        "What metrics does RAGAS use for evaluation?",
+    ]
+    
+    results = []
+    for question in questions:
+        result = query_rag(index, question, top_k=3)
+        results.append(result)
+        print("-" * 70 + "\n")
+    
+    # Save results
+    output_path = Path(__file__).parent / "llamaindex_results.json"
+    with open(output_path, 'w') as f:
+        json.dump(results, f, indent=2)
+    
+    print(f"✅ Results saved to: {output_path}")
+    print("\n" + "=" * 70)
+    print("🎉 LlamaIndex RAG pipeline completed successfully!")
+    print("=" * 70)
+    
+    # Tips for switching providers
+    print("\n💡 Tips:")
+    print("  - Change model by modifying 'model_name' parameter")
+    print("  - Set ANTHROPIC_API_KEY to use Claude models")
+    print("  - Set GROQ_API_KEY for ultra-fast inference")
+    print("  - Install Ollama for free local models")
+    print("  - LiteLLM automatically handles retries and fallbacks")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/cookbook/rag_gateway/requirements.txt b/cookbook/rag_gateway/requirements.txt
new file mode 100644
index 00000000000..8292c1a2369
--- /dev/null
+++ b/cookbook/rag_gateway/requirements.txt
@@ -0,0 +1,25 @@
+# Core LiteLLM
+litellm>=1.50.0
+
+# RAG Frameworks
+llama-index>=0.10.0
+llama-index-llms-litellm>=0.1.0
+llama-index-embeddings-huggingface>=0.2.0
+haystack-ai>=2.0.0
+
+# Evaluation
+ragas>=0.1.0
+datasets>=2.14.0
+
+# Vector Store & Embeddings
+chromadb>=0.4.0
+sentence-transformers>=2.2.0
+
+# Utilities
+python-dotenv>=1.0.0
+pyyaml>=6.0
+
+# Use --only-binary to avoid build issues on Python 3.15
+# Install these separately if needed:
+# pip install numpy --only-binary :all:
+# pip install pandas --only-binary :all:
diff --git a/cookbook/rag_gateway/sample_data/documents.json b/cookbook/rag_gateway/sample_data/documents.json
new file mode 100644
index 00000000000..51c996969bd
--- /dev/null
+++ b/cookbook/rag_gateway/sample_data/documents.json
@@ -0,0 +1,52 @@
+[
+  {
+    "id": "doc_1",
+    "title": "Introduction to Large Language Models",
+    "content": "Large Language Models (LLMs) are neural networks trained on vast amounts of text data. They use transformer architecture with self-attention mechanisms to understand and generate human-like text. Popular LLMs include GPT-4, Claude, and Llama. These models are pre-trained on diverse internet text and can be fine-tuned for specific tasks. LLMs have revolutionized natural language processing by achieving state-of-the-art results on tasks like translation, summarization, question answering, and code generation. The training process involves predicting the next token in a sequence, which allows the model to learn patterns, grammar, facts, and reasoning abilities from the training data."
+  },
+  {
+    "id": "doc_2",
+    "title": "RAG Architecture and Benefits",
+    "content": "Retrieval-Augmented Generation (RAG) combines information retrieval with language generation. The architecture has three main components: a retriever that fetches relevant documents from a knowledge base, an embedder that converts text to vector representations, and a generator (LLM) that produces answers using retrieved context. RAG reduces hallucinations by grounding responses in factual documents, enables knowledge updates without retraining, and provides source attribution for transparency. It's particularly effective for domain-specific applications where up-to-date information is critical. RAG systems typically use vector databases like ChromaDB, Pinecone, or Weaviate to store and retrieve document embeddings efficiently. The retrieval step uses semantic similarity to find the most relevant documents for a given query."
+  },
+  {
+    "id": "doc_3",
+    "title": "LiteLLM Multi-Provider Gateway",
+    "content": "LiteLLM is a unified interface for 100+ LLM providers including OpenAI, Anthropic, Azure, AWS Bedrock, Google Vertex AI, and local models like Ollama. It translates inputs to provider-specific formats and returns consistent OpenAI-compatible outputs. Key features include automatic retries with exponential backoff, fallback chains across providers for reliability, request timeouts to prevent hanging, cost tracking per request, and comprehensive logging for observability. LiteLLM's router enables load balancing across multiple model deployments, making it ideal for production RAG systems that need high availability. The proxy server adds authentication, rate limiting, and budget management capabilities. Configuration is done via YAML files that define model lists, retry policies, and fallback strategies."
+  },
+  {
+    "id": "doc_4",
+    "title": "Vector Embeddings and Semantic Search",
+    "content": "Vector embeddings are dense numerical representations of text that capture semantic meaning. Words or sentences with similar meanings have embeddings that are close together in high-dimensional space. Embedding models like sentence-transformers, OpenAI's text-embedding-ada-002, or Cohere's embed models convert text into vectors (typically 384 to 1536 dimensions). Semantic search uses these embeddings to find relevant documents by computing cosine similarity or dot product between query and document vectors. This approach outperforms traditional keyword-based search because it understands context and meaning. For RAG systems, embeddings are pre-computed for all documents and stored in a vector database with efficient indexing (HNSW, IVF) for fast retrieval. At query time, the question is embedded and used to retrieve the top-k most similar documents."
+  },
+  {
+    "id": "doc_5",
+    "title": "LlamaIndex Framework Overview",
+    "content": "LlamaIndex is a data framework for building LLM applications with external data. It provides data connectors to ingest from APIs, databases, PDFs, and other sources. The core abstraction is the Index, which structures data for efficient retrieval. VectorStoreIndex creates embeddings and enables semantic search. ListIndex stores documents sequentially. TreeIndex builds a hierarchical structure. LlamaIndex includes a query engine that orchestrates retrieval and generation, supporting various retrieval strategies like top-k similarity, MMR (Maximum Marginal Relevance), and hybrid search. It integrates with LiteLLM through the LiteLLM LLM class, allowing seamless provider switching. Response synthesizers combine retrieved context with LLM generation using techniques like refine, compact, or tree summarize. LlamaIndex also supports agents, tools, and multi-step reasoning workflows."
+  },
+  {
+    "id": "doc_6",
+    "title": "Haystack RAG Pipeline",
+    "content": "Haystack is an open-source framework for building production-ready RAG pipelines. It uses a pipeline architecture where components are connected as nodes in a directed graph. Key components include DocumentStores (Elasticsearch, Weaviate, Pinecone) for storing documents and embeddings, Retrievers (DenseRetriever, BM25Retriever) for fetching relevant documents, Readers/Generators for answer generation, and Rankers for re-ranking retrieved results. Haystack 2.0 introduced a more flexible component-based design with better typing and async support. It integrates with LiteLLM through custom generator components that call the LiteLLM completion API. Haystack excels at complex pipelines with multiple retrieval stages, question classification, and answer validation. It supports both extractive QA (extracting spans from documents) and generative QA (synthesizing answers with LLMs)."
+  },
+  {
+    "id": "doc_7",
+    "title": "RAGAS Evaluation Framework",
+    "content": "RAGAS (Retrieval-Augmented Generation Assessment) is a framework for evaluating RAG systems using LLM-based metrics. It measures both retrieval quality and generation quality. Key metrics include: Faithfulness (whether the answer is grounded in retrieved context), Answer Relevancy (how well the answer addresses the question), Context Precision (how relevant retrieved documents are), Context Recall (whether all necessary information was retrieved), and Answer Semantic Similarity (comparing generated answers to ground truth). RAGAS uses LLMs as judges to compute these metrics, making evaluation more aligned with human judgment than traditional metrics like BLEU or ROUGE. It requires a test dataset with questions, ground truth answers, retrieved contexts, and generated answers. RAGAS helps compare different retrieval strategies, chunk sizes, embedding models, and LLM providers to optimize RAG system performance."
+  },
+  {
+    "id": "doc_8",
+    "title": "Production RAG Best Practices",
+    "content": "Building production RAG systems requires careful attention to reliability, latency, and cost. Best practices include: implementing retry logic with exponential backoff for API failures, using fallback chains across multiple LLM providers for high availability, setting appropriate timeouts to prevent hanging requests, chunking documents optimally (typically 256-512 tokens with overlap), using hybrid search (combining semantic and keyword search) for better recall, implementing caching for frequently asked questions, monitoring retrieval quality metrics (MRR, NDCG), tracking LLM costs per request, adding guardrails to filter inappropriate content, and logging all requests for debugging and analysis. For scaling, consider async processing, batch embeddings, and distributed vector databases. Security measures include API key rotation, rate limiting per user, and input validation. Regular evaluation with RAGAS or similar frameworks ensures system quality over time."
+  },
+  {
+    "id": "doc_9",
+    "title": "Prompt Engineering for RAG",
+    "content": "Effective prompt engineering is crucial for RAG systems. The prompt should clearly instruct the LLM to use only the provided context, cite sources when possible, and admit when information is insufficient. A typical RAG prompt structure includes: system instructions defining the assistant's role, retrieved context documents with clear delimiters, the user's question, and output format instructions. Techniques like few-shot examples improve consistency. Chain-of-thought prompting helps with complex reasoning over multiple documents. For multi-turn conversations, include chat history while managing context window limits. Prompt compression techniques like selective context or summarization help fit more information. When using LiteLLM, prompts should be provider-agnostic since LiteLLM handles format translation. Testing prompts across different models (GPT-4, Claude, Llama) ensures robustness. Prompt versioning and A/B testing help optimize performance over time."
+  },
+  {
+    "id": "doc_10",
+    "title": "Cost Optimization for LLM Applications",
+    "content": "LLM costs can escalate quickly in production RAG systems. Optimization strategies include: using smaller models for simple queries and larger models only when needed, implementing semantic caching to avoid redundant API calls, compressing prompts by removing unnecessary context, batching requests when possible, using streaming for better user experience without cost increase, choosing cost-effective providers (Claude Haiku, GPT-3.5-turbo, or open-source models via Ollama), setting token limits to prevent runaway generation, monitoring costs per user/query with tools like LiteLLM's cost tracking, and implementing rate limiting and quotas. For embeddings, batch processing and caching reduce costs significantly. Consider fine-tuning smaller models for specific tasks instead of always using large general-purpose models. LiteLLM's router can automatically route to cheaper models based on query complexity. Regular cost analysis helps identify optimization opportunities."
+  }
+]
diff --git a/cookbook/rag_gateway/sample_data/eval_dataset.json b/cookbook/rag_gateway/sample_data/eval_dataset.json
new file mode 100644
index 00000000000..079462c4adb
--- /dev/null
+++ b/cookbook/rag_gateway/sample_data/eval_dataset.json
@@ -0,0 +1,42 @@
+[
+  {
+    "question": "What are the main components of RAG architecture?",
+    "ground_truth": "RAG architecture has three main components: a retriever that fetches relevant documents from a knowledge base, an embedder that converts text to vector representations, and a generator (LLM) that produces answers using retrieved context.",
+    "contexts": ["doc_2"]
+  },
+  {
+    "question": "How does LiteLLM help with multi-provider LLM integration?",
+    "ground_truth": "LiteLLM provides a unified interface for 100+ LLM providers, translates inputs to provider-specific formats, returns consistent OpenAI-compatible outputs, and includes features like automatic retries, fallback chains, timeouts, cost tracking, and comprehensive logging.",
+    "contexts": ["doc_3"]
+  },
+  {
+    "question": "What is the purpose of vector embeddings in semantic search?",
+    "ground_truth": "Vector embeddings are dense numerical representations that capture semantic meaning. They enable semantic search by allowing systems to find relevant documents through cosine similarity or dot product between query and document vectors, understanding context and meaning rather than just keywords.",
+    "contexts": ["doc_4"]
+  },
+  {
+    "question": "What retrieval strategies does LlamaIndex support?",
+    "ground_truth": "LlamaIndex supports various retrieval strategies including top-k similarity search, MMR (Maximum Marginal Relevance), and hybrid search that combines multiple approaches.",
+    "contexts": ["doc_5"]
+  },
+  {
+    "question": "What metrics does RAGAS use to evaluate RAG systems?",
+    "ground_truth": "RAGAS uses metrics including Faithfulness (answer grounded in context), Answer Relevancy (how well answer addresses question), Context Precision (relevance of retrieved documents), Context Recall (whether all necessary information was retrieved), and Answer Semantic Similarity (comparing to ground truth).",
+    "contexts": ["doc_7"]
+  },
+  {
+    "question": "What are best practices for production RAG systems?",
+    "ground_truth": "Best practices include implementing retry logic with exponential backoff, using fallback chains across providers, setting timeouts, optimal document chunking (256-512 tokens with overlap), hybrid search, caching, monitoring retrieval metrics, tracking costs, adding guardrails, and comprehensive logging.",
+    "contexts": ["doc_8"]
+  },
+  {
+    "question": "How can you optimize costs in LLM applications?",
+    "ground_truth": "Cost optimization strategies include using smaller models when appropriate, semantic caching, prompt compression, request batching, choosing cost-effective providers, setting token limits, monitoring per-query costs, rate limiting, and considering fine-tuned smaller models instead of large general-purpose ones.",
+    "contexts": ["doc_10"]
+  },
+  {
+    "question": "What are the key differences between LlamaIndex and Haystack?",
+    "ground_truth": "LlamaIndex focuses on data framework abstractions with various index types (VectorStoreIndex, ListIndex, TreeIndex) and response synthesizers. Haystack uses a pipeline architecture with components as nodes in a directed graph, excelling at complex multi-stage pipelines with question classification and answer validation.",
+    "contexts": ["doc_5", "doc_6"]
+  }
+]
\ No newline at end of file