# Condensed: RAG with LlamaIndex

Summary: This tutorial demonstrates implementing a Retrieval-Augmented Generation (RAG) system using LlamaIndex, focusing on integration with FAISS vector store and OpenAI's LLM. It covers essential techniques for document loading, text chunking, embedding generation using HuggingFace models, vector store setup with FAISS, and query engine configuration. The tutorial helps with tasks like building custom RAG pipelines, optimizing text splitting parameters, and customizing prompt templates. Key features include configurable chunk sizes, embedding model selection, FAISS vector store integration, and customizable query response generation, making it valuable for developers implementing production-ready RAG systems.

*This is a condensed version that preserves essential implementation details and context.*

Here's the condensed tutorial focusing on essential implementation details:

# RAG with LlamaIndex Implementation Guide

## Setup
```python
# Required packages
%pip install llama-index-llms-openai llama-index-embeddings-huggingface llama-index-vector-stores-faiss
%pip install llama_index

# OpenAI API configuration
import os
os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
```

## Key Implementation Steps

### 1. Data Loading
```python
from llama_index.core import SimpleDirectoryReader

reader = SimpleDirectoryReader("data")
documents = reader.load_data()
```

### 2. Configure RAG Settings
```python
from llama_index.core import Settings
from llama_index.core.node_parser import SentenceSplitter
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.openai import OpenAI

# Critical configurations
Settings.node_parser = SentenceSplitter(
    chunk_size=1000,    
    chunk_overlap=150,  
)
Settings.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-base-en-v1.5")
Settings.llm = OpenAI(model="gpt-4o-mini")
```

### 3. Index Creation with Faiss
```python
import faiss
from llama_index.vector_stores.faiss import FaissVectorStore
from llama_index.core import StorageContext, VectorStoreIndex

# Get embedding dimension
embedding = Settings.embed_model.get_text_embedding("Hello world")
dim = len(embedding)

# Initialize Faiss index
faiss_index = faiss.IndexFlatL2(dim)
vector_store = FaissVectorStore(faiss_index=faiss_index)
storage_context = StorageContext.from_defaults(vector_store=vector_store)

# Create index
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)
```

### 4. Query Engine Setup and Customization
```python
# Create query engine
query_engine = index.as_query_engine()

# Optional: Custom prompt template
from llama_index.core import PromptTemplate

template = """
You are a Q&A chat bot.
Use the given context only, answer the question.

<context>
{context_str}
</context>

Question: {query_str}
"""
new_template = PromptTemplate(template)
query_engine.update_prompts({"response_synthesizer:text_qa_template": new_template})
```

### 5. Query Execution
```python
response = query_engine.query("Your question here")
print(response)
```

## Important Notes
- `SimpleDirectoryReader` supports multiple file types
- Chunk size and overlap parameters are crucial for text splitting
- The embedding dimension must match the Faiss index configuration
- Custom prompt templates can be used to optimize response generation