rag-implementation

# RAG Implementation

Master Retrieval-Augmented Generation (RAG) to build LLM applications that provide accurate, grounded responses using external knowledge sources.

## When to Use This Skill

- Building Q&A systems over proprietary documents
- Creating chatbots with current, factual information
- Implementing semantic search with natural language queries
- Reducing hallucinations with grounded responses
- Enabling LLMs to access domain-specific knowledge
- Building documentation assistants
- Creating research tools with source citation

## Core Components

### 1. Vector Databases

**Purpose**: Store and retrieve document embeddings efficiently

**Options:**

- **Pinecone**: Managed, scalable, fast queries
- **Weaviate**: Open-source, hybrid search
- **Milvus**: High performance, on-premise
- **Chroma**: Lightweight, easy to use
- **Qdrant**: Fast, filtered search
- **FAISS**: Meta's library, local deployment

### 2. Embeddings

**Purpose**: Convert text to numerical vectors for similarity search

**Models:**

- **text-embedding-ada-002** (OpenAI): General purpose, 1536 dims
- **all-MiniLM-L6-v2** (Sentence Transformers): Fast, lightweight
- **e5-large-v2**: High quality, multilingual
- **Instructor**: Task-specific instructions
- **bge-large-en-v1.5**: SOTA performance

### 3. Retrieval Strategies

**Approaches:**

- **Dense Retrieval**: Semantic similarity via embeddings
- **Sparse Retrieval**: Keyword matching (BM25, TF-IDF)
- **Hybrid Search**: Combine dense + sparse
- **Multi-Query**: Generate multiple query variations
- **HyDE**: Generate hypothetical documents

### 4. Reranking

**Purpose**: Improve retrieval quality by reordering results

**Methods:**

- **Cross-Encoders**: BERT-based reranking
- **Cohere Rerank**: API-based reranking
- **Maximal Marginal Relevance (MMR)**: Diversity + relevance
- **LLM-based**: Use LLM to score relevance

## Quick Start

```python
from langchain.document_loaders import DirectoryLoader
from langchain.text_splitters import