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+# SRag — Efficient Retrieval-Augmented Generation
+
+> A modern, lightweight RAG implementation designed for efficient document parsing and contextual AI queries. Runs entirely on local models via Ollama — no cloud dependency.
+
+**Live:** [srag.jrodriiguezg.link](https://srag.jrodriiguezg.link)  
+**GitHub:** [jrodriiguezg/SRag](https://github.com/jrodriiguezg/SRag)
+
+---
+
+## Overview
+
+SRag is a clean, maintainable **Retrieval-Augmented Generation (RAG)** pipeline that connects document store embeddings to a language model. It is designed to be containerized, dependency-minimal, and completely local — no external API calls required.
+
+The system ingests documents, generates vector embeddings, and answers queries by retrieving relevant context before passing it to an LLM, drastically reducing hallucinations.
+
+## How It Works
+
+```
+Documents (text, PDF, Markdown)
+         │
+         ▼
+  ┌──────────────────┐
+  │  Ingestion Engine │  ← Parses and chunks documents
+  └──────────────────┘
+         │
+         ▼
+  ┌──────────────────┐
+  │   Vectorization   │  ← Ollama Embedding (nomic-embed-text)
+  └──────────────────┘
+         │
+         ▼
+  ┌──────────────────┐
+  │    Vector Store   │  ← In-memory / persistent index
+  └──────────────────┘
+         │
+  User Query ──────► Query Router ──► Retrieves top-k context
+                                            │
+                                            ▼
+                                    LLM (Ollama / qwen2.5)
+                                            │
+                                            ▼
+                                       Answer
+```
+
+## Core Architecture
+
+| Component | Role |
+|---|---|
+| **Ingestion Engine** | Parses text, PDFs, and Markdown into manageable chunks |
+| **Vectorization** | Generates dense embeddings optimized for semantic search |
+| **Query Router** | Intercepts user prompts, retrieves context, augments LLM input |
+| **Vector DB** | Lightweight in-memory or persistent storage for fast retrieval |
+
+## Stack
+
+| Layer | Technology |
+|---|---|
+| Framework | [LlamaIndex](https://www.llamaindex.ai/) |
+| LLM (local) | [Ollama](https://ollama.com/) — `qwen2.5:7b` |
+| Embeddings | Ollama — `nomic-embed-text` |
+| Vector Store | LlamaIndex `VectorStoreIndex` (in-memory / persistent) |
+| Containerization | Docker Compose |
+
+## Quick Start
+
+```python
+import os
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
+from llama_index.llms.ollama import Ollama
+from llama_index.embeddings.ollama import OllamaEmbedding
+
+# Setup local models via Ollama
+Settings.llm = Ollama(model="qwen2.5:7b", request_timeout=120.0)
+Settings.embed_model = OllamaEmbedding(model_name="nomic-embed-text")
+
+# Load documents and generate vector index
+documents = SimpleDirectoryReader("docs").load_data()
+index = VectorStoreIndex.from_documents(documents)
+
+# Initialize strict context-based chat engine
+chat_engine = index.as_chat_engine(
+    chat_mode="context",
+    similarity_top_k=20,
+    verbose=False
+)
+```
+
+## Key Design Decisions
+
+- **LLM Agnostic:** Compatible with Ollama for local inference or any standard API backend (OpenAI-compatible).
+- **Embeddings Flexibility:** Supports local models via Hugging Face or external APIs like OpenAI.
+- **Context-strict mode:** `chat_mode="context"` ensures the LLM answers only from retrieved documents, minimizing hallucinations.
+- **`similarity_top_k=20`:** Retrieves a generous context window to improve answer coverage.
+
+## Prerequisites
+
+- [Ollama](https://ollama.com/) installed and running
+- Models pulled: `ollama pull qwen2.5:7b` and `ollama pull nomic-embed-text`
+- Python 3.10+ with `llama-index` and related packages
+- (Optional) Docker for containerized deployment
+
+## Support
+
+☕ [Support on Ko-fi](https://ko-fi.com/jrodriiguezg)
+
+## Author
+
+**Juan Raul Rodriguez Gil** — Built with passion for Linux & AI  
+[jrodriiguezg.link](https://jrodriiguezg.link) | [Instagram](https://www.instagram.com/jrodriiguezg.link/?hl=es)
+
+---
+
+© 2026 Juan Raul Rodriguez Gil