# 🐉 Dungeon Masters Vault: Local RAG Assistant

An advanced Retrieval-Augmented Generation (RAG) system designed for Dungeon Masters. This tool ingests markdown-based campaign notes, enriches them with AI-generated metadata, and provides an interactive terminal interface to query your world’s lore using **DSPy** and **Local LLMs**.

## ⚔️ Key Features

* **Parallel Enrichment:** Utilizes a configurable multithreading to process multiple document chunks simultaneously across local LLM slots for high-speed ingestion.
* **Deep Context Retrieval:** Unlike standard RAG, this system retrieves relevant chunks and then "peeks" at the full source file to provide the LLM with broader narrative context.
* **Local-First:** Designed to run entirely on your hardware using **LM Studio**, keeping your campaign secrets private.

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## 🏗️ Architecture

1. **Ingestion:** Scans `DATA_DIR` for `.md` files.
2. **Chunking:** Splits documents into 800-character segments with overlap.
3. **Enrichment:** A DSPy `IngestionAgent` analyzes each chunk to extract:
    * **Synopsis:** A one-sentence summary.
    * **Tags:** Plot points, item names, or themes.
    * **Entities:** Specific NPCs, Locations, or Factions.
4. **Vector Store:** Chunks and metadata are embedded using `text-embedding-qwen3` and stored in a local **Turso** database.
5. **Interactive RAG:** A terminal loop that uses **ReAct (Reasoning and Acting)** to answer queries based on retrieved context.

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## 🛠️ Setup

### Prerequisites

* **UV [Link to install here](https://docs.astral.sh/uv/)**
* **LM Studio:** Running a local server at `localhost:1234` (or your specific IP).
* **Models:** * Inference & Embedding: Configurable for your preference. grab your model in LMStudio and update the conifg


### Installation

```bash
uv sync
```

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## 🚀 Usage

### 1. Ingest & Enrich

Run the ingestion script to process your markdown files and build the vector database.

```bash
uv run src/ingest.py
```

### 2. Query the LLM

Launch the interactive session to ask questions about your campaign.

```bash
uv run src/retrieve.py
```

**Example Query:**

> `📝 Query: Why did the party get free bread at the Golden Grain Inn?`  
> `📜 AI RESPONSE: Based on the session notes from 'Session_12.md', the party received free bread because the Rogue successfully intimidated the baker's assistant, and the Cleric later performed a minor miracle (Thaumaturgy) that impressed the owner.`

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## 📂 File Structure

```
.
├── config.yaml # Configuration for the app
├── load_ingestion_llms.sh  # script to load multiple LLMs (Run before ingest)
├── README.md 
├── ROADMAP.md
├── src
│   ├── config_loader.py # Loads the config yaml file
│   ├── embedding.py  # Class to talk to LMStudio Embedding Model Server
│   ├── experts
│   │   ├── ingestion_agent.py # Agent Class for ingestion enrichment
│   │   └── retrieval_agent.py # Agent Class for retrieval, with tools and database calls
│   ├── ingest.py # Ingestion script to load your DnD Campaign Notes
│   └── retrieve.py # main Q&A for your notes
├── data # GitIgnored Folder for Notes Database
│   ├── dmv.db
│   ├── dmv.db-wal
│   ├── dmv.log
│   └── time_file.txt
├── pyproject.toml
├── LICENSE
└── uv.lock
```

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## ⚙️ Configuration

In `config.yaml`, you can adjust multiple things:

* Enrichment / embedding & Retrieval Mdels
* DnD Notes Location (data_dir)
* System Prompts for Ingestion & Retrieval Agents

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