refactor: 🔨 Ollama migration
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# 🐉 Dungeon Masters Vault: Local RAG Assistant
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# Dungeon Masters Vault: Local RAG Assistant
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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**.
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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**.
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## ⚔️ Key Features
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## Key Features
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* **Parallel Enrichment:** Utilizes a configurable multithreading to process multiple document chunks simultaneously across local LLM slots for high-speed ingestion.
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* **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.
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* **Local-First:** Designed to run entirely on your hardware using **LM Studio**, keeping your campaign secrets private.
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* **Parallel Enrichment:** Configurable multithreading processes multiple document chunks simultaneously across local LLM slots for high-speed ingestion.
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* **Deep Context Retrieval:** Retrieves relevant chunks and "peeks" at the full source file to provide the LLM with broader narrative context.
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* **Local-First:** Runs entirely on your hardware using **Ollama**, keeping your campaign secrets private.
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---
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## 🏗️ Architecture
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1. **Ingestion:** Scans `DATA_DIR` for `.md` files.
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2. **Chunking:** Splits documents into 800-character segments with overlap.
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3. **Enrichment:** A DSPy `IngestionAgent` analyzes each chunk to extract:
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* **Synopsis:** A one-sentence summary.
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* **Tags:** Plot points, item names, or themes.
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* **Entities:** Specific NPCs, Locations, or Factions.
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4. **Vector Store:** Chunks and metadata are embedded using `text-embedding-qwen3` and stored in a local **Turso** database.
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5. **Interactive RAG:** A terminal loop that uses **ReAct (Reasoning and Acting)** to answer queries based on retrieved context.
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---
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## 🛠️ Setup
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## Setup
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### Prerequisites
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* **UV [Link to install here](https://docs.astral.sh/uv/)**
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* **LM Studio:** Running a local server at `localhost:1234` (or your specific IP).
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* **Models:** * Inference & Embedding: Configurable for your preference. grab your model in LMStudio and update the conifg
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* **[UV](https://docs.astral.sh/uv/)** — Python package manager
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* **Ollama** — Running a local server (default `localhost:11434`)
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* **Local Models** — Pull your inference and embedding models with `ollama pull`
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### Installation
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---
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## 🚀 Usage
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## Usage
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### 1. Ingest & Enrich
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### Ingest & Enrich
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Run the ingestion script to process your markdown files and build the vector database.
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Process your markdown campaign files and build the vector database:
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```bash
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uv run src/ingest.py
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```
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### 2. Query the LLM
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### Query the LLM
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Launch the interactive session to ask questions about your campaign.
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Launch the interactive session to ask questions about your campaign:
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```bash
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uv run src/retrieve.py
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```
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**Example Query:**
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**Example interaction:**
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> `📝 Query: Why did the party get free bread at the Golden Grain Inn?`
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> `📜 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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> Query: Why did the party get free bread at the Golden Grain Inn?
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>
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> Based on the session notes from 'Session_12.md', the party received free bread because the Rogue intimidated the baker's assistant and the Cleric performed Thaumaturgy to impress the owner.
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---
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## 📂 File Structure
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## File Structure
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```
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.
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├── config.yaml # Configuration for the app
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├── load_ingestion_llms.sh # script to load multiple LLMs (Run before ingest)
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├── config.yaml # App configuration
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├── load_ingestion_llms.sh # Script to load multiple LLMs (run before ingest)
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├── README.md
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├── ROADMAP.md
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├── src
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│ ├── config_loader.py # Loads the config yaml file
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│ ├── embedding.py # Class to talk to LMStudio Embedding Model Server
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│ ├── experts
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│ │ ├── ingestion_agent.py # Agent Class for ingestion enrichment
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│ │ └── retrieval_agent.py # Agent Class for retrieval, with tools and database calls
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│ ├── ingest.py # Ingestion script to load your DnD Campaign Notes
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│ └── retrieve.py # main Q&A for your notes
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├── data # GitIgnored Folder for Notes Database
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│ ├── dmv.db
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│ ├── dmv.db-wal
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│ ├── dmv.log
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│ └── time_file.txt
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├── src/
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│ ├── config_loader.py # Loads config.yaml
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│ ├── embedding.py # Ollama embedding model client
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│ ├── experts/
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│ │ ├── ingestion_agent.py # AI agent for document enrichment
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│ │ └── retrieval_agent.py # AI agent for queries, with tools and DB calls
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│ ├── ingest.py # Campaign notes ingestion script
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│ └── retrieve.py # Interactive Q&A interface
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├── data/ # Campaign database (gitignored)
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│ ├── dmv.db
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│ ├── dmv.log
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│ └── time_file.txt
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├── pyproject.toml
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├── LICENSE
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└── uv.lock
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@@ -93,12 +79,12 @@ uv run src/retrieve.py
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---
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## ⚙️ Configuration
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## Configuration
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In `config.yaml`, you can adjust multiple things:
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Edit `config.yaml` to customize:
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* Enrichment / embedding & Retrieval Mdels
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* DnD Notes Location (data_dir)
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* System Prompts for Ingestion & Retrieval Agents
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* Inference and embedding models
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* Campaign notes location (`data_dir`)
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* System prompts for ingestion and retrieval agents
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---
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@@ -1,5 +0,0 @@
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# lms load qwen-4b-instruct-2507 --parallel 2 --identifier "qwen-0" --ttl 1800
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# lms load qwen-4b-instruct-2507 --parallel 2 --identifier "qwen-1" --ttl 1800
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# lms load qwen-4b-instruct-2507 --parallel 2 --identifier "qwen-2" --ttl 1800
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# lms load qwen-4b-instruct-2507 --parallel 2 --identifier "qwen-3" --ttl 1800
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# lms load qwen-4b-instruct-2507 --parallel 2 --identifier "qwen-4" --ttl 1800
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