98 lines
3.4 KiB
Markdown
98 lines
3.4 KiB
Markdown
This `README.md` is designed to reflect the sophisticated local RAG pipeline you've built, highlighting the multi-threaded enrichment and the DSPy-powered "Smart Retrieval" system.
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---
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# 🐉 DnD Campaign Oracle: 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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## ⚔️ Key Features
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* **Parallel Enrichment:** Utilizes a `ThreadPoolExecutor` to process multiple document chunks simultaneously across local LLM slots for high-speed ingestion.
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* **Structured Metadata:** Uses **DSPy TypedPredictors** and **Pydantic** to force LLMs to output valid JSON synopses, tags, and entity lists.
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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** and **FAISS**, 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 **FAISS** index.
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5. **Interactive RAG:** A terminal loop that uses **Chain of Thought (CoT)** reasoning to answer queries based on retrieved context.
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---
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## 🛠️ Setup
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### Prerequisites
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* **Python 3.10+**
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* **LM Studio:** Running a local server at `http://192.168.0.49:1234` (or your specific IP).
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* **Models:** * Inference: `qwen3-8b` (or similar).
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* Embedding: `text-embedding-qwen3-embedding-8b`.
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### Installation
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```bash
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uv sync
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```
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---
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## 🚀 Usage
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### 1. Ingest & Enrich
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Run the ingestion script to process your markdown 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 Oracle
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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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> `📝 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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---
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## 📂 File Structure
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* `ingest.py`: Handles file loading, multi-threaded enrichment, and FAISS storage.
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* `retrieve.py`: The interactive terminal-based retrieval loop.
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* `experts/ingestion_agent.py`: Contains the `IngestionAgent` and Pydantic schemas.
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* `embedding.py`: Custom wrapper for `LocalLMEmbeddings` with batch processing support.
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* `local_faiss_db/`: Directory where the vector index and metadata are persisted.
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---
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## ⚙️ Configuration
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In `ingest_notes.py`, you can tune the processing speed:
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* `max_workers=8`: Adjust based on your GPU/CPU capability to handle concurrent LLM requests.
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* `chunk_size=800`: Increase for more context per chunk, decrease for more granular searching.
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---
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