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README.md

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+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.
+
+---
+
+# 🐉 DnD Campaign Oracle: 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 `ThreadPoolExecutor` to process multiple document chunks simultaneously across local LLM slots for high-speed ingestion.
+* **Structured Metadata:** Uses **DSPy TypedPredictors** and **Pydantic** to force LLMs to output valid JSON synopses, tags, and entity lists.
+* **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** and **FAISS**, keeping your campaign secrets private.
+
+---
+
+## 🏗️ 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 **FAISS** index.
+5. **Interactive RAG:** A terminal loop that uses **Chain of Thought (CoT)** reasoning to answer queries based on retrieved context.
+
+---
+
+## 🛠️ Setup
+
+### Prerequisites
+
+* **Python 3.10+**
+* **LM Studio:** Running a local server at `http://192.168.0.49:1234` (or your specific IP).
+* **Models:** * Inference: `qwen3-8b` (or similar).
+* Embedding: `text-embedding-qwen3-embedding-8b`.
+
+
+
+### Installation
+
+```bash
+uv sync
+
+```
+
+---
+
+## 🚀 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 Oracle
+
+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.`
+
+---
+
+## 📂 File Structure
+
+* `ingest.py`: Handles file loading, multi-threaded enrichment, and FAISS storage.
+* `retrieve.py`: The interactive terminal-based retrieval loop.
+* `experts/ingestion_agent.py`: Contains the `IngestionAgent` and Pydantic schemas.
+* `embedding.py`: Custom wrapper for `LocalLMEmbeddings` with batch processing support.
+* `local_faiss_db/`: Directory where the vector index and metadata are persisted.
+
+---
+
+## ⚙️ Configuration
+
+In `ingest_notes.py`, you can tune the processing speed:
+
+* `max_workers=8`: Adjust based on your GPU/CPU capability to handle concurrent LLM requests.
+* `chunk_size=800`: Increase for more context per chunk, decrease for more granular searching.
+
+---