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+# TMI RAG (rag-tmi)
+
+A lightweight **local Retrieval-Augmented Generation (RAG) system** used to index and search technical documentation and source code across the Terra-Mechanics development workspace.
+
+This tool allows Claude Code, Codex, or any LLM assistant to retrieve relevant context from engineering documentation before answering questions.
+
+The goal is to create a **searchable semantic memory layer** for projects such as:
+
+- Terra-View
+- Seismo Relay
+- Series 3 / Minimate protocol research
+- Modem / SLM documentation
+- Reverse engineering notes
+- Parser documentation
+
+Instead of manually searching repos or notes, the system retrieves relevant information using **vector similarity search**.
+
+---
+
+# How It Works
+
+The system follows a standard RAG architecture.
+
+### 1. Indexing
+
+`ingest.py`:
+
+1. Reads directories listed in `sources.yaml`
+2. Recursively scans for supported files (`.md`, `.txt`, `.py`)
+3. Splits content into chunks
+4. Generates embeddings using OpenAI
+5. Stores vectors in a FAISS index
+
+Result:
+
+```
+index/
+  index.faiss
+  meta.pkl
+```
+
+This becomes the **semantic database**.
+
+---
+
+### 2. Querying
+
+`query.py`:
+
+1. Embeds the query text
+2. Searches the FAISS vector index
+3. Returns the most relevant chunks of text
+
+These results can be pasted into an LLM chat (Claude Code, Codex, etc.) as context.
+
+Example:
+
+```
+python query.py "checksum algorithm"
+```
+
+---
+
+# Repository Structure
+
+```
+rag-tmi/
+│
+├─ ingest.py
+├─ query.py
+├─ sources.yaml
+├─ requirements.txt
+├─ README.md
+│
+├─ index/            # Generated FAISS index (not committed)
+│
+└─ .venv/            # Local Python environment (not committed)
+```
+
+---
+
+# Installation
+
+Create a virtual environment:
+
+```
+python -m venv .venv
+```
+
+Activate it.
+
+Windows (PowerShell):
+
+```
+.venv\Scripts\activate
+```
+
+Install dependencies:
+
+```
+pip install -r requirements.txt
+```
+
+---
+
+# Environment Variables
+
+Create a `.env` file in the project root.
+
+```
+OPENAI_API_KEY=your_key_here
+```
+
+The key is loaded automatically by:
+
+```
+python-dotenv
+```
+
+---
+
+# Running the Indexer
+
+Build the vector database:
+
+```
+python ingest.py
+```
+
+The script will:
+
+- scan all configured sources
+- generate embeddings
+- build a FAISS index
+
+You should see progress like:
+
+```
+Embedding 72 chunks
+```
+
+The index will be written to:
+
+```
+index/index.faiss
+index/meta.pkl
+```
+
+---
+
+# Querying the Database
+
+Run:
+
+```
+python query.py "frame checksum"
+```
+
+The system will return the most relevant chunks from indexed documents.
+
+These chunks can be copied into Claude or Codex to provide **accurate context** for responses.
+
+---
+
+# sources.yaml
+
+This file defines which directories are indexed.
+
+Example:
+
+```yaml
+sources:
+  - ../terra-view
+  - ../seismo-relay
+  - ../series3-agent
+  - ../protocol-docs
+```
+
+Paths are relative to the `rag-tmi` directory.
+
+---
+
+# Supported File Types
+
+Currently indexed:
+
+```
+.md
+.txt
+.py
+```
+
+Additional types can be added inside `ingest.py`.
+
+---
+
+# Git Notes
+
+The following files should **not be committed**:
+
+```
+.venv/
+.env
+index/
+__pycache__/
+```
+
+Example `.gitignore` entries:
+
+```
+.venv/
+.env
+index/
+__pycache__/
+```
+
+---
+
+# Why This Exists
+
+Large engineering projects accumulate knowledge across:
+
+- source code
+- documentation
+- research notes
+- reverse engineering logs
+
+Traditional search tools rely on **exact text matches**.
+
+RAG enables **semantic search**, meaning queries like:
+
+```
+"frame checksum"
+```
+
+can retrieve documentation that contains phrases like:
+
+```
+payload CRC
+frame validation
+checksum calculation
+```
+
+even if the exact words do not match.
+
+---
+
+# Future Improvements
+
+Possible upgrades:
+
+- CLI command wrapper (`rag "query"`)
+- automatic repo indexing
+- incremental indexing
+- MCP server for AI tool access
+- reranking model
+- code-aware chunking
+- web UI for search
+
+---
+
+# Philosophy
+
+This tool acts as a **local memory layer** for engineering work.
+
+Instead of searching through repos manually, developers and AI assistants can retrieve the most relevant information instantly.
+
+The system is intentionally simple:
+
+- local
+- transparent
+- easy to rebuild
+- no heavy frameworks
+
+It follows the principle that **small tools that solve real problems are better than complex systems that are hard to maintain.**
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