update

README.md

@@ -30,7 +30,9 @@ A troubleshooter receives a ticket reporting that the Apache service on a remote
 | Component | Tool |
 |-----------|------|
 | AI inference backend | [Ollama](https://ollama.com) |
-| Model | `gemma3:4b`, `llama3.1:8b`, or `qwen2.5:7b` |
+| Chat model | `gemma3:4b`, `llama3.1:8b`, or `qwen2.5:7b` |
+| Embedding model | `nomic-embed-text` (via Ollama) |
+| Vector store | [ChromaDB](https://www.trychroma.com) (embedded, local) |
 | Language | Python 3.11+ |
 
 ______________________________________________________________________
@@ -55,7 +57,7 @@ yay -S ollama-cuda
 sudo systemctl enable --now ollama
 ```
 
-### 2. Pull a model
+### 2. Pull a chat model
 
 ```bash
 ollama pull gemma3:4b       # ~3 GB — fast, good for sysadmin tasks
@@ -63,13 +65,30 @@ ollama pull llama3.1:8b     # ~5 GB — stronger reasoning
 ollama pull qwen2.5:7b      # ~4.5 GB — strong structured output
 ```
 
-### 3. Verify the model works
+### 3. Pull the embedding model
+
+`tai` uses `nomic-embed-text` to embed diagnostic data and runbooks for semantic retrieval (RAG). Pull it on the same host as Ollama:
+
+```bash
+ollama pull nomic-embed-text   # ~274 MB
+```
+
+Verify it loaded:
+
+```bash
+curl http://localhost:11434/api/embeddings \
+  -d '{"model":"nomic-embed-text","prompt":"test"}'
+```
+
+A JSON response with an `"embedding"` array confirms it is ready.
+
+### 4. Verify the chat model works
 
 ```bash
 ollama run gemma3:4b "what causes a systemd service to enter failed state?"
 ```
 
-### 4. Verify the HTTP API is running
+### 5. Verify the HTTP API is running
 
 `tai` communicates with Ollama over its OpenAI-compatible REST API:
 
@@ -80,7 +99,7 @@ curl http://localhost:11434/api/generate \
 
 A JSON response with a `response` field confirms everything is working.
 
-### 5. Point tai at your Ollama instance
+### 6. Point tai at your Ollama instance
 
 Once `tai` AI integration is complete, use these flags: