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2026-05-04 18:30:33 +02:00
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commit 739e19f595
2 changed files with 27 additions and 5 deletions

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@@ -30,7 +30,9 @@ A troubleshooter receives a ticket reporting that the Apache service on a remote
| Component | Tool | | Component | Tool |
|-----------|------| |-----------|------|
| AI inference backend | [Ollama](https://ollama.com) | | 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+ | | Language | Python 3.11+ |
______________________________________________________________________ ______________________________________________________________________
@@ -55,7 +57,7 @@ yay -S ollama-cuda
sudo systemctl enable --now ollama sudo systemctl enable --now ollama
``` ```
### 2. Pull a model ### 2. Pull a chat model
```bash ```bash
ollama pull gemma3:4b # ~3 GB — fast, good for sysadmin tasks 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 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 ```bash
ollama run gemma3:4b "what causes a systemd service to enter failed state?" 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: `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. 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: Once `tai` AI integration is complete, use these flags:

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@@ -19,6 +19,9 @@ dependencies = [
] ]
[project.optional-dependencies] [project.optional-dependencies]
rag = [
"chromadb>=0.5,<1.0",
]
dev = [ dev = [
"pytest>=8.2,<9.0", "pytest>=8.2,<9.0",
"ruff>=0.5,<1.0", "ruff>=0.5,<1.0",