# Functional Spec: AI Integration

## 1. Provider Abstraction
The system uses a pluggable architecture for LLMs. The `ModelProvider` interface abstracts:
*   **Generation:** Sending prompt + history + tools to the model.
*   **Parsing:** Extracting text content vs. tool calls from the raw response.

## 2. Ollama Implementation
*   **Endpoint:** `http://localhost:11434/api/chat`
*   **JSON Protocol:**
    *   Request: `{ model: "name", messages: [...], stream: false, tools: [...] }`
    *   Response: Standard Ollama JSON with `message.tool_calls`.
*   **Fallback:** If the specific local model doesn't support native tool calling, we may need a fallback system prompt approach, but for this story, we assume a tool-capable model (like `llama3.1` or `mistral-nemo`).

## 3. Chat Loop (Backend)
The `chat` command acts as the **Agent Loop**:
1.  Frontend sends: `User Message`.
2.  Backend appends to `SessionState.history`.
3.  Backend calls `OllamaProvider`.
4.  **If Text Response:** Return text to Frontend.
5.  **If Tool Call:**
    *   Backend executes the Tool (using the Core Tools from Story #2).
    *   Backend appends `ToolResult` to history.
    *   Backend *re-prompts* Ollama with the new history (recursion).
    *   Repeat until Text Response or Max Turns reached.

## 4. Frontend State
*   **Settings:** Store `llm_provider` ("ollama"), `ollama_model` ("llama3.2"), `ollama_base_url`.
*   **Chat:** Display the conversation. Tool calls should be visible as "System Events" (e.g., collapsed accordions).