55 lines
2.8 KiB
Markdown
55 lines
2.8 KiB
Markdown
### **Prompt Architecture: Context-Injected Action Loop**
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The bot implements a **"State-Aware Agentic Loop"** rather than a simple Chat-QA structure. The prompt is constructed dynamically at every tick of the loop.
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#### **1. Static Layer (System Definitions)**
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* **Source:** `src/core/planner/prompts/*.velin.md`
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* **Loader:** `src/core/planner/prompts/index.ts`
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* **Role:** Defines the "Soul" and "Rules".
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* **Components:**
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* **Protocol Definition (`system-action-gen-v1`):** Hardcodes the JSON schema for available tools (`send_message`, `read_unread_messages`, `sleep`) and logic flow (e.g., "Must check unread messages after sending").
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* **Persona (`personality-v1`):** Defines the character "AIRI" (tone, brevity, naturalness).
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#### **2. History Layer (Short-term Memory)**
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* **Source:** In-memory `messages` array (`ChatContext`).
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* **Role:** Provides conversational continuity.
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* **Mechanism:** A sliding window of the last ~20 messages (User/Assistant turns) is injected directly after the system prompt.
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#### **3. Dynamic State Layer (Sensory Injection)**
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* **Source:** `src/core/planner/llm-client.ts` (Runtime generated)
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* **Role:** Provides "Situational Awareness" and "Grounding".
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* **Mechanism:** A synthesized **User Message** is appended at the very end of the context window, forcing the LLM to focus on the immediate reality. It contains:
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* **Incoming Stream**: Raw content of new messages arriving *now* (passed from `scheduler`).
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* **Action History**: Results of the *immediately preceding* tool executions (e.g., "Action: send_message, Result: Success").
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* **Environment**: Current server time.
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* **Global State**: A summary of unread message counts across all channels (`unreadEvents`).
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* **Trigger**: The final instruction: *"Based on the context... Respond with the action... in JSON only."*
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---
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### **Data Flow Summary**
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```mermaid
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graph TD
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A[Static Markdown] -->|Velin Render| B(System Message)
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C[Chat History] -->|Sliding Window| D(Context Body)
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E[Runtime State] -->|Unread/Time/Results| F(State Injection)
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B --> G[Final Prompt]
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D --> G
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F --> G
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G -->|LLM API| H{Decision}
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H -->|JSON| I[Action Dispatcher]
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```
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### **Key Characteristics**
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1. **JSON Enforcement**: The bot does not use native "Function Calling" APIs (like OpenAI Tools). It relies on **Prompt Engineering** to force the model to output raw JSON, which is then parsed by `best-effort-json-parser`.
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2. **Stateless Logic**: The prompt explicitly tells the LLM "You have X unread messages" in every turn, making the LLM the sole decision-maker for flow control (Reading vs. Replying vs. Sleeping).
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3. **Observation-Reflection**: The prompt includes `History actions`, allowing the LLM to "see" the result of its previous attempt (e.g., if a read action returned empty, it knows to stop).
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