Phoenix V2: A Cognitive Architecture for AI That Remembers, Feels and Evolves — Paper, Code and Book

说明与用途

Phoenix V2 是一种本地 AI 架构，将记忆和情感上下文存储在 SQLite 中，使助手能够在模型更新后保持与用户的关系。

问题

AI assistants lose context when the model is updated, causing loss of persistent memory and relationship with the user.

结果

Phoenix V2 enables a persistent, emotionally‑aware AI assistant that survives model updates, runs on legacy hardware, and keeps all user data locally.

工作流

• 1. Store user messages and retrieved memories in a local SQLite database.
• 2. Retrieve relevant memories using semantic similarity, recency, and importance scoring.
• 3. Build a structured prompt with current message, memories, emotional state, and self‑model.
• 4. Send prompt to the LLM for reasoning (single API call).
• 5. Apply local heuristic to draft response; if flagged, revise via LLM.
• 6. Modulate tone with Personality Agent based on affective state.
• 7. Periodically run SubconsciousEngine to consolidate memories and update beliefs.
• 8. Persist all changes back to SQLite; only text of messages and embeddings are sent to external APIs.

证据

All persistent state — every memory, every emotional context, the system's model of who you are — lives outside the LLM, in a local SQLite database on your machine.

来源

The entire pipeline requires no GPU. Embedding generation uses a remote API call. LLM calls go to whichever provider you configure — the client is a single file, and swapping providers is a one-line change.

来源