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The three-level architecture is now structurally complete: all three control levels exist behind one InferenceEngine contract.

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@andreysparish andreysparish released this 06 Jul 16:42
v0.2.0
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Learn about vigilant mode.

Added

  • Level 2 — llama.cpp adapter (LlamaCppEngine). The first control
    level: Palimpsests spawns and owns a llama-server subprocess, so the
    full EngineMemoryConfig (context size, GPU offload, flash attention,
    KV-cache quantization, mmap, draft model) is applied as real launch
    flags rather than ignored. Two modes: spawn (own the server from a
    model path) and attach (talk to a user-run server by URL). Opt-in via
    PALIMPSESTS_LLAMACPP_MODEL.
  • Managed subprocess lifecycle (LlamaServerProcess). Free-port
    allocation, spawn, readiness by health poll, early-death detection, and
    idempotent shutdown — scoped to llama-server for now.
  • Level 3 slot — NativeEngine. A registered, honest placeholder:
    control_level=3 with every feature flag False, every operation
    refusing with CapabilityUnsupported, and is_available() False.
    The serving service (continuous batching, shared-prefix KV, server-side
    tool loop, KV persistence) is not implemented yet.
  • Block-memory retrieval (BlockMemory). Evicted context is embedded
    and stored in SQLite; the most relevant blocks are retrieved back on
    demand (numpy cosine, no vector DB). Injectable embedder, defaulting
    through the active engine's /api/embeddings. Backing store shared with
    future KV persistence under <workspace>/.context-memory/.
  • Block memory wired into the chat flow. chat now stores evicted
    messages and, lazily (only when eviction happened), retrieves relevant
    blocks back as a single prepended system message. Graceful: without an
    embed-capable engine or numpy, chat behaves exactly as before.
  • Ollama embeddings. OllamaEngine.embed() exposes /api/embeddings,
    the default source for block-memory vectors.
  • docs/USAGE.md — a run + settings guide for the current state.

Changed

  • AppContext.engines widened from OllamaEngine to the InferenceEngine
    protocol now that multiple adapters coexist. Callers read capabilities,
    never the concrete type, so nothing downstream changed.
  • The [llamacpp] extra is now empty and documented: the server-subprocess
    approach needs the llama-server binary out-of-band, not a Python
    package. numpy moved to its own [embeddings] extra (and [dev]).
  • Development status classifier is Beta; README, roadmap, and install
    instructions updated to reflect levels 1–2 shipped and the level-3 slot.

0.1.0 — 2026-07-06

Initial release.

Added

  • Level 1 — Ollama adapter (OllamaEngine). Thin HTTP client to an
    external Ollama daemon: streaming chat, model listing, availability
    probe, and the subset of EngineMemoryConfig Ollama honors.
  • The engine contract. InferenceEngine protocol, BaseInferenceEngine
    (derives chat from chat_stream, refuses sessions by default),
    EngineCapabilities, EngineMemoryConfig (with the flash-attention
    prerequisite for KV-quant enforced), and the level-3 InferenceSession
    protocol.
  • Context-window manager. Sink/window/evict fitting to a token budget,
    reporting what it evicted.
  • Registry — one active engine globally (radio, not checkbox).
  • Audit log — append-only, encrypted at rest (SQLCipher) with a key
    from the OS keychain, falling back to an ephemeral key headless.
  • CLI — chat, models, engine list / engine use.

Full Changelog: https://github.com/Assault-Consulting/Palimpsests/commits/v0.2.0

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