e272ed3fbf412d7c5774f7ed82463e1d905d1468
6 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 MSL
|
72cbfb5967 |
feat(privacy): blocking consent gate for env-fallback LLM API keys
Adds api_key_source provenance ('flag' | 'env' | None) to LLMProvider
so cmd_init can distinguish a key passed via --llm-api-key (explicit
opt-in) from one silently picked up via OPENAI_API_KEY / ANTHROPIC_API_KEY
shell env (stray credential).
When the endpoint is external AND api_key_source == 'env', init now
prints a blocking [y/N] prompt before any data is sent. Anything other
than 'y' drops the LLM and falls back to heuristics-only.
Adds --accept-external-llm flag for CI / non-interactive bypass.
Completes the UX gap in #1224: the URL-based warning was informational
and init kept running, so a user who didn't notice the line had already
leaked. The consent prompt is the actual gate; explicit flag-passed keys
remain treated as already-consented.
|
||
|
Igor Lins e Silva
|
5e33592ba2 |
chore(corpus-origin): address Copilot review on #1223
- cli.py: stringify each evidence entry exactly once before the startswith check (was calling str(e) twice per element). - tests: replace brittle `confidence != 0.90` assertion with an equality check against detect_origin_heuristic on the same samples. The original would have spuriously fired if the heuristic ever legitimately produced 0.90 for these samples; the new form pins the contract directly. |
||
|
Igor Lins e Silva
|
c92256f08f |
chore(corpus-origin): tag merged evidence by tier + pin confidence-source contract
Two follow-ups to PR #1221's merge-fields behavior, both raised by the Copilot review on that PR: - Evidence merge now prefixes each entry with `Tier-1 heuristic: ` or `Tier-2 LLM: ` so the on-disk `origin.json` audit record retains tier provenance. The pre-#1221 code labeled heuristic evidence; the merge-fields refactor flattened that. Re-prefixing is idempotent. - Tests now assert that the merged `confidence` is the heuristic's, not the LLM's. Added inline assertions to the two existing contradiction/disagreement tests, plus a dedicated `test_merge_tier_fields_confidence_matches_heuristic_call` that compares to `detect_origin_heuristic` directly so a future regression letting Tier 2 confidence leak through cannot pass silently. Tests: 1378 pass. Ruff check + format both clean (CI-pinned 0.4.x). |
||
|
MSL
|
4400734867 |
feat(privacy): warn when LLM tier sends content to external API
4 files changed, 248 insertions, 0 deletions. 7 new tests (4 unit + 3 integration), all RED-first. Per @milla-jovovich's question to @igorls during PR #1221 review: users running `mempalace init` with an external LLM provider (Anthropic API, OpenAI hosted, etc.) need a clear, explicit warning that their folder content will be sent to the provider, that MemPalace doesn't control how the provider logs/retains/uses that data, and how to opt out. @igorls confirmed this should be a small follow-up PR scoped to the warning itself, before the v3.3.4 tag. This PR adds: - `_endpoint_is_local(url)` helper in `mempalace/llm_client.py` — URL-based heuristic returning True if the hostname is on the user's machine or private network. Covers: localhost, 127.0.0.1, ::1, hostnames ending in .local (mDNS/Bonjour), IPv4 RFC1918 ranges (10/8, 172.16-31/12, 192.168/16), and IPv6 unique-local addresses (fc00::/7). - `is_external_service` property on the `LLMProvider` base class. Subclasses inherit; the URL determines (no provider-specific hardcoding). This means: Ollama on localhost = local. LM Studio on LAN = local. Anthropic with default `https://api.anthropic.com` = external. A user proxying Anthropic through localhost (advanced setup) = local, no false-positive warning. - One-line warning print in `cmd_init` after successful provider acquisition, gated on `is_external_service`: ⚠ {provider_name} is an EXTERNAL API. Your folder content will be sent to the provider during init. MemPalace does not control how the provider logs, retains, or uses your data. Pass --no-llm to keep init fully local. The warning fires AFTER `LLM enabled: ...` so users see both that the LLM is engaged AND the privacy implications of where it lives, before Pass 0 / entity detection actually runs. LOCAL providers (Ollama on localhost, LM Studio on localhost or LAN, llama.cpp on localhost, vLLM on localhost) DO NOT trigger the warning — nothing leaves the user's machine/network in those configurations. TDD: 7 tests added across 2 files. Unit tests in `tests/test_llm_client.py` (4 tests, all RED-first): 1. test_ollama_provider_default_endpoint_is_local — pins that the default `http://localhost:11434` is classified local. 2. test_openai_compat_provider_localhost_endpoint_is_local — covers the LM Studio / llama.cpp / vLLM common case (localhost, 127.0.0.1, and 192.168.x LAN). 3. test_openai_compat_provider_cloud_endpoint_is_external — pins that pointing openai-compat at https://api.openai.com (or any non-local URL) classifies as external. 4. test_anthropic_provider_default_endpoint_is_external — pins that AnthropicProvider's default endpoint is external (the dominant user-facing case for `--llm-provider anthropic`). Integration tests in `tests/test_corpus_origin_integration.py` (3 tests, RED-first; 1 was the critical RED — the other 2 passed by accident since nothing printed "EXTERNAL API" before this PR): 5. test_init_prints_privacy_warning_when_provider_is_external — captures stdout from cmd_init with a mocked external provider, asserts the warning text contains "EXTERNAL API" + "--no-llm" + language about MemPalace not controlling provider behavior. 6. test_init_no_privacy_warning_when_provider_is_local — same flow with a mocked local provider, asserts the warning text does NOT appear. 7. test_init_no_privacy_warning_with_no_llm_flag — pins the --no-llm path: no provider acquisition attempted, no warning fires. Tests: 1382 total mempalace tests pass. 2 pre-existing environmental failures unrelated to this change (chromadb optional dep). Ruff check + format both clean. Backwards compatible: `is_external_service` is a new property; existing callers don't reference it. The warning is a new print statement that fires only when an external endpoint is acquired. The `--no-llm` opt-out existed before this PR and continues to work identically. Out of scope for follow-up (deliberately not in this PR per Igor's "small PR" guidance): Tailscale CGNAT (100.64.0.0/10) treatment, pre-init confirmation prompt, persistent privacy-mode config flag, explicit cloud-provider name detection. Tracked for future iteration. |
||
|
MSL
|
7294b69ac8 |
feat(corpus-origin): merge LLM fields into heuristic result instead of replacing
2 files changed, 260 insertions, 7 deletions. 4 new tests (all RED-first). Per @igorls's review of PR #1211 (https://github.com/MemPalace/mempalace/pull/1211#issuecomment-4322762236): the corpus-origin Pass 0 currently lets a Tier 2 LLM result REPLACE the heuristic result wholesale. With ``--llm`` default-on (since #1211) and a small local model like Ollama gemma4:e4b, the LLM can return a wrong ``likely_ai_dialogue=False, confidence=0.90`` that overrides a confident heuristic ``True``. Tier 2's persona/user/platform extraction is the whole reason to run it; the YES/NO call should stay with the heuristic. This PR changes ``_run_pass_zero`` in ``mempalace/cli.py`` to merge fields instead of replacing: - ``likely_ai_dialogue`` → KEEP heuristic's (don't let weak LLM flip) - ``confidence`` → KEEP heuristic's (paired with the bool above) - ``primary_platform`` → TAKE LLM's when LLM provides one - ``user_name`` → TAKE LLM's when LLM provides one - ``agent_persona_names`` → TAKE LLM's when LLM provides any - ``evidence`` → COMBINE both signal trails This preserves the persona-extraction value of Tier 2 (the whole point of running it) while preventing a weak local model from flipping a confident heuristic. TDD: 4 tests added in tests/test_corpus_origin_integration.py covering the four state combinations: 1. test_merge_tier_fields_heuristic_yes_llm_no_keeps_heuristic_bool — The exact failure mode Igor caught. Heuristic confidently flags AI-dialogue; mocked LLM contradicts. Asserts merged result keeps heuristic's True AND merges LLM's persona/user/platform fields. This test was the RED that drove the implementation. 2. test_merge_tier_fields_heuristic_no_no_personas_leak — Both tiers agree NOT AI-dialogue, both report empty personas. Pins that the merge doesn't accidentally introduce personas. 3. test_merge_tier_fields_heuristic_yes_llm_yes_combines_evidence — Both tiers agree AI-dialogue, LLM extracts personas. Pins that evidence from BOTH tiers ends up in the merged audit trail and persona/user/platform come from LLM. 4. test_merge_tier_fields_no_llm_provider_returns_heuristic_only — Backwards compat: with no LLM provider (``--no-llm`` path), the merge logic doesn't fire and behavior is identical to v3.3.4. Tests: 1367 pass on the full mempalace suite. 2 pre-existing environmental failures unrelated to this change (chromadb optional dep). Ruff check + format both clean. |
||
|
MSL
|
b99e54546b |
feat(init): context-aware corpus detection
10 files changed. 2,563 insertions, 30 deletions. 48 new tests, including end-to-end coverage live-tested with Anthropic Haiku 4.5. This PR overhauls the first-run experience of `mempalace init` end-to-end, ships a new corpus-origin detection module from scratch, wires it into entity classification and LLM refinement, adds a graceful-fallback path that means `init` never crashes on a missing LLM, and ships a meta-test that prevents internal-coordination jargon from leaking into source or tests. The headline change is that `mempalace init` now understands what kind of folder you're pointing it at — AI conversations, regular writing, code, narrative — and adapts how it classifies entities accordingly. The same folder containing `Echo`, `Sparrow`, and `Cipher` (names you've assigned to AI agents) used to dump those into your "people" list alongside biological humans. Now they go into a separate `agent_personas` bucket, and your `people` list stays clean. But the broader change is that `mempalace init` got upgraded across the board — smarter defaults, smarter degradation, smarter classification, smarter persistence, and a new way to refresh as your folder grows. Built and live-verified with Anthropic Haiku 4.5; runs unmodified on the local LLM runtimes mempalace already supports. ## What changes for users (in order, from `pip install` onwards) **Install** — `pip install mempalace` is unchanged. The package itself didn't shift. **First run — `mempalace init <folder>`:** 1. **`init` examines your folder before classifying anything.** A free regex heuristic decides in milliseconds: AI conversations, regular writing, narrative, or code? If an LLM is reachable, a second pass extracts the corpus author's name and any agent persona names from the dialogue. v3.3.3 had no such step — it dove straight into entity detection with no corpus context. 2. **LLM-assisted classification is now ON by default.** v3.3.3 made `--llm` opt-in. The LLM-assisted path is qualitatively better (extracts persona names, refines ambiguous classifications, gives the model corpus context) so it now runs by default. The provider abstraction is unchanged from v3.3.3 — three buckets are supported by `mempalace.llm_client`: - **Anthropic** (`--llm-provider anthropic` + `ANTHROPIC_API_KEY`) — the official Messages API. **This is the path live-verified end-to-end in this PR with Haiku 4.5.** Cost: ~\$0.01 per `init`. - **Ollama** (`--llm-provider ollama` — the default) — local models via `http://localhost:11434`. Fully offline. Honors the "zero-API required" promise. - **OpenAI-compatible** (`--llm-provider openai-compat` + `--llm-endpoint`) — per the v3.3.3 `mempalace/llm_client.py` docstring, this covers "OpenRouter, LM Studio, llama.cpp server, vLLM, Groq, Fireworks, Together, and most self-hosted setups." We did not test each of those individually as part of this PR; the abstraction has been stable since v3.3.3. If you try this PR with a specific provider and hit a quirk, please file an issue or comment here. 3. **`init` never blocks on a missing LLM.** No Ollama running, no API key set? `init` prints a one-line message pointing at `--no-llm` and falls through to the heuristic-only path. New default behavior, new graceful fallback to support it. `--no-llm` is the new explicit opt-out. 4. **`init` shows you what it detected.** A one-line banner — `Detected: Claude (Anthropic) (user: Jordan, agents: Echo, Sparrow, Cipher)` or `Corpus origin: not AI-dialogue (confidence: 0.98)` — tells you at a glance whether mempalace understood your folder. 5. **Entity classification gets smarter across the board.** Even non-persona candidates benefit: the LLM has corpus context (this is AI-dialogue, this is the user's name, these are agent names) and uses it to disambiguate ambiguous candidates that aren't personas at all. 6. **Agent personas live in their own bucket.** Names you've assigned to AI agents (Echo, Sparrow, Cipher) go into a new `agent_personas` bucket instead of your `people` list. Your real-person entity list stays clean. 7. **Detection result persists to `<palace>/.mempalace/origin.json`** with a `schema_version: 1` envelope, so downstream tools can read it. 8. **Re-running `init` is now idempotent.** Bug fix — running `init` twice on the same folder used to give different classification results because the detection step was sampling its own `entities.json` output. Caught by integration testing during this PR. **Later — when your folder grows:** 9. **`mempalace mine --redetect-origin`** is a new flag for refreshing the stored detection without redoing the whole `init`. Heuristic-only by design (the flag is meant to be cheap). If you want the full LLM-extracted detection refreshed (persona names, user name, etc.), run `mempalace init <yourfolder>` again — `init` is now idempotent (item 8), so re-running it on the same folder is safe. ## Behind the changes - **New module** `mempalace/corpus_origin.py` (422 lines) with two-tier detection: regex heuristic with co-occurrence rule (suppresses ambiguous terms like `Claude` / `Gemini` / `Haiku` when no unambiguous AI signal is present, so French novels, astrology forums, poetry corpora, llama-rancher journals don't false-positive), and LLM tier that extracts `user_name` and `agent_persona_names` from dialogue structure with belt-and-suspenders user-vs-agent disambiguation. - **Entity-classification consumer wiring.** `entity_detector.detect_entities` and `project_scanner.discover_entities` accept an optional `corpus_origin` kwarg. When present and the corpus is identified as AI-dialogue, candidates whose name case-insensitively matches an `agent_persona_name` are routed into the `agent_personas` bucket instead of `people`. Per-entity `type` is rewritten to `"agent_persona"`. - **LLM-refine consumer wiring.** `llm_refine.refine_entities` accepts the same `corpus_origin` kwarg and prepends a `CORPUS CONTEXT` preamble to its system prompt giving the LLM the platform / user / persona context. Existing `TOPIC` / `PERSON` / `PROJECT` / `COMMON_WORD` / `AMBIGUOUS` labels are unchanged. - **`init` overhaul.** Pass 0 (corpus-origin detection) inserted before existing Pass 1 (entity discovery). `--llm` flipped to default-on. `--no-llm` added. Graceful-fallback path replaces the previous hard-error on missing LLM. Provider precedence unchanged from the existing `llm_client` module. - **`mine` flag.** `mempalace mine --redetect-origin` re-runs corpus-origin detection on the current corpus state and overwrites `<palace>/.mempalace/origin.json`. - **`CLAUDE.md` design principle reworded** — "Local-first, zero external API by default." Local LLMs running on `localhost` (Ollama, LM Studio, llama.cpp, vLLM, unsloth studio) are part of the user's machine, not external APIs. External BYOK providers (Anthropic, OpenAI, Google) are supported but always opt-in, never default, never silent fallback. ## Cost story - **Anthropic (verified path):** ~\$0.01 per `init` via Haiku 4.5 with `ANTHROPIC_API_KEY`. - **Ollama / local LLM runtime:** zero cost. Fully offline. - **OpenAI-compatible service:** depends entirely on the service. The abstraction supports any service speaking the standard `/v1/chat/completions` API; specific quirks vary per provider. Try it and tell us how it goes. - **No LLM at all:** graceful fallback to heuristic-only. Zero cost. `init` never blocks. ## Backwards compatibility - All public function signatures gained the `corpus_origin` kwarg as optional (default `None`). Callers that don't pass it see the v3.3.3 return shape unchanged — no `agent_personas` key, no behavioral change. - The `--llm` CLI flag is preserved as a deprecated alias of the default. Existing scripts that pass it continue to work. - `corpus_origin=None` keeps `llm_refine.SYSTEM_PROMPT` byte-identical to v3.3.3. ## Test coverage - **19 unit tests** in `tests/test_corpus_origin.py` covering both tiers, the co-occurrence rule, ambiguous-term suppression, word-boundary brand matching, and user/persona disambiguation. - **29 integration tests** in `tests/test_corpus_origin_integration.py` covering end-to-end through `mempalace init`, persona reclassification, the `--redetect-origin` flag, the `--llm` default flip, graceful fallback paths, and re-init idempotency. Of those 29, five specifically cover the intersection with develop's other in-flight work (Pass 0 ↔ auto-mine ordering, topics + agent_personas bucket coexistence, entities.json shape, the `wing=` kwarg threading, llm_refine TOPIC label + corpus_origin preamble composition). - **1354 total mempalace tests pass.** 2 pre-existing environmental failures (`test_mcp_stdio_protection` — chromadb optional dep) unrelated to this change; they fail on plain `develop` too. - **Live-smoke-tested** with real Anthropic Haiku 4.5 on AI-dialogue and narrative fixtures. ## Hygiene guardrail This PR also adds a meta-test (`test_no_internal_coordination_jargon_in_source_or_tests`) that walks the source tree and asserts no internal-coordination jargon (e.g. development-phase markers, internal review-section references) leaks into runtime code, comments, docstrings, or LLM prompts. RED if anything slips in. Allowlist for legitimate RFC/spec section citations in `sources/`, `backends/`, `knowledge_graph.py`, and `i18n/`. |