fix(embedding): land openai-compat EF cleanly on develop

After rebasing #1671 onto current develop:
- Opt test_embedding_api out of conftest's stable EF mock so the
  get_embedding_function selection tests exercise the real factory.
- Move the CHANGELOG entry from released 3.7.0 Performance into
  Unreleased Features (rebase context had drifted).

CHANGELOG.md

@@ -8,6 +8,10 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 
 ## [Unreleased]
 
+### Features
+
+- **Embeddings via any OpenAI-compatible `/v1/embeddings` endpoint.** New `embedding_model: "openai-compat"` option computes embeddings on a server (LM Studio, llama.cpp, vLLM, Ollama's OpenAI shim, or a self-hosted endpoint) instead of a local ONNX model — useful for larger/multilingual embedders such as Qwen3-Embedding, or GPU offload. New `OpenAICompatEmbeddingFunction` in [`mempalace/embedding.py`](mempalace/embedding.py) speaks the standard `/v1/embeddings` protocol over stdlib `urllib` (no new dependency), batches requests, re-sorts the response by `index`, and L2-normalizes for the cosine collection. Endpoint settings are resolved by `MempalaceConfig` as a single source of truth — `embedding_api_url` / `embedding_api_model` / `embedding_api_key` in `config.json`, each overridable via the matching `MEMPALACE_EMBEDDING_API_*` env var. The embedding function's `name()` encodes the model id so changing it forces `mempalace repair rebuild-index` (different vector space). Mirrors the existing `openai-compat` LLM provider naming; stays local when the endpoint is on your machine/LAN. (#1559)
+
 ---
 
 ## [3.7.0] — 2026-08-02
@@ -22,8 +26,6 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 - **HNSW capacity probes are cached** and invalidated by palace file signature, so repeated MCP status/taxonomy paths no longer re-scan native segment files on every call. (#2051, #1471)
 - **`chunk_text` line numbering is O(N)** via incremental tallies, fixing multi-second hangs on large sources. (#2054, #2055)
 
-- **Embeddings via any OpenAI-compatible `/v1/embeddings` endpoint.** New `embedding_model: "openai-compat"` option computes embeddings on a server (LM Studio, llama.cpp, vLLM, Ollama's OpenAI shim, or a self-hosted endpoint) instead of a local ONNX model — useful for larger/multilingual embedders such as Qwen3-Embedding, or GPU offload. New `OpenAICompatEmbeddingFunction` in [`mempalace/embedding.py`](mempalace/embedding.py) speaks the standard `/v1/embeddings` protocol over stdlib `urllib` (no new dependency), batches requests, re-sorts the response by `index`, and L2-normalizes for the cosine collection. Endpoint settings are resolved by `MempalaceConfig` as a single source of truth — `embedding_api_url` / `embedding_api_model` / `embedding_api_key` in `config.json`, each overridable via the matching `MEMPALACE_EMBEDDING_API_*` env var. The embedding function's `name()` encodes the model id so changing it forces `mempalace repair rebuild-index` (different vector space). Mirrors the existing `openai-compat` LLM provider naming; stays local when the endpoint is on your machine/LAN. (#1559)
-
 ### Bug Fixes
 
 - **Mining works again on the default Chroma backend.** Once Chroma began declaring `requires_explicit_embeddings`, every write started routing through `EmbeddingCollection`, whose `_embed_texts` built rows with `list(ndarray)` — that unpacks into `np.float32` *scalars*, which chromadb rejects outright (`Expected embeddings to be a list of floats or ints, a list of lists, a numpy array, or a list of numpy arrays`). `mine`, and every other write against a default palace, aborted on the first drawer. Vectors now convert to real Python floats. The suite was structurally blind to this: conftest's autouse embedding fixture replaces `_embed_texts` itself for every module outside `test_embedding` / `test_embeddinggemma`, so the defective function was never executed under test — the regression tests therefore live in `test_embedding.py`, where that stub does not apply. (#2187)

tests/conftest.py

@@ -40,6 +40,7 @@ _TEST_EMBED_DIM = 384
 _TEST_TOKEN_RE = re.compile(r"\w+", re.UNICODE)
 _REAL_EMBEDDING_TEST_MODULES = {
     "test_embedding",
+    "test_embedding_api",
     "test_embeddinggemma",
 }