engram_cosine_sim was defined but never called. Nodes have 768-dim
nomic-embed-text vectors. Now:
- engram_embed_query() embeds the query string once per activate() call
- engram_goal_bias() takes (qvec, qdim) and adds cosine-similarity bonus
up to +0.6 when sim > 0.5 — semantic relevance now augments lexical bias
- engram_wm_count() exposes working-memory-active node count to EL
- el_runtime.h declares engram_wm_count for soul-daemon linking
- Add list_set, math_exp, and float_add/sub/mul/div/gt/lt/eq/gte/lte builtins to
el_runtime.c + el_runtime.h (float arithmetic builtins needed because EL operators
+*/ operate on raw el_val_t bits, not IEEE 754 doubles)
- Remove engram_embed_query() and its forward declaration from el_runtime.c
- Remove Ollama cosine-similarity blend from activation scoring (reverts 9af2482):
drops query_emb/query_edim variables, bias *= (1 + 0.3 * sim) block, and all
free(query_emb) calls from the activation loop
- Implement BM25+ scoring in server.el (k1=1.2, b=0.75, delta=1.0):
bm25_tokenize, bm25_count_term, bm25_score_doc, bm25_search_json
V1 uses n_t=1 approximation (constant IDF per corpus size); acceptable as a
first pass without an inverted index
- Wire /api/bm25/search POST/GET route in server.el dispatcher
- Zero Ollama calls in the activation/search path; embeddings on nodes are
untouched (still written at node-creation time)
engram_cosine_sim() was defined and embeddings were computed per-node
via nomic-embed-text on write, but the function was never called during
activation scoring. The goal_bias computation used only lexical substring
matching, ignoring all stored embedding vectors.
This change adds engram_embed_query() to embed the query string at search
time (5s timeout so Ollama latency never blocks activation), then blends
cosine similarity into the working-memory bias with α=0.3:
bias_final = goal_bias(lexical) * (1 + 0.3 * max(0, cosine_sim))
Nodes with high semantic similarity to the query but low lexical overlap
now receive up to 30% bias boost into working memory promotion. Gracefully
degrades to pure lexical when Ollama is unavailable or node has no embedding.
- Add ML-KEM-1024 + AES-256-GCM binary persistence to el_runtime.c with
two-key scheme (Neuron master + user key); SHAKE-256 key derivation
- Add nomic-embed-text 768-dim float32 embeddings on every node write
via Ollama; graceful fallback when Ollama is not running
- Wire all /api/neuron/* MCP routes directly into Engram (server.el),
eliminating the Kotlin server as the MCP backend
- Set ENGRAM_CHECKPOINT_INTERVAL = 1 (write binary on every node write,
not every 50)
- Add el_runtime.h declarations for engram_write_binary_el and
engram_load_binary_el builtins
will.anderson
Will Anderson