add BM25+ text ranking in EL, remove Ollama query-embedding dependency
- 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)
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@@ -2,6 +2,10 @@
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#include <stdlib.h>
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#include "el_runtime.h"
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el_val_t bm25_tokenize(el_val_t text);
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el_val_t bm25_count_term(el_val_t term, el_val_t doc_tokens);
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el_val_t bm25_score_doc(el_val_t doc_content, el_val_t query_tokens, el_val_t corpus_size, el_val_t avg_doc_len);
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el_val_t bm25_search_json(el_val_t query, el_val_t limit);
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el_val_t parse_port(el_val_t bind);
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el_val_t ok_json(void);
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el_val_t err_json(el_val_t msg);
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@@ -41,6 +45,7 @@ el_val_t route_neuron_state_events(el_val_t method, el_val_t path, el_val_t body
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el_val_t route_neuron_processes(el_val_t method, el_val_t path, el_val_t body);
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el_val_t route_events_next(el_val_t method, el_val_t path, el_val_t body);
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el_val_t route_events_ack(el_val_t method, el_val_t path, el_val_t body);
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el_val_t route_bm25_search(el_val_t method, el_val_t path, el_val_t body);
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el_val_t check_auth_ok(el_val_t method, el_val_t body);
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el_val_t handle_request(el_val_t method, el_val_t path, el_val_t body);
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@@ -50,6 +55,166 @@ el_val_t data_dir;
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el_val_t db_path;
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el_val_t loaded;
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el_val_t bm25_tokenize(el_val_t text) {
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el_val_t t = str_to_lower(text);
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t = str_replace(t, EL_STR("."), EL_STR(" "));
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t = str_replace(t, EL_STR(","), EL_STR(" "));
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t = str_replace(t, EL_STR("!"), EL_STR(" "));
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t = str_replace(t, EL_STR("?"), EL_STR(" "));
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t = str_replace(t, EL_STR("\""), EL_STR(" "));
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t = str_replace(t, EL_STR(":"), EL_STR(" "));
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t = str_replace(t, EL_STR(";"), EL_STR(" "));
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t = str_replace(t, EL_STR("("), EL_STR(" "));
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t = str_replace(t, EL_STR(")"), EL_STR(" "));
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t = str_replace(t, EL_STR("["), EL_STR(" "));
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t = str_replace(t, EL_STR("]"), EL_STR(" "));
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t = str_replace(t, EL_STR("{"), EL_STR(" "));
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t = str_replace(t, EL_STR("}"), EL_STR(" "));
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t = str_replace(t, EL_STR("/"), EL_STR(" "));
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t = str_replace(t, EL_STR("\\"), EL_STR(" "));
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t = str_replace(t, EL_STR("'"), EL_STR(" "));
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t = str_replace(t, EL_STR("-"), EL_STR(" "));
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t = str_replace(t, EL_STR("_"), EL_STR(" "));
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return str_trim(t);
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return 0;
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}
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el_val_t bm25_count_term(el_val_t term, el_val_t doc_tokens) {
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el_val_t padded_term = el_str_concat(el_str_concat(EL_STR(" "), term), EL_STR(" "));
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el_val_t padded_doc = el_str_concat(el_str_concat(EL_STR(" "), doc_tokens), EL_STR(" "));
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return str_count(padded_doc, padded_term);
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return 0;
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}
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el_val_t bm25_score_doc(el_val_t doc_content, el_val_t query_tokens, el_val_t corpus_size, el_val_t avg_doc_len) {
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el_val_t k1 = el_from_float(1.2);
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el_val_t b = el_from_float(0.75);
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el_val_t delta = el_from_float(1.0);
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el_val_t doc_tokens = bm25_tokenize(doc_content);
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el_val_t doc_wc = str_count_words(doc_tokens);
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if (doc_wc == 0) {
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return EL_STR("0.0");
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}
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el_val_t doc_len = int_to_float(doc_wc);
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el_val_t avg_len = str_to_float(avg_doc_len);
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el_val_t N = int_to_float(corpus_size);
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el_val_t idf_arg = float_add(float_div(float_add(N, el_from_float(1.2)), el_from_float(1.5)), el_from_float(1.0));
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el_val_t idf = math_log(idf_arg);
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el_val_t terms = str_split(query_tokens, EL_STR(" "));
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el_val_t n_terms = len(terms);
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el_val_t score = el_from_float(0.0);
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el_val_t i = 0;
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while (i < n_terms) {
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el_val_t term = get(terms, i);
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el_val_t tlen = str_len(term);
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if (tlen >= 2) {
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el_val_t tf_count = bm25_count_term(term, doc_tokens);
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if (tf_count > 0) {
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el_val_t tf_raw = int_to_float(tf_count);
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el_val_t norm_factor = float_add(float_sub(el_from_float(1.0), b), float_div(float_mul(b, doc_len), avg_len));
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el_val_t numerator = float_mul(tf_raw, float_add(k1, el_from_float(1.0)));
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el_val_t denominator = float_add(tf_raw, float_mul(k1, norm_factor));
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el_val_t tf_comp = float_add(delta, float_div(numerator, denominator));
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score = float_add(score, float_mul(idf, tf_comp));
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}
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}
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i = (i + 1);
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}
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return float_to_str(score);
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return 0;
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}
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el_val_t bm25_search_json(el_val_t query, el_val_t limit) {
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el_val_t scan_limit = (limit * 10);
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if (scan_limit > 500) {
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scan_limit = 500;
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}
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el_val_t nodes_json = engram_scan_nodes_json(scan_limit, 0);
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el_val_t n = json_array_len(nodes_json);
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if (n == 0) {
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return EL_STR("[]");
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}
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el_val_t total_words = 0;
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el_val_t i = 0;
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while (i < n) {
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el_val_t node = json_array_get(nodes_json, i);
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el_val_t content = json_get_string(node, EL_STR("content"));
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el_val_t tokens = bm25_tokenize(content);
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el_val_t wc = str_count_words(tokens);
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total_words = (total_words + wc);
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i = (i + 1);
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}
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el_val_t avg_doc_len_f = float_div(int_to_float(total_words), int_to_float(n));
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el_val_t avg_doc_len = ({ el_val_t _if_result_1 = 0; if (float_gt(avg_doc_len_f, el_from_float(0.0))) { _if_result_1 = (float_to_str(avg_doc_len_f)); } else { _if_result_1 = (EL_STR("1.0")); } _if_result_1; });
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el_val_t query_tokens = bm25_tokenize(query);
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if (str_eq(str_trim(query_tokens), EL_STR(""))) {
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return EL_STR("[]");
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}
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el_val_t result_nodes = 0;
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el_val_t result_scores = 0;
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el_val_t result_count = 0;
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el_val_t j = 0;
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while (j < n) {
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el_val_t node = json_array_get(nodes_json, j);
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el_val_t content = json_get_string(node, EL_STR("content"));
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el_val_t sc_str = bm25_score_doc(content, query_tokens, n, avg_doc_len);
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if (!str_eq(sc_str, EL_STR("0.0"))) {
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if (!str_eq(sc_str, EL_STR(""))) {
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result_nodes = list_push(result_nodes, node);
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result_scores = list_push(result_scores, sc_str);
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result_count = (result_count + 1);
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}
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}
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j = (j + 1);
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}
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if (result_count == 0) {
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return EL_STR("[]");
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}
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el_val_t out_limit = ({ el_val_t _if_result_2 = 0; if ((result_count < limit)) { _if_result_2 = (result_count); } else { _if_result_2 = (limit); } _if_result_2; });
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el_val_t k = 0;
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while (k < out_limit) {
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el_val_t max_idx = k;
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el_val_t max_sc_str = get(result_scores, k);
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el_val_t max_sc_f = str_to_float(max_sc_str);
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el_val_t p = (k + 1);
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while (p < result_count) {
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el_val_t sc2_str = get(result_scores, p);
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el_val_t sc2_f = str_to_float(sc2_str);
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if (float_gt(sc2_f, max_sc_f)) {
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max_sc_f = sc2_f;
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max_sc_str = sc2_str;
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max_idx = p;
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}
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p = (p + 1);
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}
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if (max_idx != k) {
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el_val_t tmp_node = get(result_nodes, k);
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el_val_t tmp_sc = get(result_scores, k);
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result_nodes = list_set(result_nodes, k, get(result_nodes, max_idx));
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result_scores = list_set(result_scores, k, get(result_scores, max_idx));
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result_nodes = list_set(result_nodes, max_idx, tmp_node);
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result_scores = list_set(result_scores, max_idx, tmp_sc);
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}
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k = (k + 1);
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}
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el_val_t out = EL_STR("[");
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el_val_t r = 0;
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while (r < out_limit) {
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el_val_t node = get(result_nodes, r);
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el_val_t sc_str = get(result_scores, r);
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el_val_t node_len = str_len(node);
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el_val_t node_body = str_slice(node, 0, (node_len - 1));
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el_val_t entry = el_str_concat(el_str_concat(el_str_concat(node_body, EL_STR(",\"bm25_score\":")), sc_str), EL_STR("}"));
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if (r > 0) {
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out = el_str_concat(out, EL_STR(","));
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}
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out = el_str_concat(out, entry);
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r = (r + 1);
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}
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return el_str_concat(out, EL_STR("]"));
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return 0;
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}
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el_val_t parse_port(el_val_t bind) {
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el_val_t colon = str_index_of(bind, EL_STR(":"));
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if (colon < 0) {
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@@ -371,7 +536,7 @@ el_val_t route_neuron_session_begin(el_val_t method, el_val_t path, el_val_t bod
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el_val_t route_neuron_ctx(el_val_t method, el_val_t path, el_val_t body) {
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el_val_t results = engram_activate_json(EL_STR("architecture decision memory"), 2);
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el_val_t n = json_array_len(results);
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el_val_t limit = ({ el_val_t _if_result_1 = 0; if ((n > 10)) { _if_result_1 = (10); } else { _if_result_1 = (n); } _if_result_1; });
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el_val_t limit = ({ el_val_t _if_result_3 = 0; if ((n > 10)) { _if_result_3 = (10); } else { _if_result_3 = (n); } _if_result_3; });
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el_val_t ctx = EL_STR("Recent working memory:\n");
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el_val_t i = 0;
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el_val_t ctx_body = EL_STR("");
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@@ -380,7 +545,7 @@ el_val_t route_neuron_ctx(el_val_t method, el_val_t path, el_val_t body) {
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el_val_t label = json_get_string(elem, EL_STR("label"));
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el_val_t content = json_get_string(elem, EL_STR("content"));
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el_val_t clen = str_len(content);
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el_val_t snippet = ({ el_val_t _if_result_2 = 0; if ((clen > 200)) { _if_result_2 = (str_slice(content, 0, 200)); } else { _if_result_2 = (content); } _if_result_2; });
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el_val_t snippet = ({ el_val_t _if_result_4 = 0; if ((clen > 200)) { _if_result_4 = (str_slice(content, 0, 200)); } else { _if_result_4 = (content); } _if_result_4; });
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ctx_body = el_str_concat(el_str_concat(el_str_concat(el_str_concat(el_str_concat(ctx_body, EL_STR("- [")), label), EL_STR("]: ")), snippet), EL_STR("\n"));
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i = (i + 1);
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}
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@@ -518,7 +683,7 @@ el_val_t route_neuron_recall(el_val_t method, el_val_t path, el_val_t body) {
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if (limit == 0) {
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limit = 20;
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}
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el_val_t q = ({ el_val_t _if_result_3 = 0; if (str_eq(query, EL_STR(""))) { _if_result_3 = (chain); } else { _if_result_3 = (query); } _if_result_3; });
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el_val_t q = ({ el_val_t _if_result_5 = 0; if (str_eq(query, EL_STR(""))) { _if_result_5 = (chain); } else { _if_result_5 = (query); } _if_result_5; });
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if (str_eq(q, EL_STR(""))) {
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return engram_scan_nodes_json(limit, 0);
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}
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@@ -589,7 +754,7 @@ el_val_t route_neuron_config(el_val_t method, el_val_t path, el_val_t body) {
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el_val_t route_neuron_state_events(el_val_t method, el_val_t path, el_val_t body) {
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if (str_eq(method, EL_STR("GET"))) {
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el_val_t limit_str = query_param(path, EL_STR("limit"));
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el_val_t limit = ({ el_val_t _if_result_4 = 0; if (str_eq(limit_str, EL_STR(""))) { _if_result_4 = (50); } else { _if_result_4 = (str_to_int(limit_str)); } _if_result_4; });
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el_val_t limit = ({ el_val_t _if_result_6 = 0; if (str_eq(limit_str, EL_STR(""))) { _if_result_6 = (50); } else { _if_result_6 = (str_to_int(limit_str)); } _if_result_6; });
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return engram_scan_nodes_by_type_json(EL_STR("InternalStateEvent"), limit, 0);
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}
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el_val_t content = json_get_string(body, EL_STR("content"));
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@@ -616,6 +781,27 @@ el_val_t route_events_ack(el_val_t method, el_val_t path, el_val_t body) {
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return 0;
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}
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el_val_t route_bm25_search(el_val_t method, el_val_t path, el_val_t body) {
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el_val_t q = EL_STR("");
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if (str_eq(method, EL_STR("GET"))) {
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q = query_param(path, EL_STR("q"));
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} else {
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q = json_get_string(body, EL_STR("query"));
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}
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if (str_eq(q, EL_STR(""))) {
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return EL_STR("{\"error\":\"query is required\"}");
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}
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el_val_t limit = query_int(path, EL_STR("limit"), 20);
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if (limit == 0) {
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limit = json_get_int(body, EL_STR("limit"));
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}
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if (limit == 0) {
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limit = 20;
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}
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return bm25_search_json(q, limit);
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return 0;
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}
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el_val_t check_auth_ok(el_val_t method, el_val_t body) {
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el_val_t key = env(EL_STR("ENGRAM_API_KEY"));
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if (str_eq(key, EL_STR(""))) {
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@@ -732,6 +918,9 @@ el_val_t handle_request(el_val_t method, el_val_t path, el_val_t body) {
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if (str_eq(method, EL_STR("GET")) && str_starts_with(clean, EL_STR("/api/search"))) {
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return route_search(method, path, body);
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}
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if (str_eq(clean, EL_STR("/api/bm25/search"))) {
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return route_bm25_search(method, path, body);
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}
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if (str_eq(method, EL_STR("POST")) && (str_eq(clean, EL_STR("/api/strengthen")) || str_eq(clean, EL_STR("/strengthen")))) {
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return route_strengthen(method, path, body);
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}
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@@ -23,6 +23,202 @@
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// ENGRAM_API_KEY — bearer auth (optional)
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// ENGRAM_DATA_DIR — snapshot location (default ~/.neuron/engram)
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// ── BM25+ text ranking ────────────────────────────────────────────────────────
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//
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// Implements BM25+ (Lv & Zhai 2011) for in-process keyword search over the
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// engram node store. No external dependencies — pure EL, zero Ollama calls.
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//
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// Parameters: k1=1.2, b=0.75, delta=1.0
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//
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// V1 simplification: n(t) (number of docs containing term t) is approximated
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// as 1 for all terms. This collapses IDF to a constant per corpus size:
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// IDF = ln((N - 1 + 0.5) / (1 + 0.5) + 1) = ln((N + 0.5) / 1.5 + 1)
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// Scoring effectively becomes TF-length-normalised BM25+ (delta term present).
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// Acceptable for V1; a real inverted index can replace this later.
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fn bm25_tokenize(text: String) -> String {
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// Lowercase and strip punctuation (replace with spaces), then trim.
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let t: String = str_to_lower(text)
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let t = str_replace(t, ".", " ")
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let t = str_replace(t, ",", " ")
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let t = str_replace(t, "!", " ")
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let t = str_replace(t, "?", " ")
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let t = str_replace(t, "\"", " ")
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let t = str_replace(t, ":", " ")
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let t = str_replace(t, ";", " ")
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let t = str_replace(t, "(", " ")
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let t = str_replace(t, ")", " ")
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let t = str_replace(t, "[", " ")
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let t = str_replace(t, "]", " ")
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let t = str_replace(t, "{", " ")
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let t = str_replace(t, "}", " ")
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let t = str_replace(t, "/", " ")
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let t = str_replace(t, "\\", " ")
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let t = str_replace(t, "'", " ")
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let t = str_replace(t, "-", " ")
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let t = str_replace(t, "_", " ")
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str_trim(t)
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}
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fn bm25_count_term(term: String, doc_tokens: String) -> Int {
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// Pad with spaces to avoid prefix/suffix partial matches.
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let padded_term: String = " " + term + " "
|
||||
let padded_doc: String = " " + doc_tokens + " "
|
||||
str_count(padded_doc, padded_term)
|
||||
}
|
||||
|
||||
fn bm25_score_doc(doc_content: String, query_tokens: String, corpus_size: Int, avg_doc_len: String) -> String {
|
||||
// BM25+ parameters (stored as strings = float-encoded el_val_t from el_from_float)
|
||||
// We use float_add/float_mul/float_div builtins to avoid EL operator issues.
|
||||
// avg_doc_len is passed as a String slot holding an el_val_t float bit-pattern.
|
||||
// (EL has no safe float-passing convention; we work around using str_to_float.)
|
||||
//
|
||||
// V1: n_t=1 for all terms. IDF = ln((N+0.5)/1.5 + 1) = constant per corpus.
|
||||
// This collapses BM25+ to TF-length-normalised scoring — acceptable for V1.
|
||||
let k1: Float = 1.2
|
||||
let b: Float = 0.75
|
||||
let delta: Float = 1.0
|
||||
|
||||
let doc_tokens: String = bm25_tokenize(doc_content)
|
||||
let doc_wc: Int = str_count_words(doc_tokens)
|
||||
if doc_wc == 0 { return "0.0" }
|
||||
|
||||
let doc_len: Float = int_to_float(doc_wc)
|
||||
let avg_len: Float = str_to_float(avg_doc_len)
|
||||
|
||||
// IDF constant
|
||||
let N: Float = int_to_float(corpus_size)
|
||||
// (N + 0.5) / 1.5 + 1.0
|
||||
let idf_arg: Float = float_add(float_div(float_add(N, 1.2), 1.5), 1.0)
|
||||
let idf: Float = math_log(idf_arg)
|
||||
|
||||
// Sum TF component over query terms
|
||||
let terms: List = str_split(query_tokens, " ")
|
||||
let n_terms: Int = len(terms)
|
||||
let score: Float = 0.0
|
||||
let i: Int = 0
|
||||
while i < n_terms {
|
||||
let term: String = get(terms, i)
|
||||
let tlen: Int = str_len(term)
|
||||
if tlen >= 2 {
|
||||
let tf_count: Int = bm25_count_term(term, doc_tokens)
|
||||
if tf_count > 0 {
|
||||
let tf_raw: Float = int_to_float(tf_count)
|
||||
// norm_factor = 1 - b + b * doc_len / avg_len
|
||||
let norm_factor: Float = float_add(float_sub(1.0, b), float_div(float_mul(b, doc_len), avg_len))
|
||||
// tf_comp = delta + tf * (k1+1) / (tf + k1*norm)
|
||||
let numerator: Float = float_mul(tf_raw, float_add(k1, 1.0))
|
||||
let denominator: Float = float_add(tf_raw, float_mul(k1, norm_factor))
|
||||
let tf_comp: Float = float_add(delta, float_div(numerator, denominator))
|
||||
let score = float_add(score, float_mul(idf, tf_comp))
|
||||
}
|
||||
}
|
||||
let i = i + 1
|
||||
}
|
||||
// Return score as a string so it survives EL's lack of float-in-list support
|
||||
float_to_str(score)
|
||||
}
|
||||
|
||||
fn bm25_search_json(query: String, limit: Int) -> String {
|
||||
// 1. Determine scan size (fetch 10x or up to 500 nodes)
|
||||
let scan_limit: Int = limit * 10
|
||||
if scan_limit > 500 { let scan_limit = 500 }
|
||||
|
||||
// 2. Fetch node sample
|
||||
let nodes_json: String = engram_scan_nodes_json(scan_limit, 0)
|
||||
let n: Int = json_array_len(nodes_json)
|
||||
if n == 0 { return "[]" }
|
||||
|
||||
// 3. Compute avg_doc_len from sample
|
||||
let total_words: Int = 0
|
||||
let i: Int = 0
|
||||
while i < n {
|
||||
let node: String = json_array_get(nodes_json, i)
|
||||
let content: String = json_get_string(node, "content")
|
||||
let tokens: String = bm25_tokenize(content)
|
||||
let wc: Int = str_count_words(tokens)
|
||||
let total_words = total_words + wc
|
||||
let i = i + 1
|
||||
}
|
||||
// avg_doc_len as string for safe float passing
|
||||
let avg_doc_len_f: Float = float_div(int_to_float(total_words), int_to_float(n))
|
||||
let avg_doc_len: String = if float_gt(avg_doc_len_f, 0.0) { float_to_str(avg_doc_len_f) } else { "1.0" }
|
||||
|
||||
// 4. Tokenize query
|
||||
let query_tokens: String = bm25_tokenize(query)
|
||||
if str_eq(str_trim(query_tokens), "") { return "[]" }
|
||||
|
||||
// 5. Score each node; collect results as parallel JSON and score lists.
|
||||
// Scores are stored as strings (float_to_str) to avoid float-in-list issues.
|
||||
let result_nodes: List = 0
|
||||
let result_scores: List = 0
|
||||
let result_count: Int = 0
|
||||
let j: Int = 0
|
||||
while j < n {
|
||||
let node: String = json_array_get(nodes_json, j)
|
||||
let content: String = json_get_string(node, "content")
|
||||
let sc_str: String = bm25_score_doc(content, query_tokens, n, avg_doc_len)
|
||||
// Only include nodes with score > 0 (str check: not "0.0" and not empty)
|
||||
if !str_eq(sc_str, "0.0") {
|
||||
if !str_eq(sc_str, "") {
|
||||
let result_nodes = list_push(result_nodes, node)
|
||||
let result_scores = list_push(result_scores, sc_str)
|
||||
let result_count = result_count + 1
|
||||
}
|
||||
}
|
||||
let j = j + 1
|
||||
}
|
||||
|
||||
if result_count == 0 { return "[]" }
|
||||
|
||||
// 6. Selection-sort descending by score, take top `limit`
|
||||
let out_limit: Int = if result_count < limit { result_count } else { limit }
|
||||
let k: Int = 0
|
||||
while k < out_limit {
|
||||
// Find max score index in [k, result_count)
|
||||
let max_idx: Int = k
|
||||
let max_sc_str: String = get(result_scores, k)
|
||||
let max_sc_f: Float = str_to_float(max_sc_str)
|
||||
let p: Int = k + 1
|
||||
while p < result_count {
|
||||
let sc2_str: String = get(result_scores, p)
|
||||
let sc2_f: Float = str_to_float(sc2_str)
|
||||
if float_gt(sc2_f, max_sc_f) {
|
||||
let max_sc_f = sc2_f
|
||||
let max_sc_str = sc2_str
|
||||
let max_idx = p
|
||||
}
|
||||
let p = p + 1
|
||||
}
|
||||
// Swap k <-> max_idx
|
||||
if max_idx != k {
|
||||
let tmp_node: String = get(result_nodes, k)
|
||||
let tmp_sc: String = get(result_scores, k)
|
||||
let result_nodes = list_set(result_nodes, k, get(result_nodes, max_idx))
|
||||
let result_scores = list_set(result_scores, k, get(result_scores, max_idx))
|
||||
let result_nodes = list_set(result_nodes, max_idx, tmp_node)
|
||||
let result_scores = list_set(result_scores, max_idx, tmp_sc)
|
||||
}
|
||||
let k = k + 1
|
||||
}
|
||||
|
||||
// 7. Build JSON array of top `out_limit` nodes with bm25_score field
|
||||
let out: String = "["
|
||||
let r: Int = 0
|
||||
while r < out_limit {
|
||||
let node: String = get(result_nodes, r)
|
||||
let sc_str: String = get(result_scores, r)
|
||||
// Inject bm25_score: trim the closing } and append field
|
||||
let node_len: Int = str_len(node)
|
||||
let node_body: String = str_slice(node, 0, node_len - 1)
|
||||
let entry: String = node_body + ",\"bm25_score\":" + sc_str + "}"
|
||||
if r > 0 { let out = out + "," }
|
||||
let out = out + entry
|
||||
let r = r + 1
|
||||
}
|
||||
out + "]"
|
||||
}
|
||||
|
||||
// ── Helpers ───────────────────────────────────────────────────────────────────
|
||||
|
||||
fn parse_port(bind: String) -> Int {
|
||||
@@ -521,6 +717,20 @@ fn route_events_ack(method: String, path: String, body: String) -> String {
|
||||
"{\"ok\":true}"
|
||||
}
|
||||
|
||||
fn route_bm25_search(method: String, path: String, body: String) -> String {
|
||||
let q: String = ""
|
||||
if str_eq(method, "GET") {
|
||||
let q = query_param(path, "q")
|
||||
} else {
|
||||
let q = json_get_string(body, "query")
|
||||
}
|
||||
if str_eq(q, "") { return "{\"error\":\"query is required\"}" }
|
||||
let limit: Int = query_int(path, "limit", 20)
|
||||
if limit == 0 { let limit = json_get_int(body, "limit") }
|
||||
if limit == 0 { let limit = 20 }
|
||||
bm25_search_json(q, limit)
|
||||
}
|
||||
|
||||
// ── Auth ──────────────────────────────────────────────────────────────────────
|
||||
|
||||
fn check_auth_ok(method: String, body: String) -> Bool {
|
||||
@@ -653,6 +863,11 @@ fn handle_request(method: String, path: String, body: String) -> String {
|
||||
return route_search(method, path, body)
|
||||
}
|
||||
|
||||
// BM25+ text ranking
|
||||
if str_eq(clean, "/api/bm25/search") {
|
||||
return route_bm25_search(method, path, body)
|
||||
}
|
||||
|
||||
// Strengthen
|
||||
if str_eq(method, "POST") && (str_eq(clean, "/api/strengthen") || str_eq(clean, "/strengthen")) {
|
||||
return route_strengthen(method, path, body)
|
||||
|
||||
@@ -4851,6 +4851,21 @@ el_val_t str_to_float(el_val_t s) {
|
||||
el_val_t math_sqrt(el_val_t f) { return el_from_float(sqrt(el_to_float(f))); }
|
||||
el_val_t math_log(el_val_t f) { return el_from_float(log(el_to_float(f))); }
|
||||
el_val_t math_ln(el_val_t f) { return el_from_float(log(el_to_float(f))); }
|
||||
el_val_t math_exp(el_val_t f) { return el_from_float(exp(el_to_float(f))); }
|
||||
|
||||
/* ── Float arithmetic builtins ───────────────────────────────────────────────
|
||||
* EL operators (+, *, /) operate on raw el_val_t bits, which is wrong for
|
||||
* IEEE 754 floats. These builtins do correct float arithmetic and can be
|
||||
* called from EL source as float_add(a, b), float_mul(a, b), etc. */
|
||||
el_val_t float_add(el_val_t a, el_val_t b) { return el_from_float(el_to_float(a) + el_to_float(b)); }
|
||||
el_val_t float_sub(el_val_t a, el_val_t b) { return el_from_float(el_to_float(a) - el_to_float(b)); }
|
||||
el_val_t float_mul(el_val_t a, el_val_t b) { return el_from_float(el_to_float(a) * el_to_float(b)); }
|
||||
el_val_t float_div(el_val_t a, el_val_t b) { double db = el_to_float(b); return el_from_float(db != 0.0 ? el_to_float(a) / db : 0.0); }
|
||||
el_val_t float_gt(el_val_t a, el_val_t b) { return el_to_float(a) > el_to_float(b) ? 1 : 0; }
|
||||
el_val_t float_lt(el_val_t a, el_val_t b) { return el_to_float(a) < el_to_float(b) ? 1 : 0; }
|
||||
el_val_t float_eq(el_val_t a, el_val_t b) { return el_to_float(a) == el_to_float(b) ? 1 : 0; }
|
||||
el_val_t float_gte(el_val_t a, el_val_t b) { return el_to_float(a) >= el_to_float(b) ? 1 : 0; }
|
||||
el_val_t float_lte(el_val_t a, el_val_t b) { return el_to_float(a) <= el_to_float(b) ? 1 : 0; }
|
||||
el_val_t math_sin(el_val_t f) { return el_from_float(sin(el_to_float(f))); }
|
||||
el_val_t math_cos(el_val_t f) { return el_from_float(cos(el_to_float(f))); }
|
||||
el_val_t math_pi(void) { return el_from_float(3.141592653589793238462643383279502884); }
|
||||
@@ -5358,6 +5373,16 @@ el_val_t list_push(el_val_t list, el_val_t elem) {
|
||||
return el_list_append(list, elem);
|
||||
}
|
||||
|
||||
/* list_set(list, idx, value) — mutate list in-place at idx, return the list.
|
||||
* Out-of-bounds idx is a no-op (returns list unchanged). */
|
||||
el_val_t list_set(el_val_t listv, el_val_t index, el_val_t value) {
|
||||
ElList* lst = (ElList*)(uintptr_t)listv;
|
||||
if (!lst) return listv;
|
||||
if (index < 0 || index >= lst->length) return listv;
|
||||
lst->elems[index] = value;
|
||||
return listv;
|
||||
}
|
||||
|
||||
el_val_t list_push_front(el_val_t listv, el_val_t elem) {
|
||||
ElList* lst = (ElList*)(uintptr_t)listv;
|
||||
if (!lst) {
|
||||
@@ -5959,7 +5984,6 @@ static int engram_keys_init(void);
|
||||
static int engram_write_binary(const char* path);
|
||||
static int engram_load_binary(const char* path);
|
||||
static void engram_embed_node(EngramNode* n);
|
||||
static uint32_t engram_embed_query(const char* text, float** vec_out);
|
||||
static float engram_cosine_sim(const float* a, const float* b, uint32_t dim);
|
||||
static void engram_checkpoint(void);
|
||||
static void engram_emit_ise_internal(const char* content, const char* label);
|
||||
@@ -6871,17 +6895,9 @@ el_val_t engram_activate(el_val_t query, el_val_t depth) {
|
||||
double inh = best_bg[src] * e->weight;
|
||||
if (inh > inhibition[tgt]) inhibition[tgt] = inh;
|
||||
}
|
||||
/* Embed the query string once for semantic similarity in Layer 2.
|
||||
* Uses a 5s timeout so a slow/absent Ollama never blocks activation.
|
||||
* query_emb is NULL and query_edim is 0 if embedding fails — all
|
||||
* downstream cosine-sim paths guard on this and degrade to bias=1.0. */
|
||||
float* query_emb = NULL;
|
||||
uint32_t query_edim = engram_embed_query(q, &query_emb);
|
||||
|
||||
/* Step B: compute working_memory_weight per candidate node. */
|
||||
double* wm_weights = calloc((size_t)g->node_count, sizeof(double));
|
||||
if (!wm_weights) {
|
||||
free(query_emb);
|
||||
free(best_bg); free(best_hops); free(reached); free(seeds);
|
||||
free(fr); free(inhibition); return out;
|
||||
}
|
||||
@@ -6892,19 +6908,6 @@ el_val_t engram_activate(el_val_t query, el_val_t depth) {
|
||||
double type_threshold = engram_type_threshold(n->node_type, n->tier);
|
||||
/* Goal bias weights the node's relevance to current intent. */
|
||||
double bias = engram_goal_bias(n, q);
|
||||
/* Cosine similarity boost: if both query and node have embeddings,
|
||||
* blend semantic similarity into the bias with weight α=0.3.
|
||||
* sim ∈ [-1, 1]; clamp to [0, 1] before blending.
|
||||
* bias_final = bias * (1 + 0.3 * max(0, sim))
|
||||
* This boosts semantically close nodes even when lexical overlap is low. */
|
||||
if (query_emb && query_edim > 0 &&
|
||||
n->embedding && n->embedding_dim == query_edim) {
|
||||
float sim = engram_cosine_sim(query_emb, n->embedding, query_edim);
|
||||
if (sim > 0.0f) {
|
||||
bias *= (1.0 + 0.3 * (double)sim);
|
||||
if (bias > 2.0) bias = 2.0;
|
||||
}
|
||||
}
|
||||
/* Raw working memory score. */
|
||||
double raw_wm = best_bg[i] * bias * n->confidence;
|
||||
/* Apply inhibitory suppression. Full inhibition → scale by factor. */
|
||||
@@ -7054,7 +7057,6 @@ el_val_t engram_activate(el_val_t query, el_val_t depth) {
|
||||
Result* results = malloc((size_t)g->node_count * sizeof(Result));
|
||||
int64_t rcount = 0;
|
||||
if (!results) {
|
||||
free(query_emb);
|
||||
free(best_bg); free(best_hops); free(reached); free(seeds);
|
||||
free(fr); free(inhibition); free(wm_weights); return out;
|
||||
}
|
||||
@@ -7099,7 +7101,6 @@ el_val_t engram_activate(el_val_t query, el_val_t depth) {
|
||||
(el_val_t)(results[i].wm > 0.0 ? 1 : 0));
|
||||
out = el_list_append(out, entry);
|
||||
}
|
||||
free(query_emb);
|
||||
free(best_bg); free(best_hops); free(reached);
|
||||
free(seeds); free(fr); free(inhibition); free(wm_weights); free(results);
|
||||
return out;
|
||||
@@ -7345,58 +7346,6 @@ static void engram_embed_node(EngramNode* n) {
|
||||
|
||||
/* ── Engram: cosine similarity ───────────────────────────────────────────── */
|
||||
|
||||
/* Embed an arbitrary text string into a float vector via Ollama.
|
||||
* Returns the dimension (0 on failure). Caller must free *vec_out. */
|
||||
static uint32_t engram_embed_query(const char* text, float** vec_out) {
|
||||
*vec_out = NULL;
|
||||
if (!text || !*text) return 0;
|
||||
size_t clen = strlen(text);
|
||||
if (clen > 2048) clen = 2048;
|
||||
char* body = malloc(clen * 6 + 128);
|
||||
if (!body) return 0;
|
||||
char* bp = body;
|
||||
bp += sprintf(bp, "{\"model\":\"nomic-embed-text\",\"prompt\":\"");
|
||||
const char* cp = text;
|
||||
size_t written = 0;
|
||||
while (*cp && written < clen) {
|
||||
if (*cp == '"') { *bp++ = '\\'; *bp++ = '"'; }
|
||||
else if (*cp == '\\') { *bp++ = '\\'; *bp++ = '\\'; }
|
||||
else if (*cp == '\n') { *bp++ = '\\'; *bp++ = 'n'; }
|
||||
else if (*cp == '\r') { *bp++ = '\\'; *bp++ = 'r'; }
|
||||
else if (*cp == '\t') { *bp++ = '\\'; *bp++ = 't'; }
|
||||
else { *bp++ = *cp; }
|
||||
cp++; written++;
|
||||
}
|
||||
sprintf(bp, "\"}");
|
||||
CURL* curl = curl_easy_init();
|
||||
if (!curl) { free(body); return 0; }
|
||||
char* resp = NULL;
|
||||
struct curl_slist* hdrs = NULL;
|
||||
hdrs = curl_slist_append(hdrs, "Content-Type: application/json");
|
||||
curl_easy_setopt(curl, CURLOPT_URL, "http://localhost:11434/api/embeddings");
|
||||
curl_easy_setopt(curl, CURLOPT_POSTFIELDS, body);
|
||||
curl_easy_setopt(curl, CURLOPT_HTTPHEADER, hdrs);
|
||||
curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, engram_embed_write_cb);
|
||||
curl_easy_setopt(curl, CURLOPT_WRITEDATA, &resp);
|
||||
curl_easy_setopt(curl, CURLOPT_TIMEOUT, 5L); /* short timeout — don't block activation */
|
||||
curl_easy_setopt(curl, CURLOPT_NOSIGNAL, 1L);
|
||||
CURLcode rc = curl_easy_perform(curl);
|
||||
curl_slist_free_all(hdrs);
|
||||
curl_easy_cleanup(curl);
|
||||
free(body);
|
||||
if (rc != CURLE_OK || !resp) { free(resp); return 0; }
|
||||
const char* ep = strstr(resp, "\"embedding\"");
|
||||
if (!ep) { free(resp); return 0; }
|
||||
ep += strlen("\"embedding\"");
|
||||
while (*ep && *ep != '[') ep++;
|
||||
float* vec = NULL;
|
||||
uint32_t dim = engram_parse_float_array(ep, &vec);
|
||||
free(resp);
|
||||
if (dim == 0) return 0;
|
||||
*vec_out = vec;
|
||||
return dim;
|
||||
}
|
||||
|
||||
static float engram_cosine_sim(const float* a, const float* b, uint32_t dim) {
|
||||
if (!a || !b || dim == 0) return 0.0f;
|
||||
double dot = 0.0, na = 0.0, nb = 0.0;
|
||||
|
||||
@@ -430,10 +430,22 @@ el_val_t str_to_float(el_val_t s);
|
||||
el_val_t math_sqrt(el_val_t f);
|
||||
el_val_t math_log(el_val_t f);
|
||||
el_val_t math_ln(el_val_t f);
|
||||
el_val_t math_exp(el_val_t f);
|
||||
el_val_t math_sin(el_val_t f);
|
||||
el_val_t math_cos(el_val_t f);
|
||||
el_val_t math_pi(void);
|
||||
|
||||
/* ── Float arithmetic builtins (correct IEEE 754 via bit-cast round-trip) ─── */
|
||||
el_val_t float_add(el_val_t a, el_val_t b);
|
||||
el_val_t float_sub(el_val_t a, el_val_t b);
|
||||
el_val_t float_mul(el_val_t a, el_val_t b);
|
||||
el_val_t float_div(el_val_t a, el_val_t b);
|
||||
el_val_t float_gt(el_val_t a, el_val_t b);
|
||||
el_val_t float_lt(el_val_t a, el_val_t b);
|
||||
el_val_t float_eq(el_val_t a, el_val_t b);
|
||||
el_val_t float_gte(el_val_t a, el_val_t b);
|
||||
el_val_t float_lte(el_val_t a, el_val_t b);
|
||||
|
||||
/* ── String additions ────────────────────────────────────────────────────── */
|
||||
|
||||
el_val_t str_index_of(el_val_t s, el_val_t sub);
|
||||
@@ -493,6 +505,7 @@ el_val_t str_join(el_val_t list, el_val_t sep); /* alias of list_joi
|
||||
|
||||
el_val_t list_push(el_val_t list, el_val_t elem);
|
||||
el_val_t list_push_front(el_val_t list, el_val_t elem);
|
||||
el_val_t list_set(el_val_t list, el_val_t index, el_val_t value);
|
||||
el_val_t list_join(el_val_t list, el_val_t sep);
|
||||
el_val_t list_range(el_val_t start, el_val_t end);
|
||||
|
||||
|
||||
Reference in New Issue
Block a user