Compare commits
2 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
| 0c5b966773 | |||
| b2008f4894 |
@@ -40,9 +40,43 @@ fn engram_compile(intent: String) -> String {
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""
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""
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}
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}
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// Affective context: always include the most recent high-emotion memory if one
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// exists within 72 hours. This ensures continuity of care across turns — when
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// the user was in distress earlier in the session (or recently), that context
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// travels into every subsequent LLM call so the response register stays aware.
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// We search for BellEvent nodes specifically; these are written by auto_persist
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// when safety_detect_bell_level fires. The 72h window (259200 seconds) is wide
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// enough to span a multi-session day without pulling ancient history.
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let bell_nodes: String = engram_search_json("bell:soft bell:hard BellEvent", 3)
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let bell_ok: Bool = !str_eq(bell_nodes, "") && !str_eq(bell_nodes, "[]")
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let now_ts: Int = time_now()
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let cutoff_ts: Int = now_ts - 259200
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let recent_bell: String = if bell_ok {
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let bn0: String = json_array_get(bell_nodes, 0)
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// created_at is not present in engram node JSON for BellEvent nodes.
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// Extract the timestamp embedded in the content string as " | ts:NNNNN".
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// Fall back to created_at / updated_at JSON fields if the marker is absent.
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let bn_content: String = json_get(bn0, "content")
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let ts_marker: String = " | ts:"
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let ts_pos: Int = str_index_of(bn_content, ts_marker)
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let bn_ts_raw: String = if ts_pos >= 0 {
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let ts_start: Int = ts_pos + str_len(ts_marker)
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let rest: String = str_slice(bn_content, ts_start, str_len(bn_content))
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let next_sep: Int = str_index_of(rest, " | ")
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if next_sep < 0 { rest } else { str_slice(rest, 0, next_sep) }
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} else {
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let ca: String = json_get(bn0, "created_at")
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if str_eq(ca, "") { json_get(bn0, "updated_at") } else { ca }
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}
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let bn_ts: Int = if str_eq(bn_ts_raw, "") { 0 } else { str_to_int(bn_ts_raw) }
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if bn_ts > cutoff_ts { bn0 } else { "" }
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} else { "" }
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let affective_part: String = if !str_eq(recent_bell, "") { recent_bell } else { "" }
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let sep1: String = if !str_eq(act_part, "") && !str_eq(srch_part, "") { "\n" } else { "" }
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let sep1: String = if !str_eq(act_part, "") && !str_eq(srch_part, "") { "\n" } else { "" }
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let sep2: String = if (!str_eq(act_part, "") || !str_eq(srch_part, "")) && !str_eq(scan_part, "") { "\n" } else { "" }
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let sep2: String = if (!str_eq(act_part, "") || !str_eq(srch_part, "")) && !str_eq(scan_part, "") { "\n" } else { "" }
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let ctx: String = act_part + sep1 + srch_part + sep2 + scan_part
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let sep3: String = if (!str_eq(act_part, "") || !str_eq(srch_part, "") || !str_eq(scan_part, "")) && !str_eq(affective_part, "") { "\n" } else { "" }
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let ctx: String = act_part + sep1 + srch_part + sep2 + scan_part + sep3 + affective_part
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if str_eq(ctx, "") { return "" }
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if str_eq(ctx, "") { return "" }
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@@ -108,6 +142,69 @@ fn hist_trim(hist: String) -> String {
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return hist
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return hist
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}
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}
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// hist_trim_with_bell_guard — trim the history window exactly as hist_trim does, but
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// before dropping the oldest user/assistant pair check whether the user turn triggered
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// a bell event. If it did, write a preservation node to engram so the distress exchange
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// survives the 20-turn window. The LLM window drops it; engram retains it permanently
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// and engram_compile will surface it again via the affective context path.
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fn hist_trim_with_bell_guard(hist: String) -> String {
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// Extract the first turn (should be a user message) to inspect it.
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let inner: String = str_slice(hist, 1, str_len(hist) - 1)
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let marker: String = "{\"role\":"
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let i1: Int = str_index_of(inner, marker)
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// i1 is the start of the first entry within inner.
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// Find where the second entry begins to delimit the first entry's JSON.
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let tail1: String = str_slice(inner, i1 + 1, str_len(inner))
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let i2: Int = str_index_of(tail1, marker)
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// The first entry spans from i1 to (i1 + 1 + i2 - 1) within inner.
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let first_entry_raw: String = if i2 > 0 {
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str_slice(inner, i1, i1 + 1 + i2 - 1)
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} else {
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str_slice(inner, i1, str_len(inner))
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}
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let first_role: String = json_get(first_entry_raw, "role")
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let first_content: String = json_get(first_entry_raw, "content")
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// Only inspect user turns — assistant content doesn't carry bell signals.
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let bell_level: String = if str_eq(first_role, "user") {
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safety_detect_bell_level(first_content)
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} else {
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"none"
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}
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// If the turn being evicted triggered a bell, preserve it to engram.
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// This is distinct from the BellEvent written by auto_persist: that node
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// carries a short summary. This node carries the full exchange content so
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// it is recoverable for clinical/continuity review.
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if !str_eq(bell_level, "none") {
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let ts: Int = time_now()
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let ts_str: String = int_to_str(ts)
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let safe_content: String = str_replace(first_content, "\"", "'")
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let preserve_content: String = "PRESERVED_BELL:" + bell_level
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+ " | evicted_at:" + ts_str
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+ " | message:" + safe_content
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let preserve_tags: String = "[\"bell-history\",\"bell:" + bell_level + "\",\"evicted\",\"affective\",\"BellEvent\"]"
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let discard: String = engram_node_full(
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preserve_content,
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"BellEvent",
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"bell:" + bell_level + ":preserved",
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el_from_float(0.9),
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el_from_float(0.9),
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el_from_float(1.0),
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"Episodic",
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preserve_tags
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)
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}
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// Now perform the standard trim (drop oldest 2 entries = 1 user + 1 assistant pair).
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let tail2: String = str_slice(tail1, i2 + 1, str_len(tail1))
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let i3: Int = str_index_of(tail2, marker)
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if i3 >= 0 {
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return "[" + str_slice(tail2, i3, str_len(tail2)) + "]"
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}
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return hist
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}
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// clean_llm_response — strips GPT-2 BPE byte-to-unicode artifacts that vLLM
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// clean_llm_response — strips GPT-2 BPE byte-to-unicode artifacts that vLLM
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// emits when the tokenizer hasn't decoded back to raw bytes.
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// emits when the tokenizer hasn't decoded back to raw bytes.
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//
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//
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@@ -200,8 +297,10 @@ fn handle_chat(body: String) -> String {
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let updated_hist: String = hist_append(stored_hist, "user", message)
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let updated_hist: String = hist_append(stored_hist, "user", message)
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let updated_hist2: String = hist_append(updated_hist, "assistant", raw_response)
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let updated_hist2: String = hist_append(updated_hist, "assistant", raw_response)
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// Use bell-guarded trim: if the evicted turn triggered a bell event, it is
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// preserved to engram before being dropped from the in-memory window.
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let final_hist: String = if json_array_len(updated_hist2) > 20 {
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let final_hist: String = if json_array_len(updated_hist2) > 20 {
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hist_trim(updated_hist2)
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hist_trim_with_bell_guard(updated_hist2)
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} else {
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} else {
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updated_hist2
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updated_hist2
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}
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}
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@@ -1135,14 +1234,28 @@ fn auto_persist(req: String, resp: String) -> Void {
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let safe_msg: String = str_replace(message, "\"", "'")
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let safe_msg: String = str_replace(message, "\"", "'")
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let safe_reply: String = str_replace(reply2, "\"", "'")
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let safe_reply: String = str_replace(reply2, "\"", "'")
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// Detect emotional salience before persisting. safety_detect_bell_level uses the
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// same phrase lists as the safety layer (safety.el), so the classification is
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// consistent with what safety_screen already evaluated for this turn.
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let bell_level: String = safety_detect_bell_level(message)
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let is_bell: Bool = !str_eq(bell_level, "none")
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// Tag the Conversation node with bell metadata when distress is present so
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// subsequent affective queries (e.g. engram_compile) can find this exchange.
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let tags: String = if is_bell {
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"[\"Conversation\",\"chat\",\"timestamped\",\"bell:" + bell_level + "\",\"affective\"]"
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} else {
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"[\"Conversation\",\"chat\",\"timestamped\"]"
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}
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let content: String = "{\"q\":\"" + safe_msg + "\""
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let content: String = "{\"q\":\"" + safe_msg + "\""
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+ ",\"a\":\"" + safe_reply + "\""
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+ ",\"a\":\"" + safe_reply + "\""
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+ ",\"created_at\":" + ts_str
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+ ",\"created_at\":" + ts_str
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+ ",\"source\":\"chat\""
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+ ",\"source\":\"chat\""
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+ ",\"bell\":\"" + bell_level + "\""
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+ ",\"label\":\"chat:" + ts_str + "\"}"
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+ ",\"label\":\"chat:" + ts_str + "\"}"
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let tags: String = "[\"Conversation\",\"chat\",\"timestamped\"]"
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let conv_node_id: String = engram_node_full(
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engram_node_full(
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content,
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content,
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"Conversation",
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"Conversation",
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"chat:" + ts_str,
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"chat:" + ts_str,
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@@ -1152,6 +1265,72 @@ fn auto_persist(req: String, resp: String) -> Void {
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"Episodic",
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"Episodic",
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tags
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tags
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)
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)
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// When a bell fires, write a dedicated BellEvent node in addition to the
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// Conversation node. This makes distress moments directly findable by label
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// ("bell:soft" / "bell:hard") without having to scan all Conversation nodes.
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// The BellEvent carries higher salience so engram_compile pulls it into context.
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// The message content is truncated to 120 chars — enough signal, not a full dump.
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if is_bell {
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let summary: String = if str_len(message) > 120 { str_slice(message, 0, 120) } else { message }
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let safe_summary: String = str_replace(summary, "\"", "'")
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let bell_content: String = "BELL:" + bell_level
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+ " | ts:" + ts_str
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+ " | summary:" + safe_summary
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// bell:hard gets peak salience; bell:soft is slightly lower.
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let sal_a: String = if str_eq(bell_level, "hard") { el_from_float(0.98) } else { el_from_float(0.88) }
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let sal_b: String = if str_eq(bell_level, "hard") { el_from_float(0.98) } else { el_from_float(0.88) }
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let sal_c: String = if str_eq(bell_level, "hard") { el_from_float(1.0) } else { el_from_float(0.95) }
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let bell_tags: String = "[\"safety\",\"bell\",\"bell:" + bell_level + "\",\"affective\",\"BellEvent\"]"
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let bell_ts_str: String = int_to_str(time_now())
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let bell_label: String = "bell:" + bell_level + ":" + bell_ts_str
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let bell_node_id: String = engram_node_full(
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bell_content,
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"BellEvent",
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bell_label,
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sal_a,
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sal_b,
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sal_c,
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"Episodic",
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bell_tags
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)
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// Increment session-level bell counter so session_hist_save knows whether
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// any bell fired during this session when writing a boundary summary.
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let sess_id: String = json_get(req, "session_id")
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let bell_key: String = if str_eq(sess_id, "") {
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"session_bell_count"
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} else {
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"session_bell_count:" + sess_id
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}
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let prior_count: String = state_get(bell_key)
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let prior_n: Int = if str_eq(prior_count, "") { 0 } else { str_to_int(prior_count) }
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state_set(bell_key, int_to_str(prior_n + 1))
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// Also record the highest bell level seen this session so the boundary
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// summary can classify the session correctly (hard takes precedence).
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let level_key: String = if str_eq(sess_id, "") {
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"session_bell_level"
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} else {
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"session_bell_level:" + sess_id
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}
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let prior_level: String = state_get(level_key)
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let new_level: String = if str_eq(bell_level, "hard") { "hard" } else {
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if str_eq(prior_level, "hard") { "hard" } else { "soft" }
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}
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state_set(level_key, new_level)
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// Stash a short signal summary for the boundary node (last bell wins for
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// the one-liner; the full history is in per-bell BellEvent nodes).
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let signal_key: String = if str_eq(sess_id, "") {
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"session_bell_signal"
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} else {
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"session_bell_signal:" + sess_id
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}
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state_set(signal_key, safe_summary)
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}
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}
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}
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// strengthen_chat_nodes — strengthen the engram nodes that were activated during a chat.
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// strengthen_chat_nodes — strengthen the engram nodes that were activated during a chat.
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+42
@@ -368,6 +368,48 @@ fn session_hist_save(session_id: String, hist: String) -> Void {
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el_from_float(0.6), el_from_float(0.6), el_from_float(0.9),
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el_from_float(0.6), el_from_float(0.6), el_from_float(0.9),
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"Episodic", tags
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"Episodic", tags
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)
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)
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// Session boundary emotional summary — written once per session the first time
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// a bell event has fired. The summary node is findable by future sessions via
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// broad affective queries ("session:emotional-summary" or "bell distress session").
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// It is NOT rewritten on every save — the state flag prevents duplicate nodes.
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let summary_written_key: String = "session_bell_summary_written:" + session_id
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let already_written: String = state_get(summary_written_key)
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if str_eq(already_written, "") {
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let bell_count_key: String = "session_bell_count:" + session_id
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let bell_count_raw: String = state_get(bell_count_key)
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let bell_count: Int = if str_eq(bell_count_raw, "") { 0 } else { str_to_int(bell_count_raw) }
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if bell_count > 0 {
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let bell_level_key: String = "session_bell_level:" + session_id
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let bell_signal_key: String = "session_bell_signal:" + session_id
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let dominant_level: String = state_get(bell_level_key)
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let last_signal: String = state_get(bell_signal_key)
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let eff_level: String = if str_eq(dominant_level, "") { "soft" } else { dominant_level }
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let eff_signal: String = if str_eq(last_signal, "") { "(no signal captured)" } else { last_signal }
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let ts_now: Int = time_now()
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let summary_content: String = "session:emotional-summary"
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+ " | session:" + session_id
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+ " | bell_count:" + int_to_str(bell_count)
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+ " | dominant_level:" + eff_level
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+ " | last_signal:" + eff_signal
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+ " | ts:" + int_to_str(ts_now)
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let summary_tags: String = "[\"session-emotional-summary\",\"affective\",\"bell:" + eff_level + "\",\"BellEvent\"]"
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let summary_sal: String = if str_eq(eff_level, "hard") { el_from_float(0.95) } else { el_from_float(0.85) }
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let sum_discard: String = engram_node_full(
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summary_content,
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"BellEvent",
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"session:emotional-summary",
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summary_sal,
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summary_sal,
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el_from_float(1.0),
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"Episodic",
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summary_tags
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)
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// Mark written so we do not create duplicate summary nodes as the
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// session continues accumulating more turns.
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state_set(summary_written_key, "1")
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}
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}
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}
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}
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// session_update_meta_timestamp — update the updated_at field in the session:meta node.
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// session_update_meta_timestamp — update the updated_at field in the session:meta node.
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Reference in New Issue
Block a user