vibecoded a better prompt because ai know best about ai. and also maybe don't just hardcode the token teehee

2 files changed, 75 insertions, 17 deletions

diff --git a/bot.py b/bot.py
index 44c961d..d2999d1 100644
--- a/bot.py
+++ b/bot.py
@@ -40,11 +40,9 @@ def init_files():
 
 init_files()
 
-#TOKEN= "your token goes here"
-TOKEN = "yourtokengoeshere" #real token
-
-#server = "put your serevr id here"
-server = 1000000000000005 # real server
+_config_lines = [l.strip() for l in (CONFIG_DIR / "tokens.txt").read_text(encoding="utf-8").splitlines() if l.strip() and not l.strip().startswith("#")]
+TOKEN  = _config_lines[0]
+server = int(_config_lines[1])
 
 intents = discord.Intents.default()
 intents.message_content = True
diff --git a/main.py b/main.py
index bf1e1d1..7baa30b 100644
--- a/main.py
+++ b/main.py
@@ -82,19 +82,79 @@ async def ollamachat(websocket):
         user_query = nots(user_input)
         retrieved_knowledge = retrieve(user_query) if VECTOR_DB else []
 
-        context_message = (
-            "These are memory notes to help you continue naturally. "
-            "Do NOT imitate tone, style, structure, or wording.\n\n"
-            "Short-term memory, these are the most 15 recent chats sent by various people:\n"
-            + "\n".join(f"- {line}" for line in shortterm)
-            + "\n\nLong-term memory, these are retrieved older messages using cosine similarity:\n"
-            + "\n".join(f"- {chunk}" for chunk, _ in retrieved_knowledge)
-        )
+        # rag chunks go in system so they don't pollute turn history
+        rag_block = ""
+        if retrieved_knowledge:
+            rag_block = "\n\nOlder relevant messages for context:\n" + "\n".join(f"- {chunk}" for chunk, _ in retrieved_knowledge)
+
+        # lines to skip — not real dialogue
+        skip_fragments = ("sent sticker:", "sent media:", "sent a random dm to:", "saved media:")
+        def is_skip(line):
+            c = nots(line)
+            if any(f in c for f in skip_fragments):
+                return True
+            # empty said: like "username said: "
+            if "said:" in c and c.split("said:", 1)[1].strip() == "":
+                return True
+            return False
+
+        BOT_NAME = "Faggot-chan"
+
+        # parse shortterm lines into (speaker, text) pairs
+        def parse_line(line):
+            c = nots(line).strip()
+            # "X said: Y", "X said in DM: Y", "X replied to ... with reply: Y"
+            if " said in DM: " in c:
+                speaker, text = c.split(" said in DM: ", 1)
+                return speaker.strip(), text.strip()
+            if " with reply: " in c:
+                # "X replied to Y saying: "Z" with reply: "W""
+                speaker = c.split(" replied to ", 1)[0].strip()
+                text = c.rsplit(' with reply: "', 1)[-1].rstrip('"').strip()
+                return speaker.strip(), text.strip()
+            if " said: " in c:
+                speaker, text = c.split(" said: ", 1)
+                return speaker.strip(), text.strip()
+            return None, None
+
+        # build alternating user/assistant turns from shortterm
+        # group consecutive non-bot lines into one user blob, bot lines become assistant turns
+        messages = []
+        current_user_lines = []
+        seen_last = None  # for dedup
+
+        for raw in shortterm:
+            if is_skip(raw):
+                continue
+            speaker, text = parse_line(raw)
+            if not speaker or not text:
+                continue
+            entry = f"{speaker}: {text}"
+            if entry == seen_last:  # skip duplicate consecutive lines
+                continue
+            seen_last = entry
+
+            if speaker == BOT_NAME:
+                # flush accumulated user lines first
+                if current_user_lines:
+                    messages.append({"role": "user", "content": "\n".join(current_user_lines)})
+                    current_user_lines = []
+                messages.append({"role": "assistant", "content": text})
+            else:
+                current_user_lines.append(entry)
+
+        # flush any trailing user lines before the final turn
+        if current_user_lines:
+            messages.append({"role": "user", "content": "\n".join(current_user_lines)})
+
+        # final isolated turn — the specific message she needs to reply to
+        messages.append({"role": "user", "content": f"[responding to]: {nots(user_input)}"})
+
+        # make sure we start with a user turn (model requirement)
+        if messages and messages[0]["role"] != "user":
+            messages.insert(0, {"role": "user", "content": ""})
 
-        messages = [
-            {"role": "system", "content": system_prompt + "\n\n" + context_message},
-            {"role": "user",   "content": user_input},
-        ]
+        messages.insert(0, {"role": "system", "content": system_prompt + rag_block})
 
         response = ollama.chat(
             model=chatmodel,