mirror of
https://git.squeakyrat.moe/faggot-chan/
synced 2026-11-04 18:35:45 +01:00
grr
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david committed
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@@ -120,9 +120,6 @@ async def call_main(payload: str) -> tuple[bool, str]:
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if "error" in data:
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return False, data["error"]
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return True, data["ok"]
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except websockets.exceptions.ConnectionClosedOK:
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_main_ws = None
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return False, f"[{now_str()}] websocket closed cleanly (no reply)"
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except Exception as e:
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_main_ws = None
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if attempt == 0:
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@@ -65,128 +65,130 @@ def rebuild_vector_db(longterm_lines: list[str]):
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async def ollamachat(websocket):
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try:
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# bot sends one json payload with everything main needs
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raw = await websocket.recv()
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payload = json.loads(raw)
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if payload.get("auth_token") != AUTH_TOKEN:
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await websocket.send(json.dumps({"error": "bad auth token"}))
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return
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system_prompt = payload["system_prompt"]
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user_input = payload["user_input"]
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shortterm = payload.get("shortterm", []) # list of strings
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longterm = payload.get("longterm", []) # list of strings
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# rebuild vector db if bot signals it (every 150 interactions bot sends full longterm),
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# or if we come up empty but longterm isn't — e.g. we just restarted and lost VECTOR_DB
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if payload.get("rebuild_vectors") or (not VECTOR_DB and longterm):
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await asyncio.to_thread(rebuild_vector_db, longterm)
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print(f"[{now_str()}] Vector DB reconciled, {len(VECTOR_DB)} chunks")
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user_query = nots(user_input)
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retrieved_knowledge = await asyncio.to_thread(retrieve, user_query) if VECTOR_DB else []
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# rag chunks go in system so they don't pollute turn history
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rag_block = ""
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if retrieved_knowledge:
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rag_block = "\n\nOlder relevant messages for context:\n" + "\n".join(f"- {chunk}" for chunk, _ in retrieved_knowledge)
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# lines to skip — not real dialogue
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skip_fragments = ("sent sticker:", "sent media:", "sent a random dm to:", "saved media:")
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def is_skip(line):
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c = nots(line)
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if any(f in c for f in skip_fragments):
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return True
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# empty said: like "username said: "
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if "said:" in c and c.split("said:", 1)[1].strip() == "":
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return True
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return False
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BOT_NAME = "Faggot-chan"
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# parse shortterm lines into (speaker, text) pairs
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def parse_line(line):
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c = nots(line).strip()
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# "X said: Y", "X said in DM: Y", "X replied to ... with reply: Y"
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if " said in DM: " in c:
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speaker, text = c.split(" said in DM: ", 1)
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return speaker.strip(), text.strip()
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if " with reply: " in c:
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# "X replied to Y saying: "Z" with reply: "W""
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speaker = c.split(" replied to ", 1)[0].strip()
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text = c.rsplit(' with reply: "', 1)[-1].rstrip('"').strip()
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return speaker.strip(), text.strip()
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if " said: " in c:
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speaker, text = c.split(" said: ", 1)
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return speaker.strip(), text.strip()
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return None, None
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# build alternating user/assistant turns from shortterm
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# group consecutive non-bot lines into one user blob, bot lines become assistant turns
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messages = []
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current_user_lines = []
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seen_last = None # for dedup
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for raw in shortterm:
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if is_skip(raw):
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continue
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speaker, text = parse_line(raw)
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if not speaker or not text:
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continue
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entry = f"{speaker}: {text}"
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if entry == seen_last: # skip duplicate consecutive lines
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continue
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seen_last = entry
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if speaker == BOT_NAME:
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# flush accumulated user lines first
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if current_user_lines:
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messages.append({"role": "user", "content": "\n".join(current_user_lines)})
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current_user_lines = []
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messages.append({"role": "assistant", "content": text})
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else:
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current_user_lines.append(entry)
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# flush any trailing user lines before the final turn
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if current_user_lines:
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messages.append({"role": "user", "content": "\n".join(current_user_lines)})
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# final isolated turn — the specific message she needs to reply to
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messages.append({"role": "user", "content": f"[responding to]: {nots(user_input)}"})
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# make sure we start with a user turn (model requirement)
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if messages and messages[0]["role"] != "user":
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messages.insert(0, {"role": "user", "content": ""})
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messages.insert(0, {"role": "system", "content": system_prompt + rag_block})
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params = json.loads((CONFIG_DIR / "params.json").read_text())
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async with chat_semaphore:
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response = await asyncio.to_thread(
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ollama.chat,
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model=chatmodel,
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messages=messages,
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options=params,
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)
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# strip <think> blocks in case the model ignores think=False, thank you claude
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bot_reply = re.sub(r'<think>.*?</think>', '', response["message"]["content"], flags=re.DOTALL).strip()
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print(messages)
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print(f"[{now_str()}] reply: {bot_reply}\n")
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await websocket.send(json.dumps({"ok": bot_reply}))
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except Exception as e:
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error_msg = f"[{now_str()}] error in ollamachat: {e}"
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print(error_msg)
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# loop over every message on this connection instead of handling one and returning —
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# returning from this function is what makes the websockets lib close the connection,
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# which was breaking bot.py's persistent connection after every single reply
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async for raw in websocket:
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try:
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await websocket.send(json.dumps({"error": error_msg}))
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except Exception:
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pass # if we can't even send the error, nothing we can do
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payload = json.loads(raw)
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if payload.get("auth_token") != AUTH_TOKEN:
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await websocket.send(json.dumps({"error": "bad auth token"}))
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continue
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system_prompt = payload["system_prompt"]
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user_input = payload["user_input"]
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shortterm = payload.get("shortterm", []) # list of strings
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longterm = payload.get("longterm", []) # list of strings
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# rebuild vector db if bot signals it (every 150 interactions bot sends full longterm),
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# or if we come up empty but longterm isn't — e.g. we just restarted and lost VECTOR_DB
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if payload.get("rebuild_vectors") or (not VECTOR_DB and longterm):
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await asyncio.to_thread(rebuild_vector_db, longterm)
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print(f"[{now_str()}] Vector DB reconciled, {len(VECTOR_DB)} chunks")
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user_query = nots(user_input)
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retrieved_knowledge = await asyncio.to_thread(retrieve, user_query) if VECTOR_DB else []
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# rag chunks go in system so they don't pollute turn history
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rag_block = ""
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if retrieved_knowledge:
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rag_block = "\n\nOlder relevant messages for context:\n" + "\n".join(f"- {chunk}" for chunk, _ in retrieved_knowledge)
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# lines to skip — not real dialogue
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skip_fragments = ("sent sticker:", "sent media:", "sent a random dm to:", "saved media:")
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def is_skip(line):
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c = nots(line)
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if any(f in c for f in skip_fragments):
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return True
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# empty said: like "username said: "
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if "said:" in c and c.split("said:", 1)[1].strip() == "":
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return True
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return False
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BOT_NAME = "Faggot-chan"
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# parse shortterm lines into (speaker, text) pairs
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def parse_line(line):
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c = nots(line).strip()
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# "X said: Y", "X said in DM: Y", "X replied to ... with reply: Y"
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if " said in DM: " in c:
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speaker, text = c.split(" said in DM: ", 1)
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return speaker.strip(), text.strip()
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if " with reply: " in c:
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# "X replied to Y saying: "Z" with reply: "W""
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speaker = c.split(" replied to ", 1)[0].strip()
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text = c.rsplit(' with reply: "', 1)[-1].rstrip('"').strip()
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return speaker.strip(), text.strip()
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if " said: " in c:
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speaker, text = c.split(" said: ", 1)
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return speaker.strip(), text.strip()
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return None, None
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# build alternating user/assistant turns from shortterm
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# group consecutive non-bot lines into one user blob, bot lines become assistant turns
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messages = []
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current_user_lines = []
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seen_last = None # for dedup
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for line in shortterm:
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if is_skip(line):
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continue
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speaker, text = parse_line(line)
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if not speaker or not text:
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continue
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entry = f"{speaker}: {text}"
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if entry == seen_last: # skip duplicate consecutive lines
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continue
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seen_last = entry
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if speaker == BOT_NAME:
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# flush accumulated user lines first
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if current_user_lines:
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messages.append({"role": "user", "content": "\n".join(current_user_lines)})
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current_user_lines = []
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messages.append({"role": "assistant", "content": text})
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else:
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current_user_lines.append(entry)
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# flush any trailing user lines before the final turn
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if current_user_lines:
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messages.append({"role": "user", "content": "\n".join(current_user_lines)})
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# final isolated turn — the specific message she needs to reply to
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messages.append({"role": "user", "content": f"[responding to]: {nots(user_input)}"})
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# make sure we start with a user turn (model requirement)
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if messages and messages[0]["role"] != "user":
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messages.insert(0, {"role": "user", "content": ""})
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messages.insert(0, {"role": "system", "content": system_prompt + rag_block})
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params = json.loads((CONFIG_DIR / "params.json").read_text())
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async with chat_semaphore:
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response = await asyncio.to_thread(
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ollama.chat,
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model=chatmodel,
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messages=messages,
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options=params,
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)
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# strip <think> blocks in case the model ignores think=False, thank you claude
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bot_reply = re.sub(r'<think>.*?</think>', '', response["message"]["content"], flags=re.DOTALL).strip()
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print(messages)
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print(f"[{now_str()}] reply: {bot_reply}\n")
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await websocket.send(json.dumps({"ok": bot_reply}))
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except Exception as e:
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error_msg = f"[{now_str()}] error in ollamachat: {e}"
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print(error_msg)
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try:
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await websocket.send(json.dumps({"error": error_msg}))
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except Exception:
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pass # if we can't even send the error, nothing we can do
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async def main():
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