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https://github.com/Laurent2916/nio-llm.git
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✨ use chat history in prompt
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parent
35b987dd14
commit
8abae999e0
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@ -2,8 +2,8 @@
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import logging
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import time
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from collections import deque
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from pathlib import Path
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from textwrap import dedent
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from llama_cpp import Llama
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from nio import AsyncClient, MatrixRoom, RoomMessageText
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@ -41,8 +41,12 @@ class LLMClient(AsyncClient):
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self.llm = Llama(
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model_path=str(ggml_path),
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n_threads=12,
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n_ctx=512 + 128,
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)
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# create message history queue
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self.history: deque[RoomMessageText] = deque(maxlen=10)
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# add callbacks
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self.add_event_callback(self.message_callback, RoomMessageText) # type: ignore
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@ -50,11 +54,6 @@ class LLMClient(AsyncClient):
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"""Process new messages as they come in."""
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logger.debug(f"New RoomMessageText: {event.source}")
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# ignore our own messages
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if event.sender == self.user:
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logger.debug("Ignoring our own message.")
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return
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# ignore messages pre-dating our spawn time
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if event.server_timestamp < self.spawn_time:
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logger.debug("Ignoring message pre-spawn.")
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@ -70,6 +69,14 @@ class LLMClient(AsyncClient):
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logger.debug("Ignoring edited message.")
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return
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# update history
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self.history.append(event)
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# ignore our own messages
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if event.sender == self.user:
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logger.debug("Ignoring our own message.")
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return
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# ignore messages not mentioning us
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if not (
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"format" in event.source["content"]
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@ -81,15 +88,24 @@ class LLMClient(AsyncClient):
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logger.debug("Ignoring message not directed at us.")
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return
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# generate prompt from message
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prompt = dedent(
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f"""
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{self.preprompt}
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<{event.sender}>: {event.body}
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<{self.username}>:
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""",
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).strip()
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logger.debug(f"Prompt: {prompt}")
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# generate prompt from message and history
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history = "\n".join(f"<{message.sender}>: {message.body}" for message in self.history)
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prompt = "\n".join([self.preprompt, history, f"<{self.uid}>:"])
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tokens = self.llm.tokenize(str.encode(prompt))
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logger.debug(f"Prompt:\n{prompt}")
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logger.debug(f"Tokens: {len(tokens)}")
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if len(tokens) > 512:
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logger.debug("Prompt too long, skipping.")
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await self.room_send(
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room_id=self.room,
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message_type="m.room.message",
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content={
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"msgtype": "m.emote",
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"body": "reached prompt token limit",
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},
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)
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return
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# enable typing indicator
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await self.room_typing(
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@ -99,10 +115,11 @@ class LLMClient(AsyncClient):
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)
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# generate response using llama.cpp
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senders = [f"<{message.sender}>" for message in self.history]
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output = self.llm(
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prompt,
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max_tokens=100,
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stop=[f"<{event.sender}>"],
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max_tokens=128,
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stop=[f"<{self.uid}>", "### Human", "### Assistant", *senders],
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echo=True,
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)
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