{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# ChatGLM\n", "\n", "[ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B) is an open bilingual language model based on General Language Model (GLM) framework, with 6.2 billion parameters. With the quantization technique, users can deploy locally on consumer-grade graphics cards (only 6GB of GPU memory is required at the INT4 quantization level). \n", "\n", "[ChatGLM2-6B](https://github.com/THUDM/ChatGLM2-6B) is the second-generation version of the open-source bilingual (Chinese-English) chat model ChatGLM-6B. It retains the smooth conversation flow and low deployment threshold of the first-generation model, while introducing the new features like better performance, longer context and more efficient inference.\n", "\n", "[ChatGLM3](https://github.com/THUDM/ChatGLM3) is a new generation of pre-trained dialogue models jointly released by Zhipu AI and Tsinghua KEG. ChatGLM3-6B is the open-source model in the ChatGLM3 series" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Install required dependencies\n", "\n", "%pip install -qU langchain langchain-community" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ChatGLM3\n", "\n", "This examples goes over how to use LangChain to interact with ChatGLM3-6B Inference for text completion." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from langchain.chains import LLMChain\n", "from langchain.schema.messages import AIMessage\n", "from langchain_community.llms.chatglm3 import ChatGLM3\n", "from langchain_core.prompts import PromptTemplate" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "template = \"\"\"{question}\"\"\"\n", "prompt = PromptTemplate.from_template(template)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "endpoint_url = \"http://127.0.0.1:8000/v1/chat/completions\"\n", "\n", "messages = [\n", " AIMessage(content=\"我将从美国到中国来旅游，出行前希望了解中国的城市\"),\n", " AIMessage(content=\"欢迎问我任何问题。\"),\n", "]\n", "\n", "llm = ChatGLM3(\n", " endpoint_url=endpoint_url,\n", " max_tokens=80000,\n", " prefix_messages=messages,\n", " top_p=0.9,\n", ")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'北京和上海是中国两个不同的城市,它们在很多方面都有所不同。\\n\\n北京是中国的首都,也是历史悠久的城市之一。它有着丰富的历史文化遗产,如故宫、颐和园等,这些景点吸引着众多游客前来观光。北京也是一个政治、文化和教育中心,有很多政府机构和学术机构总部设在北京。\\n\\n上海则是一个现代化的城市,它是中国的经济中心之一。上海拥有许多高楼大厦和国际化的金融机构,是中国最国际化的城市之一。上海也是一个美食和购物天堂,有许多著名的餐厅和购物中心。\\n\\n北京和上海的气候也不同。北京属于温带大陆性气候,冬季寒冷干燥,夏季炎热多风;而上海属于亚热带季风气候,四季分明,春秋宜人。\\n\\n北京和上海有很多不同之处,但都是中国非常重要的城市,每个城市都有自己独特的魅力和特色。'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "llm_chain = LLMChain(prompt=prompt, llm=llm)\n", "question = \"北京和上海两座城市有什么不同？\"\n", "\n", "llm_chain.run(question)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ChatGLM and ChatGLM2\n", "\n", "The following example shows how to use LangChain to interact with the ChatGLM2-6B Inference to complete text.\n", "ChatGLM-6B and ChatGLM2-6B has the same api specs, so this example should work with both." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "from langchain.chains import LLMChain\n", "from langchain_community.llms import ChatGLM\n", "from langchain_core.prompts import PromptTemplate\n", "\n", "# import os" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "template = \"\"\"{question}\"\"\"\n", "prompt = PromptTemplate.from_template(template)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# default endpoint_url for a local deployed ChatGLM api server\n", "endpoint_url = \"http://127.0.0.1:8000\"\n", "\n", "# direct access endpoint in a proxied environment\n", "# os.environ['NO_PROXY'] = '127.0.0.1'\n", "\n", "llm = ChatGLM(\n", " endpoint_url=endpoint_url,\n", " max_token=80000,\n", " history=[\n", " [\"我将从美国到中国来旅游，出行前希望了解中国的城市\", \"欢迎问我任何问题。\"]\n", " ],\n", " top_p=0.9,\n", " model_kwargs={\"sample_model_args\": False},\n", ")\n", "\n", "# turn on with_history only when you want the LLM object to keep track of the conversation history\n", "# and send the accumulated context to the backend model api, which make it stateful. By default it is stateless.\n", "# llm.with_history = True" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "llm_chain = LLMChain(prompt=prompt, llm=llm)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ChatGLM payload: {'prompt': '北京和上海两座城市有什么不同？', 'temperature': 0.1, 'history': [['我将从美国到中国来旅游，出行前希望了解中国的城市', '欢迎问我任何问题。']], 'max_length': 80000, 'top_p': 0.9, 'sample_model_args': False}\n" ] }, { "data": { "text/plain": [ "'北京和上海是中国的两个首都，它们在许多方面都有所不同。\\n\\n北京是中国的政治和文化中心，拥有悠久的历史和灿烂的文化。它是中国最重要的古都之一，也是中国历史上最后一个封建王朝的都城。北京有许多著名的古迹和景点，例如紫禁城、天安门广场和长城等。\\n\\n上海是中国最现代化的城市之一，也是中国商业和金融中心。上海拥有许多国际知名的企业和金融机构，同时也有许多著名的景点和美食。上海的外滩是一个历史悠久的商业区，拥有许多欧式建筑和餐馆。\\n\\n除此之外，北京和上海在交通和人口方面也有很大差异。北京是中国的首都，人口众多，交通拥堵问题较为严重。而上海是中国的商业和金融中心，人口密度较低，交通相对较为便利。\\n\\n总的来说，北京和上海是两个拥有独特魅力和特点的城市，可以根据自己的兴趣和时间来选择前往其中一座城市旅游。'" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "question = \"北京和上海两座城市有什么不同？\"\n", "\n", "llm_chain.run(question)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.1" } }, "nbformat": 4, "nbformat_minor": 4 }