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https://github.com/hwchase17/langchain.git
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community: Add ChatGLM3 (#15265)
Add [ChatGLM3](https://github.com/THUDM/ChatGLM3) and updated [chatglm.ipynb](https://python.langchain.com/docs/integrations/llms/chatglm) --------- Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This commit is contained in:
@@ -11,7 +11,102 @@
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"\n",
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"[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",
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"\n",
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"This example goes over how to use LangChain to interact with ChatGLM2-6B Inference for text completion.\n",
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"[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"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Install required dependencies\n",
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"\n",
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"%pip install -qU langchain langchain-community"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## ChatGLM3\n",
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"\n",
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"This examples goes over how to use LangChain to interact with ChatGLM3-6B Inference for text completion."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chains import LLMChain\n",
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"from langchain.prompts import PromptTemplate\n",
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"from langchain.schema.messages import AIMessage\n",
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"from langchain_community.llms.chatglm3 import ChatGLM3"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"template = \"\"\"{question}\"\"\"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"endpoint_url = \"http://127.0.0.1:8000/v1/chat/completions\"\n",
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"\n",
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"messages = [\n",
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" 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",
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" AIMessage(content=\"欢迎问我任何问题。\"),\n",
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"]\n",
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"\n",
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"llm = ChatGLM3(\n",
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" endpoint_url=endpoint_url,\n",
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" max_tokens=80000,\n",
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" prefix_messages=messages,\n",
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" top_p=0.9,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'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。'"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
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"question = \"北京和上海两座城市有什么不同?\"\n",
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"\n",
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"llm_chain.run(question)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## ChatGLM and ChatGLM2\n",
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"\n",
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"The following example shows how to use LangChain to interact with the ChatGLM2-6B Inference to complete text.\n",
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"ChatGLM-6B and ChatGLM2-6B has the same api specs, so this example should work with both."
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]
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},
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@@ -106,7 +201,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "langchain-dev",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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@@ -120,9 +215,9 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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"nbformat_minor": 4
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}
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