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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 如何使用LangChain插件\n",
"\n",
"为了便利大家结合文心大模型与[LangChain](https://www.langchain.com/)开发应用，ERNIE Bot Agent扩展LangChain框架的功能，提供大语言模型（large language model）、聊天模型（chat model）、文本嵌入模型（text embedding model）等组件（这些组件的集合称为LangChain插件）。本文档将介绍ERNIE Bot Agent的LangChain插件的基础用法。\n",
"\n",
"ERNIE Bot Agent的LangChain插件目前包含如下组件：\n",
"\n",
"- `ErnieBot`：大语言模型，用于完成文本补全任务。\n",
"- `ErnieBotChat`：聊天模型，用于完成对话补全任务。\n",
"- `ErnieEmbeddings`：文本嵌入模型，用于生成文本的向量表示。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 准备工作\n",
"\n",
"安装`erniebot-agent`与`langchain`："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install erniebot-agent langchain"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"根据[ERNIE Bot SDK认证鉴权文档](https://github.com/PaddlePaddle/ERNIE-Bot-SDK/blob/develop/docs/authentication.md)中的说明，获取AI Studio星河社区的access token。执行如下代码，填写access token并敲击回车键："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"\n",
"access_token = getpass.getpass(prompt=\"Access token: \")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## `ErnieBot`\n",
"\n",
"`ErnieBot`是LangChain大语言模型组件，可用于完成文本补全任务。本文档仅介绍`ErnieBot`的用法，大家可以在[LangChain官方文档](https://python.langchain.com/docs/modules/model_io/llms/)了解关于大语言模型组件的更多信息。\n",
"\n",
"创建一个`ErnieBot`对象："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from erniebot_agent.extensions.langchain.llms import ErnieBot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"llm = ErnieBot(aistudio_access_token=access_token)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 基本使用"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"question = \"What does SFINAE mean in C++ template metaprogramming?\"\n",
"\n",
"print(llm(question))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 在chain中使用"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain.prompts import PromptTemplate\n",
"from langchain.chains import LLMChain"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"template = \"Tell me a joke about {content}.\"\n",
"\n",
"prompt = PromptTemplate(template=template, input_variables=[\"content\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"llm_chain = LLMChain(prompt=prompt, llm=llm)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"content = \"rabbits\"\n",
"\n",
"print(llm_chain.run(content=content))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 异步调用"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"question = \"Please write a Python program that checks if an integer is a prime number.\"\n",
"\n",
"answer = await llm.agenerate([question])\n",
"print(answer)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 流式回复"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"question = \"What is the difference between capybara and kiwi?\"\n",
"\n",
"for chunk in llm.stream(question):\n",
" print(chunk, end=\"\", flush=True)\n",
"print(\"\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## `ErnieBotChat`\n",
"\n",
"`ErnieBotChat`是LangChain聊天模型组件，可用于完成文本补全任务。本文档仅介绍`ErnieBotChat`的用法，大家可以在[LangChain官方文档](https://python.langchain.com/docs/modules/model_io/chat/)了解关于聊天模型模型组件的更多信息。\n",
"\n",
"创建一个`ErnieBotChat`对象："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from erniebot_agent.extensions.langchain.chat_models import ErnieBotChat"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"chat = ErnieBotChat(aistudio_access_token=access_token)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 基本使用"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain.chat_models.base import HumanMessage"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"message = HumanMessage(content=\"What does SFINAE mean in C++ template metaprogramming?\")\n",
"print(chat([message]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 在chain中使用"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain.prompts import ChatPromptTemplate"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"message = \"Tell me a joke about {content}.\"\n",
"prompt = ChatPromptTemplate.from_messages([(\"human\", message)])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"chain = prompt | chat"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(chain.invoke({\"content\": \"rabbits\"}))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 异步调用"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain.chat_models.base import HumanMessage"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"message = HumanMessage(content=\"Please write a Python program that checks if an integer is a prime number.\")\n",
"\n",
"response = await chat.agenerate([[message]])\n",
"print(response)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 流式回复"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain.chat_models.base import HumanMessage"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"message = HumanMessage(content=\"What is the difference between capybara and kiwi?\")\n",
"\n",
"for chunk in chat.stream([message]):\n",
" print(chunk.content, end=\"\", flush=True)\n",
"print(\"\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## `ErnieEmbeddings`\n",
"\n",
"`ErnieEmbeddings`是LangChain文本嵌入模型组件，可用于生成文本的向量表示。本文档仅介绍`ErnieEmbeddings`的用法，大家可以在[LangChain官方文档](https://python.langchain.com/docs/modules/data_connection/text_embedding/)了解关于聊天模型模型组件的更多信息。\n",
"\n",
"创建一个`ErnieEmbeddings`对象："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from erniebot_agent.extensions.langchain.embeddings import ErnieEmbeddings"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"embeddings = ErnieEmbeddings(aistudio_access_token=access_token)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 处理单段输入文本"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"text = \"This is a test document.\"\n",
"\n",
"query_result = embeddings.embed_query(text)\n",
"print(len(query_result))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 处理多段输入文本"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"texts = [\"doc1\", \"doc2\"]\n",
"\n",
"docs_result = embeddings.embed_documents(texts)\n",
"print(len(docs_result))\n",
"for res in docs_result:\n",
" print(len(res))"
]
}
],
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