mirror of
https://github.com/hwchase17/langchain
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62fa2bc518
--------- Co-authored-by: zhanghexian1 <zhanghexian1@jd.com> Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
414 lines
21 KiB
Plaintext
414 lines
21 KiB
Plaintext
{
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"cells": [
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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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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/export/anaconda3/envs/langchainGLM6B/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n",
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"INFO 2023-08-28 18:26:07,485-1d: \n",
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"loading model config\n",
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"llm device: cuda\n",
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"embedding device: cuda\n",
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"dir: /data/zhx/zhx/langchain-ChatGLM_new\n",
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"flagging username: e2fc35b8e87c4de18d692e951a5f7c46\n",
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"\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"True\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Loading checkpoint shards: 100%|██████████| 7/7 [00:06<00:00, 1.01it/s]\n"
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]
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}
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],
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"source": [
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"\n",
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"import os, sys, torch\n",
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"from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModel\n",
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"from langchain import HuggingFacePipeline, ConversationChain\n",
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"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
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"from langchain.vectorstores.vearch import VearchDb\n",
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"from langchain.document_loaders import TextLoader\n",
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"from langchain.prompts import PromptTemplate\n",
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"from langchain.chains import RetrievalQA\n",
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"from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
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"\n",
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"# your local model path\n",
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"model_path =\"/data/zhx/zhx/langchain-ChatGLM_new/chatglm2-6b\" \n",
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"\n",
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"tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)\n",
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"model = AutoModel.from_pretrained(model_path, trust_remote_code=True).half().cuda(0)"
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Human: 你好!\n",
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"ChatGLM:你好👋！我是人工智能助手 ChatGLM2-6B，很高兴见到你，欢迎问我任何问题。\n",
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"\n",
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"Human: 你知道凌波微步吗，你知道都有谁学会了吗?\n",
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"ChatGLM:凌波微步是一种步伐，最早出自于《倚天屠龙记》。在小说中，灭绝师太曾因与练习凌波微步的杨过的恩怨纠葛，而留下了一部经书，内容是记载凌波微步的起源和作用。后来，凌波微步便成为杨过和小龙女的感情象征。在现实生活中，凌波微步是一句口号，是清华大学学生社团“模型社”的社训。\n",
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"\n"
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]
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}
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],
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"source": [
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"query = \"你好!\"\n",
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"response, history = model.chat(tokenizer, query, history=[])\n",
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"print(f\"Human: {query}\\nChatGLM:{response}\\n\")\n",
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"query = \"你知道凌波微步吗，你知道都有谁学会了吗?\"\n",
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"response, history = model.chat(tokenizer, query, history=history)\n",
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"print(f\"Human: {query}\\nChatGLM:{response}\\n\")"
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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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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"INFO 2023-08-28 18:27:36,037-1d: Load pretrained SentenceTransformer: /data/zhx/zhx/langchain-ChatGLM_new/text2vec/text2vec-large-chinese\n",
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"WARNING 2023-08-28 18:27:36,038-1d: No sentence-transformers model found with name /data/zhx/zhx/langchain-ChatGLM_new/text2vec/text2vec-large-chinese. Creating a new one with MEAN pooling.\n",
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"INFO 2023-08-28 18:27:38,936-1d: Use pytorch device: cuda\n"
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]
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}
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],
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"source": [
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"# Add your local knowledge files\n",
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"file_path = \"/data/zhx/zhx/langchain-ChatGLM_new/knowledge_base/天龙八部/lingboweibu.txt\"#Your local file path\"\n",
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"loader = TextLoader(file_path,encoding=\"utf-8\")\n",
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"documents = loader.load()\n",
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"\n",
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"# split text into sentences and embedding the sentences\n",
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"text_splitter = RecursiveCharacterTextSplitter(\n",
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" chunk_size=500, chunk_overlap=100)\n",
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"texts = text_splitter.split_documents(documents)\n",
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"\n",
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"#your model path\n",
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"embedding_path = '/data/zhx/zhx/langchain-ChatGLM_new/text2vec/text2vec-large-chinese'\n",
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"embeddings = HuggingFaceEmbeddings(model_name=embedding_path)\n",
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"\n"
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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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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Batches: 100%|██████████| 1/1 [00:00<00:00, 4.56it/s]"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"['7aae36236f784105a0004d8ff3c7c3ad', '7e495d4e5962497db2080e84d52e75ed', '9a640124fc324a8abb0eaa31acb638b7']\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\n"
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]
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}
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],
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"source": [
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"#first add your document into vearch vectorstore\n",
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"vearch_db = VearchDb.from_documents(texts,embeddings,table_name=\"your_table_name\",metadata_path=\"/data/zhx/zhx/langchain-ChatGLM_new/knowledge_base/your_table_name\")"
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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": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Batches: 100%|██████████| 1/1 [00:00<00:00, 22.49it/s]\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"####################第1段相关文档####################\n",
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"\n",
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"午饭过后，段誉又练“凌波微步”，走一步，吸一口气，走第二步时将气呼出，六十四卦走完，四肢全无麻痹之感，料想呼吸顺畅，便无害处。第二次再走时连走两步吸一口气，再走两步始行呼出。这“凌波微步”是以动功修习内功，脚步踏遍六十四卦一个周天，内息自然而然地也转了一个周天。因此他每走一遍，内力便有一分进益。\n",
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"\n",
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"这般练了几天，“凌波微步”已走得颇为纯熟，不须再数呼吸，纵然疾行，气息也已无所窒滞。心意既畅，跨步时渐渐想到《洛神赋》中那些与“凌波微步”有关的句子：“仿佛兮若轻云之蔽月，飘飘兮若流风之回雪”，“竦轻躯以鹤立，若将飞而未翔”，“体迅飞凫，飘忽若神”，“动无常则，若危若安。进止难期，若往若还”。\n",
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"\n",
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"\n",
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"\n",
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"百度简介\n",
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"\n",
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"凌波微步是「逍遥派」独门轻功身法，精妙异常。\n",
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"\n",
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"凌波微步乃是一门极上乘的轻功，所以列于卷轴之末，以易经八八六十四卦为基础，使用者按特定顺序踏着卦象方位行进，从第一步到最后一步正好行走一个大圈。此步法精妙异常，原是要待人练成「北冥神功」，吸人内力，自身内力已【颇为深厚】之后再练。\n",
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"\n",
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"####################第2段相关文档####################\n",
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"\n",
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"《天龙八部》第五回 微步縠纹生\n",
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"\n",
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"卷轴中此外诸种经脉修习之法甚多，皆是取人内力的法门，段誉虽自语宽解，总觉习之有违本性，单是贪多务得，便非好事，当下暂不理会。\n",
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"\n",
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"卷到卷轴末端，又见到了“凌波微步”那四字，登时便想起《洛神赋》中那些句子来：“凌波微步，罗袜生尘……转眄流精，光润玉颜。含辞未吐，气若幽兰。华容婀娜，令我忘餐。”曹子建那些千古名句，在脑海中缓缓流过：“秾纤得衷，修短合度，肩若削成，腰如约素。延颈秀项，皓质呈露。芳泽无加，铅华弗御。云髻峨峨，修眉连娟。丹唇外朗，皓齿内鲜。明眸善睐，靥辅承权。瑰姿艳逸，仪静体闲。柔情绰态，媚于语言……”这些句子用在木婉清身上，“这话倒也有理”；但如用之于神仙姊姊，只怕更为适合。想到神仙姊姊的姿容体态，“皎若太阳升朝霞，灼若芙蓉出绿波”，但觉依她吩咐行事，实为人生至乐，心想：“我先来练这‘凌波微步’，此乃逃命之妙法，非害人之手段也，练之有百利而无一害。”\n",
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"\n",
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"####################第3段相关文档####################\n",
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"\n",
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"《天龙八部》第二回 玉壁月华明\n",
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"\n",
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"再展帛卷，长卷上源源皆是裸女画像，或立或卧，或现前胸，或见后背。人像的面容都是一般，但或喜或愁，或含情凝眸，或轻嗔薄怒，神情各异。一共有三十六幅图像，每幅像上均有颜色细线，注明穴道部位及练功法诀。\n",
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"\n",
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"帛卷尽处题着“凌波微步”四字，其后绘的是无数足印，注明“妇妹”、“无妄”等等字样，尽是《易经》中的方位。段誉前几日还正全心全意地钻研《易经》，一见到这些名称，登时精神大振，便似遇到故交良友一般。只见足印密密麻麻，不知有几千百个，自一个足印至另一个足印均有绿线贯串，线上绘有箭头，最后写着一行字道：“步法神妙，保身避敌，待积内力，再取敌命。”\n",
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"\n",
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"段誉心道：“神仙姊姊所遗的步法，必定精妙之极，遇到强敌时脱身逃走，那就很好，‘再取敌命’也就不必了。”\n",
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"卷好帛卷，对之作了两个揖，珍而重之地揣入怀中，转身对那玉像道：“神仙姊姊，你吩咐我朝午晚三次练功，段誉不敢有违。今后我对人加倍客气，别人不会来打我，我自然也不会去吸他内力。你这套‘凌波微步’我更要用心练熟，眼见不对，立刻溜之大吉，就吸不到他内力了。”至于“杀尽我逍遥派弟子”一节，却想也不敢去想。\n",
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"\n",
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"********ChatGLM:凌波微步是一种轻功身法，属于逍遥派独门轻功。它以《易经》中的六十四卦为基础，按照特定顺序踏着卦象方位行进，从第一步到最后一步正好行走一个大圈。凌波微步精妙异常，可以让人内力相助，自身内力颇为深厚之后再练。《天龙八部》第五回中有描述。\n",
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"\n"
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]
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}
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],
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"source": [
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"\n",
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"res=vearch_db.similarity_search(query, 3)\n",
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"query = \"你知道凌波微步吗，你知道都有谁会凌波微步?\"\n",
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"for idx,tmp in enumerate(res): \n",
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" print(f\"{'#'*20}第{idx+1}段相关文档{'#'*20}\\n\\n{tmp.page_content}\\n\")\n",
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"\n",
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"# combine your local knowleadge and query \n",
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"context = \"\".join([tmp.page_content for tmp in res])\n",
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"new_query = f\"基于以下信息，尽可能准确的来回答用户的问题。背景信息:\\n {context} \\n 回答用户这个问题:{query}\\n\\n\"\n",
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"response, history = model.chat(tokenizer, new_query, history=[])\n",
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"print(f\"********ChatGLM:{response}\\n\")\n"
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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": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Human: 你知道vearch是什么吗?\n",
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"ChatGLM:是的，我知道 Vearch。Vearch 是一种矩阵分解 technique，用于将矩阵分解为若干个不可约矩阵的乘积。它是由 Linus Torvalds 开发的，旨在提高 Linux 内核中矩阵操作的性能。\n",
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"\n",
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"Vearch 可以通过使用特殊的操作来对矩阵进行操作，从而避免了使用昂贵的矩阵操作库。它也被广泛用于其他操作系统中，如 FreeBSD 和 Solaris。\n",
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"\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Batches: 100%|██████████| 1/1 [00:00<00:00, 31.59it/s]"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"['04bc84fff5074b7b8990441e92e6df07', 'e221906153bb4e03bc7095dadea144de', '126034ba51934093920d8732860f340b']\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"['04bc84fff5074b7b8990441e92e6df07',\n",
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" 'e221906153bb4e03bc7095dadea144de',\n",
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" '126034ba51934093920d8732860f340b']"
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]
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},
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"execution_count": 8,
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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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"query = \"你知道vearch是什么吗?\"\n",
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"response, history = model.chat(tokenizer, query, history=history)\n",
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"print(f\"Human: {query}\\nChatGLM:{response}\\n\")\n",
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"\n",
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"\n",
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"vearch_info = [\"Vearch 是一款存储大语言模型数据的向量数据库，用于存储和快速搜索模型embedding后的向量，可用于基于个人知识库的大模型应用\",\n",
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" \"Vearch 支持OpenAI, Llama, ChatGLM等模型，以及LangChain库\",\n",
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" \"vearch 是基于C语言,go语言开发的，并提供python接口，可以直接通过pip安装\"]\n",
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"vearch_source=[{'source': '/data/zhx/zhx/langchain-ChatGLM_new/knowledge_base/tlbb/three_body.txt'},{'source': '/data/zhx/zhx/langchain-ChatGLM_new/knowledge_base/tlbb/three_body.txt'},{'source': '/data/zhx/zhx/langchain-ChatGLM_new/knowledge_base/tlbb/three_body.txt'}]\n",
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"vearch_db.add_texts(vearch_info,vearch_source)\n",
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"\n"
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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": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Batches: 100%|██████████| 1/1 [00:00<00:00, 25.57it/s]\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"####################第1段相关文档####################\n",
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"\n",
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"Vearch 是一款存储大语言模型数据的向量数据库，用于存储和快速搜索模型embedding后的向量，可用于基于个人知识库的大模型应用\n",
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"\n",
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"####################第2段相关文档####################\n",
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"\n",
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"Vearch 支持OpenAI, Llama, ChatGLM等模型，以及LangChain库\n",
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"\n",
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"####################第3段相关文档####################\n",
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"\n",
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"vearch 是基于C语言,go语言开发的，并提供python接口，可以直接通过pip安装\n",
|
||
"\n",
|
||
"***************ChatGLM:是的，Varch是一个向量数据库，旨在存储和快速搜索模型embedding后的向量。它支持OpenAI、Llama和ChatGLM等模型，并可以直接通过pip安装。Varch是一个基于C语言和Go语言开发的项目，并提供了Python接口。\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"query3 = \"你知道vearch是什么吗?\"\n",
|
||
"res1 = vearch_db.similarity_search(query3, 3)\n",
|
||
"for idx,tmp in enumerate(res1): \n",
|
||
" print(f\"{'#'*20}第{idx+1}段相关文档{'#'*20}\\n\\n{tmp.page_content}\\n\")\n",
|
||
"\n",
|
||
"context1 = \"\".join([tmp.page_content for tmp in res1])\n",
|
||
"new_query1 = f\"基于以下信息，尽可能准确的来回答用户的问题。背景信息:\\n {context1} \\n 回答用户这个问题:{query3}\\n\\n\"\n",
|
||
"response, history = model.chat(tokenizer, new_query1, history=[])\n",
|
||
"\n",
|
||
"print(f\"***************ChatGLM:{response}\\n\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"delete docid True\n",
|
||
"Human: 你知道vearch是什么吗?\n",
|
||
"ChatGLM:Vearch是一种高分子化合物,也称为聚合物、高分子材料或合成材料。它是由重复单元组成的大型聚合物,通常由一些重复单元组成,这些单元在聚合过程中结合在一起形成一个连续的高分子链。\n",
|
||
"\n",
|
||
"Vearch具有许多独特的性质,例如高强度、高刚性、耐磨、耐腐蚀、耐高温等。它们通常用于制造各种应用,例如塑料制品、橡胶、纤维、建筑材料等。\n",
|
||
"\n",
|
||
"after delete docid to query again: {}\n",
|
||
"get existed docid {'7aae36236f784105a0004d8ff3c7c3ad': Document(page_content='《天龙八部》第二回 玉壁月华明\\n\\n再展帛卷，长卷上源源皆是裸女画像，或立或卧，或现前胸，或见后背。人像的面容都是一般，但或喜或愁，或含情凝眸，或轻嗔薄怒，神情各异。一共有三十六幅图像，每幅像上均有颜色细线，注明穴道部位及练功法诀。\\n\\n帛卷尽处题着“凌波微步”四字，其后绘的是无数足印，注明“妇妹”、“无妄”等等字样，尽是《易经》中的方位。段誉前几日还正全心全意地钻研《易经》，一见到这些名称，登时精神大振，便似遇到故交良友一般。只见足印密密麻麻，不知有几千百个，自一个足印至另一个足印均有绿线贯串，线上绘有箭头，最后写着一行字道：“步法神妙，保身避敌，待积内力，再取敌命。”\\n\\n段誉心道：“神仙姊姊所遗的步法，必定精妙之极，遇到强敌时脱身逃走，那就很好，‘再取敌命’也就不必了。”\\n卷好帛卷，对之作了两个揖，珍而重之地揣入怀中，转身对那玉像道：“神仙姊姊，你吩咐我朝午晚三次练功，段誉不敢有违。今后我对人加倍客气，别人不会来打我，我自然也不会去吸他内力。你这套‘凌波微步’我更要用心练熟，眼见不对，立刻溜之大吉，就吸不到他内力了。”至于“杀尽我逍遥派弟子”一节，却想也不敢去想。', metadata={'source': '/data/zhx/zhx/langchain-ChatGLM_new/knowledge_base/天龙八部/lingboweibu.txt'}), '7e495d4e5962497db2080e84d52e75ed': Document(page_content='《天龙八部》第五回 微步縠纹生\\n\\n卷轴中此外诸种经脉修习之法甚多，皆是取人内力的法门，段誉虽自语宽解，总觉习之有违本性，单是贪多务得，便非好事，当下暂不理会。\\n\\n卷到卷轴末端，又见到了“凌波微步”那四字，登时便想起《洛神赋》中那些句子来：“凌波微步，罗袜生尘……转眄流精，光润玉颜。含辞未吐，气若幽兰。华容婀娜，令我忘餐。”曹子建那些千古名句，在脑海中缓缓流过：“秾纤得衷，修短合度，肩若削成，腰如约素。延颈秀项，皓质呈露。芳泽无加，铅华弗御。云髻峨峨，修眉连娟。丹唇外朗，皓齿内鲜。明眸善睐，靥辅承权。瑰姿艳逸，仪静体闲。柔情绰态，媚于语言……”这些句子用在木婉清身上，“这话倒也有理”；但如用之于神仙姊姊，只怕更为适合。想到神仙姊姊的姿容体态，“皎若太阳升朝霞，灼若芙蓉出绿波”，但觉依她吩咐行事，实为人生至乐，心想：“我先来练这‘凌波微步’，此乃逃命之妙法，非害人之手段也，练之有百利而无一害。”', metadata={'source': '/data/zhx/zhx/langchain-ChatGLM_new/knowledge_base/天龙八部/lingboweibu.txt'})}\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"##delete and get function need to maintian docids \n",
|
||
"##your docid\n",
|
||
"res_d=vearch_db.delete(['04bc84fff5074b7b8990441e92e6df07', 'e221906153bb4e03bc7095dadea144de', '126034ba51934093920d8732860f340b'])\n",
|
||
"print(\"delete docid\",res_d)\n",
|
||
"query = \"你知道vearch是什么吗?\"\n",
|
||
"response, history = model.chat(tokenizer, query, history=[])\n",
|
||
"print(f\"Human: {query}\\nChatGLM:{response}\\n\")\n",
|
||
"get_id_doc=vearch_db.get(['04bc84fff5074b7b8990441e92e6df07'])\n",
|
||
"print(\"after delete docid to query again:\",get_id_doc)\n",
|
||
"get_delet_doc=vearch_db.get(['7aae36236f784105a0004d8ff3c7c3ad', '7e495d4e5962497db2080e84d52e75ed'])\n",
|
||
"print(\"get existed docid\",get_delet_doc)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3.10.12 ('langchainGLM6B')",
|
||
"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": "3.10.12"
|
||
},
|
||
"orig_nbformat": 4,
|
||
"vscode": {
|
||
"interpreter": {
|
||
"hash": "1fd24e7ef183310e43cbf656d21568350c6a30580b6df7fe3b34654b3770f74d"
|
||
}
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|