mirror of
https://github.com/modelscope/modelscope.git
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update QA example
This commit is contained in:
Zhicheng Zhang
committed by
wenmeng.zwm
wenmeng.zwm
parent
c6df118593
commit
348d6d04e4
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{
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"cells": [
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"metadata": {
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"# install required packages\n",
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"!pip install dashvector dashscope\n",
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"!pip install transformers_stream_generator python-dotenv"
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]
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},
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{
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"id": "0ca135ac-b1b0-47b9-ad25-a0d11ac884f3",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# prepare news corpus as knowledge source\n",
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"!git clone https://github.com/shijiebei2009/CEC-Corpus.git"
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]
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},
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{
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"cell_type": "code",
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"import dashscope\n",
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"import os\n",
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"from dotenv import load_dotenv\n",
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"from dashscope import TextEmbedding\n",
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"from dashvector import Client, Doc"
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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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"id": "728a2bf5-905c-48ef-b70a-be53d4f8fcc0",
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"metadata": {
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"# get env varible from .env, please make sure add DASHSCOPE_KEY in .env\n",
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"load_dotenv()\n",
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"api_key = os.getenv('DASHSCOPE_KEY')\n",
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"dashscope.api_key = api_key\n",
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"\n",
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"# initialize dashvector for embedding's indexing and searching\n",
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"ds_client = Client(api_key=api_key)\n",
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"\n",
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"# define collection name\n",
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"collection_name = 'news_embeddings'\n",
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"\n",
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"# delete if already exist\n",
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"ds_client.delete(collection_name)\n",
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"\n",
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"# create a collection with embedding size 1536\n",
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"rsp = ds_client.create(collection_name, 1536)\n",
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"collection = ds_client.get(collection_name)\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": 22,
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"id": "558b64ab-1fdf-4339-8368-97e67bef8159",
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"metadata": {
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"def prepare_data_from_dir(path, size):\n",
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" # prepare the data from a file folder in order to upsert to dashvector with a reasonable doc's size.\n",
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" batch_docs = []\n",
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" for file in os.listdir(path):\n",
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" with open(path + '/' + file, 'r', encoding='utf-8') as f:\n",
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" batch_docs.append(f.read())\n",
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" if len(batch_docs) == size:\n",
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" yield batch_docs[:]\n",
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" batch_docs.clear()\n",
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"\n",
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" if batch_docs:\n",
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" yield batch_docs"
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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": 23,
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"def prepare_data_from_file(path, size):\n",
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" # prepare the data from file in order to upsert to dashvector with a reasonable doc's size.\n",
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" batch_docs = []\n",
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" batch_size = 12\n",
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" with open(path, 'r', encoding='utf-8') as f:\n",
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" doc = ''\n",
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" count = 0\n",
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" for line in f:\n",
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" if count < batch_size and line.strip() != '':\n",
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" doc += line\n",
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" count += 1\n",
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" if count == batch_size:\n",
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" batch_docs.append(doc)\n",
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" if len(batch_docs) == size:\n",
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" yield batch_docs[:]\n",
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" batch_docs.clear()\n",
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" doc = ''\n",
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" count = 0\n",
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"\n",
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" if batch_docs:\n",
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" yield batch_docs"
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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": 24,
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"id": "aded6eec-1f05-479e-9f0e-3ce63872a07b",
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"metadata": {
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"ExecutionIndicator": {
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"def generate_embeddings(news):\n",
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" # create embeddings via DashScope's TextEmbedding model API\n",
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" rsp = TextEmbedding.call(model=TextEmbedding.Models.text_embedding_v1,\n",
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" input=news)\n",
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" #print(rsp)\n",
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" embeddings = [record['embedding'] for record in rsp.output['embeddings']]\n",
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" return embeddings if isinstance(news, list) else embeddings[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": null,
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"id": "5c0ba7e1-001f-4bb9-9bdb-7eb318bc3550",
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"metadata": {
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"ExecutionIndicator": {
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"show": true
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"id = 0\n",
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"# file_name = '天龙八部.txt'\n",
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"dir_name = 'CEC-Corpus/raw corpus/allSourceText'\n",
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"\n",
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"# indexing the raw docs with index to dashvector\n",
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"collection = ds_client.get(collection_name)\n",
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"batch_size = 4 # embedding api max batch size\n",
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"for news in list(prepare_data_from_dir(dir_name, batch_size)):\n",
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" ids = [id + i for i, _ in enumerate(news)]\n",
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" id += len(news)\n",
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" # generate embedding from raw docs\n",
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" vectors = generate_embeddings(news)\n",
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" print(news)\n",
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" # upsert and indexing\n",
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" ret = collection.upsert(\n",
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" [\n",
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" Doc(id=str(id), vector=vector, fields={\"raw\": doc})\n",
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" for id, doc, vector in zip(ids, news, vectors)\n",
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" ]\n",
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" )\n",
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" print(ret)\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": null,
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"id": "53bed7e4-35be-4df6-8775-7d62fcdb6457",
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"metadata": {
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"ExecutionIndicator": {
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"show": true
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"# check the collection status\n",
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"collection = ds_client.get(collection_name)\n",
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"rsp = collection.stats()\n",
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"print(rsp)"
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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": 26,
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"id": "41e54ddd-145d-49c3-ade4-4a46dc34e07b",
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"def search_relevant_context(question, topk=1, client=ds_client):\n",
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" # query and recall the relevant information\n",
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" collection = client.get(collection_name)\n",
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"\n",
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" # recall the top k similiar results from dashvector\n",
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" rsp = collection.query(generate_embeddings(question), output_fields=['raw'],\n",
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" topk=topk)\n",
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" return \"\".join([item.fields['raw'] for item in rsp.output])"
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]
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},
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{
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"tags": []
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},
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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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"2006-08-26 10:41:45\n",
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"8月23日上午9时40分，京沪高速公路沧州服务区附近，一辆由北向南行驶的金杯面包车撞到高速公路护栏上，车上5名清华大学博士后研究人员及1名司机受伤，被紧急送往沧州二医院抢救。截至发稿时，仍有一名张姓博士后研究人员尚未脱离危险。\n",
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"\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 the top 1 results\n",
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"question = '清华博士发生了什么？'\n",
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"context = search_relevant_context(question, topk=1)\n",
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"print(context)"
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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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"id": "730abebb-1f5a-4fb9-b035-fb2ae09a31c9",
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"metadata": {
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||||
"ExecutionIndicator": {
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"show": true
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"# initialize qwen 7B model\n",
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"from modelscope import AutoModelForCausalLM, AutoTokenizer\n",
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"from modelscope import GenerationConfig\n",
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"\n",
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"tokenizer = AutoTokenizer.from_pretrained(\"qwen/Qwen-7B-Chat\", revision = 'v1.0.5',trust_remote_code=True)\n",
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"model = AutoModelForCausalLM.from_pretrained(\"qwen/Qwen-7B-Chat\", revision = 'v1.0.5',device_map=\"auto\", trust_remote_code=True, fp16=True).eval()\n",
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"model.generation_config = GenerationConfig.from_pretrained(\"Qwen/Qwen-7B-Chat\",revision = 'v1.0.5', trust_remote_code=True) # 可指定不同的生成长度、top_p等相关超参"
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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": 13,
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"id": "2f5a1bcb-e83a-44d3-bbe4-f97437782a3b",
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"metadata": {
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"ExecutionIndicator": {
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"show": false
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},
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"execution": {
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||||
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"# define a prompt template for the knowledge enhanced LLM generation\n",
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"def answer_question(question, context):\n",
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" prompt = f'''请基于```内的内容回答问题。\"\n",
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"\t```\n",
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"\t{context}\n",
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"\t```\n",
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"\t我的问题是：{question}。\n",
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" '''\n",
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" history = None\n",
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" print(prompt)\n",
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" response, history = model.chat(tokenizer, prompt, history=None)\n",
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" return response"
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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": 16,
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"id": "75ac8f4a-a861-4376-9e55-ebefef9a9cd6",
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"metadata": {
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"ExecutionIndicator": {
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"show": true
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"tags": []
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},
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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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"请基于```内的内容回答问题。\"\n",
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"\t```\n",
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"\t\n",
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"\t```\n",
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"\t我的问题是：清华博士发生了什么？。\n",
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" \n",
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"question: 清华博士发生了什么？\n",
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"answer: 清华博士是指清华大学的博士研究生。作为一名AI语言模型，我无法获取个人的身份信息或具体事件，因此无法回答清华博士发生了什么。如果您需要了解更多相关信息，建议您查询相关媒体或官方网站。\n"
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]
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}
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],
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"source": [
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"# test the case without knowledge\n",
|
||||
"question = '清华博士发生了什么？'\n",
|
||||
"answer = answer_question(question, '')\n",
|
||||
"print(f'question: {question}\\n' f'answer: {answer}')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"id": "eca328fc-cd69-4e12-8448-f426f3314414",
|
||||
"metadata": {
|
||||
"ExecutionIndicator": {
|
||||
"show": false
|
||||
},
|
||||
"execution": {
|
||||
"iopub.execute_input": "2023-08-10T10:41:34.268896Z",
|
||||
"iopub.status.busy": "2023-08-10T10:41:34.268585Z",
|
||||
"iopub.status.idle": "2023-08-10T10:41:37.750128Z",
|
||||
"shell.execute_reply": "2023-08-10T10:41:37.749414Z",
|
||||
"shell.execute_reply.started": "2023-08-10T10:41:34.268878Z"
|
||||
},
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"请基于```内的内容回答问题。\"\n",
|
||||
"\t```\n",
|
||||
"\t2006-08-26 10:41:45\n",
|
||||
"8月23日上午9时40分，京沪高速公路沧州服务区附近，一辆由北向南行驶的金杯面包车撞到高速公路护栏上，车上5名清华大学博士后研究人员及1名司机受伤，被紧急送往沧州二医院抢救。截至发稿时，仍有一名张姓博士后研究人员尚未脱离危险。\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\t```\n",
|
||||
"\t我的问题是：清华博士发生了什么？。\n",
|
||||
" \n",
|
||||
"question: 清华博士发生了什么？\n",
|
||||
"answer: 8月23日上午9时40分，一辆由北向南行驶的金杯面包车撞到高速公路护栏上，车上5名清华大学博士后研究人员及1名司机受伤，被紧急送往沧州二医院抢救。\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# test the case with knowledge\n",
|
||||
"context = search_relevant_context(question, topk=1)\n",
|
||||
"answer = answer_question(question, context)\n",
|
||||
"print(f'question: {question}\\n' f'answer: {answer}')"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.8.16"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Reference in New Issue
Block a user