在阿里的灵积模型中嵌入垂直领域知识库

灵积模型api调用

1	pip install dashscope

你的api key建议直接加入环境变量中

from http import HTTPStatus
import dashscope

def sample_sync_call():
    prompt_text = '用萝卜、土豆、茄子做饭，给我个菜谱。'
    resp = dashscope.Generation.call(
        model='qwen-turbo',
        prompt=prompt_text
    )
    # The response status_code is HTTPStatus.OK indicate success,
    # otherwise indicate request is failed, you can get error code
    # and message from code and message.
    if resp.status_code == HTTPStatus.OK:
        print(resp.output)  # The output text
        print(resp.usage)  # The usage information
    else:
        print(resp.code)  # The error code.
        print(resp.message)  # The error message.

sample_sync_call()

embedding

本地知识库的向量化。通过文本向量模型将其转化为高质量低维度的向量数据，再写入DashVector向量检索服务。这里数据的向量化我们采用了灵积模型服务上的Embedding API实现。
相关知识点的提取。将提问文本向量化后，通过DashVector提取相关知识点的原文。
构造Prompt进行提问。将相关知识点作为“限定上下文+提问” 一起作为prompt询问通义千问。

本地知识库的向量化

1	pip install dashvector

dashscope.api_key,dashvector的api_key和endpoint需要写上你自己的，加载预料处的路径也改为你数据集的目录路径。batch_size根据自己需求调整。我这里用的数据集是从“人人都是产品经理”上爬下来的文章。

import os

import dashscope
from dashscope import TextEmbedding
import dashvector
from dashvector import Client, Doc

def prepare_data(path, batch_size=25):
    batch_docs = []
    for file in os.listdir(path):
        with open(path + '/' + file, 'r', encoding='utf-8') as f:
            batch_docs.append(f.read())
            if len(batch_docs) == batch_size:
                yield batch_docs
                batch_docs = []

    if batch_docs:
        yield batch_docs


def generate_embeddings(news):
    rsp = TextEmbedding.call(
        model=TextEmbedding.Models.text_embedding_v1,
        input=news
    )
    embeddings = [record['embedding'] for record in rsp.output['embeddings']]
    return embeddings if isinstance(news, list) else embeddings[0]


if __name__ == '__main__':
    dashscope.api_key = ''
    
    # 初始化 client
    client = dashvector.Client(
        api_key='',
        endpoint=''
    )
    assert client

    # 创建集合：指定集合名称和向量维度, text_embedding_v1 模型产生的向量统一为 1536 维
    rsp = client.create('product', 1536)
    assert rsp

    # 加载语料
    id = 0
    collection = client.get('product')
    for news in list(prepare_data('knowledgedata/PM-data-master/woshipm.com')):
        print(news)
        ids = [id + i for i, _ in enumerate(news)]

        id += len(news)

        vectors = generate_embeddings(news)
        # 写入 dashvector 构建索引
        rsp = collection.upsert(
            [
                Doc(id=str(id), vector=vector, fields={"raw": doc})
                for id, vector, doc in zip(ids, vectors, news)
            ]
        )
        assert rsp

知识点的提取

import dashvector
from dashvector import Client
from dashvector import Doc
from embedding import generate_embeddings


def search_relevant_news(question):
    # 初始化 dashvector client
    client = dashvector.Client(
        api_key='',
        endpoint=''
    )
    assert client

    # 获取刚刚存入的集合
    collection = client.get('product')
    assert collection

    # 向量检索：指定 topk = 1 
    rsp = collection.query(generate_embeddings(question), output_fields=['raw'],topk=1)
    assert rsp
    return rsp.output[0].fields['raw']


question = input("请输入问题：")
print(search_relevant_news(question))

构造Prompt进行提问

import dashscope
from http import HTTPStatus
import os
import webbrowser

import dashvector
from dashvector import Client
from dashvector import Doc
from embedding import generate_embeddings

def generate_response(prompt):
    response_generator = dashscope.Generation.call(
        model='chatglm3-6b',
        prompt=prompt,
        stream=True,
        top_p=0.8)

    head_idx = 0
    print("\n")
    for resp in response_generator:
        paragraph = resp.output['text']
        # print("\r%s" % paragraph[head_idx:len(paragraph)], end='')
        if(paragraph.rfind('\n') != -1):
            head_idx = paragraph.rfind('\n') + 1

    if resp.status_code == HTTPStatus.OK:
        return resp.output.text  # The output text
        print(resp.usage)  # The usage information
    else:
        print(resp.code)  # The error code.
        print(resp.message)  # The error message.

def search_relevant_news(question):
    # 初始化 dashvector client
    client = dashvector.Client(
        api_key='',
        endpoint=''
    )
    assert client

    # 获取刚刚存入的集合
    collection = client.get('product')
    assert collection

    # 向量检索：指定 topk = 1 
    rsp = collection.query(generate_embeddings(question), output_fields=['raw'],topk=1)
    assert rsp
    return rsp.output[0].fields['raw']

# 初始化对话历史列表
dialogue_history = []

while True:
    # 获取用户输入的对话内容
    origin_user_input = input("\n\n请描述您的问题：")

    # 结束循环条件：用户未输入新对话
    if not origin_user_input:
        break

    # 添加知识库参考
    user_input = search_relevant_news(origin_user_input) + "\n请参考以上资料回答下列问题\n" + origin_user_input
    # print(user_input)

    # 将用户对话内容添加到历史记录中
    dialogue_history.append(user_input)

    # 构建包含对话历史的新提示
    prompt_text = "\n".join(dialogue_history)

    # 使用新的对话上下文生成回复
    generated_response = generate_response(prompt_text)

    print(generated_response)

    # 获取当前工作目录
    current_dir = os.getcwd()

    with open('answer.txt', 'w', encoding='utf-8') as f:
        f.write(generated_response)

# 循环结束后，程序会在这里结束

如此一来，大模型就会根据你知识库的内容来回答你的问题

提问：需求分析怎么做

这是通过dashvector从知识库中检索到的内容

这是构造prompt后的对话

很明显大模型参考了知识库，然后根据问题回答

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