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大模型#xff0c;包括部署微调prompt/Agent应用开发、知识库增强、数据库增强、知识图谱增强、自然语言处理、多模态等大模型应用开发内容 从0起步#xff0c;扬帆起航。
大模型应用向开发路径及一点个人思考大模型应用开发实用开源项目汇总大模型问答项目…大模型相关目录
大模型包括部署微调prompt/Agent应用开发、知识库增强、数据库增强、知识图谱增强、自然语言处理、多模态等大模型应用开发内容 从0起步扬帆起航。
大模型应用向开发路径及一点个人思考大模型应用开发实用开源项目汇总大模型问答项目问答性能评估方法大模型数据侧总结大模型token等基本概念及参数和内存的关系大模型应用开发-华为大模型生态规划从零开始的LLaMA-Factory的指令增量微调基于实体抽取-SMC-语义向量的大模型能力评估通用算法附代码基于Langchain-chatchat的向量库构建及检索附代码一文教你成为合格的Prompt工程师最简明的大模型agent教程批量使用API调用langchain-chatchat知识库能力langchin-chatchat部分开发笔记持续更新文心一言、讯飞星火、GPT、通义千问等线上API调用示例 文章目录 大模型相关目录接口使用文心一言讯飞星火通义千问GPT 接口使用
import ask_Tongyi
import ask_Wenxin
import ask_Xunfei
import pandas as pd
import numpy as np# llm ask_Tongyi.TongyiAPI()
# llm ask_Wenxin.WenxinAPI()
llm ask_Xunfei.XunfeiAPI()data pd.read_excel(rC:\Users\12258\Desktop\123\线上API和14B性能对比.xlsx)prompt_ls data[Docs].tolist()
question_ls data[Question].tolist()
n len(data)anwser_ls []
for index in range(n):
# for index in range(1):single_query prompt_ls[index] \n question_ls[index]single_anwser llm.get_one_response_by_prompt(single_query)print(single_query)print(single_anwser)anwser_ls.append(single_anwser[text])pd.DataFrame(anwser_ls).to_excel(rC:\Users\12258\Desktop\123\xunfei_anwser.xlsx,indexFalse)文心一言
import requests
import jsonclass WenxinAPI:def __init__(self):self.API_KEY *self.SECRET_KEY *self.token self.get_access_token()def get_access_token(self):使用 API KeySecret Key 获取access_token替换下列示例中的应用API Key、应用Secret Keyurl https://aip.baidubce.com/oauth/2.0/token?grant_typeclient_credentialsclient_id{api}client_secret{secret}.format(apiself.API_KEY,secretself.SECRET_KEY)payload json.dumps()headers {Content-Type: application/json,Accept: application/json}response requests.request(POST, url, headersheaders, datapayload)return response.json().get(access_token)def get_one_response_by_prompt(self, question):url https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-3.5-8k-0205?access_token self.tokenpayload json.dumps({messages: [{role: user,content: question}]})headers {Content-Type: application/json}response requests.request(POST, url, headersheaders, datapayload)# print(response.text)return {text:json.loads(response.text)[result]}if __name__ __main__:test_query 你是谁llm WenxinAPI()anwser llm.get_one_response_by_prompt(test_query)print(anwser)讯飞星火
需要一个引用文件SparkApi.py
import _thread as thread
import base64
import datetime
import hashlib
import hmac
import json
from urllib.parse import urlparse
import ssl
from datetime import datetime
from time import mktime
from urllib.parse import urlencode
from wsgiref.handlers import format_date_timeimport websocket # 使用websocket_client
answer class Ws_Param(object):# 初始化def __init__(self, APPID, APIKey, APISecret, Spark_url):self.APPID APPIDself.APIKey APIKeyself.APISecret APISecretself.host urlparse(Spark_url).netlocself.path urlparse(Spark_url).pathself.Spark_url Spark_url# 生成urldef create_url(self):# 生成RFC1123格式的时间戳now datetime.now()date format_date_time(mktime(now.timetuple()))# 拼接字符串signature_origin host: self.host \nsignature_origin date: date \nsignature_origin GET self.path HTTP/1.1# 进行hmac-sha256进行加密signature_sha hmac.new(self.APISecret.encode(utf-8), signature_origin.encode(utf-8),digestmodhashlib.sha256).digest()signature_sha_base64 base64.b64encode(signature_sha).decode(encodingutf-8)authorization_origin fapi_key{self.APIKey}, algorithmhmac-sha256, headershost date request-line, signature{signature_sha_base64}authorization base64.b64encode(authorization_origin.encode(utf-8)).decode(encodingutf-8)# 将请求的鉴权参数组合为字典v {authorization: authorization,date: date,host: self.host}# 拼接鉴权参数生成urlurl self.Spark_url ? urlencode(v)# 此处打印出建立连接时候的url,参考本demo的时候可取消上方打印的注释比对相同参数时生成的url与自己代码生成的url是否一致return url# 收到websocket错误的处理
def on_error(ws, error):# print(### error:, error)pass# 收到websocket关闭的处理
def on_close(ws,one,two):# print( )pass# 收到websocket连接建立的处理
def on_open(ws):thread.start_new_thread(run, (ws,))def run(ws, *args):data json.dumps(gen_params(appidws.appid, domain ws.domain,questionws.question))ws.send(data)# 收到websocket消息的处理
def on_message(ws, message):# print(message)data json.loads(message)code data[header][code]if code ! 0:# print(f请求错误: {code}, {data})ws.close()else:choices data[payload][choices]status choices[status]content choices[text][0][content]global answeranswer content# print(1)if status 2:ws.close()def gen_params(appid, domain,question):通过appid和用户的提问来生成请参数data {header: {app_id: appid,uid: 1234},parameter: {chat: {domain: domain,random_threshold: 0.5,max_tokens: 8192,auditing: default}},payload: {message: {text: question}}}return datadef main(appid, api_key, api_secret, Spark_url,domain, question):# print(星火:)wsParam Ws_Param(appid, api_key, api_secret, Spark_url)websocket.enableTrace(False)wsUrl wsParam.create_url()ws websocket.WebSocketApp(wsUrl, on_messageon_message, on_erroron_error, on_closeon_close, on_openon_open)ws.appid appidws.question questionws.domain domainws.run_forever(sslopt{cert_reqs: ssl.CERT_NONE})
import requests
import json
import SparkApiclass XunfeiAPI:def __init__(self):self.appid *self.api_secret *self.api_key *self.domain generalv2self.url ws://spark-api.xf-yun.com/v2.1/chat # v2.0def getText(self, text, role, content):获取角色和文本存入text中jsoncon {}jsoncon[role] rolejsoncon[content] contenttext.append(jsoncon)return textdef get_one_response_by_prompt(self, question):SparkApi.answer SparkApi.main(self.appid, self.api_key, self.api_secret, self.url, self.domain, self.getText([], user, question))return {text:SparkApi.answer}if __name__ __main__:test_query 你是谁llm XunfeiAPI()anwser llm.get_one_response_by_prompt(test_query)print(anwser)通义千问
import requests
import json
import dashscope
from dashscope import Generation
from http import HTTPStatusclass TongyiAPI:def __init__(self):API_KEY sk-*dashscope.api_key API_KEYself.gen Generation()def get_one_response_by_prompt(self, prompt):response self.gen.call(modeldashscope.Generation.Models.qwen_turbo,promptprompt)# 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 response.status_code HTTPStatus.OK:# print(response.output) # The output textprint(response.usage) # The usage informationreturn response.outputelse:print(response.code) # The error code.print(response.message) # The error message.
GPT
通过openai-sb调用
import requests
import json
import openaiclass GPTAPI:def __init__(self):self.API_KEY sb-*self.url https://api.openai-sb.com/v1/chat/completionsopenai.api_key self.API_KEYopenai.api_base https://api.openai-sb.com/v1def getText(self, text, role, content):获取角色和文本存入text中jsoncon {}jsoncon[role] rolejsoncon[content] contenttext.append(jsoncon)return textdef get_one_response(self, question):payload json.dumps({model: gpt-3.5-turbo,messages: [{role: user,content: question}]})headers {Authorization: Bearer self.API_KEY,Content-Type: application/json}response requests.request(POST, self.url, headersheaders, datapayload)try:return response.json()[choices][0][message][content]except Exception as e:print(e)return def get_one_response_by_prompt(self, prompt):payload json.dumps({model: gpt-3.5-turbo,messages: prompt})headers {Authorization: Bearer self.API_KEY,Content-Type: application/json}response requests.request(POST, self.url, headersheaders, datapayload)return response.json()if __name__ __main__:test_query 你是谁llm GPTAPI()anwser llm.get_one_response_by_prompt(test_query)print(anwser)
