利用openai，Llama-index库实现多功能的agent [Datawhale学习]
参考资料：GitHub - datawhalechina/wow-agent: A simple and trans-platform agent framework and tutorial目次
一、借助Llama-index实现算数agent
注意：要在.env文件中配置好apikey
1.先构建大模子（智谱glm-4-flash模子）
2.构建其需要利用的算数工具
3.完整代码
二、多模子接入agent
借助Llama-index和OpenWeatherMap实现实时天气
1.构建大模子与（一、借助Llama-index实现agent）相同，这里着重说明后半部分
2.由于OpenWeatherMap是利用经度纬度获取天气，以是需要将地区转换为经纬度
3.再利用经纬度获取天气信息
4.完整代码如下
效果如下
三、数据库接入agent
1.本地安装ollama
运行效果
2.毗连数据库的模子
首先创建数据库，并添加数据
假如你是用的是pycharm，以下方法可以直观的查看表中数据
注意事项：
3.再用本地摆设的模子ollama作为llm实现agent
注意需要提前安装这些库
效果如下
四、RAG接入agent
1.首先接入本地的模子
2.构建索引
3.构建问答引擎
4.完整代码体验RAG
注意：将以下内容命名为问答手册.txt，放置在代码运行目次下（供模子学习）
运行效果
5.将RAG作为工具供agent利用
完整代码
效果如下
五、搜索引擎接入agent
1.接入模子
2.构建llm模子
3.配置搜索引擎
4.完整代码
效果如下
一、借助Llama-index实现算数agent
注意：要在.env文件中配置好apikey
申请一个智谱的免费api参考利用openai，Llama-index实现agent -CSDN博客
1.先构建大模子（智谱glm-4-flash模子）
本次由于要借助react框架，因此启用流式相应更能反映模子自我推理本领
import os
from dotenv import load_dotenv
from openai import OpenAI
from pydantic import Field# 导入Field，用于Pydantic模型中定义字段的元数据
from llama_index.core.llms import (
CustomLLM,
CompletionResponse,
LLMMetadata,
)
from llama_index.core.embeddings import BaseEmbedding
from llama_index.core.llms.callbacks import llm_completion_callback
from typing import List, Any, Generator
# 加载环境变量
load_dotenv()
# 从环境变量中读取api_key
api_key = os.getenv('ZHIPU_API_KEY')
base_url = "https://open.bigmodel.cn/api/paas/v4"
chat_model = "glm-4-flash"
# 定义OurLLM类，继承自CustomLLM基类
class OurLLM(CustomLLM):
api_key: str = Field(default=api_key)
base_url: str = Field(default=base_url)
model_name: str = Field(default=chat_model)
client: OpenAI = Field(default=None, exclude=True)# 显式声明 client 字段
def __init__(self, api_key: str, base_url: str, model_name: str = chat_model, **data: Any):
super().__init__(**data)
self.api_key = api_key
self.base_url = base_url
self.model_name = model_name
self.client = OpenAI(api_key=self.api_key, base_url=self.base_url)# 使用传入的api_key和base_url初始化 client 实例
@property
def metadata(self) -> LLMMetadata:
"""Get LLM metadata."""
return LLMMetadata(
model_name=self.model_name,
)
@llm_completion_callback()
def complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
response = self.client.chat.completions.create(model=self.model_name, messages=[{"role": "user", "content": prompt}])
if hasattr(response, 'choices') and len(response.choices) > 0:
response_text = response.choices.message.content
return CompletionResponse(text=response_text)
else:
raise Exception(f"Unexpected response format: {response}")
@llm_completion_callback()
def stream_complete(
self, prompt: str, **kwargs: Any
) -> Generator:
response = self.client.chat.completions.create(
model=self.model_name,
messages=[{"role": "user", "content": prompt}],
stream=True
)
try:
for chunk in response:
chunk_message = chunk.choices.delta
if not chunk_message.content:
continue
content = chunk_message.content
yield CompletionResponse(text=content, delta=content)
except Exception as e:
raise Exception(f"Unexpected response format: {e}")
llm = OurLLM(api_key=api_key, base_url=base_url, model_name=chat_model)
2.构建其需要利用的算数工具
import sys
import os
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))
from llama_index.core.agent import ReActAgent
from llama_index.core.tools import FunctionTool
def multiply(a: float, b: float) -> float:
"""Multiply two numbers and returns the product"""
return a * b
def add(a: float, b: float) -> float:
"""Add two numbers and returns the sum"""
return a + b
def main():
multiply_tool = FunctionTool.from_defaults(fn=multiply)
add_tool = FunctionTool.from_defaults(fn=add)
# 创建ReActAgent实例
agent = ReActAgent.from_tools(, llm=llm, verbose=True)
response = agent.chat("20+（2*4）等于多少？使用工具计算每一步")
print(response)3.完整代码
import os
from dotenv import load_dotenv
from openai import OpenAI
from pydantic import Field# 导入Field，用于Pydantic模型中定义字段的元数据
from llama_index.core.llms import (
CustomLLM,
CompletionResponse,
LLMMetadata,
)
from llama_index.core.embeddings import BaseEmbedding
from llama_index.core.llms.callbacks import llm_completion_callback
from typing import List, Any, Generator
# 加载环境变量
load_dotenv()
# 从环境变量中读取api_key
api_key = os.getenv('ZHIPU_API_KEY')
base_url = "https://open.bigmodel.cn/api/paas/v4"
chat_model = "glm-4-flash"
# 定义OurLLM类，继承自CustomLLM基类
class OurLLM(CustomLLM):
api_key: str = Field(default=api_key)
base_url: str = Field(default=base_url)
model_name: str = Field(default=chat_model)
client: OpenAI = Field(default=None, exclude=True)# 显式声明 client 字段
def __init__(self, api_key: str, base_url: str, model_name: str = chat_model, **data: Any):
super().__init__(**data)
self.api_key = api_key
self.base_url = base_url
self.model_name = model_name
self.client = OpenAI(api_key=self.api_key, base_url=self.base_url)# 使用传入的api_key和base_url初始化 client 实例
@property
def metadata(self) -> LLMMetadata:
"""Get LLM metadata."""
return LLMMetadata(
model_name=self.model_name,
)
@llm_completion_callback()
def complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
response = self.client.chat.completions.create(model=self.model_name, messages=[{"role": "user", "content": prompt}])
if hasattr(response, 'choices') and len(response.choices) > 0:
response_text = response.choices.message.content
return CompletionResponse(text=response_text)
else:
raise Exception(f"Unexpected response format: {response}")
@llm_completion_callback()
def stream_complete(
self, prompt: str, **kwargs: Any
) -> Generator:
response = self.client.chat.completions.create(
model=self.model_name,
messages=[{"role": "user", "content": prompt}],
stream=True
)
try:
for chunk in response:
chunk_message = chunk.choices.delta
if not chunk_message.content:
continue
content = chunk_message.content
yield CompletionResponse(text=content, delta=content)
except Exception as e:
raise Exception(f"Unexpected response format: {e}")
llm = OurLLM(api_key=api_key, base_url=base_url, model_name=chat_model)
import sys
import os
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))
from llama_index.core.agent import ReActAgent
from llama_index.core.tools import FunctionTool
def multiply(a: float, b: float) -> float:
"""Multiply two numbers and returns the product"""
return a * b
def add(a: float, b: float) -> float:
"""Add two numbers and returns the sum"""
return a + b
def main():
multiply_tool = FunctionTool.from_defaults(fn=multiply)
add_tool = FunctionTool.from_defaults(fn=add)
# 创建ReActAgent实例
agent = ReActAgent.from_tools(, llm=llm, verbose=True)
response = agent.chat("20+（2*4）等于多少？使用工具计算每一步")
print(response)if __name__ == "__main__": main()用智谱glm-4-flash运行效果
https://i-blog.csdnimg.cn/direct/9e5f4f36860a4f728a802edbeee6fd4d.png
GPT-4o运行效果
https://i-blog.csdnimg.cn/direct/f9dc346313da413dbf7fa296e837bb99.png
可以两个效果对比一下，好像也差不多，可能模子简单的情况下不易区分
二、多模子接入agent
借助Llama-index和OpenWeatherMap实现实时天气
OpenWeatherMap具体利用请参考OpenWeatherMap API指南-CSDN博客
1.构建大模子与（一、借助Llama-index实现agent）相同，这里着重说明后半部分
（首先需要去OpenWeatherMap官网注册申请“API密钥”：Members，并将密钥储存在.env文件中）
即OpenWeatherMap_API_KEY=
2.由于OpenWeatherMap是利用经度纬度获取天气，以是需要将地区转换为经纬度
def get_coordinates(city: str) -> dict:
api_key = os.getenv('OpenWeatherMap_API_KEY')
# OpenWeatherMap API endpoint for current weather
url = f"http://api.openweathermap.org/data/2.5/weather?q={city}&appid={api_key}"
try:
# Send a GET request to the OpenWeatherMap API
response = requests.get(url)
# Check if the response is successful (status code 200)
if respo
免责声明：如果侵犯了您的权益，请联系站长，我们会及时删除侵权内容，谢谢合作！更多信息从访问主页：qidao123.com:ToB企服之家，中国第一个企服评测及商务社交产业平台。
页:
[1]