用示例提升大语言模型的查询分析能力！

引言

随着查询分析的复杂性增加，大语言模型（LLM）在理解如何响应某些场景时可能会面临挑战。为了提高性能，我们可以在提示中添加示例，以便更好地引导模型。本文将详细介绍如何为我们在Quickstart中构建的LangChain YouTube视频查询分析器添加示例，以优化其响应准确性。

主要内容

设置环境

安装依赖项

我们需要安装langchain-core和langchain-openai库。

# %pip install -qU langchain-core langchain-openai

设置环境变量

我们将使用OpenAI，你需要配置API密钥。

import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass()
# 使用API代理服务提高访问稳定性，可以根据需要配置API代理

查询架构定义

我们需要定义一个查询架构，让模型输出更结构化的数据。我们将增加一个sub_queries字段来包含从顶级问题中派生出的更具体的问题。

from typing import List, Optional
from langchain_core.pydantic_v1 import BaseModel, Field

sub_queries_description = """\
If the original question contains multiple distinct sub-questions, \
or if there are more generic questions that would be helpful to answer in \
order to answer the original question, write a list of all relevant sub-questions. \
Make sure this list is comprehensive and covers all parts of the original question. \
It's ok if there's redundancy in the sub-questions. \
Make sure the sub-questions are as narrowly focused as possible."""

class Search(BaseModel):
    """Search over a database of tutorial videos about a software library."""
    query: str = Field(
        ...,
        description="Primary similarity search query applied to video transcripts.",
    )
    sub_queries: List[str] = Field(
        default_factory=list, description=sub_queries_description
    )
    publish_year: Optional