LLM之LangChain（五）| 使用LangChain Agent分析非结构化数据

       想象一下，你有一家面包店，你派出了甜食商情报团队来收集竞争对手的数据。他们会汇报竞争情况，他们有很多很棒的想法，你想把它们应用到你的业务中。然而，数据是非结构化的！您如何分析这些数据，以了解最需要什么，并为您业务的下一步计划做出最佳的策略？在第1部分中，我们使用“PydanticOutputParser”来分析我们的数据并添加所需的结构。在第2部分中，我们将创建一个LangChain Agent来进行数据分析。

       为了探索这个用例，创建了一个玩具数据集[1]。以下是数据集中的一个示例样本：

At Velvet Frosting Cupcakes, our team learned about the unveiling of a seasonal pastry menu that changes monthly. Introducing a rotating seasonal menu at our bakery using the “SeasonalJoy” subscription platform and adding a special touch to our cookies with the “FloralStamp” cookie stamper could keep our offerings fresh and exciting for customers.

第一部分：从非结构化数据抽取结构化信息

方法一：create_extract_chain

      定义数据抽取的结构，并且使用LangChain创建一个提取链。

from langchain.chains import create_extraction_chainfrom langchain.chat_models import ChatOpenAI​# Schemaschema = {
      "properties": {
          "company": {"type": "string"},        "offering": {"type": "string"},        "advantage": {"type": "string"},        "products_and_services": {"type": "string"},        "additional_details": {"type": "string"},    }}

        定义测试样本

# Inputsin1 = """Sweet Delights Bakery introduced lavender-infused vanilla cupcakes with a honey buttercream frosting, using the "Frosting-Spreader-3000". This innovation could inspire our next cupcake creation"""in2 = """Whisked Away Cupcakes introduced a dessert subscription service, ensuring regular customers receive fresh batches of various sweets. Exploring a similar subscription model using the "SweetSubs" program could boost customer loyalty."""in3 = """At Velvet Frosting Cupcakes, our team learned about the unveiling of a seasonal pastry menu that changes monthly. Introducing a rotating seasonal menu at our bakery using the "SeasonalJoy" subscription platform and adding a special touch to our cookies with the "FloralStamp" cookie stamper could keep our offerings fresh and exciting for customers."""​inputs = [in1, in2