这份笔记提供了一个使用 Pinecone 作为向量数据库来存储 OpenAI 嵌入的分步指南。作为示例，它演示了如何将此设置与 Retool 集成以创建 REST 端点，从而实现与 ChatGPT 作为操作的无缝交互。但是，Retool 只是连接 Pinecone 数据库与 ChatGPT 的众多可用方法之一。

Pinecone 是一个完全托管的向量数据库，专为存储、索引和查询大规模向量嵌入而设计。它支持快速高效的相似性搜索，非常适合推荐系统、语义搜索和自然语言处理等人工智能驱动的应用程序。

Retool 是一个低代码平台，通过连接数据库、API 和第三方服务来简化自定义内部工具的构建。它使用户能够以最少的编码创建强大、用户友好的界面和工作流，非常适合简化业务运营和集成复杂系统。

先决条件

  • Pinecone 账户
  • Retool 账户
  • 启用了操作的自定义 GPT
  • OpenAI API 密钥

目录

  1. 设置 Pinecone
  2. 设置笔记
  3. 准备数据
  4. 创建 Pinecone 索引
  5. 填充 Pinecone 索引
  6. 创建 Retool 工作流
  7. 创建自定义 GPT 操作

设置 Pinecone

如果您还没有 Pinecone 账户，请注册一个账户。一旦您看到以下屏幕，您就可以继续进行下一部分。转到 API 密钥并创建一个新的 API 密钥。

Pinecone 中的向量

设置笔记

从 OpenAI 和 Pinecone 安装所需的库。

!pip install -qU openai pinecone

导入 OpenAI 和 Pinecone 库。

from pinecone.grpc import PineconeGRPC as Pinecone
from pinecone import ServerlessSpec
from openai import OpenAI
client = OpenAI() 

pc = Pinecone(api_key="YOUR API KEY")
## OpenAI 密钥默认设置为环境变量 `OPENAI_API_KEY`

准备数据

定义一个样本数据集，用于嵌入存储在 Pinecone 中，并用于从 ChatGPT 进行搜索。

data = [
    {"id": "vec1", "text": "OpenAI is a leading AI research organization focused on advancing artificial intelligence."},
    {"id": "vec2", "text": "The ChatGPT platform is renowned for its natural language processing capabilities."},
    {"id": "vec3", "text": "Many users leverage ChatGPT for tasks like creative writing, coding assistance, and customer support."},
    {"id": "vec4", "text": "OpenAI has revolutionized AI development with innovations like GPT-4 and its user-friendly APIs."},
    {"id": "vec5", "text": "ChatGPT makes AI-powered conversations accessible to millions, enhancing productivity and creativity."},
    {"id": "vec6", "text": "OpenAI was founded in December 2015 as an organization dedicated to advancing digital intelligence for the benefit of humanity."}
]

我们现在准备将文本转换为嵌入。下面的示例是此函数最简单的实现。如果您的文本长度超过了您正在使用的模型的上下文窗口，您需要将文本分块成更小的部分。

def embed(text):
    text = text.replace("\n", " ")  # 确保文本不包含换行符
    res = client.embeddings.create(input=[text], model="text-embedding-3-large")

    return res.data[0].embedding

doc_embeds = [embed(d["text"]) for d in data]

print(doc_embeds)

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