Many businesses are looking to implement AI chatbots to streamline their operations and provide quick answers to common queries. 許多企業希望實施 AI 聊天機器人，以簡化其運營並快速回答常見問題。 A common request is to analyze sales data, such as determining how much was sold in a specific timeframe. 一個常見的請求是分析銷售數據，例如確定在特定時間範圍內售出了多少。 However, traditional vector database solutions often fall short for numerical queries, as they are designed for string comparisons rather than structured data analysis. 然而，傳統的向量數據庫解決方案在數值查詢方面往往不夠理想，因為它們是為字符串比較而設計的，而不是為結構化數據分析。
Vector databases excel at finding similar documents based on vectorized queries but struggle with numerical data. 向量數據庫擅長根據向量化查詢查找相似文檔，但在處理數據時卻面臨困難。 For instance, while a question like 'What is the PR of X?' may yield results, asking for sales figures lacks the necessary context for the AI to provide accurate answers. 例如，像“X 的 PR 是多少？”這樣的問題可能會產生結果，但詢問銷售數字卻缺乏 AI 提供準確答案所需的上下文。 Additionally, sending large datasets to a language model (LM) is impractical due to context window limits and cost inefficiencies. 此外，由於上下文窗口限制和成本低效，將大型數據集發送到語言模型（LM）是不可行的。
To effectively address the client's needs, a more traditional approach using SQL is recommended. 為了有效滿足客戶的需求，建議使用更傳統的 SQL 方法。 This method does not require extensive SQL knowledge and can be implemented for free. 這種方法不需要廣泛的 SQL 知識，並且可以免費實施。 The workflow involves reading data from Google Sheets, creating a PostgreSQL table, and inserting the data into that table, allowing for efficient querying. 該工作流程涉及從 Google Sheets 讀取數據，創建 PostgreSQL 表，並將數據插入該表中，從而實現高效查詢。
The proposed workflow begins by connecting to Google Sheets and fetching the data. 所提議的工作流程首先通過連接到 Google Sheets 並提取數據開始。 Once the data is retrieved, it is transformed into SQL queries that can be executed within the PostgreSQL database. 一旦數據被檢索，它將轉換為可以在 PostgreSQL 數據庫中執行的 SQL 查詢。 This process ensures that the data is structured correctly, enabling the AI to generate accurate responses to queries about sales figures. 這個過程確保數據正確結構化，使 AI 能夠生成對銷售數字查詢的準確回應。
When working with large datasets, it is crucial to optimize how data is sent to the AI. 在處理大型數據集時，優化數據發送到 AI 的方式至關重要。 Instead of overwhelming the LM with excessive data, it is more effective to filter the data based on specific criteria. 與其用過多數據淹沒 LM，不如根據特定標準過濾數據更為有效。 This approach allows the AI to focus on relevant information, improving response accuracy and reducing costs. 這種方法使 AI 能夠專注於相關信息，提高回應的準確性並降低成本。
After setting up the PostgreSQL database, the next step is to ensure that the table is created correctly. 在設置 PostgreSQL 數據庫後，下一步是確保表格正確創建。 The workflow includes a trigger that checks if the table exists and fetches data from Google Sheets accordingly. 該工作流程包括一個觸發器，用於檢查表格是否存在並相應地從 Google Sheets 獲取數據。 This ensures that the database is always up-to-date with the latest information from the spreadsheet. 這確保數據庫始終與電子表格中的最新信息保持同步。
Once the data is in the PostgreSQL database, the AI can be prompted to answer specific questions. 一旦數據進入 PostgreSQL 數據庫，AI 就可以被提示回答特定問題。 For example, asking how much was made during a particular period can yield precise results. 例如，詢問在特定期間內賺了多少可以產生精確的結果。 The AI must first understand the schema of the table to generate the correct SQL queries, which can be achieved through a structured prompting process. AI 必須首先理解表格的架構，以生成正確的 SQL 查詢，這可以通過結構化的提示過程來實現。
While the current setup provides a functional solution, there is always room for improvement. 雖然當前的設置提供了一個功能性解決方案，但總有改進的空間。 Future enhancements may include refining the AI's ability to understand and execute queries more efficiently. 未來的增強可能包括改善 AI 理解和執行查詢的能力。 By continuously testing and updating the workflow, users can ensure that the AI remains effective in answering complex queries. 通過不斷測試和更新工作流程，用戶可以確保 AI 在回答複雜查詢時保持有效。
In summary, leveraging SQL and a structured workflow can significantly enhance the ability of an AI chatbot to answer numerical queries related to sales data. 總之，利用 SQL 和結構化工作流程可以顯著提高 AI 聊天機器人回答與銷售數據相關的數值查詢的能力。 By focusing on efficient data retrieval and processing, businesses can improve their operational efficiency and provide better service to their clients. 通過專注於高效的數據檢索和處理，企業可以提高其運營效率，並為客戶提供更好的服務。
Q: Why do businesses need AI chatbots?
問：為什麼企業需要 AI 聊天機器人？
A: Businesses implement AI chatbots to streamline operations and provide quick answers to common queries, such as analyzing sales data.
答：企業實施 AI 聊天機器人以簡化運營並快速回答常見問題，例如分析銷售數據。
Q: What are the limitations of vector databases?
問：向量數據庫的局限性是什麼？
A: Vector databases excel at finding similar documents but struggle with numerical data, making them less effective for queries like sales figures.
答：向量數據庫擅長查找相似文檔，但在處理數據時卻面臨困難，這使得它們在查詢銷售數字等方面的效果較差。
Q: Why is SQL recommended for data analysis?
問：為什麼推薦使用 SQL 進行數據分析？
A: SQL is recommended because it allows for efficient querying of structured data without requiring extensive SQL knowledge and can be implemented for free.
答：推薦使用 SQL，因為它允許高效查詢結構化數據，而不需要廣泛的 SQL 知識，並且可以免費實施。
Q: How does the workflow for implementing SQL with Google Sheets work?
問：實施 SQL 與 Google Sheets 的工作流程是如何運作的？
A: The workflow involves connecting to Google Sheets, fetching data, transforming it into SQL queries, and executing those queries within a PostgreSQL database.
答：該工作流程涉及連接到 Google Sheets、提取數據、將其轉換為 SQL 查詢，並在 PostgreSQL 數據庫中執行這些查詢。
Q: What is the importance of optimizing data retrieval?
問：優化數據檢索的重要性是什麼？
A: Optimizing data retrieval is crucial to avoid overwhelming the AI with excessive data, allowing it to focus on relevant information for improved accuracy and cost efficiency.
答：優化數據檢索至關重要，以避免用過多數據淹沒 AI，使其能夠專注於相關信息，以提高準確性和成本效率。
Q: How is a PostgreSQL database created and maintained?
問：如何創建和維護 PostgreSQL 數據庫？
A: A PostgreSQL database is created by ensuring the table exists and fetching data from Google Sheets, keeping the database updated with the latest information.
答：通過確保表格存在並從 Google Sheets 獲取數據來創建 PostgreSQL 數據庫，保持數據庫與最新信息的同步。
Q: How can AI be used to query data in PostgreSQL?
問：如何使用 AI 查詢 PostgreSQL 中的數據？
A: Once data is in PostgreSQL, AI can be prompted to answer specific questions, provided it understands the table schema to generate correct SQL queries.
答：一旦數據進入 PostgreSQL，AI 可以被提示回答特定問題，前提是它理解表格架構以生成正確的 SQL 查詢。
Q: What are some ways to improve the AI's performance?
問：有哪些方法可以改善 AI 的性能？
A: Improving the AI's performance can involve refining its ability to understand and execute queries more efficiently through continuous testing and updates.
答：改善 AI 的性能可以通過不斷測試和更新來提高其理解和執行查詢的能力。
Q: What is the conclusion regarding the use of SQL and AI chatbots?
問：關於使用 SQL 和 AI 聊天機器人的結論是什麼？
A: Leveraging SQL and a structured workflow can significantly enhance an AI chatbot's ability to answer numerical queries, improving operational efficiency and client service.
答：利用 SQL 和結構化工作流程可以顯著提高 AI 聊天機器人回答數值查詢的能力，從而改善運營效率和客戶服務。
