使用 Zhparser 來解決 PostgreSQL 中文全文檢索

# 先查看一下你要前往 postgres 的 container 是哪一個, 記下 id
docker ps -a

# 進入 container 並且停在 bash 的 shell(環境)畫面中
# 請將下方 container_id 換成你環境中的目標的 id
docker exec -it container_id bash

## 接下來要來安裝 Zhparser, 但是我們要先下載來編譯一下喔
# 首先, 確定一下你的 postgres 版本, 先登入到 postgres 裡面
# 登入 postgres, 帳號如果你是依照官方的文件安裝, 那應該是 docmost
# 或是你自己設定的其他帳號
psql -U docmost

# 輸入以下指令
SELECT VERSION();

# 板主這邊看到的是
 PostgreSQL 18.3 (Debian 18.3-1.pgdg13+1) on x86_64-pc-linux-gnu, compiled by gcc (Debian 14.2.0-19) 14.2.0, 64-bit

# 確認完版本之後, 請輸入 exit 跳出, 回到 bash 畫面

# 那麼我們就可以透過以下指令準備好相關的環境
apt update -y && apt install wget gcc make git bzip2 postgresql-server-dev-18 -y

# 接下來我們要下載 Zhparser, 版本的部分你可以查閱下方網址
https://www.xunsearch.com/scws/download.php

# 確認好版本之下就來進行以下步驟
cd /tmp
wget http://www.xunsearch.com/scws/down/scws-1.2.3.tar.bz2
tar -jxvf scws-1.2.3.tar.bz2 
cd scws-1.2.3
./configure && make && make install

cd ..
git clone https://github.com/amutu/zhparser.git
cd zhparser/
make && make install

# 完成之後, 請登入 postgres
psql -U docmost

# 輸入以下指令
CREATE EXTENSION zhparser;
CREATE TEXT SEARCH CONFIGURATION chinese (PARSER = zhparser);

# 完成後請退出
exit

# 接下來你可以回到 docmost 試試看中文搜尋是否可以作用了

環境設定

首先你當然是會先有 python 的開發與執行環境對吧!! 好比說筆者這邊用的就是 PyCharm, 當然有些人會選擇 jupyter, 就看你自己習慣囉!

如果是 IDE, 你就可以再輸入 source code 時, 依依帶入所需要的 library, 如果是純文字模式, 那你可能就需要先安裝環境套見到你的電腦中.

# jupyter
!pip install yfinance

# terminal mode
pip install yfinance
# or, maybe 如果你的環境中有 python2 與 3 的時候 就會分 pip 與 pip3 兩種指令
pip3 install yfinance

# 其實我們還需要另外兩個 library
# numpy 與 pandas, 只是通常我們的環境內都有, 所以沒有特別提出來

首先我們先小試一下抓取資料

以下就是我們試著抓取美國 Tesla 股價的一小段程式碼

import numpy as np
import pandas as pd

import yfinance as yf

my_data = yf.download('TSLA', start='2025-01-02', end='2025-07-02')
print(my_data.info)

以上存檔執行後你就會看到類似以下畫面的結果, 當然這邊僅顯示一部分而已, 這僅是示意圖

            Close.      High        Low         Open        Volume
Date                                                                 
2025-01-02  379.279999  392.730011  373.040009  390.100006  109710700
2025-01-03  410.440002  411.880005  379.450012  381.480011   95423300
2025-01-06  411.049988  426.429993  401.700012  423.200012   85516500
2025-01-07  394.359985  414.329987  390.000000  405.829987   75699500
2025-01-08  394.940002  402.500000  387.399994  392.950012   73038800
...                ...         ...         ...         ...        ...
2025-06-25  327.549988  343.000000  320.399994  342.700012  119845100
2025-06-26  325.779999  331.049988  323.609985  324.609985   80440900
2025-06-27  323.630005  329.339996  317.500000  324.510010   89067000
2025-06-30  317.660004  325.579987  316.600006  319.899994   76695100
2025-07-01  300.709991  305.890015  293.209991  298.459991  145085700

[123 rows x 5 columns]>

這邊的程式碼有一段關鍵, 那就是 yf.download(‘TSLA’, start=’2025-01-02′, end=’2025-07-02′)
TSLA 這是股價代號, 這個代號要怎麼查呢? 各位可以到 yahoo finance 的網站去查看, 搜尋到你要抓取的股票資訊後, 你可以查看網址列就會發現他的代號是多少了, 未來我再準備一篇查詢教學 building !!
那麼後面那兩個參數就是一個是開始日期, 一個是結束日期. 應該很容易理解.

以下就是執行出來的結果

最後我將 source code 列出如下, 歡迎大家拿去改喔!!!

# For data manipulation
from datetime import datetime

import numpy as np
import pandas as pd

# To fetch financial data
import yfinance as yf

# For visualisation
import matplotlib.pyplot as plt
#print(plt.style.available)
#exit(0)
#plt.style.use('seaborn-darkgrid')
#plt.style.use('seaborn-v0_8-darkgrid')

# Set the ticker as 'EURUSD=X'
endToday = datetime.today().strftime('%Y-%m-%d')
#forex_data = yf.download('EURUSD=X', start='2025-01-02', end='2021-12-31')
#forex_data = yf.download('EURUSD=X', start='2025-01-02', end=endToday)
forex_data = yf.download('TSLA', start='2025-01-02', end=endToday)
#forex_data = yf.download('TSLA', start='2025-01-02', end='2025-07-02')


print(forex_data.info)
#exit(0)
# Set the index to a datetime object
forex_data.index = pd.to_datetime(forex_data.index)

# Display the last five rows
#forex_data.tail()

# Plot the close price
plt.figure(figsize=(15, 7))
#forex_data['Adj Close'].plot()

# 就是這一行讓繪圖先出現, 之後再呼叫 show 就出現一個空白的
#forex_data['Close'].plot()
plt.plot(forex_data['Close'])

# Set the title and axis label
plt.title('Tesla Data', fontsize=16)
plt.xlabel('Year-Month', fontsize=12)
plt.ylabel('Price', fontsize=12)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
plt.legend(['Close'], prop={'size': 12})

# Show the plot
plt.show()

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