كيفية إنشاء وكيل ذكاء اصطناعي باستخدام DSPy وخادم ClickHouse MCP

في هذا الدليل، ستتعلّم كيفية إنشاء وكيل ذكاء اصطناعي باستخدام DSPy يمكنه التفاعل مع بيئة SQL التفاعلية الخاصة بـ ClickHouse باستخدام خادم ClickHouse MCP.

المتطلبات الأساسية

ستحتاج إلى تثبيت بايثون على نظامك.
ستحتاج إلى تثبيت pip على نظامك.
ستحتاج إلى مفتاح API من Anthropic، أو مفتاح API من موفّر LLM آخر

يمكنك تنفيذ الخطوات التالية إمّا من بايثون REPL أو عبر برنامج نصي.

دفتر ملاحظات مثاليمكن العثور على هذا المثال على هيئة دفتر ملاحظات في مستودع الأمثلة.

تثبيت المكتبات

نفّذ الأوامر التالية باستخدام pip لتثبيت المكتبات المطلوبة:

pip install -q --upgrade pip
pip install -q dspy
pip install -q mcp

إعداد بيانات الاعتماد

بعد ذلك، ستحتاج إلى إدخال مفتاح واجهة برمجة تطبيقات Anthropic الخاص بك:

import os
os.environ["ANTHROPIC_API_KEY"] = getpass.getpass("Enter Anthropic API Key:")

استخدام موفّر LLM آخرإذا لم يكن لديك مفتاح واجهة برمجة تطبيقات Anthropic، وكنت تريد استخدام موفّر LLM آخر، فيمكنك العثور على تعليمات إعداد بيانات الاعتماد في توثيق DSPy

بعد ذلك، حدِّد بيانات الاعتماد اللازمة للاتصال بـ ClickHouse SQL playground:

env = {
    "CLICKHOUSE_HOST": "sql-clickhouse.clickhouse.com",
    "CLICKHOUSE_PORT": "8443",
    "CLICKHOUSE_USER": "demo",
    "CLICKHOUSE_PASSWORD": "",
    "CLICKHOUSE_SECURE": "true"
}

تهيئة خادم MCP

الآن اضبط خادم ClickHouse MCP ليشير إلى ClickHouse SQL playground.

from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
import dspy

server_params = StdioServerParameters(
    command="uv",
    args=[
        'run',
        '--with', 'mcp-clickhouse',
        '--python', '3.13',
        'mcp-clickhouse'
    ],
    env=env
)

تهيئة LLM

بعد ذلك، قم بتهيئة LLM باستخدام السطر التالي:

dspy.configure(lm=dspy.LM("anthropic/claude-sonnet-4-20250514"))

تشغيل الوكيل

أخيرًا، قم بتهيئة الوكيل وتشغيله:

class DataAnalyst(dspy.Signature):
    """You are a data analyst. You'll be asked questions and you need to try to answer them using the tools you have access to. """

    user_request: str = dspy.InputField()
    process_result: str = dspy.OutputField(
        desc=(
            "Answer to the query"
        )
    )

from utils import print_dspy_result

async with stdio_client(server_params) as (read, write):
    async with ClientSession(read, write) as session:
        await session.initialize()
        tools = await session.list_tools()

        dspy_tools = []
        for tool in tools.tools:
            dspy_tools.append(dspy.Tool.from_mcp_tool(session, tool))

        react = dspy.ReAct(DataAnalyst, tools=dspy_tools)
        result = await react.acall(user_request="What's the most popular Amazon product category")
        print_dspy_result(result)

Response

================================================================================
🤖 DSPy ReAct Result
================================================================================

📍 STEP 1
----------------------------------------
🧠 THINKING: I need to find information about Amazon product categories and determine which one is most popular. First, I should explore what databases are available to see if there's any Amazon-related data.

🔧 TOOL: list_databases

📊 RESULT:
   amazon
bluesky
country
covid
default
dns
environmental
food
forex
geo
git
github
hackernews
imdb
logs
metrica
mgbench
mta
noaa
nyc_taxi
nypd
ontime
opensky
otel
otel_v2
pypi
random
reddit
rubygems
sta...

📍 STEP 2
----------------------------------------
🧠 THINKING: Great! I can see there's an "amazon" database available. This is exactly what I need to find information about Amazon product categories. Let me explore the tables in the amazon database to see what data is available.

🔧 TOOL: list_tables
   Args: {'database': 'amazon'}

📊 RESULT:
   {
  "database": "amazon",
  "name": "amazon_reviews",
  "comment": "",
  "columns": [
    {
      "name": "review_date",
      "type": "Date",
      "default_type": "",
      "default_expression": "",...

📍 STEP 3
----------------------------------------
🧠 THINKING: Perfect! I found the amazon_reviews table which contains a product_category column. This table has over 150 million rows of Amazon review data, which should give me a good representation of product popularity. To find the most popular Amazon product category, I need to count the number of reviews per category and order them by count in descending order. The category with the most reviews would be considered the most popular.

🔧 TOOL: run_select_query
   Args: {'query': 'SELECT product_category, COUNT(*) as review_count FROM amazon.amazon_reviews GROUP BY product_category ORDER BY review_count DESC LIMIT 10'}

📊 RESULT:
   1. Books: 19,530,930 reviews
   2. Digital_Ebook_Purchase: 17,622,107 reviews
   3. Wireless: 9,001,567 reviews
   4. PC: 6,908,245 reviews
   5. Home: 6,221,321 reviews
   ... and 5 more

📍 STEP 4
----------------------------------------
🧠 THINKING: Perfect! I have found the answer to the user's question. Based on the Amazon reviews data, I can clearly see that "Books" is the most popular Amazon product category with 19,530,930 reviews, followed by "Digital_Ebook_Purchase" with 17,622,107 reviews. The data shows a clear ranking of product categories by popularity based on the number of reviews each category has received. I now have all the information needed to answer the user's question.

🔧 TOOL: finish

📊 RESULT:
   Completed.

🎯 REASONING
----------------------------------------
To answer the question about the most popular Amazon product category, I accessed the Amazon database and found a table called "amazon_reviews" with over 150 million review records. This table contains a "product_category" column that allows me to analyze product popularity based on review volume. I ran a query to count the number of reviews per product category and ordered them by count in descending order. The assumption here is that categories with more reviews indicate higher popularity, as more reviews typically correlate with more purchases and customer engagement.

✅ FINAL RESULT
----------------------------------------
Based on Amazon review data containing over 150 million records, the most popular Amazon product category is **Books** with 19,530,930 reviews. 

Here are the top 10 most popular Amazon product categories by review count:

1. **Books** - 19,530,930 reviews
2. **Digital_Ebook_Purchase** - 17,622,107 reviews  
3. **Wireless** - 9,001,567 reviews
4. **PC** - 6,908,245 reviews
5. **Home** - 6,221,321 reviews
6. **Apparel** - 5,906,085 reviews
7. **Health & Personal Care** - 5,331,239 reviews
8. **Beauty** - 5,115,462 reviews
9. **Video DVD** - 5,069,014 reviews
10. **Mobile_Apps** - 5,033,164 reviews

It's interesting to note that Books and Digital Ebook Purchase (which are related categories) together account for over 37 million reviews, showing the strong popularity of reading materials on Amazon's platform.
================================================================================

أدلة ClickHouse

أدلة حالات الاستخدام

كيفية إنشاء وكيل ذكاء اصطناعي باستخدام DSPy وخادم ClickHouse MCP

المتطلبات الأساسية

​المتطلبات الأساسية

المتطلبات الأساسية