> ## Documentation Index
> Fetch the complete documentation index at: https://clickhouse.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Como criar um agente de IA com DSPy e o ClickHouse MCP server
> Aprenda a criar um agente de IA com DSPy e o ClickHouse MCP server
Neste guia, você aprenderá a criar um agente de IA com [DSPy](https://github.com/langchain-ai/langgraph) que
pode interagir com o [SQL playground do ClickHouse](https://sql.clickhouse.com/) usando o [ClickHouse MCP server](https://github.com/ClickHouse/mcp-clickhouse).
## Pré-requisitos
* Você precisará ter o Python instalado no sistema.
* Você precisará ter o `pip` instalado no sistema.
* Você precisará de uma API key da Anthropic ou de outro provedor de LLM
Você pode executar as etapas a seguir no REPL do Python ou por meio de um script.
**Notebook de exemplo**
Este exemplo pode ser encontrado como um notebook no [repositório de exemplos](https://github.com/ClickHouse/examples/blob/main/ai/mcp/dspy/dspy.ipynb).
Execute os comandos a seguir com o `pip` para instalar as bibliotecas necessárias:
```shell theme={null}
pip install -q --upgrade pip
pip install -q dspy
pip install -q mcp
```
Em seguida, você precisará fornecer sua chave de API da Anthropic:
```python theme={null}
import os
os.environ["ANTHROPIC_API_KEY"] = getpass.getpass("Enter Anthropic API Key:")
```
**Usando outro provedor de LLM**
Se você não tiver uma chave de API da Anthropic e quiser usar outro provedor de LLM,
consulte as instruções para configurar suas credenciais na [documentação do DSPy](https://dspy.ai/#__tabbed_1_1)
Em seguida, defina as credenciais necessárias para se conectar ao playground de SQL do ClickHouse:
```python theme={null}
env = {
"CLICKHOUSE_HOST": "sql-clickhouse.clickhouse.com",
"CLICKHOUSE_PORT": "8443",
"CLICKHOUSE_USER": "demo",
"CLICKHOUSE_PASSWORD": "",
"CLICKHOUSE_SECURE": "true"
}
```
Agora, configure o servidor MCP do ClickHouse para apontar para o Playground SQL do ClickHouse.
```python theme={null}
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
)
```
Em seguida, inicialize o LLM com a linha a seguir:
```python theme={null}
dspy.configure(lm=dspy.LM("anthropic/claude-sonnet-4-20250514"))
```
Por fim, inicialize e execute o agente:
```python theme={null}
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 title="Response" theme={null}
================================================================================
🤖 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.
================================================================================
```