> ## 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.
# ClickHouse MCP 서버를 사용하여 LangChain/LangGraph AI 에이전트를 구축하는 방법
> ClickHouse MCP 서버를 사용해 ClickHouse SQL playground와 상호작용할 수 있는 LangChain/LangGraph AI 에이전트를 구축하는 방법을 알아봅니다.
이 가이드에서는 [LangChain/LangGraph](https://github.com/langchain-ai/langgraph) AI 에이전트를 구축하여
[ClickHouse SQL playground](https://sql.clickhouse.com/)와 [ClickHouse MCP 서버](https://github.com/ClickHouse/mcp-clickhouse)로 상호작용하는 방법을 알아봅니다.
**예시 노트북**
이 예시는 [examples 리포지토리](https://github.com/ClickHouse/examples/blob/main/ai/mcp/langchain/langchain.ipynb)에서 노트북 형태로 확인할 수 있습니다.
## 사전 요구 사항
* 시스템에 Python이 설치되어 있어야 합니다.
* 시스템에 `pip`가 설치되어 있어야 합니다.
* Anthropic API Key 또는 다른 LLM 제공업체의 API Key가 필요합니다.
다음 단계는 Python REPL 또는 스크립트에서 실행할 수 있습니다.
다음 명령어를 실행하여 필요한 라이브러리를 설치합니다:
```python theme={null}
pip install -q --upgrade pip
pip install -q langchain-mcp-adapters langgraph "langchain[anthropic]"
```
다음으로 Anthropic API Key를 입력해야 합니다:
```python theme={null}
import os, getpass
os.environ["ANTHROPIC_API_KEY"] = getpass.getpass("Enter Anthropic API Key:")
```
```response title="Response" theme={null}
Enter Anthropic API Key: ········
```
**다른 LLM 제공업체 사용**
Anthropic API Key가 없고 다른 LLM 제공업체를 사용하려는 경우,
[Langchain Providers docs](https://python.langchain.com/docs/integrations/providers/)에서 자격 증명 설정 방법을 확인할 수 있습니다.
이제 ClickHouse MCP 서버가 ClickHouse SQL playground를 가리키도록 설정합니다:
```python theme={null}
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
server_params = StdioServerParameters(
command="uv",
args=[
"run",
"--with", "mcp-clickhouse",
"--python", "3.13",
"mcp-clickhouse"
],
env={
"CLICKHOUSE_HOST": "sql-clickhouse.clickhouse.com",
"CLICKHOUSE_PORT": "8443",
"CLICKHOUSE_USER": "demo",
"CLICKHOUSE_PASSWORD": "",
"CLICKHOUSE_SECURE": "true"
}
)
```
Langchain과 ClickHouse MCP 서버를 사용할 때 쿼리 결과는 단일 응답이 아니라 스트리밍 방식의 데이터로 반환되는 경우가 많습니다. 대용량 데이터셋이나 처리에 시간이 걸릴 수 있는 복잡한 분석 쿼리에서는 스트림 핸들러를 구성하는 것이 중요합니다. 이를 적절히 처리하지 않으면 애플리케이션에서 이 스트리밍 출력을 다루기 어려울 수 있습니다.
스트리밍 출력을 애플리케이션에서 더 쉽게 활용할 수 있도록 핸들러를 구성하십시오:
```python theme={null}
class UltraCleanStreamHandler:
def __init__(self):
self.buffer = ""
self.in_text_generation = False
self.last_was_tool = False
def handle_chunk(self, chunk):
event = chunk.get("event", "")
if event == "on_chat_model_stream":
data = chunk.get("data", {})
chunk_data = data.get("chunk", {})
# Only handle actual text content, skip tool invocation streams
if hasattr(chunk_data, 'content'):
content = chunk_data.content
if isinstance(content, str) and not content.startswith('{"'):
# Add space after tool completion if needed
if self.last_was_tool:
print(" ", end="", flush=True)
self.last_was_tool = False
print(content, end="", flush=True)
self.in_text_generation = True
elif isinstance(content, list):
for item in content:
if (isinstance(item, dict) and
item.get('type') == 'text' and
'partial_json' not in str(item)):
text = item.get('text', '')
if text and not text.startswith('{"'):
# Add space after tool completion if needed
if self.last_was_tool:
print(" ", end="", flush=True)
self.last_was_tool = False
print(text, end="", flush=True)
self.in_text_generation = True
elif event == "on_tool_start":
if self.in_text_generation:
print(f"\n🔧 {chunk.get('name', 'tool')}", end="", flush=True)
self.in_text_generation = False
elif event == "on_tool_end":
print(" ✅", end="", flush=True)
self.last_was_tool = True
```
마지막으로, 에이전트를 호출해 ClickHouse에 가장 많은 코드를 커밋한 사람이 누구인지 물어보세요:
```python theme={null}
async with stdio_client(server_params) as (read, write):
async with ClientSession(read, write) as session:
await session.initialize()
tools = await load_mcp_tools(session)
agent = create_react_agent("anthropic:claude-sonnet-4-0", tools)
handler = UltraCleanStreamHandler()
async for chunk in agent.astream_events(
{"messages": [{"role": "user", "content": "Who's committed the most code to ClickHouse?"}]},
version="v1"
):
handler.handle_chunk(chunk)
print("\n")
```
다음과 유사한 응답이 표시됩니다:
```response title="Response" theme={null}
I'll help you find who has committed the most code to ClickHouse by exploring the available databases and tables to locate git commit data.
🔧 list_databases ✅ I can see there's a `git` database which likely contains git commit information. Let me explore the tables in that database:
🔧 list_tables ✅ Perfect! I can see the `clickhouse_commits` table in the git database contains ClickHouse commit data with 80,644 commits. This table has information about each commit including the author, lines added/deleted, files modified, etc. Let me query this table to find who has committed the most code based on different metrics.
🔧 run_select_query ✅ Let me also look at just the lines added to see who has contributed the most new code:
🔧 run_select_query ✅ Based on the ClickHouse git commit data, **Alexey Milovidov** has committed the most code to ClickHouse by several measures:
## Key Statistics:
1. **Most Total Lines Changed**: Alexey Milovidov with **1,696,929 total lines changed** (853,049 added + 843,880 deleted)
2. **Most Lines Added**: Alexey Milovidov with **853,049 lines added**
3. **Most Commits**: Alexey Milovidov with **15,375 commits**
4. **Most Files Changed**: Alexey Milovidov with **73,529 files changed**
## Top Contributors by Lines Added:
1. **Alexey Milovidov**: 853,049 lines added (15,375 commits)
2. **s-kat**: 541,609 lines added (50 commits)
3. **Nikolai Kochetov**: 219,020 lines added (4,218 commits)
4. **alesapin**: 193,566 lines added (4,783 commits)
5. **Vitaly Baranov**: 168,807 lines added (1,152 commits)
Alexey Milovidov is clearly the most prolific contributor to ClickHouse, which makes sense as he is one of the original creators and lead developers of the project. His contribution dwarfs others both in terms of total code volume and number of commits, with nearly 16,000 commits and over 850,000 lines of code added to the project.
```