LangChain: Add example using MCP

Author: amotl

.github/workflows/ml-llamaindex.yml

@@ -43,7 +43,12 @@ jobs:
           '3.8',
           '3.13',
         ]
-        cratedb-version: [ 'nightly' ]
+        cratedb-version: [
+          'nightly',
+        ]
+        cratedb-mcp-version: [
+          'pr-50',
+        ]
 
     services:
       cratedb:
@@ -53,6 +58,15 @@ jobs:
           - 5432:5432
         env:
           CRATE_HEAP_SIZE: 4g
+      cratedb-mcp:
+        image: ghcr.io/crate/cratedb-mcp:${{ matrix.cratedb-mcp-version }}
+        ports:
+          - 8000:8000
+        env:
+          CRATEDB_MCP_TRANSPORT: streamable-http
+          CRATEDB_MCP_HOST: 0.0.0.0
+          CRATEDB_MCP_PORT: 8000
+          CRATEDB_CLUSTER_URL: http://crate:crate@cratedb:4200/
 
     env:
       OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}

topic/machine-learning/llm-langchain/agent_with_mcp.py

@@ -0,0 +1,54 @@
+"""
+Exercise LangChain/LangGraph with the CrateDB MCP server.
+
+## Synopsis
+
+# Install prerequisites.
+pip install -U -r requirements.txt
+
+# Start database.
+docker run --rm -it --publish=4200:4200 crate/crate:nightly
+
+# Start MCP server.
+export CRATEDB_MCP_TRANSPORT=streamable-http
+export CRATEDB_MCP_HOST=0.0.0.0
+export CRATEDB_MCP_PORT=8000
+export CRATEDB_CLUSTER_URL=http://crate:crate@localhost:4200/
+docker run --rm -it --network=host --publish=8000:8000 ghcr.io/crate/cratedb-mcp:pr-50
+
+# Run program.
+export OPENAI_API_KEY=<your_openai_api_key>
+python agent_with_mcp.py
+"""
+import asyncio
+
+from langchain_mcp_adapters.client import MultiServerMCPClient
+from langgraph.prebuilt import create_react_agent
+
+
+async def amain():
+    client = MultiServerMCPClient(
+        {
+            "cratedb": {
+                "transport": "streamable_http",
+                "url": "http://localhost:8000/mcp/"
+            },
+        }
+    )
+    tools = await client.get_tools()
+    agent = create_react_agent("openai:gpt-4.1", tools)
+
+    QUERY_STR = "What is the average value for sensor 1?"
+    response = await agent.ainvoke({"messages": QUERY_STR})
+    answer = response["messages"][-1].content
+
+    print("Query was:", QUERY_STR)
+    print("Answer was:", answer)
+
+
+def main():
+    asyncio.run(amain())
+
+
+if __name__ == "__main__":
+    main()

topic/machine-learning/llm-langchain/requirements.txt

@@ -1,9 +1,11 @@
 crash
 google-cloud-aiplatform<2
 langchain-cratedb<0.1.2
+langchain-mcp-adapters<0.2
 langchain-google-vertexai<3
 langchain-openai<0.4
 langchain-text-splitters<0.4
+langgraph<0.6
 pueblo[cli,nlp]>=0.0.10
 pypdf<6
 python-dotenv<2

topic/machine-learning/llm-langchain/test.py

@@ -29,6 +29,14 @@ def reset_database(cratedb):
     time.sleep(0.01)
 
 
+@pytest.fixture(scope="function", autouse=True)
+def init_database(cratedb):
+    """
+    Initialize database.
+    """
+    cratedb.run_sql((HERE / "init.sql").read_text())
+
+
 def pytest_generate_tests(metafunc):
     """
     Generate pytest test case per Jupyter Notebook.