silencing some log messages for examples (generative-computing#181)

add MCP example back in own folder and explain how to use in Langflow 

Co-authored-by: jakelorocco <[email protected]>

Author: 2 people

docs/examples/agents/react.py

@@ -13,8 +13,12 @@
 import mellea.stdlib
 import mellea.stdlib.base
 import mellea.stdlib.chat
+from mellea.backends import model_ids
+from mellea.helpers.fancy_logger import FancyLogger
 from mellea.stdlib.base import ChatContext
 
+FancyLogger.get_logger().setLevel("ERROR")
+
 react_system_template: Template = Template(
     """Answer the user's question as best you can.
 

docs/examples/mcp/README.md

@@ -0,0 +1,46 @@
+# Write a poem MCP
+This is a simple example to show how to write a MCP tool 
+with Mellea and instruct-validate-repair. Being able to 
+speak the tool language allows you to integrate with
+Claude Desktop, Langflow, ...
+
+See code in [mcp_example.py](mcp_example.py)
+
+## Run the example
+You need to install the mcp package:
+```bash
+uv pip install "mcp[cli]"
+```
+
+and run the example in MCP debug UI:
+```bash
+uv run mcp dev docs/examples/tutorial/mcp_example.py
+```
+
+
+## Use in Langflow
+Follow this path (JSON) to use it in Langflow: [https://docs.langflow.org/mcp-client#mcp-stdio-mode](https://docs.langflow.org/mcp-client#mcp-stdio-mode)
+
+The JSON to register your MCP tool is the following. Be sure to insert the absolute path to the directory containing the mcp_example.py file:
+
+```json
+{
+  "mcpServers": {
+    "mellea_mcp_server": {
+      "command": "uv",
+      "args": [
+        "--directory",
+        "<ABSOLUTE PATH>/mellea/docs/examples/mcp",
+        "run",
+        "mcp",
+        "run",
+        "mcp_example.py"
+      ]
+    }
+  }
+}
+```
+
+
+
+

docs/examples/mcp/mcp_example.py

@@ -0,0 +1,54 @@
+"""Example of an MCP server.
+
+You need to install the mcp package:
+uv pip install "mcp[cli]"
+
+and run the example in MCP debug UI:
+uv run mcp dev docs/examples/tutorial/mcp_example.py
+"""
+
+from mcp.server.fastmcp import FastMCP
+
+from mellea import MelleaSession
+from mellea.backends import ModelOption, model_ids
+from mellea.backends.ollama import OllamaModelBackend
+from mellea.stdlib.base import ModelOutputThunk
+from mellea.stdlib.requirement import Requirement, simple_validate
+from mellea.stdlib.sampling import RejectionSamplingStrategy
+
+# #################
+# run MCP debug UI with: uv run mcp dev docs/examples/tutorial/mcp_example.py
+# ##################
+
+
+# Create an MCP server
+mcp = FastMCP("Demo")
+
+
+@mcp.tool()
+def write_a_poem(word_limit: int) -> str:
+    """Write a poem with a word limit."""
+    m = MelleaSession(
+        OllamaModelBackend(
+            model_ids.HF_SMOLLM2_2B,
+            model_options={ModelOption.MAX_NEW_TOKENS: word_limit + 10},
+        )
+    )
+    wl_req = Requirement(
+        f"Use only {word_limit} words.",
+        validation_fn=simple_validate(lambda x: len(x.split(" ")) < word_limit),
+    )
+
+    res = m.instruct(
+        "Write a poem",
+        requirements=[wl_req],
+        strategy=RejectionSamplingStrategy(loop_budget=2),
+    )
+    assert isinstance(res, ModelOutputThunk)
+    return str(res.value)
+
+
+@mcp.resource("greeting://{name}")
+def get_greeting(name: str) -> str:
+    """Get a personalized greeting."""
+    return f"Hello, {name}!"

docs/examples/mcp/mcp_server.json

@@ -0,0 +1,15 @@
+{
+  "mcpServers": {
+    "mellea_mcp_server": {
+      "command": "uv",
+      "args": [
+        "--directory",
+        "<ABSOLUTE PATH>/mellea/docs/examples/mcp",
+        "run",
+        "mcp",
+        "run",
+        "mcp_example.py"
+      ]
+    }
+  }
+}

docs/examples/mify/rich_table_execute_basic.py

@@ -4,8 +4,11 @@
 from mellea import start_session
 from mellea.backends import model_ids
 from mellea.backends.types import ModelOption
+from mellea.helpers.fancy_logger import FancyLogger
 from mellea.stdlib.docs.richdocument import RichDocument, Table
 
+FancyLogger.get_logger().setLevel("ERROR")
+
 """
 Here we demonstrate the use of the (internally m-ified) class
 RichDocument and Table that are wrappers around Docling documents.
@@ -42,7 +45,7 @@ def cached_doc():
     model_id=model_ids.META_LLAMA_3_2_3B,
     model_options={ModelOption.MAX_NEW_TOKENS: 500},
 )
-
+print("==> Outputs:")
 # apply transform on the Table and make sure that the returned object is a Table. Try up to 5 times.
 for seed in [x * 12 for x in range(5)]:
     table_transformed = m.transform(