You've already forked adk-python
mirror of
https://github.com/encounter/adk-python.git
synced 2026-03-30 10:57:20 -07:00
Eliza Huang
d3796f9b33
Close #2893 Co-authored-by: Eliza Huang <heliza@google.com> PiperOrigin-RevId: 826070077
82 lines
2.6 KiB
Python
82 lines
2.6 KiB
Python
# Copyright 2025 Google LLC
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import logging
|
|
import os
|
|
|
|
from fastmcp import Context
|
|
from fastmcp import FastMCP
|
|
from fastmcp.experimental.sampling.handlers.openai import OpenAISamplingHandler
|
|
from openai import OpenAI
|
|
|
|
logging.basicConfig(level=logging.INFO)
|
|
API_KEY = os.getenv("OPENAI_API_KEY")
|
|
|
|
# Set up the server's LLM handler using the OpenAI API.
|
|
# This handler will be used for all sampling requests from tools on this server.
|
|
llm_handler = OpenAISamplingHandler(
|
|
default_model="gpt-4o",
|
|
client=OpenAI(
|
|
api_key=API_KEY,
|
|
),
|
|
)
|
|
|
|
|
|
# Create the FastMCP Server instance.
|
|
# The `sampling_handler` is configured to use the server's own LLM.
|
|
# `sampling_handler_behavior="always"` ensures the server never delegates
|
|
# sampling back to the ADK agent.
|
|
mcp = FastMCP(
|
|
name="SentimentAnalysis",
|
|
sampling_handler=llm_handler,
|
|
sampling_handler_behavior="always",
|
|
)
|
|
|
|
|
|
@mcp.tool
|
|
async def analyze_sentiment(text: str, ctx: Context) -> dict:
|
|
"""Analyzes sentiment by delegating to the server's own LLM."""
|
|
logging.info("analyze_sentiment tool called with text: %s", text)
|
|
prompt = f"""Analyze the sentiment of the following text as positive,
|
|
negative, or neutral. Just output a single word.
|
|
Text to analyze: {text}"""
|
|
|
|
# This delegates the LLM call to the server's own sampling handler,
|
|
# as configured in the FastMCP instance.
|
|
logging.info("Attempting to call ctx.sample()")
|
|
try:
|
|
response = await ctx.sample(prompt)
|
|
logging.info("ctx.sample() successful. Response: %s", response)
|
|
except Exception as e:
|
|
logging.error("ctx.sample() failed: %s", e, exc_info=True)
|
|
raise
|
|
|
|
sentiment = response.text.strip().lower()
|
|
|
|
if "positive" in sentiment:
|
|
result = "positive"
|
|
elif "negative" in sentiment:
|
|
result = "negative"
|
|
else:
|
|
result = "neutral"
|
|
|
|
logging.info("Sentiment analysis result: %s", result)
|
|
return {"text": text, "sentiment": result}
|
|
|
|
|
|
if __name__ == "__main__":
|
|
print("Starting FastMCP server with tool 'analyze_sentiment'...")
|
|
# This runs the server process, which the ADK agent will connect to.
|
|
mcp.run()
|