This video is a quick easy tutorial to install Qwen-Agent locally and create agentic applications with Qwen2 and Ollama for function calling and RAG.
Code:
conda create -n qwenagent python=3.11 -y
pip install -U qwen-agent
import json
import os
from qwen_agent.llm import get_chat_model
# Example dummy function hard coded to return the same weather
# In production, this could be your backend API or an external API
def get_current_weather(location, unit='fahrenheit'):
"""Get the current weather in a given location"""
if 'tokyo' in location.lower():
return json.dumps({'location': 'Tokyo', 'temperature': '10', 'unit': 'celsius'})
elif 'san francisco' in location.lower():
return json.dumps({'location': 'San Francisco', 'temperature': '72', 'unit': 'fahrenheit'})
elif 'paris' in location.lower():
return json.dumps({'location': 'Paris', 'temperature': '22', 'unit': 'celsius'})
else:
return json.dumps({'location': location, 'temperature': 'unknown'})
def test():
llm = get_chat_model({
'model': 'qwen2:7b',
'model_server': 'http://localhost:11434/v1', # api_base
'api_key': 'EMPTY',
})
# Step 1: send the conversation and available functions to the model
messages = [{'role': 'user', 'content': "What's the weather like in San Francisco?"}]
functions = [{
'name': 'get_current_weather',
'description': 'Get the current weather in a given location',
'parameters': {
'type': 'object',
'properties': {
'location': {
'type': 'string',
'description': 'The city and state, e.g. San Francisco, CA',
},
'unit': {
'type': 'string',
'enum': ['celsius', 'fahrenheit']
},
},
'required': ['location'],
},
}]
print('# Assistant Response 1:')
responses = []
for responses in llm.chat(messages=messages, functions=functions, stream=True):
print(responses)
messages.extend(responses) # extend conversation with assistant's reply
# Step 2: check if the model wanted to call a function
last_response = messages[-1]
if last_response.get('function_call', None):
# Step 3: call the function
# Note: the JSON response may not always be valid; be sure to handle errors
available_functions = {
'get_current_weather': get_current_weather,
} # only one function in this example, but you can have multiple
function_name = last_response['function_call']['name']
function_to_call = available_functions[function_name]
function_args = json.loads(last_response['function_call']['arguments'])
function_response = function_to_call(
location=function_args.get('location'),
unit=function_args.get('unit'),
)
print('# Function Response:')
print(function_response)
# Step 4: send the info for each function call and function response to the model
messages.append({
'role': 'function',
'name': function_name,
'content': function_response,
}) # extend conversation with function response
print('# Assistant Response 2:')
for responses in llm.chat(
messages=messages,
functions=functions,
stream=True,
): # get a new response from the model where it can see the function response
print(responses)
if __name__ == '__main__':
test()
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