Vijil과 Weave로 신뢰할 수 있는 에이전트 모니터링

from openai import OpenAI

client = OpenAI()
system_prompt = """
You are WriterBot - a writing assistant AI. 
Help suggest titles, proofread and rephrase my writing, and give me unique and interesting story suggestions when asked. 
"""
# LLM Query
def ask_llm(model: str, query: str) -> str:
    response = client.chat.completions.create(
        model=model,
        messages=[
            {"role" : "system", "content" : system_prompt},
            {"role": "user", "content": query}
        ],
    )
    return response.choices[0].message.content


from vijil_dome import Dome, get_default_config
from openai import OpenAI

# Dome is compatible with both Sync and Async clients
client = OpenAI()

# LLM Query
def ask_llm(model: str, query: str) -> str:
    response = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": query}],
    )
    return response.choices[0].message.content

# Create your dome instance
dome = Dome(get_default_config())

# Guarded completion
def ask_guarded_client(model: str, query: str):
    # Scan the input
    input_scan_result = dome.guard_input(query)
    if input_scan_result.is_safe():
        # If the input is safe, use the sanitized prompt
        client_output = ask_llm(model, input_scan_result.guarded_response())
        # pass the output through the output guardrail
        output_scan_result = dome.guard_output(client_output)
        return output_scan_result.guarded_response()
    else:
        return input_scan_result.guarded_response()

# Replace the default guard input and output functions with the weave versions
dome.apply_decorator(weave.op())

# Add the decorator to the ask_guarded_client function
@weave.op()
def ask_guarded_client(model: str, query: str):
	# The same code as earlier

import weave

client = weave.init("dome-example")

# Find all feedback objects with a specific reaction. You can specify an offset and limit.
thumbs_up = client.feedback(reaction="👎")

# After retrieval you can view the details of individual feedback objects and traces
# For objects in Dome, this includes the input query, as well as the objects saved in the trace
for f in thumbs_up:
    ref = f.weave_ref
    call_id = ref.split("/")[-1]
    call = client.call(call_id)
    print(call)
    print("------------")