LangChain

from langchain.chains import LLMChain
import uuid

# muster_emit(  # add after job completes
# agent_id="contract-reviewer", version="1.3.0")

def run_contract_review(contract_text: str) -> dict:
    job_id = str(uuid.uuid4())

    chain = LLMChain(llm=llm, prompt=prompt)
    result = chain.invoke({"contract": contract_text})

    muster_emit(
        job_id=job_id,
        overall_passed=bool(result.get("output")),
        token_input=result.get("token_usage", {}).get("prompt_tokens", 0),
        token_output=result.get("token_usage", {}).get("completion_tokens", 0),
        model="gpt-4o",
        checks=[
            quality.Check("output_not_empty",   "HIGH", bool(result.get("output"))),
            quality.Check("risk_flag_present",  "HIGH", "risk_flags" in result),
            quality.Check("source_cited",       "HIGH", "source" in result),
        ]
    )
    return result

from langchain.callbacks import get_openai_callback

with get_openai_callback() as cb:
    result = chain.invoke({"contract": contract_text})

muster_emit(
    job_id=job_id,
    overall_passed=bool(result),
    token_input=cb.prompt_tokens,
    token_output=cb.completion_tokens,
    model="gpt-4o",
    checks=[
        quality.Check("output_not_empty", "HIGH", bool(result)),
    ]
)