personal-consumption-service/app/blueprints/risk/routes.py

# @description: 
# @author: licanglong
# @date: 2025/11/20 14:22
import json

import openai
from flask import jsonify, request
from openai.types.chat import ChatCompletion

from app.blueprints.risk import risk_bp
from app.client.VectorStoreClient import vector_store_client
from app.constants.vector_store import VectorStoreCollection
from app.core import BizException, CTX
from app.models.Result import SysResult
from app.models.dto import FinalDecisionResult, RiskEvidenceResult
from app.prompt import person_consumption_prompt, external_evidence_search_prompt
from app.utils import AIStreamJSONParser


@risk_bp.route('/decide', methods=['POST'])
def risk_decide():
    """
    发票风险裁决
    :return:
    """
    invoice_data = request.json
    vector = vector_store_client.embedding.encode(f"""
        特定业务类型：{invoice_data['tdywlx'] or ''} 
        购买方名称： {invoice_data['gmfmc'] or ''} 
        货物名称：{invoice_data['hwmc'] or ''} 
        规格型号：{invoice_data['ggxh'] or ''} 
        开票人：{invoice_data['kpr'] or ''}
        """)
    rules = vector_store_client.client.query_points(
        collection_name=VectorStoreCollection.RULE_EMBED_STORE,
        query=vector.tolist(),
        limit=5,
        score_threshold=0.5
    )
    cases = vector_store_client.client.query_points(
        collection_name=VectorStoreCollection.CASE_EMBED_STORE,
        query=vector.tolist(),
        limit=5,
        score_threshold=0.5
    )

    merchants = vector_store_client.client.query_points(
        collection_name=VectorStoreCollection.MERCHANTS_EMBED_STORE,
        query=vector.tolist(),
        limit=5,
        score_threshold=0.5
    )

    edges = vector_store_client.client.query_points(
        collection_name=VectorStoreCollection.EDGES_EMBED_STORE,
        query=vector.tolist(),
        limit=5,
        score_threshold=0.5
    )
    input_data = {
        "invoice_context": invoice_data,
        "rules": [hit.payload for hit in rules.points],
        "cases": [hit.payload for hit in cases.points],
        "industry": [hit.payload for hit in merchants.points],
        "signals": [hit.payload for hit in edges.points]
    }

    final_user_prompt = person_consumption_prompt.get_person_consumption_user_prompt(
        json.dumps(input_data, ensure_ascii=False))

    final_user_prompt = final_user_prompt.replace("{{input_data_desc}}", "")
    client = openai.OpenAI(
        api_key=CTX.ENV.getprop("llm.qwen.api_key", raise_error=True),
        base_url=CTX.ENV.getprop("llm.qwen.base_url", raise_error=True),
    )
    completion = client.chat.completions.create(
        model="qwen-plus",
        messages=[{'role': 'system', 'content': person_consumption_prompt.system_prompt},
                  {'role': 'user', 'content': final_user_prompt}],
        stream=True,
        stream_options={"include_usage": True}
    )
    parser = AIStreamJSONParser()
    for chunk in completion:
        parser.feed_chunk(chunk.model_dump_json())
        if parser.is_finished(chunk.model_dump_json()):
            break
    result = parser.get_result()
    decision_result: FinalDecisionResult = FinalDecisionResult.model_validate(result)
    return jsonify(SysResult.success(data=decision_result.dict()))


@risk_bp.route('/evidence', methods=['POST'])
def evidence_replenish():
    invoice_data = request.json

    input_data = {
        "invoice_context": invoice_data
    }
    final_external_evidence_user_prompt = external_evidence_search_prompt.get_external_evidence_user_prompt(
        json.dumps(input_data, ensure_ascii=False))
    client = openai.OpenAI(
        api_key=CTX.ENV.getprop("llm.qwen.api_key", raise_error=True),
        base_url=CTX.ENV.getprop("llm.qwen.base_url", raise_error=True),
    )
    completion: ChatCompletion = client.chat.completions.create(
        model="qwen-plus",
        messages=[
            {'role': 'system', 'content': external_evidence_search_prompt.external_evidence_system_prompt},
            {'role': 'user', 'content': final_external_evidence_user_prompt}],
        extra_body={"enable_search": True,
                    "search_options": {"search_strategy": "agent", "enable_source": True}}

    )
    if not completion.choices:
        raise BizException("LLM响应异常")
    generate_content = completion.choices[0].message.content
    evidence_result: RiskEvidenceResult = RiskEvidenceResult.model_validate(json.loads(generate_content))
    return jsonify(SysResult.success(data=evidence_result.dict()))