253 lines
8.3 KiB
Python
253 lines
8.3 KiB
Python
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from flask import Flask, request, jsonify
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from llama_index.llms.ollama import Ollama
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from llama_index.core.llms import MockLLM
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import logging
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import time
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# 初始化日志
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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app = Flask(__name__)
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# 定义任务映射
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TASK_MAPPING = {
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"1": ["文章内容", 400], # 表示文章
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"2": ["文章标题", 20], # 表示标题
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"3": ["文章摘要", 200], # 表示摘要
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"4": ["文章关键词", 10], # 表示关键词
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"5": ["文章描述", 50] # 表示问题描述
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}
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# 自定义 MockLLM 实现
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class CustomMockLLM(MockLLM):
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def complete(self, prompt):
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# 模拟生成过程
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time.sleep(1) # 模拟处理时间
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return f"这是MockLLM生成的测试响应。\n原始输入: {prompt}"
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# 统一的模型接口
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class ModelInterface:
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def __init__(self, model):
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self.model = model
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def generate_text(self, prompt):
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if isinstance(self.model, Ollama):
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response = self.model.complete(prompt)
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return response.text
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elif isinstance(self.model, CustomMockLLM):
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return self.model.complete(prompt)
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else:
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raise ValueError("Unsupported model type")
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# 初始化LLM
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try:
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logging.info("尝试初始化 Ollama 模型...")
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raw_llm = Ollama(
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model="qwen2.5",
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temperature=0,
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request_timeout=120.0,
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base_url="http://localhost:11434"
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)
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logging.info("Ollama 模型初始化成功！")
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except Exception as e:
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logging.warning(f"无法连接到 Ollama 服务器，使用 MockLLM。错误: {e}")
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raw_llm = CustomMockLLM()
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# 创建统一的模型接口
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llm = ModelInterface(raw_llm)
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# 获取映射后的任务类型
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def get_mapped_task(task_id):
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return TASK_MAPPING.get(task_id, ["未知任务类型", 200]) # 默认返回未知任务类型和200字限制
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# 调用模型生成内容
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def generate_with_ollama(x, y, text):
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source_type, _ = get_mapped_task(x)
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target_type, word_limit = get_mapped_task(y)
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prompt = (
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f"你是一名专业的SEO内容策划专家。\n"
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f"任务：根据以下{source_type}生成符合要求的高质量SEO{target_type}。\n"
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f"- 内容要求：清晰、连贯，准确概括核心内容。包含专业但易懂的科学解释，自然地融入关键词，不要出现特殊符号和表情符号，地名要在[]之中\n"
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f"- 写作风格：专业但通俗易懂,适合一般读者阅读,保持科学准确性\n"
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f"- 输出限制：仅返回生成的{target_type}，不需要任何解释或补充内容。\n"
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f"- 字数限制：{word_limit}字以内。\n"
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f"- 语言要求：输出内容符合中文语言习惯。\n"
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f"- 输出格式：请严格按照以下格式返回：\n"
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f"{target_type}\n\n"
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f"输入：\n{text}\n"
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)
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logging.info(f"生成 Prompt：{prompt}")
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try:
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response = llm.generate_text(prompt)
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logging.info(f"生成结果：{response}")
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return {"result": response}
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except Exception as e:
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logging.error(f"生成失败：{e}")
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return {"error": f"模型生成失败: {e}"}
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# 调用模型优化内容
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def optimize_with_ollama(x, text):
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task_type, word_limit = get_mapped_task(x)
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prompt = (
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f"任务：对以下{task_type}进行优化。\n"
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f"要求：\n"
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f"1. 保持原文信息完整性，优化内容更清晰、逻辑更连贯。\n"
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f"2. 适当简化句式，使内容更简洁流畅。\n"
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f"3. 控制字数在{word_limit}字以内。\n"
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f"4. 请直接输出优化后的{task_type}内容，不需要额外解释。\n"
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f"\n输入：\n{text}\n"
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)
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logging.info(f"优化 Prompt：{prompt}")
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try:
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response = llm.generate_text(prompt)
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logging.info(f"优化结果：{response}")
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return {"result": response}
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except Exception as e:
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logging.error(f"优化失败：{e}")
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return {"error": f"模型优化失败: {e}"}
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# 调用模型根据 x 和 y 进行优化
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def optimize_with_target(x, y, text):
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source_type, _ = get_mapped_task(x)
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target_type, word_limit = get_mapped_task(y)
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prompt = (
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f"任务：将以下{source_type}优化为{target_type}。\n"
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f"要求：\n"
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f"1. 确保内容符合{target_type}的表达特点，逻辑清晰，语言简洁。\n"
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f"2. 控制内容在{word_limit}字以内。\n"
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f"3. 请直接输出优化后的{target_type}内容，不要附加多余解释。\n"
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f"\n输入：\n{text}\n"
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)
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logging.info(f"优化（带目标类型）Prompt：{prompt}")
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try:
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response = llm.generate_text(prompt)
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logging.info(f"优化结果：{response}")
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return {"result": response}
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except Exception as e:
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logging.error(f"优化失败：{e}")
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return {"error": f"模型优化失败: {e}"}
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# 调用模型生成关键词
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def generate_keywords(x, y, text):
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task_type, word_limit = get_mapped_task(x)
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# 构建关键词生成的 Prompt
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prompt = (
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f"任务：根据以下{task_type}提取关键词。\n"
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f"要求：\n"
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f"1. 提取{y}个关键词，每个关键词应准确概括内容核心思想。\n"
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f"2. 关键词之间用逗号分隔。\n"
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f"3. 请仅输出关键词列表，不要添加其他内容。\n"
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f"\n输入：\n{text}\n"
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)
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logging.info(f"生成关键词 Prompt：{prompt}")
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try:
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response = llm.generate_text(prompt)
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logging.info(f"生成关键词结果：{response}")
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return {"result": response}
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except Exception as e:
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logging.error(f"生成关键词失败：{e}")
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return {"error": f"模型生成关键词失败: {e}"}
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# 定义生成接口
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@app.route('/generate', methods=['POST'])
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def generate():
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params = request.json
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if not params:
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return jsonify({"error": "缺少参数"}), 400
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x = params.get("x")
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y = params.get("y")
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text = params.get("text")
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if not x or not y or not text:
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return jsonify({"error": "参数不完整"}), 400
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if x not in TASK_MAPPING:
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return jsonify({"error": f"无效的源任务类型: {x}"}), 400
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if y not in TASK_MAPPING:
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return jsonify({"error": f"无效的目标任务类型: {y}"}), 400
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result = generate_with_ollama(x, y, text)
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return jsonify(result)
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# 定义优化接口
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@app.route('/optimize', methods=['POST'])
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def optimize():
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params = request.json
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if not params:
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return jsonify({"error": "缺少参数"}), 400
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x = params.get("x")
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text = params.get("text")
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if not x or not text:
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return jsonify({"error": "参数不完整"}), 400
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if x not in TASK_MAPPING:
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return jsonify({"error": f"无效的任务类型: {x}"}), 400
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result = optimize_with_ollama(x, text)
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return jsonify(result)
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# 定义优化带目标接口
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@app.route('/optimize_with_target', methods=['POST'])
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def optimize_with_target_api():
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params = request.json
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if not params:
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return jsonify({"error": "缺少参数"}), 400
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x = params.get("x")
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y = params.get("y")
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text = params.get("text")
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if not x or not y or not text:
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return jsonify({"error": "参数不完整"}), 400
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if x not in TASK_MAPPING:
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return jsonify({"error": f"无效的源任务类型: {x}"}), 400
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if y not in TASK_MAPPING:
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return jsonify({"error": f"无效的目标任务类型: {y}"}), 400
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result = optimize_with_target(x, y, text)
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return jsonify(result)
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# 定义关键词生成接口
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@app.route('/generate_keywords', methods=['POST'])
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def generate_keywords_api():
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params = request.json
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if not params:
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return jsonify({"error": "缺少参数"}), 400
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x = params.get("x")
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y = params.get("y")
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text = params.get("text")
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if not x or not y or not text:
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return jsonify({"error": "参数不完整"}), 400
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# 验证任务类型是否有效
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if x not in TASK_MAPPING:
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return jsonify({"error": f"无效的任务类型: {x}"}), 400
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try:
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y = int(y)
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if y <= 0:
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return jsonify({"error": "关键词数量必须为正整数"}), 400
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except ValueError:
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return jsonify({"error": "关键词数量必须为整数"}), 400
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result = generate_keywords(x, y, text)
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return jsonify(result)
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if __name__ == '__main__':
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app.run(host='0.0.0.0', port=5000)
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