AI Agent与Web3融合：构建自主链上智能体

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from typing import List, Dict
import hashlib
import json

class OnChainInferenceNetwork:
    """链上AI推理网络"""

    def __init__(self, blockchain_rpc: str):
        self.web3 = Web3(Web3.HTTPProvider(blockchain_rpc))
        self.private_key = os.getenv("PRIVATE_KEY")
        self.account = self.web3.eth.account.from_key(self.private_key)

        # 加载推理合约ABI
        self.inference_contract = self.web3.eth.contract(
            address="0x...",  # AI推理合约地址
            abi=[...]  # 合约ABI
        )

    def submit_inference_task(
        self,
        model_id: str,
        input_data: Dict,
        reward: int
    ) -> str:
        """提交推理任务"""

        # 准备任务数据
        task_data = {
            "modelId": model_id,
            "input": input_data,
            "reward": reward,
            "timeout": 3600,  # 1小时超时
            "timestamp": int(time.time())
        }

        # 计算任务哈希
        task_hash = self._compute_task_hash(task_data)

        # 提交到链上
        tx_hash = self.inference_contract.functions.submitTask(
            task_hash,
            json.dumps(input_data),
            reward,
            task_data["timeout"]
        ).transact({'from': self.account.address})

        self.web3.eth.wait_for_transaction_receipt(tx_hash)

        return task_hash

    def submit_inference_result(
        self,
        task_hash: str,
        output_data: Dict
    ) -> str:
        """提交推理结果"""

        # 准备结果数据
        result_hash = self._compute_result_hash({
            "taskHash": task_hash,
            "output": output_data,
            "submitter": self.account.address
        })

        # 提交结果
        tx_hash = self.inference_contract.functions.submitResult(
            task_hash,
            json.dumps(output_data),
            result_hash
        ).transact({'from': self.account.address})

        self.web3.eth.wait_for_transaction_receipt(tx_hash)

        return tx_hash

    def claim_reward(self, task_hash: str) -> str:
        """领取奖励"""

        # 检查任务是否完成
        task = self.inference_contract.functions.tasks(task_hash).call()

        if not task["completed"]:
            raise Exception("Task not completed yet")

        # 领取奖励
        tx_hash = self.inference_contract.functions.claimReward(
            task_hash
        ).transact({'from': self.account.address})

        self.web3.eth.wait_for_transaction_receipt(tx_hash)

        return tx_hash

    def verify_result(
        self,
        task_hash: str,
        output_data: Dict
    ) -> bool:
        """验证结果"""

        # 从链上获取任务
        task = self.inference_contract.functions.tasks(task_hash).call()

        # 计算期望的输出哈希
        expected_hash = self._compute_output_hash(
            task["input"],
            task["modelId"]
        )

        # 验证结果哈希
        result_hash = self._compute_result_hash({
            "taskHash": task_hash,
            "output": output_data
        })

        return result_hash == expected_hash

    def _compute_task_hash(self, task_data: Dict) -> str:
        """计算任务哈希"""
        data_string = json.dumps(task_data, sort_keys=True)
        return hashlib.sha256(data_string.encode()).hexdigest()

    def _compute_result_hash(self, result_data: Dict) -> str:
        """计算结果哈希"""
        data_string = json.dumps(result_data, sort_keys=True)
        return hashlib.sha256(data_string.encode()).hexdigest()

    def _compute_output_hash(self, input_data: Dict, model_id: str) -> str:
        """计算输出哈希（模拟AI推理）"""
        # 实际应用中，这里应该运行AI模型
        # 这里简化处理
        output = self._run_model(input_data, model_id)

        return hashlib.sha256(json.dumps(output).encode()).hexdigest()

    def _run_model(self, input_data: Dict, model_id: str) -> Dict:
        """运行AI模型"""
        # 实际应用中，这里应该调用真实的AI模型
        # 可以使用OpenAI API、本地模型等

        if model_id == "text-classifier":
            return self._classify_text(input_data["text"])
        elif model_id == "image-analyzer":
            return self._analyze_image(input_data["imageUrl"])
        elif model_id == "sentiment-analyzer":
            return self._analyze_sentiment(input_data["text"])
        else:
            raise Exception(f"Unknown model: {model_id}")

    def _classify_text(self, text: str) -> Dict:
        """文本分类"""
        # 简化实现，实际应该调用真实模型
        categories = {
            "technology": 0.8,
            "finance": 0.6,
            "sports": 0.1
        }

        predicted_category = max(categories, key=categories.get)

        return {
            "category": predicted_category,
            "confidence": categories[predicted_category]
        }

    def _analyze_image(self, image_url: str) -> Dict:
        """图像分析"""
        return {
            "objects": ["person", "car", "building"],
            "scene": "street",
            "confidence": 0.95
        }

    def _analyze_sentiment(self, text: str) -> Dict:
        """情感分析"""
        # 简化实现
        positive_words = ["good", "great", "excellent", "happy"]
        negative_words = ["bad", "terrible", "awful", "sad"]

        words = text.lower().split()

        positive_count = sum(1 for word in words if word in positive_words)
        negative_count = sum(1 for word in words if word in negative_words)

        if positive_count > negative_count:
            sentiment = "positive"
        elif negative_count > positive_count:
            sentiment = "negative"
        else:
            sentiment = "neutral"

        return {
            "sentiment": sentiment,
            "score": (positive_count - negative_count) / len(words)
        }

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from transformers import AutoTokenizer, AutoModelForCausalLM
from typing import List, Dict

class AIContractAuditor:
    """AI智能合约审计助手"""

    def __init__(self, model_name="microsoft/CodeGPT-small"):
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForCausalLM.from_pretrained(model_name)

    def audit_contract(
        self,
        contract_source: str
    ) -> Dict:
        """审计智能合约"""

        # 1. 代码结构分析
        structure_analysis = self._analyze_structure(contract_source)

        # 2. 漏洞检测
        vulnerabilities = self._detect_vulnerabilities(contract_source)

        # 3. 最佳实践检查
        best_practices = self._check_best_practices(contract_source)

        # 4. Gas优化建议
        gas_optimization = self._suggest_gas_optimization(contract_source)

        # 5. 生成审计报告
        report = self._generate_report({
            "structure": structure_analysis,
            "vulnerabilities": vulnerabilities,
            "best_practices": best_practices,
            "gas_optimization": gas_optimization
        })

        return report

    def _analyze_structure(self, source: str) -> Dict:
        """分析合约结构"""

        prompt = f"""
        Analyze the following smart contract code structure:

        {source}

        Provide:
        1. Contract architecture
        2. Key functions and their roles
        3. State variables and their purposes
        4. Access control mechanisms
        5. External dependencies
        """

        response = self._generate(prompt)

        return {
            "architecture": self._parse_response(response, "architecture"),
            "functions": self._parse_response(response, "functions"),
            "state_variables": self._parse_response(response, "state_variables"),
            "access_control": self._parse_response(response, "access_control"),
            "dependencies": self._parse_response(response, "dependencies")
        }

    def _detect_vulnerabilities(self, source: str) -> List[Dict]:
        """检测漏洞"""

        known_vulnerabilities = {
            "reentrancy": {
                "patterns": [
                    r"\.call\{.*value:\s*msg\.value",
                    r"\.send\{.*value:\s*msg\.value"
                ],
                "severity": "critical",
                "description": "Reentrancy vulnerability detected"
            },
            "overflow": {
                "patterns": [
                    r"uint256.*=.*\+.*(?!\.add\()",
                    r"uint256.*=.*-.*(?!\.sub\()"
                ],
                "severity": "high",
                "description": "Potential integer overflow/underflow"
            },
            "access_control": {
                "patterns": [
                    r"function\s+\w+\s*\(\s*\)\s*public(?!\s*onlyOwner|onlyRole)",
                    r"tx\.origin"
                ],
                "severity": "high",
                "description": "Weak access control"
            },
            "unchecked_call": {
                "patterns": [
                    r"\.call\s*\(",
                    r"\.send\s*\("
                ],
                "severity": "medium",
                "description": "Unchecked external call"
            }
        }

        detected = []

        for vuln_type, vuln_info in known_vulnerabilities.items():
            for pattern in vuln_info["patterns"]:
                matches = re.finditer(pattern, source)

                for match in matches:
                    detected.append({
                        "type": vuln_type,
                        "severity": vuln_info["severity"],
                        "description": vuln_info["description"],
                        "location": match.span(),
                        "code_snippet": source[match.start()-20:match.end()+20]
                    })

        return detected

    def _check_best_practices(self, source: str) -> List[Dict]:
        """检查最佳实践"""

        checks = {
            "uses_safe_math": r"SafeMath|\.add\(|\.sub\(" in source,
            "has_reentrancy_guard": r"ReentrancyGuard|nonReentrant" in source,
            "uses_openzeppelin": r"@openzeppelin" in source,
            "has_events": r"event\s+\w+" in source,
            "uses_checks_effects_interactions": r"Checks-Effects-Interactions" in source,
            "has_pause": r"whenNotPaused|Pausable" in source,
            "has_timelock": r"TimelockController|releaseTimeLock" in source
        }

        results = []

        for check_name, check_result in checks.items():
            results.append({
                "check": check_name,
                "passed": check_result,
                "description": self._get_check_description(check_name)
            })

        return results

    def _suggest_gas_optimization(self, source: str) -> List[str]:
        """建议Gas优化"""

        optimizations = []

        # 检查循环
        if "for (" in source:
            optimizations.append("Consider using unchecked blocks for loop iterations")

        # 检查storage操作
        if re.search(r"uint256\s+public\s+\w+", source):
            optimizations.append("Consider packing struct variables to save storage")

        # 检查重复计算
        if re.search(r"keccak256\(", source):
            optimizations.append("Cache keccak256 results in local variables")

        # 检查memory vs storage
        if re.search(r".*\.\w+\s*=\s*\w+\[.*\]\s*\+\s*1", source):
            optimizations.append("Consider using calldata instead of memory for arrays")

        return optimizations

    def _generate_report(self, audit_data: Dict) -> Dict:
        """生成审计报告"""

        # 计算风险评分
        risk_score = self._calculate_risk_score(audit_data)

        # 生成总结
        summary = self._generate_summary(audit_data, risk_score)

        # 生成修复建议
        recommendations = self._generate_recommendations(audit_data)

        return {
            "risk_score": risk_score,
            "summary": summary,
            "vulnerabilities": audit_data["vulnerabilities"],
            "best_practices": audit_data["best_practices"],
            "optimizations": audit_data["gas_optimization"],
            "recommendations": recommendations
        }

    def _calculate_risk_score(self, audit_data: Dict) -> int:
        """计算风险评分（0-100）"""

        score = 100

        for vuln in audit_data["vulnerabilities"]:
            if vuln["severity"] == "critical":
                score -= 30
            elif vuln["severity"] == "high":
                score -= 15
            elif vuln["severity"] == "medium":
                score -= 5
            elif vuln["severity"] == "low":
                score -= 2

        return max(score, 0)

    def _generate_summary(self, audit_data: Dict, risk_score: int) -> str:
        """生成审计总结"""

        vuln_count = len(audit_data["vulnerabilities"])
        critical_count = sum(1 for v in audit_data["vulnerabilities"] if v["severity"] == "critical")

        summary = f"""
        Smart Contract Audit Summary
        ==========================

        Risk Score: {risk_score}/100
        Total Vulnerabilities: {vuln_count}
        Critical Issues: {critical_count}

        """

        if risk_score >= 80:
            summary += "Overall Assessment: LOW RISK"
        elif risk_score >= 50:
            summary += "Overall Assessment: MEDIUM RISK"
        else:
            summary += "Overall Assessment: HIGH RISK"

        return summary

    def _generate_recommendations(self, audit_data: Dict) -> List[str]:
        """生成修复建议"""

        recommendations = []

        for vuln in audit_data["vulnerabilities"]:
            if vuln["type"] == "reentrancy":
                recommendations.append(
                    "Use ReentrancyGuard or implement Checks-Effects-Interactions pattern"
                )
            elif vuln["type"] == "overflow":
                recommendations.append(
                    "Use Solidity 0.8.0+ or SafeMath library for arithmetic operations"
                )
            elif vuln["type"] == "access_control":
                recommendations.append(
                    "Implement proper access control using onlyOwner or role-based access"
                )

        return recommendations

    def _generate(self, prompt: str) -> str:
        """生成文本"""
        inputs = self.tokenizer(prompt, return_tensors="pt")

        with torch.no_grad():
            outputs = self.model.generate(
                **inputs,
                max_new_tokens=500,
                temperature=0.3,
                do_sample=True
            )

        response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)

        return response

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from typing import List, Dict, Optional
from datetime import datetime
import ccxt
import pandas as pd
import numpy as np

class DeFiTradingAgent:
    """DeFi交易Agent"""

    def __init__(
        self,
        initial_capital: float,
        exchanges: List[str],
        llm_model: str = "gpt-4"
    ):
        self.capital = initial_capital
        self.portfolio = {}  # {token: amount}
        self.exchanges = {}
        self.trade_history = []

        # 初始化交易所连接
        for exchange_name in exchanges:
            if exchange_name == "uniswap":
                exchange = ccxt.uniswap({
                    "enableRateLimit": True
                })
            elif exchange_name == "pancakeswap":
                exchange = ccxt.pancakeswap({
                    "enableRateLimit": True
                })
            else:
                exchange = ccxt.binance({
                    "enableRateLimit": True
                })

            self.exchanges[exchange_name] = exchange

        # 初始化LLM
        self.llm = self._init_llm(llm_model)

    def _init_llm(self, model_name: str):
        """初始化LLM"""
        # 实际应用中，这里应该连接到真实的LLM API
        # 或运行本地模型

        from transformers import AutoTokenizer, AutoModelForCausalLM

        model = AutoModelForCausalLM.from_pretrained(model_name)
        tokenizer = AutoTokenizer.from_pretrained(model_name)

        return {
            "model": model,
            "tokenizer": tokenizer
        }

    def analyze_market(
        self,
        tokens: List[str],
        timeframe: str = "1h"
    ) -> Dict:
        """分析市场"""

        # 获取市场数据
        market_data = self._fetch_market_data(tokens, timeframe)

        # 技术分析
        ta_analysis = self._technical_analysis(market_data)

        # 使用LLM生成市场洞察
        market_insight = self._generate_market_insight(
            market_data,
            ta_analysis
        )

        return {
            "market_data": market_data,
            "technical_analysis": ta_analysis,
            "insight": market_insight
        }

    def _fetch_market_data(
        self,
        tokens: List[str],
        timeframe: str
    ) -> Dict:
        """获取市场数据"""

        data = {}

        for token in tokens:
            # 从各个交易所获取数据
            for exchange_name, exchange in self.exchanges.items():
                try:
                    ohlcv = exchange.fetch_ohlcv(
                        f"{token}/USDT",
                        timeframe,
                        limit=100
                    )

                    if token not in data:
                        data[token] = []

                    # 转换为DataFrame
                    df = pd.DataFrame(
                        ohlcv,
                        columns=['timestamp', 'open', 'high', 'low', 'close', 'volume']
                    )
                    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')

                    data[token].append(df)

                    break  # 使用第一个成功的数据源

                except Exception as e:
                    print(f"Error fetching data for {token}: {e}")

        return data

    def _technical_analysis(self, market_data: Dict) -> Dict:
        """技术分析"""

        analysis = {}

        for token, dfs in market_data.items():
            if not dfs:
                continue

            df = dfs[0]  # 使用第一个数据源

            # 计算技术指标
            df['sma_20'] = df['close'].rolling(window=20).mean()
            df['sma_50'] = df['close'].rolling(window=50).mean()
            df['rsi'] = self._calculate_rsi(df['close'], 14)
            df['macd'] = self._calculate_macd(df['close'])

            # 趋势分析
            latest_close = df['close'].iloc[-1]
            sma_20 = df['sma_20'].iloc[-1]
            sma_50 = df['sma_50'].iloc[-1]

            trend = "bullish" if latest_close > sma_20 > sma_50 else "bearish"

            analysis[token] = {
                "current_price": latest_close,
                "sma_20": sma_20,
                "sma_50": sma_50,
                "rsi": df['rsi'].iloc[-1],
                "trend": trend,
                "support_levels": self._find_support_levels(df),
                "resistance_levels": self._find_resistance_levels(df)
            }

        return analysis

    def _calculate_rsi(self, prices: pd.Series, period: int = 14) -> pd.Series:
        """计算RSI"""
        delta = prices.diff()
        gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
        loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()

        rs = gain / loss
        rsi = 100 - (100 / (1 + rs))

        return rsi

    def _calculate_macd(self, prices: pd.Series) -> Dict:
        """计算MACD"""
        exp1 = prices.ewm(span=12, adjust=False).mean()
        exp2 = prices.ewm(span=26, adjust=False).mean()

        macd = exp1 - exp2
        signal = macd.ewm(span=9, adjust=False).mean()
        histogram = macd - signal

        return {
            "macd": macd.iloc[-1],
            "signal": signal.iloc[-1],
            "histogram": histogram.iloc[-1]
        }

    def _find_support_levels(self, df: pd.DataFrame) -> List[float]:
        """寻找支撑位"""
        # 简化实现：使用局部最小值
        from scipy.signal import argrelextrema

        prices = df['close'].values
        local_min = argrelextrema(prices, np.less, order=20)

        support_levels = sorted(prices[local_min])
        return support_levels[-5:]  # 返回最近的5个支撑位

    def _find_resistance_levels(self, df: pd.DataFrame) -> List[float]:
        """寻找阻力位"""
        from scipy.signal import argrelextrema

        prices = df['close'].values
        local_max = argrelextrema(prices, np.greater, order=20)

        resistance_levels = sorted(prices[local_max], reverse=True)
        return resistance_levels[-5:]  # 返回最近的5个阻力位

    def _generate_market_insight(
        self,
        market_data: Dict,
        ta_analysis: Dict
    ) -> str:
        """生成市场洞察"""

        # 准备prompt
        prompt = f"""
        Analyze the following cryptocurrency market data and provide trading insights:

        Technical Analysis:
        {json.dumps(ta_analysis, indent=2)}

        Based on this analysis, provide:
        1. Market trend analysis
        2. Key support and resistance levels
        3. Trading recommendations
        4. Risk factors to consider
        5. Optimal entry and exit points

        Be specific and actionable.
        """

        # 调用LLM
        response = self._generate(prompt)

        return response

    def execute_trade(
        self,
        exchange: str,
        symbol: str,
        side: str,
        amount: float,
        price: Optional[float] = None
    ) -> Dict:
        """执行交易"""

        exchange_obj = self.exchanges[exchange]

        try:
            if side == "buy":
                # 限价买单
                if price:
                    order = exchange_obj.create_limit_buy_order(
                        symbol,
                        amount,
                        price
                    )
                else:
                    # 市价买单
                    order = exchange_obj.create_market_buy_order(
                        symbol,
                        amount
                    )
            else:
                # 卖单
                if price:
                    order = exchange_obj.create_limit_sell_order(
                        symbol,
                        amount,
                        price
                    )
                else:
                    order = exchange_obj.create_market_sell_order(
                        symbol,
                        amount
                    )

            # 记录交易
            trade_record = {
                "exchange": exchange,
                "symbol": symbol,
                "side": side,
                "amount": amount,
                "price": price,
                "timestamp": datetime.now().isoformat(),
                "status": "executed"
            }

            self.trade_history.append(trade_record)

            return trade_record

        except Exception as e:
            print(f"Trade execution failed: {e}")

            return {
                "status": "failed",
                "error": str(e)
            }

    def run_strategy(
        self,
        strategy_config: Dict
    ) -> List[Dict]:
        """运行交易策略"""

        # 1. 分析市场
        market_analysis = self.analyze_market(
            strategy_config["tokens"],
            strategy_config.get("timeframe", "1h")
        )

        # 2. 生成交易信号
        signals = self._generate_trading_signals(
            market_analysis,
            strategy_config
        )

        # 3. 执行交易
        executed_trades = []

        for signal in signals:
            if signal["action"] == "hold":
                continue

            trade = self.execute_trade(
                exchange=signal["exchange"],
                symbol=signal["symbol"],
                side=signal["side"],
                amount=signal["amount"],
                price=signal.get("price")
            )

            if trade.get("status") == "executed":
                executed_trades.append(trade)

        return executed_trades

    def _generate_trading_signals(
        self,
        market_analysis: Dict,
        strategy_config: Dict
    ) -> List[Dict]:
        """生成交易信号"""

        signals = []

        ta_analysis = market_analysis["technical_analysis"]

        for token, analysis in ta_analysis.items():
            # 简单的移动平均策略
            if (analysis["trend"] == "bullish" and
                analysis["rsi"] < 70 and
                analysis["current_price"] > analysis["sma_20"]):

                signals.append({
                    "action": "buy",
                    "exchange": "uniswap",
                    "symbol": f"{token}/USDT",
                    "side": "buy",
                    "amount": strategy_config.get("trade_size", 100),
                    "reason": "Bullish trend with RSI below overbought"
                })

            elif (analysis["trend"] == "bearish" and
                  analysis["rsi"] > 30):

                signals.append({
                    "action": "sell",
                    "exchange": "uniswap",
                    "symbol": f"{token}/USDT",
                    "side": "sell",
                    "amount": strategy_config.get("trade_size", 100),
                    "reason": "Bearish trend detected"
                })

        return signals

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from typing import List, Dict, Optional

class DAOGovernanceAgent:
    """DAO治理Agent"""

    def __init__(
        self,
        dao_address: str,
        llm_model: str = "gpt-4"
    ):
        self.dao_address = dao_address
        self.llm = self._init_llm(llm_model)

        # 治理历史
        self.governance_history = []

        # 提案分析
        self.proposals_db = {}

    def analyze_proposal(
        self,
        proposal_data: Dict
    ) -> Dict:
        """分析提案"""

        # 1. 提取关键信息
        key_info = self._extract_proposal_info(proposal_data)

        # 2. 风险评估
        risk_assessment = self._assess_risk(proposal_data)

        # 3. 财务影响分析
        financial_impact = self._analyze_financial_impact(proposal_data)

        # 4. 生成投票建议
        voting_recommendation = self._generate_voting_recommendation({
            "key_info": key_info,
            "risk_assessment": risk_assessment,
            "financial_impact": financial_impact
        })

        return {
            "proposal_id": proposal_data["id"],
            "key_info": key_info,
            "risk_assessment": risk_assessment,
            "financial_impact": financial_impact,
            "recommendation": voting_recommendation
        }

    def _extract_proposal_info(self, proposal: Dict) -> Dict:
        """提取提案关键信息"""

        prompt = f"""
        Extract key information from this DAO proposal:

        Title: {proposal.get('title', '')}
        Description: {proposal.get('description', '')}

        Please extract:
        1. Proposal type (e.g., parameter change, spending, governance change)
        2. Key changes proposed
        3. Affected stakeholders
        4. Implementation timeline
        5. Required resources
        """

        response = self._generate(prompt)

        # 解析LLM响应
        key_info = {
            "type": self._parse_field(response, "Proposal type"),
            "changes": self._parse_field(response, "Key changes"),
            "stakeholders": self._parse_field(response, "Stakeholders"),
            "timeline": self._parse_field(response, "Timeline"),
            "resources": self._parse_field(response, "Resources")
        }

        return key_info

    def _assess_risk(self, proposal: Dict) -> Dict:
        """评估风险"""

        risk_factors = []

        # 检查提案类型
        proposal_type = self._extract_proposal_type(proposal)

        if proposal_type == "spending":
            # 检查金额
            amount = self._extract_amount(proposal)
            if amount > 1000000:
                risk_factors.append({
                    "type": "financial",
                    "severity": "high",
                    "description": "Large expenditure proposed"
                })

        elif proposal_type == "parameter_change":
            # 检查参数范围
            params = self._extract_parameters(proposal)
            if self._is_risk_parameter_change(params):
                risk_factors.append({
                    "type": "governance",
                    "severity": "medium",
                    "description": "Parameter changes may affect protocol stability"
                })

        return {
            "risk_score": self._calculate_risk_score(risk_factors),
            "risk_factors": risk_factors,
            "mitigation_strategies": self._suggest_mitigation(risk_factors)
        }

    def _generate_voting_recommendation(self, analysis: Dict) -> Dict:
        """生成投票建议"""

        prompt = f"""
        Based on the following DAO proposal analysis:

        Key Information: {json.dumps(analysis['key_info'], indent=2)}
        Risk Assessment: {json.dumps(analysis['risk_assessment'], indent=2)}
        Financial Impact: {json.dumps(analysis['financial_impact'], indent=2)}

        Provide a voting recommendation:
        1. Vote: For/Against/Abstain
        2. Confidence: High/Medium/Low
        3. Reasoning: Detailed explanation
        4. Conditions: Any conditions for changing the vote

        Consider:
        - Long-term sustainability
        - Community impact
        - Financial health
        - Innovation vs stability
        """

        response = self._generate(prompt)

        # 解析建议
        recommendation = {
            "vote": self._parse_field(response, "Vote"),
            "confidence": self._parse_field(response, "Confidence"),
            "reasoning": self._parse_field(response, "Reasoning"),
            "conditions": self._parse_field(response, "Conditions")
        }

        return recommendation

    def automate_governance(self) -> None:
        """自动化治理决策"""

        # 获取待处理提案
        pending_proposals = self._fetch_pending_proposals()

        for proposal in pending_proposals:
            # 分析提案
            analysis = self.analyze_proposal(proposal)

            # 根据建议自动投票
            if analysis["recommendation"]["vote"].lower() == "for":
                self._cast_vote(
                    proposal["id"],
                    "for",
                    analysis["recommendation"]["reasoning"]
                )

    def _fetch_pending_proposals(self) -> List[Dict]:
        """获取待处理提案"""

        # 实际应用中，这里应该从链上或DAO的API获取
        # 简化实现

        proposals = []

        # 示例提案
        proposals.append({
            "id": "proposal-123",
            "title": "Grant Program Funding",
            "description": "Allocate $500,000 for grants",
            "status": "pending",
            "voting_deadline": datetime.now() + timedelta(days=7)
        })

        return proposals

    def _cast_vote(self, proposal_id: str, vote: str, reason: str) -> str:
        """投票"""

        # 实际应用中，这里应该调用链上治理合约
        print(f"Voting {vote} on proposal {proposal_id}")
        print(f"Reason: {reason}")

        return f"voted-{vote}-{proposal_id}"