Feat: Add uv python enviorment, dspy chatbot, ollama, an easy frontend by flask

.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml
+# Ignored default folder with query files
+/queries/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+
+*.env
+
+# Ignore Python cache files
+__pycache__/
+*.py[cod]

.python-version

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+3.13

.vscode/settings.json

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+{
+    "python-envs.pythonProjects": []
+}

pyproject.toml

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+[project]
+name = "atlas-world"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "dspy>=3.1.0",
+    "ollama>=0.6.1",
+    "flask>=3.0.0",
+    "dotenv>=0.9.9",
+    "flask-cors>=6.0.2",
+]

script/chatbot.py

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+from flask import Flask, request, jsonify, render_template
+from flask_cors import CORS
+import os
+import dspy
+from script.llm_config import configure_lm
+from script.llm_module.investigator.module import InvestigatorModule
+from script.llm_module.opposition.module import OppositionModule
+
+# Ensure Flask can find the templates folder
+base_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
+template_dir = os.path.join(base_dir, 'templates')
+app = Flask(__name__, template_folder=template_dir)
+CORS(app)
+
+# Initialize DSPy with user-chosen LM
+print("正在啟動 LLM 配置引導...")
+configure_lm()
+investigator = InvestigatorModule()
+opposer = OppositionModule()
+
+# 使用官方 dspy.History 管理
+chat_history = dspy.History(messages=[])
+
+port = int(os.getenv("PORT") or 5000)
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+@app.route('/investigate', methods=['POST'])
+def investigate_endpoint():
+    try:
+        data = request.json
+        user_content = data.get('userContent')
+        system_prompt = data.get('systemPrompt') or ""
+
+        readme_path = os.path.join(base_dir, 'README.md')
+        if os.path.exists(readme_path):
+            with open(readme_path, 'r', encoding='utf-8') as f:
+                readme_content = f.read()
+            system_prompt += "\n--- 背景資訊 ---\n" + readme_content
+
+        if not user_content:
+            user_content = "請進行調查"
+
+        # 使用調查者模組
+        result = investigator(system_prompt=system_prompt, user_query=user_content, history=chat_history)
+
+        # 更新歷史軌跡
+        chat_history.messages.append({"user_query": user_content, "response": result.response})
+
+        return jsonify({'response': result.response})
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+@app.route('/oppose', methods=['POST'])
+def oppose_endpoint():
+    try:
+        data = request.json
+        system_prompt = data.get('systemPrompt') or ""
+
+        readme_path = os.path.join(base_dir, 'README.md')
+        if os.path.exists(readme_path):
+            with open(readme_path, 'r', encoding='utf-8') as f:
+                readme_content = f.read()
+            system_prompt += "\n--- 背景資訊 ---\n" + readme_content
+
+        # 反對者模組主要基於當前對話歷史提出挑戰
+        result = opposer(system_prompt=system_prompt, history=chat_history)
+
+        # 反對者的回應通常也要更新進歷史，以便調查者下一次能回應反對點
+        chat_history.messages.append({"user_query": "[監察反對請求]", "response": result})
+
+        return jsonify({'response': result})
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+@app.route('/reset', methods=['POST'])
+def reset_history():
+    try:
+        global chat_history
+        chat_history = dspy.History(messages=[])
+        return jsonify({'status': '歷史記錄已重置'})
+    except Exception as e:
+        print(f"Reset error: {e}")
+        return jsonify({'error': str(e)}), 500
+
+# 保留 /chat 以相容舊前端，指向預設調查者
+@app.route('/chat', methods=['POST'])
+def chat_endpoint():
+    return investigate_endpoint()
+
+if __name__ == '__main__':
+    # 注意：debug=True 會自動啟動 Reloader 導致兩次 Prompt
+    # 這裡關閉它以確保只需輸入一次模型配置
+    app.run(debug=True, use_reloader=False, host='0.0.0.0', port=port)

script/llm_config.py

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+import dspy
+import os
+
+CONFIGED_LM = None
+
+def configure_lm():
+    from dotenv import load_dotenv
+    load_dotenv()
+    global CONFIGED_LM
+    if CONFIGED_LM is not None:
+        return CONFIGED_LM
+    print("\n--- 🌐 選擇要用的 LLM 模型來源 ---")
+    print("1) 本地模型 (Ollama/HF)")
+    print("2) OpenAI API")
+    print("3) Google Gemini API")
+    print("4) Anthropic Claude API")
+
+    choice = input("請輸入數字 (1/2/3/4): ").strip()
+
+    model_name = None
+    api_key = None
+
+
+    if choice == "1":
+        # 預設幾個常見本地模型參考
+        env_model = os.getenv("OLLAMA_MODEL")
+        if env_model:
+            print(f"\n偵測到預設模型: {env_model}")
+            model_name = input(f"輸入本地模型名稱 (直接按 Enter 使用 {env_model}): ").strip()
+            if not model_name:
+                model_name = env_model
+        else:
+            print("\n可用本地模型例子: llama3, gemma3:1b, mistral, phi3")
+            model_name = input("輸入本地模型名稱: ").strip()
+
+        # 確保本地模型有名稱前綴 (LiteLM 要求)
+        if "/" not in model_name:
+            model_name = f"ollama/{model_name}"
+            print(f"自動修正為 LiteLM 格式: {model_name}")
+
+        lm = dspy.LM(model_name, cache=False)
+
+    elif choice == "2":
+        print("\nOpenAI 模型選擇:")
+        print("1) openai/gpt-5.2\n2) openai/gpt-4o\n3) openai/gpt-4o-mini\n4) openai/o4-mini\n5) openai/o3-mini")
+        idx = input("選擇模型 (1-5): ").strip()
+        mapping = {
+            "1": "openai/gpt-5.2",
+            "2": "openai/gpt-4o",
+        }
+        model_name = mapping.get(idx, "openai/gpt-5.2")
+        api_key = input("輸入 OpenAI API Key (或留空用 OPENAI_API_KEY 環境變數): ").strip() or os.getenv("OPENAI_API_KEY")
+        lm = dspy.LM(model_name, api_key=api_key, cache=False)
+
+    elif choice == "3":
+        print("\nGoogle Gemini 模型選擇:\n1) gemini-2.5-flash\n2) gemini-2.5-pro\n3) gemini-3-flash-preview\n4) gemini-3-pro-preview")
+        idx = input("選擇模型 (1-4): ").strip()
+        mapping = {
+            "1": "gemini/gemini-2.5-flash",
+            "2": "gemini/gemini-2.5-pro",
+            "3": "gemini/gemini-3-flash-preview",
+            "4": "gemini/gemini-3-pro-preview"
+        }
+        model_name = mapping.get(idx, "gemini-2.5-pro")
+        api_key = input("輸入 Gemini API Key (或留空用 GEMINI_API_KEY 環境變數): ").strip() or os.getenv("GEMINI_API_KEY")
+        lm = dspy.LM(model_name, api_key=api_key, cache=False)
+
+    elif choice == "4":
+        print("\nAnthropic Claude 模型選擇:")
+        print("1) claude-opus-4.5-20251101\n2) claude-sonnet-4.5\n3) claude-haiku-4.5")
+        idx = input("選擇模型 (1-3): ").strip()
+        mapping = {
+            "1": "claude/claude-opus-4.5-20251101",
+            "2": "claude/claude-sonnet-4.5",
+            "3": "claude/claude-haiku-4.5"
+        }
+        model_name = mapping.get(idx, "claude-opus-4.5-20251101")
+        api_key = input("輸入 Claude API Key (或留空用 ANTHROPIC_API_KEY): ").strip() or os.getenv("ANTHROPIC_API_KEY")
+        lm = dspy.LM(model_name, api_key=api_key, cache=False)
+
+    else:
+        print(" 選擇無效，預設用 openai/gpt-5.2")
+        model_name = "openai/gpt-5.2"
+        api_key = os.getenv("OPENAI_API_KEY")
+        lm = dspy.LM(model_name, api_key=api_key, cache=False)
+
+    # 設定為全域預設 LLM
+    dspy.configure(lm=lm)
+    CONFIGED_LM = lm
+    print(f"\n 已設定模型: {model_name}")
+    return lm

script/llm_module/investigator/module.py

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+import dspy
+from .signature import InvestigatorSignature
+
+class InvestigatorModule(dspy.Module):
+    """
+    實作調查者邏輯的 DSPy 模組。
+    """
+    def __init__(self):
+        super().__init__()
+        self.investigate = dspy.ChainOfThought(InvestigatorSignature)
+
+    def forward(self, system_prompt, user_query, history):
+        return self.investigate(
+            system_prompt=system_prompt, 
+            user_query=user_query, 
+            history=history
+        )

script/llm_module/investigator/signature.py

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+import dspy
+
+class InvestigatorSignature(dspy.Signature):
+    """
+    妳是 Atlas-World 的「調查者」(Investigator)。
+    妳的職責是深入分析使用者的問題，結合背景資訊與對話歷史，
+    進行邏輯推演、事實查核或情境解析，給出詳盡且具洞察力的調查報告或回應。
+    """
+    system_prompt = dspy.InputField(desc="目前的文件、與使用者給予的系統提示")
+    user_query = dspy.InputField(desc="使用者的當前問題或調查標的")
+    history: dspy.History = dspy.InputField(desc="目前的調查對話歷史記錄")
+    response = dspy.OutputField(desc="生成的調查分析、洞察或回應")

script/llm_module/opposition/module.py

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+import dspy
+from .signature import OppositionSignature
+
+class OppositionModule(dspy.Module):
+    """
+    提供反向思考與監察邏輯的 DSPy 模組。
+    """
+    def __init__(self):
+        super().__init__()
+        self.predictor = dspy.ChainOfThought(OppositionSignature)
+
+    def forward(self, system_prompt, history):
+        # history 應為 dspy.History 物件
+        result = self.predictor(system_prompt=system_prompt, history=history)
+        return result.response