智模学院

# Step 1: Initialize specialized customer service LLM
llm_cs = CustomerServiceLLM()
summarizer = SummaryModule()
translator = TranslationModule()

# Step 2: Input handling (email, chat, attachment)
input_data = get_user_input()
if is_email(input_data):
    content = extract_email_content(input_data)
else:
    content = extract_chat_turns(input_data)

# Step 3: Multi-task: summarization + translation
summary = summarizer.run(content)
translated = translator.translate(content, target_lang="en")

# Step 4: Understand intent and classify
intent = llm_cs.detect_intent(content)
context = llm_cs.extract_context(content)

# Step 5: Generate intelligent response
prompt = f"""You are a professional customer service agent.
Context: {context}
Intent: {intent}
Summary: {summary}
Reply professionally:"""
response = llm_cs.generate(prompt)

# Step 6: Log + return result
log_interaction(input_data, summary, intent)
display(response)

🎯 核心锻炼点

• ✓ 构建多模态输入接口（文本 + 图像 + 语音），实现信息统一解析
• ✓ 接入并调优客服领域专属大模型，实现行业语境理解
• ✓ 实现邮件和对话的自动摘要、内容归类与情绪识别
• ✓ 支持中英文翻译、多语言交互与国际客服场景模拟
• ✓ 利用大模型完成工单生成、FAQ匹配、智能回复等自动化任务
• ✓ 部署Web界面或企业内嵌插件，模拟真实客服交互流程

# Initialize vector database and embedding model
db = VectorDB()
embed_model = EmbeddingModel()

# Load and preprocess documents
docs = load_documents(path)
chunks = split_documents(docs)
vectors = embed_model.encode(chunks)
db.insert(chunks, vectors)

# User inputs a question
question = get_user_input()
q_vector = embed_model.encode([question])[0]

# Retrieve top-k similar chunks
top_k = db.search(q_vector, k=5)
retrieved_docs = [item['text'] for item in top_k]

# Construct prompt for LLM
context = "\n".join(retrieved_docs)
prompt = f"""Based on the following content, answer the question:
{context}

Question: {question}
Answer:"""

# Generate answer using LLM
llm = LargeLanguageModel()
answer = llm.generate(prompt)
display(answer)

🎯 核心锻炼点

• ✓ 构建向量数据库（FAISS、Milvus）存储企业知识文档嵌入
• ✓ 编写文档预处理与切片模块，支持多格式文档解析
• ✓ 接入嵌入模型（bge、OpenAI Embedding）完成语义索引构建
• ✓ 使用LangChain、LlamaIndex等工具实现RAG检索增强流程
• ✓ 编写高质量Prompt，实现问答准确性与企业知识风格控制
• ✓ 构建问答前端界面，支持引用追溯、问答记录、权限管理

# Initialize embedding model and knowledge engine
embed_model = EmbeddingModel()
knowledge_base = VectorDB()

# Import and index external content
files = upload_files()
docs = parse_files(files)
chunks = split_documents(docs)
vectors = embed_model.encode(chunks)
knowledge_base.insert(chunks, vectors)

# User searches or drafts content
user_input = get_user_input()

# Semantic search over knowledge base
query_vec = embed_model.encode([user_input])[0]
matches = knowledge_base.search(query_vec, k=5)
context = "\n".join([m['text'] for m in matches])

# Generate content suggestion
llm = LargeLanguageModel()
prompt = f"""Use the following material to assist content creation:
{context}

Instruction: {user_input}
Generated:"""
output = llm.generate(prompt)

# Display AI-assisted content
render(output)

🎯 核心锻炼点

• ✓ 调用大语言模型实现写作、润色、摘要、风格迁移等功能模块
• ✓ 集成图像模型（DALL·E、SDXL）支持图文生成、图片描述
• ✓ 搭建知识管理引擎，支持文献资料的批量导入、语义索引
• ✓ 实现语义检索与写作任务的联动，如素材召回、智能引用
• ✓ 构建创作工具集，如内容大纲生成器、标题建议器、SEO优化助手
• ✓ 提供清晰的用户交互界面，支持内容预览、导出、协作与权限管理

# Initialize models and vector database
image_model = VisionEncoder()
ocr_model = OCRModule()
embed_model = TextEmbedding()
vector_db = VectorDB()
llm = LLM()

# Receive user image input
image = get_uploaded_image()

# Extract and interpret image content
image_caption = image_model.describe(image)
ocr_text = ocr_model.extract_text(image)
combined_text = image_caption + "\n" + ocr_text

# Embed and search local vector DB
query_vector = embed_model.encode([combined_text])[0]
results = vector_db.search(query_vector, k=5)
retrieved_chunks = [r['text'] for r in results]

# Build LangChain-style prompt chain
context = "\n".join(retrieved_chunks)
chain_input = {
    "image_description": image_caption,
    "ocr_text": ocr_text,
    "matched_docs": context
}
prompt = f"""You are a dictionary assistant.
Image says: {chain_input['image_description']}
Detected text: {chain_input['ocr_text']}
Related entries:\n{chain_input['matched_docs']}
Give a useful summary or translation."""

response = llm.generate(prompt)
display(response)

🎯 核心锻炼点

• ✓ 使用NIM接入视觉识别、OCR文字提取、翻译模型等多模态接口
• ✓ 构建LangChain链式逻辑，实现“图像→提取文本→翻译→检索→总结”任务流
• ✓ 搭建本地向量数据库（FAISS）存储词汇用法示例、教材解释
• ✓ 使用嵌入模型完成语义索引与匹配，增强词义检索准确性
• ✓ 调用LLM对查询结果进行精炼总结、语境扩展或双语对照输出
• ✓ 提供统一的图文输入界面，支持移动端或网页端交互使用

# Initialize model registry and workflow engine
model_pool = ModelHub()
flow_engine = FlowOrchestrator()
policy_db = VectorDB()
edu_repo = LearningGraph()

# Define agent types
agent_policy = Agent(name="PolicyAgent", model=model_pool.get("LLM-Gov"))
agent_edu = Agent(name="TutorAgent", model=model_pool.get("LLM-Edu"))

# Load and embed knowledge bases
docs_policy = load_docs("gov_files/")
chunks = split_documents(docs_policy)
vecs = model_pool.embedder.encode(chunks)
policy_db.insert(chunks, vecs)

# Create a visual flow for policy linking
flow_engine.add_node(agent_policy)
flow_engine.connect(agent_policy, step="retrieve_related_policies", db=policy_db)

# Create education path builder
student_profile = get_user_input()
topics = edu_repo.match(student_profile)
flow_engine.add_node(agent_edu)
flow_engine.connect(agent_edu, step="generate_learning_path", input=topics)

# Execute the flow
results = flow_engine.run()
display(results)

🎯 核心锻炼点

• ✓ 构建支持多模型调度的智能体执行框架（OpenAI、BGE、Claude等）
• ✓ 实现可视化工作流设计器，支持任务节点拖拽、依赖定义与执行追踪
• ✓ 使用LangChain/Agent模块实现任务链构建与上下文传递
• ✓ 针对央国企文档场景，实现政策解读、多文档对比、引用溯源功能
• ✓ 针对教育场景，设计学习者画像构建、能力评估与个性化路径推荐模块
• ✓ 支持智能体任务链部署、权限管理、日志监控与持续更新

# Step 1: Data ingestion and preprocessing
raw_data = collect_enterprise_data(sources=["CRM", "ERP", "logs"])
clean_data = preprocess(raw_data)
features, labels = extract_features_labels(clean_data)

# Step 2: Model selection and training
model = AutoModelSelector().select(task="classification", data=features)
trained_model = model.train(features, labels)

# Step 3: Evaluate and refine
metrics = trained_model.evaluate(test_split=0.2)
if metrics["accuracy"] < 0.9:
    tuned_model = trained_model.hyperparameter_tune()
else:
    tuned_model = trained_model

# Step 4: Deploy model as service
endpoint = deploy_model(tuned_model, target="inference-api")

# Step 5: Monitor and retrain if needed
new_data = stream_new_data()
if drift_detected(new_data):
    retrained_model = tuned_model.retrain(new_data)
    update_model(endpoint, retrained_model)

# Step 6: Generate insights
insights = tuned_model.explain_decision()
report(insights)

🎯 核心锻炼点

• ✓ 梳理企业业务场景，提炼可建模目标（预测、分类、问答、自动化）
• ✓ 设计数据采集、清洗与结构化方案，构建高质量训练数据
• ✓ 根据任务特点选择合适模型（分类模型、RAG系统、微调大模型等）
• ✓ 实现模型训练、评估与优化流程（超参数搜索、模型压缩等）
• ✓ 构建应用层接口（API/前端/集成插件），支持业务系统对接
• ✓ 进行系统部署、权限管理、日志监控与性能追踪

# Step 1: Connect to multiple data sources
sources = ["SQL", "NoSQL", "API", "Logs"]
raw_batches = [fetch_data(src) for src in sources]

# Step 2: Data cleaning, normalization, integration
cleaned = [clean_and_align(batch) for batch in raw_batches]
data_warehouse = DataIntegrator().merge(cleaned)

# Step 3: Model library registration
model_registry = ModelHub()
model_registry.register(name="SalesPredictor", type="regression")
model_registry.register(name="AnomalyDetector", type="unsupervised")

# Step 4: Training and deployment pipeline
trainer = Pipeline(model_registry.get("SalesPredictor"))
trained = trainer.fit(data_warehouse["sales"])
deployed = deploy_model(trained, endpoint="predict/sales")

# Step 5: Monitoring and feedback loop
monitor = ModelMonitor(endpoint)
if monitor.detect_drift():
    retrain_model(endpoint)

# Step 6: Analytics and visualization
dashboard = BIBuilder()
dashboard.load(data_warehouse)
dashboard.visualize(metrics=["revenue", "growth", "risk"])

# Step 7: Serve to downstream innovation apps
serve_predictions(dashboard, target_apps=["CRM", "ERP", "Portal"])