LangChain Skills 模式实战：构建按需加载知识的 SQL 助手
在先前的文章中，我们探讨了如何通过 Deep Agents CLI 模拟 Deep Agent 使用 Skills 的模式。如今，LangChain 已原生支持这一特性，极大地简化了开发流程。本文将带领大家深入体验这一功能，构建一个更智能的 SQL 助手。
构建复杂的 AI Agent 时，开发者往往陷入两难境地：是将所有上下文（数据库表结构、API 文档、业务规则）一次性注入 System Prompt，导致上下文窗口（Context Window）溢出且分散模型注意力？还是选择成本高昂的频繁微调（Fine-tuning）？
**Skills 模式（Skills Pattern）**提供了一条优雅的中间路线。它通过动态加载所需知识，实现了上下文的高效利用。LangChain 对此模式的原生支持，意味着我们可以更轻松地构建具备“按需学习”能力的 Agent。
本文将结合官方文档 Build a SQL assistant with on-demand skills，引导读者从零开始，构建一个支持“按需加载知识”的 SQL Assistant。
1. 核心概念：为何选择 Skills 模式？
传统 SQL Agent 的局限性
在传统的 SQL Agent 架构中，我们通常需要在 System Prompt 中提供完整的 Database Schema。随着业务发展，当表数量扩展到数百张时，这种方式会带来显著问题：
-
Token 消耗巨大：每次对话都携带大量无关的表结构，造成资源浪费。
-
幻觉风险增加：过多的无关干扰信息会降低模型的推理准确性。
-
维护困难：所有业务线的知识紧密耦合，难以独立迭代。
Skills 模式：基于渐进式披露的解决方案
Skills 模式基于**渐进式披露（Progressive Disclosure）**原则，将知识获取过程分层处理：
-
Agent 初始状态：仅掌握有哪些“技能”（Skills）及其简要描述（Description），保持轻量级。
-
运行时加载：当面对具体问题（如“查询库存”）时，Agent 主动调用工具（
load_skill）加载该技能详细的上下文（Schema + Prompt）。 -
执行任务：基于加载的精确上下文，执行具体的任务（如编写并执行 SQL）。
这种模式有效支持了无限扩展和团队解耦，使 Agent 能够适应日益复杂的业务场景。
2. 系统架构设计
本实战项目将构建一个包含两个核心 Skills 的 SQL Assistant，以演示该模式的实际应用：
-
Sales Analytics（销售分析）：负责
sales_data表，处理收入统计、订单趋势分析等。 -
Inventory Management（库存管理）：负责
inventory_items表，处理库存水平监控、位置查询等。
3. 开发环境搭建
本项目采用 Pythonuv进行高效的依赖管理。
核心依赖安装
uv add langchain langchain-openai langgraph psycopg2-binary python-dotenv langchain-community
PostgreSQL 环境配置
本地启动一个 Postgres 实例，并创建agent_platform数据库。我们提供了setup_db.py脚本来自动初始化表结构和测试数据（详见文末源码）。
4. 核心实现步骤详解
Langkah 1: Mendefinisikan Keterampilan Domain (The Knowledge)
Kita akan mendefinisikan keterampilan sebagai struktur kamus, mensimulasikan proses pemuatan dari sistem file atau database. Harap perhatikan perbedaan antara description (untuk digunakan oleh Agent dalam memilih) dan content (konteks detail yang sebenarnya dimuat).
SKILLS = {"sales_analytics": {"description":"Berguna untuk menganalisis pendapatan penjualan, tren...","content":"""... Skema Tabel: sales_data ..."" },"inventory_management": {"description":"Berguna untuk memeriksa tingkat stok...","content":"""... Skema Tabel: inventory_items ..."" }}
Langkah 2: Mengimplementasikan Alat Inti (The Capabilities)
Agent bergantung pada dua alat penting untuk menyelesaikan tugas:
-
load_skill(skill_name): Memuat detail keterampilan yang ditentukan secara dinamis saat runtime. -
run_sql_query(query): Mengeksekusi pernyataan SQL tertentu.
Langkah 3: Menyusun Logika Agent (The Brain)
Memanfaatkan LangGraph untuk membangun ReAct Agent. System Prompt memainkan peran penting di sini, membimbing Agent untuk secara ketat mengikuti prosedur operasi standar (SOP) Identify -> Load -> Query.
system_prompt ="""1. Identify the relevant skill.2. Use 'load_skill' to get schema.3. Write and execute SQL using 'run_sql_query'....Do not guess table names. Always load the skill first.""
5. Verifikasi Efek Operasi
Dengan menjalankan test_agent.py, kami menguji kueri di dua domain berbeda, Penjualan dan Inventaris. Berikut adalah log keluaran aktual dari konsol, yang menunjukkan bagaimana Agent memuat keterampilan secara dinamis berdasarkan pertanyaan:
Testing Sales Query...Agent calling tools: [{'name': 'load_skill', 'args': {'skill_name': 'sales_analytics'}, 'id': 'call_f270d76b7ce4404cb5f61bf2', 'type': 'tool_call'}]Tool output:You are a Sales Analytics Expert.You have access to the 'sales_data' table.Table Schema:- id: integer...Agent calling tools: [{'name': 'run_sql_query', 'args': {'query': 'SELECT SUM(amount) as total_revenue FROM sales_data;'}, 'id': 'call_b4f3e686cc7f4f22b3bb9ea7', 'type': 'tool_call'}]Tool output: [(Decimal('730.50'),)]...Agent response: The total revenue is $730.50.Testing Inventory Query...Agent calling tools: [{'name': 'load_skill', 'args': {'skill_name': 'inventory_management'}, 'id': 'call_18c823b2d5064e95a0cfe2e3', 'type': 'tool_call'}]Tool output:You are an Inventory Management Expert.You have access to the 'inventory_items' table.Table Schema...Agent calling tools: [{'name': 'run_sql_query', 'args': {'query': "SELECT warehouse_location FROM inventory_items WHERE product_name = 'Laptop';"}, 'id': 'call_647ee3a444804bd98a045f00', 'type': 'tool_call'}]Tool output: [('Warehouse A',)]...Agent response: The Laptop is located in **Warehouse A**.## 6. Referensi Kode Sumber Lengkap
Berikut adalah kode sumber lengkap proyek, termasuk skrip inisialisasi basis data dan program utama Agent.
1. Inisialisasi Basis Data (setup_db.py)
`import psycopg2 from psycopg2.extensions import ISOLATION_LEVEL_AUTOCOMMIT import os from dotenv import load_dotenv
load_dotenv()
请确保在 .env 中配置数据库连接信息
Pastikan untuk mengonfigurasi informasi koneksi basis data di .env
DB_HOST = os.getenv("DB_HOST", "localhost") DB_PORT = os.getenv("DB_PORT", "5432") DB_USER = os.getenv("DB_USER", "postgres") DB_PASSWORD = os.getenv("DB_PASSWORD", "your_password") # 请替换为实际密码 # Harap ganti dengan kata sandi yang sebenarnya DB_NAME = os.getenv("DB_NAME", "agent_platform")
def create_database(): try: # Connect to default 'postgres' database to create new db # Hubungkan ke basis data 'postgres' default untuk membuat db baru conn = psycopg2.connect( host=DB_HOST, port=DB_PORT, user=DB_USER, password=DB_PASSWORD, dbname="postgres", ) conn.set_isolation_level(ISOLATION_LEVEL_AUTOCOMMIT) cur = conn.cursor()
# Check if database exists
# Periksa apakah basis data ada
cur.execute(f"SELECT 1 FROM pg_catalog.pg_database WHERE datname = '{DB_NAME}'")
exists = cur.fetchone()
if not exists:
print(f"Creating database {DB_NAME}...")
cur.execute(f"CREATE DATABASE {DB_NAME}")
else:
print(f"Database {DB_NAME} already exists.")
cur.close()
conn.close()
except Exception as e:
print(f"Error creating database: {e}")
def create_tables_and_data(): try: conn = psycopg2.connect( host=DB_HOST, port=DB_PORT, user=DB_USER, password=DB_PASSWORD, dbname=DB_NAME, ) cur = conn.cursor()
# Create Sales Table
# Buat Tabel Penjualan
print("Creating sales_data table...")
cur.execute(
"""
CREATE TABLE IF NOT EXISTS sales_data (
id SERIAL PRIMARY KEY,
transaction_date DATE,
product_id VARCHAR(50),
amount DECIMAL(10, 2),
region VARCHAR(50)
)
"""
)
# Create Inventory Table
# Buat Tabel Inventaris
print("Creating inventory_items table...")
cur.execute(
"""
CREATE TABLE IF NOT EXISTS inventory_items (
id SERIAL PRIMARY KEY,
product_id VARCHAR(50),
product_name VARCHAR(100),
stock_count INTEGER,
warehouse_location VARCHAR(50)
)
"""
)
# Insert Mock Data
# Masukkan Data Mock
print("Inserting mock data...")
cur.execute("TRUNCATE sales_data, inventory_items")
sales_data = [
('2023-01-01', 'P001', 100.00, 'North'),
('2023-01-02', 'P002', 150.50, 'South'),
('2023-01-03', 'P001', 120.00, 'East'),
('2023-01-04', 'P003', 200.00, 'West'),
('2023-01-05', 'P002', 160.00, 'North'),
]
cur.executemany(
"INSERT INTO sales_data (transaction_date, product_id, amount, region) VALUES (%s, %s, %s, %s)",
sales_data,
)
inventory_data = [
('P001', 'Laptop', 50, 'Warehouse A'),
('P002', 'Mouse', 200, 'Warehouse B'),
('P003', 'Keyboard', 150, 'Warehouse A'),
('P004', 'Monitor', 30, 'Warehouse C'),
]
cur.executemany(
"INSERT INTO inventory_items (product_id, product_name, stock_count, warehouse_location) VALUES (%s, %s, %s, %s)",
inventory_data,
)
conn.commit()
cur.close()
conn.close()
print("Database setup complete.")
except Exception as e:
print(f"Error setting up tables: {e}")
if name == "main": create_database() create_tables_and_data() `### 2. Agent 主程序 (main.py)
`importosfromtypingimportAnnotated, Literal, TypedDict, Union, Dictfromdotenvimportload_dotenvfromlangchain_openaiimportChatOpenAIfromlangchain_core.toolsimporttoolfromlangchain_core.messagesimportSystemMessage, HumanMessage, AIMessage, ToolMessagefromlangchain_community.utilitiesimportSQLDatabasefromlangchain_community.agent_toolkitsimportSQLDatabaseToolkitfromlanggraph.graphimportStateGraph, START, END, MessagesStatefromlanggraph.prebuiltimportToolNode, tools_conditionload_dotenv()# --- Configuration ---BASE_URL = os.getenv("BASIC_MODEL_BASE_URL")API_KEY = os.getenv("BASIC_MODEL_API_KEY")MODEL_NAME = os.getenv("BASIC_MODEL_MODEL")DB_URI =f"postgresql://{os.getenv('DB_USER')}:{os.getenv('DB_PASSWORD')}@{os.getenv('DB_HOST')}:{os.getenv('DB_PORT')}/{os.getenv('DB_NAME')}"# --- Database Setup ---db = SQLDatabase.from_uri(DB_URI)# --- Skills Definition ---SKILLS: Dict[str, Dict[str, str]] = {"sales_analytics": {"description":"Berguna untuk menganalisis pendapatan penjualan, tren, dan kinerja regional.","content":"""Anda adalah Pakar Analisis Penjualan.Anda memiliki akses ke tabel 'sales_data'.Skema Tabel:- id: integer (primary key)- transaction_date: date- product_id: varchar(50)- amount: decimal(10, 2)- region: varchar(50)Kueri umum:- Total pendapatan: SUM(amount)- Pendapatan per wilayah: GROUP BY region- Tren penjualan: GROUP BY transaction_date""" },"inventory_management": {"description":"Berguna untuk memeriksa tingkat stok, lokasi produk, dan manajemen gudang.","content":"""Anda adalah Pakar Manajemen Inventaris.Anda memiliki akses ke 'inventory_items' table.Table Schema:
- id: integer (primary key)
- product_id: varchar(50)
- product_name: varchar(100)
- stock_count: integer
- warehouse_location: varchar(50) Common queries:
- Check stock: WHERE product_name = '...'
- Low stock: WHERE stock_count < threshold""" }}# --- Tools ---@tooldefload_skill(skill_name: str)-> str:""" Muat prompt dan skema detail untuk keterampilan tertentu. Keterampilan yang tersedia:
- sales_analytics: Untuk analisis penjualan, pendapatan, dan transaksi.
- inventory_management: Untuk kueri stok, produk, dan gudang. """ skill = SKILLS.get(skill_name) ifnotskill: returnf"Error: Skill '{skill_name}' not found. Available skills:{list(SKILLS.keys())}" returnskill["content"] @tool def run_sql_query(query: str)-> str: """ Eksekusi kueri SQL terhadap database. Hanya gunakan alat ini SETELAH memuat keterampilan yang sesuai untuk memahami skema. """ try: returndb.run(query) exceptExceptionase: returnf"Error executing SQL:{e}" @tool def list_tables()-> str: """Daftar semua tabel yang tersedia di database.""" returnstr(db.get_usable_table_names()) tools = [load_skill, run_sql_query, list_tables]
--- Agent Setup ---
llm = ChatOpenAI( base_url=BASE_URL, api_key=API_KEY, model=MODEL_NAME, temperature=0 ) llm_with_tools = llm.bind_tools(tools)
--- Graph Definition ---
classAgentState(MessagesState):
We can add custom state if needed, but MessagesState is sufficient for simple chat
pass def agent_node(state: AgentState): messages = state["messages"] response = llm_with_tools.invoke(messages) return{"messages": [response]} workflow = StateGraph(AgentState) workflow.add_node("agent", agent_node)workflow.add_node(\ke({"messages": messages}) last_msg = final_state["messages"][-1]ifisinstance(last_msg, AIMessage): print(last_msg.content) messages = final_state["messages"]# Update history print("-"*50)exceptExceptionase: print(f"\nError:{e}")break`
