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LangChain Skills 模式实战：构建按需加载知识的 SQL 助手
- Authors
- Name
- Shoukai Huang
LangChain（Photo by insung yoon on Unsplash ）
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。
系统工作流 (Workflow)
3. 开发环境搭建
本项目采用 Python uv 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
步骤一：定义领域技能 (The Knowledge)
我们将技能定义为字典结构，模拟从文件系统或数据库加载的过程。请注意区分 description（供 Agent 决策选型使用）和 content（实际加载的详细上下文）。
SKILLS = {
"sales_analytics": {
"description": "Useful for analyzing sales revenue, trends...",
"content": """... Table Schema: sales_data ..."""
},
"inventory_management": {
"description": "Useful for checking stock levels...",
"content": """... Table Schema: inventory_items ..."""
}
}
步骤二：实现核心工具 (The Capabilities)
Agent 依赖两个关键工具来完成任务：
load_skill(skill_name): 在运行时动态加载指定技能的详情。run_sql_query(query): 执行具体的 SQL 语句。
步骤三：编排 Agent 逻辑 (The Brain)
利用 LangGraph 构建 ReAct Agent。System Prompt 在此处起着关键作用，它指导 Agent 严格遵循 Identify -> Load -> Query 的标准作业程序（SOP）。
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. 运行效果验证
通过运行 test_agent.py，我们对 Sales 和 Inventory 两个不同领域的查询进行了测试。以下是控制台的实际输出日志，展示了 Agent 如何根据问题动态加载技能：
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. 完整源码参考
以下是项目的完整源代码，包含数据库初始化脚本和 Agent 主程序。
1. 数据库初始化 (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
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
DB_NAME = os.getenv("DB_NAME", "agent_platform")
def create_database():
try:
# Connect to default 'postgres' database to create new db
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
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
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
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
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)
import os
from typing import Annotated, Literal, TypedDict, Union, Dict
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage, ToolMessage
from langchain_community.utilities import SQLDatabase
from langchain_community.agent_toolkits import SQLDatabaseToolkit
from langgraph.graph import StateGraph, START, END, MessagesState
from langgraph.prebuilt import ToolNode, tools_condition
load_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": "Useful for analyzing sales revenue, trends, and regional performance.",
"content": """
You are a Sales Analytics Expert.
You have access to the 'sales_data' table.
Table Schema:
- id: integer (primary key)
- transaction_date: date
- product_id: varchar(50)
- amount: decimal(10, 2)
- region: varchar(50)
Common queries:
- Total revenue: SUM(amount)
- Revenue by region: GROUP BY region
- Sales trend: GROUP BY transaction_date
"""
},
"inventory_management": {
"description": "Useful for checking stock levels, product locations, and warehouse management.",
"content": """
You are an Inventory Management Expert.
You have access to the '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 ---
@tool
def load_skill(skill_name: str) -> str:
"""
Load the detailed prompt and schema for a specific skill.
Available skills:
- sales_analytics: For sales, revenue, and transaction analysis.
- inventory_management: For stock, products, and warehouse queries.
"""
skill = SKILLS.get(skill_name)
if not skill:
return f"Error: Skill '{skill_name}' not found. Available skills: {list(SKILLS.keys())}"
return skill["content"]
@tool
def run_sql_query(query: str) -> str:
"""
Execute a SQL query against the database.
Only use this tool AFTER loading the appropriate skill to understand the schema.
"""
try:
return db.run(query)
except Exception as e:
return f"Error executing SQL: {e}"
@tool
def list_tables() -> str:
"""List all available tables in the database."""
return str(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 ---
class AgentState(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("tools", ToolNode(tools))
workflow.add_edge(START, "agent")
workflow.add_conditional_edges("agent", tools_condition)
workflow.add_edge("tools", "agent")
app = workflow.compile()
# --- Main Execution ---
if __name__ == "__main__":
system_prompt = """You are a helpful SQL Assistant.
You have access to specialized skills that contain database schemas and domain knowledge.
To answer a user's question:
1. Identify the relevant skill (sales_analytics or inventory_management).
2. Use the 'load_skill' tool to get the schema and instructions.
3. Based on the loaded skill, write and execute a SQL query using 'run_sql_query'.
4. Answer the user's question based on the query results.
Do not guess table names. Always load the skill first.
"""
print("SQL Assistant initialized. Type 'quit' to exit.")
print("-" * 50)
messages = [SystemMessage(content=system_prompt)]
# Pre-warm connection check
try:
print(f"Connected to database: {DB_URI.split('@')[-1]}")
except Exception as e:
print(f"Database connection warning: {e}")
while True:
try:
user_input = input("User: ")
if user_input.lower() in ["quit", "exit"]:
break
messages.append(HumanMessage(content=user_input))
# Stream the execution
print("Agent: ", end="", flush=True)
final_response = None
for event in app.stream({"messages": messages}, stream_mode="values"):
# In 'values' mode, we get the full state. We just want to see the last message if it's new.
last_message = event["messages"][-1]
# Update our message history with the latest state
pass
# After stream finishes, the last state has the final answer
final_state = app.invoke({"messages": messages})
last_msg = final_state["messages"][-1]
if isinstance(last_msg, AIMessage):
print(last_msg.content)
messages = final_state["messages"] # Update history
print("-" * 50)
except Exception as e:
print(f"\nError: {e}")
break