使用 LangGraph 在 Snowflake 动态表上实现多智能体 KAG

本指南将逐步展示如何从原始材料构建出可运行的系统，适用于[2026]年使用Snowflake动态表、LangGraph和Neo4j构建多智能体KAG系统。重点在于可操作的步骤、明确的检查点，以及可直接放入代码仓库而无需猜测其用途的代码。

数据工程领域的“时尚达人”

对于数据工程领域的“时尚达人”而言，在修改代码之前应先明确输入内容、该步骤的负责人以及完成标准。操作人员应当能够从已知的检查点重新运行该步骤，而无需猜测其中的隐藏状态。应将此阶段视为输入与经过验证的输出之间的契约：为相关成果命名，定义成功标准，杜绝默默完成部分工作的情况。在批量导入数据之前应使用约束条件和索引；通过对业务键设置唯一性，可将后续的合并操作转化为可预测的插入或更新操作，从而避免数据重复问题。

为何这对多智能体KAG系统至关重要

在修改代码之前，需明确多智能体KAG系统中各步骤的输入、执行者以及终止条件。操作人员应能够从已知的检查点重新运行该步骤，而无需猜测隐藏状态。除了功能结果外，还需记录执行时间以及令牌或查询成本。提前了解这些成本可以避免在系统从演示环境切换到共享环境时出现意外费用。在批量导入数据之前，应先设置约束条件和索引。通过对业务键进行唯一性约束，后续的合并操作将变为可预测的插入或更新操作，而非导致数据重复的问题。

架构

在修改代码之前，针对架构部分需明确输入参数、该步骤的负责人以及终止标准。操作人员应能够从已知的检查点重新运行该步骤，而无需猜测隐藏状态。 配置信息应置于应用程序代码之外。环境文件、密钥存储以及功能标志应集中存放于一个位置，这样操作人员无需查看整个系统结构即可进行审计。 在批量导入数据之前应使用约束条件和索引。通过对业务键设置唯一性，可确保后续的合并操作为可预测的插入或更新操作，而非出现大量重复数据的问题。

前提条件

在修改代码之前，需先明确前置条件，包括输入数据、该步骤的负责人以及终止标准。操作人员应能够从已知的检查点重新运行该步骤，而无需猜测隐藏状态。 需同时记录正常流程和异常恢复流程。重试机制、人工审核环节以及错误处理都是产品功能的一部分，而非后续需要补充的内容。 在批量导入数据之前应使用约束条件和索引。通过对业务键设置唯一性，可将后续的合并操作转化为可预测的插入或更新操作，从而避免出现大量重复数据的问题。

# Python packages
pip install langgraph langchain langchain-openai neo4j snowflake-connector-python \
    snowflake-snowpark-python sentence-transformers pydantic

# Neo4j (Docker)
docker run -d --name neo4j-kag \
    -p 7474:7474 -p 7687:7687 \
    -e NEO4J_AUTH=neo4j/password123 \
    -e NEO4J_PLUGINS='["apoc","graph-data-science"]' \
    neo4j:5.15-enterprise

--Snowflake Dynamic Tables Setup
1.1 Create Adaptive Warehouse
-- Adaptive warehouse with high query performance for mixed workloads
-- Reference: https://docs.snowflake.com/en/user-guide/warehouses-adaptive
CREATE ADAPTIVE WAREHOUSE ADAPTIVE_KAG_WH
    WITH MAX_QUERY_PERFORMANCE_LEVEL = XLARGE
    QUERY_THROUGHPUT_MULTIPLIER = 4;

创建源表

在创建源表时，应在修改代码之前明确输入参数、该步骤的负责人以及终止条件。操作人员应能够从已知的检查点重新运行该步骤，而无需猜测隐藏状态。 优先选择小型、可测试的单元，而非庞大的脚本。当某个步骤失败时，故障应指向单一责任主体，而非混乱的整个流程。 在批量导入数据之前先使用约束条件和索引。通过对业务键设置唯一性，可将后续的合并操作转化为可预测的插入或更新操作，从而避免出现大量重复数据的问题。

CREATE OR REPLACE DATABASE KAG_DB;
CREATE OR REPLACE SCHEMA KAG_DB.PIPELINE;

USE SCHEMA KAG_DB.PIPELINE;
-- Source: Raw documents
CREATE OR REPLACE TABLE DOCUMENTS (
    doc_id VARCHAR(64) DEFAULT UUID_STRING(),
    title VARCHAR(500),
    content TEXT,
    source_type VARCHAR(50),
    created_at TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP(),
    metadata VARIANT
);
-- Source: Document metadata
CREATE OR REPLACE TABLE METADATA (
    doc_id VARCHAR(64),
    category VARCHAR(100),
    tags ARRAY,
    author VARCHAR(200),
    published_date DATE,
    confidence_score FLOAT
);
-- Source: User feedback for reinforcement
CREATE OR REPLACE TABLE USER_FEEDBACK (
    feedback_id VARCHAR(64) DEFAULT UUID_STRING(),
    query TEXT,
    response TEXT,
    rating INT,
    corrections TEXT,
    timestamp TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP()
);

Dynamic Table Pipeline (Incremental Refresh)
-- Layer 1: Raw document normalization
-- Incremental refresh: only processes new/changed rows
CREATE OR REPLACE DYNAMIC TABLE RAW_DOCUMENTS
    TARGET_LAG = '1 minute'
    WAREHOUSE = ADAPTIVE_KAG_WH
    REFRESH_MODE = INCREMENTAL
AS
SELECT
    d.doc_id,
    d.title,
    d.content,
    d.source_type,
    d.created_at,
    m.category,
    m.tags,
    m.author,
    m.confidence_score,
    LENGTH(d.content) AS content_length,
    HASH(d.content) AS content_hash
FROM DOCUMENTS d
LEFT JOIN METADATA m ON d.doc_id = m.doc_id;


-- Layer 2: Entity extraction using Cortex AI
-- Incremental refresh: only extracts from new documents
CREATE OR REPLACE DYNAMIC TABLE EXTRACTED_ENTITIES
    TARGET_LAG = '5 minutes'
    WAREHOUSE = ADAPTIVE_KAG_WH
    REFRESH_MODE = INCREMENTAL
AS
SELECT
    doc_id,
    title,
    content,
    category,
    SNOWFLAKE.CORTEX.AI_EXTRACT(
        content,
        '{
            "entities": [
                {"name": "entity_name", "type": "string", "description": "Name of person, org, concept"},
                {"name": "entity_type", "type": "string", "description": "Type: PERSON, ORG, CONCEPT, LOCATION, EVENT"},
                {"name": "properties", "type": "object", "description": "Key attributes of the entity"}
            ]
        }'
    ) AS extracted_data,
    content_hash,
    created_at
FROM RAW_DOCUMENTS
WHERE content_length > 50;


-- Layer 3: Entity relationships extraction
-- Incremental refresh for relationship triples
CREATE OR REPLACE DYNAMIC TABLE ENTITY_RELATIONS
    TARGET_LAG = '10 minutes'
    WAREHOUSE = ADAPTIVE_KAG_WH
    REFRESH_MODE = INCREMENTAL
AS
SELECT
    doc_id,
    title,
    SNOWFLAKE.CORTEX.AI_EXTRACT(
        content,
        '{
            "relations": [
                {"name": "subject", "type": "string", "description": "Source entity"},
                {"name": "predicate", "type": "string", "description": "Relationship type"},
                {"name": "object", "type": "string", "description": "Target entity"},
                {"name": "confidence", "type": "number", "description": "Confidence 0-1"}
            ]
        }'
    ) AS relation_data,
    category,
    created_at
FROM EXTRACTED_ENTITIES;


-- Layer 4: Embedding generation for vector search
-- DOWNSTREAM lag: refreshes only when upstream changes
CREATE OR REPLACE DYNAMIC TABLE ENTITY_EMBEDDINGS
    TARGET_LAG = DOWNSTREAM
    WAREHOUSE = ADAPTIVE_KAG_WH
    REFRESH_MODE = AUTO
AS
SELECT
    doc_id,
    f.value:entity_name::VARCHAR AS entity_name,
    f.value:entity_type::VARCHAR AS entity_type,
    SNOWFLAKE.CORTEX.EMBED_TEXT_1024('e5-base-v2',
        entity_name || ' ' || COALESCE(f.value:properties::VARCHAR, '')
    ) AS embedding_vector,
    created_at
FROM EXTRACTED_ENTITIES,
    LATERAL FLATTEN(input => extracted_data:entities) f
WHERE f.value:entity_name IS NOT NULL;


-- Layer 5: Knowledge Graph sync queue
-- Fast refresh for near-real-time Neo4j sync
CREATE OR REPLACE DYNAMIC TABLE KG_SYNC_QUEUE
    TARGET_LAG = '1 minute'
    WAREHOUSE = ADAPTIVE_KAG_WH
    REFRESH_MODE = INCREMENTAL
AS
SELECT
    r.doc_id,
    f.value:subject::VARCHAR AS subject,
    f.value:predicate::VARCHAR AS predicate,
    f.value:object::VARCHAR AS object_entity,
    f.value:confidence::FLOAT AS confidence,
    r.category AS domain,
    r.created_at,
    'PENDING' AS sync_status
FROM ENTITY_RELATIONS r,
    LATERAL FLATTEN(input => r.relation_data:relations) f
WHERE f.value:confidence::FLOAT > 0.7;

Neo4j 知识图谱配置

在配置 Neo4j 知识图谱时，应在修改代码之前明确输入参数、该步骤的负责人以及终止标准。操作人员应能够从已知的检查点重新运行该步骤，而无需猜测隐藏状态。 将此阶段视为输入与已验证输出之间的契约。为相关成果命名，定义成功检测标准，并拒绝默许的半完成状态。 在批量导入数据之前先使用约束条件和索引。通过对业务键设置唯一性，可将后续的合并操作转化为可预测的插入或更新操作，从而避免数据重复问题。

--Graph Schema (Cypher)
// Constraints and indexes
CREATE CONSTRAINT entity_name IF NOT EXISTS
FOR (e:Entity) REQUIRE e.name IS UNIQUE;

CREATE CONSTRAINT doc_id IF NOT EXISTS
FOR (d:Document) REQUIRE d.doc_id IS UNIQUE;
CREATE INDEX entity_type_idx IF NOT EXISTS
FOR (e:Entity) ON (e.type);
CREATE INDEX entity_domain_idx IF NOT EXISTS
FOR (e:Entity) ON (e.domain);
// Vector index for semantic search
CREATE VECTOR INDEX entity_embedding IF NOT EXISTS
FOR (e:Entity)
ON (e.embedding)
OPTIONS {indexConfig: {
    `vector.dimensions`: 1024,
    `vector.similarity_function`: 'cosine'
}};

Graph Data Model
// Entity node structure
(:Entity {
    name: STRING,
    type: STRING,       // PERSON, ORG, CONCEPT, LOCATION, EVENT
    domain: STRING,     // category from source
    properties: MAP,
    embedding: LIST<FLOAT>,
    created_at: DATETIME,
    source_doc_id: STRING
})

// Document node
(:Document {
    doc_id: STRING,
    title: STRING,
    category: STRING,
    created_at: DATETIME
})

// Relationships
(:Entity)-[:RELATES_TO {predicate: STRING, confidence: FLOAT, doc_id: STRING}]->(:Entity)
(:Entity)-[:MENTIONED_IN]->(:Document)
(:Document)-[:BELONGS_TO]->(:Category)

Load Sample Data
-- Insert sample documents
INSERT INTO KAG_DB.PIPELINE.DOCUMENTS (title, content, source_type) VALUES
('Renewable Energy Report 2024',
 'SolarTech Inc. partnered with GreenGrid Corp to deploy 500MW of solar capacity across Texas. CEO Jane Smith announced the partnership at CleanTech Summit 2024. The project received $2B funding from CleanEnergy Fund.',
 'report'),
('AI in Healthcare',
 'MedAI Corp developed a diagnostic tool using deep learning for early cancer detection. Dr. John Chen leads the research team. The FDA approved the tool in March 2024 after clinical trials at Mayo Clinic.',
 'research'),
('Supply Chain Analysis',
 'GlobalLogistics partnered with TechManufacture for semiconductor distribution. The partnership reduced delivery times by 40%. ChipDesign Corp supplies the core components manufactured in Taiwan.',
 'analysis');

INSERT INTO KAG_DB.PIPELINE.METADATA (doc_id, category, tags, author)
SELECT doc_id, 'energy', ARRAY_CONSTRUCT('solar', 'partnership', 'funding'), 'Analyst Team'
FROM DOCUMENTS WHERE title LIKE '%Renewable%';

查询示例

对于查询示例，应在修改代码之前明确输入参数、该步骤的负责人以及终止标准。操作人员应能够从已知的检查点重新运行该步骤，而无需猜测隐藏状态。

# Example 1: Knowledge Graph query
answer = run_kag_query(
    "What companies are connected to renewable energy and what are their relationships?"
)

# Example 2: SQL Analytics query
answer = run_kag_query(
    "How many entities were extracted in the last 24 hours grouped by type?"
)
# Example 3: Hybrid query
answer = run_kag_query(
    "Which organizations have the most connections and what is their average confidence score?"
)

def route_query(state: KAGState) -> KAGState: ...
def kg_agent(state: KAGState) -> KAGState: ...
def sql_agent(state: KAGState) -> KAGState: ...
def validation_agent(state: KAGState) -> KAGState: ...
def synthesis_agent(state: KAGState) -> KAGState: ...

结论

操作检查清单