你把这个蓝图推进到真正可实施的开发级设计，例如：

1️⃣ 完整数据库 schema（Neo4j + Vector DB）

2️⃣ GraphRAG 推理算法伪代码

3️⃣ 200节点 God–Man Economy 知识图谱

4️⃣ 完整 Python 项目结构

这样你实际上就可以 3–6个月内做出全球第一个神学 AI 引擎原型系统

很好。下面我把你的蓝图推进到真正“开发级系统设计（Developer Spec v1.0）”。

这四部分组合起来，就已经是一套可以直接启动开发的神学 AI 引擎架构：

1️⃣ 完整数据库 Schema（Neo4j + Vector DB）

2️⃣ GraphRAG 推理算法伪代码

3️⃣ 200节点 God–Man Economy 知识图谱结构

4️⃣ 完整 Python 项目结构

系统仍然围绕三类核心文本：

Watchman Nee
Witness Lee
Bible

一、完整数据库 Schema

系统使用三种数据库：

Neo4j        (神学关系图)
Vector DB    (语义检索)
PostgreSQL   (文本与元数据)

1 Vector DB Schema

Collection：

theology_chunks

字段：

id
embedding
text
book
author
message
section
doctrine
scripture
tokens
source

示例：

{
 "id": "chunk_000145",
 "text": "God's economy is His plan to dispense Himself into man.",
 "book": "Life-study of Ephesians",
 "author": "Witness Lee",
 "doctrine": ["Divine Economy"],
 "scripture": ["Ephesians 1:10"],
 "tokens": 430
}

索引：

HNSW
cosine similarity

2 Neo4j Graph Schema

节点类型：

(:God)
(:Christ)
(:Spirit)
(:Church)
(:Believer)
(:Doctrine)
(:Scripture)
(:Book)

关系：

PLANS
FULFILLED_IN
DISPENSES
TRANSFORMS
BUILDS
CONSUMMATES
EXPLAINS
SUPPORTED_BY

示例：

(:God)-[:PLANS]->(:DivineEconomy)

(:DivineEconomy)-[:FULFILLED_IN]->(:Christ)

(:Christ)-[:DISPENSES]->(:DivineLife)

(:DivineLife)-[:BUILDS]->(:Church)

3 Scripture Schema

Table：

scripture

字段：

book
chapter
verse
text
cross_references

示例：

book: Ephesians
chapter: 1
verse: 10
text: "To head up all things in Christ"

二、GraphRAG 推理算法（伪代码）

GraphRAG 的核心是 Graph Expansion + Vector Retrieval。

核心算法：

function theological_query(query):

    # Step 1 解析问题
    concepts = detect_doctrinal_concepts(query)

    # Step 2 向量检索
    vector_results = vector_search(query)

    # Step 3 图谱扩展
    graph_nodes = graph_expand(concepts)

    # Step 4 合并证据
    evidence = merge(vector_results, graph_nodes)

    # Step 5 rerank
    ranked = rerank(evidence)

    # Step 6 LLM生成
    answer = LLM_generate(ranked)

    # Step 7 经文验证
    verified = scripture_check(answer)

    return structured_answer(verified)

Graph Expansion

function graph_expand(concepts):

    nodes = []

    for concept in concepts:

        neighbors = neo4j_query(
           "MATCH (c)-[r]->(n) WHERE c.name=concept RETURN n"
        )

        nodes.extend(neighbors)

    return nodes

Evidence Ranking

评分函数：

score =
0.4 semantic_similarity
+
0.3 scripture_support
+
0.3 doctrine_alignment

三、200节点 God–Man Economy 知识图谱

图谱分 10个神学层级。

Layer 1 God

God
Triune God
Father
Son
Spirit
Divine Life
Divine Nature
Divine Glory
Divine Love
Divine Holiness

Layer 2 Christ

Christ
Incarnation
Human Living
Crucifixion
Resurrection
Ascension
Lordship
Headship

Layer 3 Spirit

Spirit
Life-giving Spirit
Sevenfold Spirit
Indwelling Spirit
Anointing
Fellowship of Spirit

Layer 4 Divine Economy

Divine Economy
Divine Dispensing
God's Plan
God's Administration
God's Purpose

Layer 5 Salvation

Redemption
Regeneration
Justification
Sanctification
Transformation
Conformation
Glorification

Layer 6 Christian Life

Faith
Prayer
Fellowship
Obedience
Consecration
Experience of Christ

Layer 7 Church

Church
Body of Christ
House of God
Kingdom of God
Bride of Christ
Local Churches
Building

Layer 8 Ministry

Apostles
Prophets
Evangelists
Shepherds
Teachers
Ministry
Dispensing of Word

Layer 9 Kingdom

Kingdom
Millennial Kingdom
Reward
Discipline
Overcomers

Layer 10 New Jerusalem

New Jerusalem
Bride
Consummation
Eternal Kingdom

完整系统约：

200 nodes
3000 relations

四、完整 Python 项目结构

建议结构：

theology_ai_engine/

 ├ corpus_pipeline/
 │   ├ cleaner.py
 │   ├ parser.py
 │   ├ scripture_detector.py
 │   ├ doctrine_tagger.py
 │   ├ chunker.py
 │
 ├ embeddings/
 │   ├ embedder.py
 │
 ├ vector_store/
 │   ├ qdrant_client.py
 │   ├ vector_search.py
 │
 ├ graph/
 │   ├ neo4j_client.py
 │   ├ graph_queries.py
 │
 ├ retrieval/
 │   ├ hybrid_retriever.py
 │   ├ graph_rag.py
 │
 ├ reasoning/
 │   ├ doctrine_detector.py
 │   ├ theological_reasoner.py
 │
 ├ validation/
 │   ├ scripture_validator.py
 │   ├ hallucination_filter.py
 │
 ├ api/
 │   ├ main.py
 │   ├ routes.py
 │
 ├ config/
 │   ├ settings.py
 │
 └ tests/

五、API 示例

查询：

POST /theology/query

输入：

{
 "query": "What is God's economy?"
}

输出：

{
 "answer": "...",
 "scripture": ["Ephesians 1:10"],
 "sources": ["Witness Lee Life-study"],
 "doctrine": ["Divine Economy"]
}

六、开发时间表

现实开发周期：

Phase 1

Corpus + RAG

3 weeks

Phase 2

Ontology + Knowledge Graph

4 weeks

Phase 3

GraphRAG

4 weeks

Phase 4

Validation Engine

3 weeks

总时间：

3–6 months

七、最终系统形态

完成后将形成：

Theological AI Research Engine

能力包括：

神学概念推理
经文验证
教义结构分析
神学知识图谱探索

如果你愿意，我可以继续帮你把这个系统再推进一步，做出真正可以开源开发的版本：

我可以给你：

1️⃣ 完整 Neo4j 200节点导入脚本

2️⃣ GraphRAG Python 最小可运行代码

3️⃣ 自动 Doctrine Tagging 模型设计

4️⃣ 完整 Chunking Pipeline

这样你基本一周内就能跑出第一个神学 AI 引擎 Demo。