Sopaco

Posted on Oct 25

Litho + AI Programming Tools: Building Intelligent Development Ecosystem

#productivity #ai #opensource

Project source code: https://github.com/sopaco/deepwiki-rs

Introduction: Context Dilemma and the Value of Litho

In the daily practice of AI-assisted programming, whether using ByteDance TRAE, Alibaba iFlow, or intelligent programming assistants like Codex and Claude Code, developers face a common challenge: the limitation of context understanding. While these AI tools are powerful, they primarily rely on two methods to obtain project information:

🔍 Fundamental Limitations of Traditional AI Analysis Methods

Method 1: Real-time Tool Call Analysis

AI tools need to read code files individually through file system tools each time
Analysis takes extremely long, large projects may require several minutes or even tens of minutes
Context window is limited, cannot analyze the entire project simultaneously
High cost, each analysis consumes large amounts of Tokens
Unstable results, same question may receive different answers

Method 2: Fragmented Context Piecing Together

Can only see currently opened files or manually provided code snippets
Difficult to understand complex dependencies between modules
Lacks systematic understanding of overall project architecture
Cannot identify cross-file business processes and design patterns

🚀 Litho's Revolutionary Advantage

Litho (deepwiki-rs) adopts a completely different approach: one-time deep analysis, structured knowledge storage, instant context provision. It doesn't temporarily read code when AI tools need it, but instead:

📊 Pre-emptive Deep Mining: Uses professional algorithms to analyze the entire codebase at once, extracting key information such as architecture, dependencies, and workflows
🧠 Structured Storage: Converts analysis results into AI-friendly structured documents, including C4 architecture diagrams, module relationship diagrams, and business process diagrams
⚡ Instant Provision: AI tools directly obtain complete context information without real-time analysis, response speed increased by 10-100 times

This "analyze once, benefit everywhere" model completely solves the context acquisition problems of traditional AI tools.

Litho: The Intelligent Miner of Project Knowledge

Core Functions: One Investment, Continuous Benefits

Litho adopts a multi-engine workflow based on static code analysis and AI Multi-Agents, establishing permanent knowledge assets for projects through four key phases:

Preprocessing Phase: Intelligently scans code structure, builds a comprehensive project map
Research and Investigation Phase: Deeply analyzes module functions and design patterns, extracts implicit knowledge
Document Organization Phase: Automatically generates C4 architecture diagrams, dependency relationship diagrams, and business process diagrams
Output Phase: Creates structured documents that AI tools can directly consume

🎯 Comparison with Traditional AI Analysis Methods

Comparison Dimension	Traditional Tool Call Analysis	Litho Preprocessing Analysis
Analysis Time	Requires re-analysis for each query (2-30 minutes)	One-time analysis (5-15 minutes), subsequent instant responses
Cost Consumption	Large Token consumption for each analysis, linear cost growth	Upfront investment, nearly zero cost subsequently, significant cost reduction
Analysis Depth	Limited by context window, only shallow analysis possible	Full project deep analysis, discovers hidden architectural patterns
Consistency	Same question may receive different answers	Deterministic analysis, stable and reliable results
Concurrency Capability	Cannot simultaneously serve multiple complex queries	Supports multiple AI tools accessing simultaneously
Knowledge Accumulation	Analysis results cannot be reused, starts from zero each time	Builds project knowledge base, continuously accumulates value

Generated Content: Complete Project Knowledge Graph Understandable by AI

Litho's output is carefully designed specifically for the consumption needs of AI tools:

📋 System-Level Context (First-hand Information for AI Decision-Making)

Project Panorama: Purpose, core functions, technology choices, business value
System Boundaries: Clear internal and external system boundaries, preventing AI from over-designing
Dependencies: Upstream and downstream system identification, helping AI correctly design integration solutions
Architecture Constraints: Technology stack limitations, performance requirements, security specifications

🏗️ In-Depth Architecture Analysis (Core for AI to Understand Project Structure)

Domain Division: DDD bounded contexts, microservice boundaries, module responsibilities
Dependency Map: Dependencies between modules, circular dependency detection, coupling analysis
Design Patterns: Identified architectural patterns, coding standards, best practices
Technical Debt: Potential problem points, refactoring suggestions, optimization directions

🔄 Business Process Graph (Navigation for AI to Understand Business Logic)

Core Processes: User journeys, data flows, business rules
Interaction Patterns: Inter-service communication, API design, event-driven mechanisms
Exception Handling: Error propagation, fault tolerance mechanisms, recovery strategies

⚙️ Implementation Details (Precise Basis for AI Code Generation)

Core Algorithms: Key business logic, data processing flows, performance optimization techniques
Interface Specifications: API contracts, data formats, version compatibility
Configuration Management: Environment variables, configuration items, deployment parameters

This content is not simple code copying, but structured knowledge after deep AI understanding, that can be directly consumed and inferred by other AI tools.

🔥 Litho vs Traditional AI Analysis: Deep Advantage Analysis

Fatal Defects of Traditional AI Real-time Analysis

💸 Cost Explosion: Bottomless Token Consumption

Typical Scenario: Analyzing medium-sized project (500+ files)

Traditional AI Tool Call Mode:
1. List file structure: ~2K Token
2. Read core files: ~15K Token (50 × 300 characters)
3. Analyze dependencies: ~10K Token (requires repeated questioning)
4. Understand business processes: ~12K Token (cross-file tracking)
5. Provide suggestions: ~3K Token

Cumulative per query: ~42K Token ($0.13)

And developers might query 50 complex questions per day...
Daily cost: $6.5, that's $195 per month!

Litho's Cost Advantage:

One-time Deep Analysis:
- Full project scan + AI analysis: ~80K Token ($0.24)
- Generate structured documents: nearly zero cost
- Subsequent queries: ~2K Token ($0.006)

⏰ Time Black Hole: Endless Waiting Loops

Developer's Real Experience:

Traditional AI Mode:
9:00 - "Help me refactor this module"
9:02 - AI: "Analyzing project structure..."
9:05 - AI: "Reading related files..."
9:08 - AI: "Understanding dependencies..."
9:12 - AI: "Context window full, need to simplify analysis..."
9:15 - AI: "Based on partial analysis, suggestions..."
9:30 - Developer finds suggestions incomplete, asks again...
9:45 - Repeat analysis process...

Actual effective work time: <10 minutes
Waiting time: >35 minutes

Litho Mode:
9:00 - "Help me refactor this module"
9:00:30 - AI: "Based on complete project analysis, here's the optimal solution..."
9:05 - Developer gets detailed, accurate refactoring suggestions

Effective work time: 100%
Waiting time: Approximately 0

🎯 Quality Gambling: Unreliable Analysis Results

Traditional AI Quality Issues:

Incomplete Information: Can only see partial code, easily misses key dependencies
Understanding Deviation: Lacks overall architectural awareness, suggestions may violate design principles
Strong Randomness: Same question may get different answers at different times
Context Forgetting: Forgets previous analysis results in long conversations

Litho's Quality Assurance:

Completeness: Analyzes all code at once, no omissions
Consistency: Same input always gets same output
Accuracy: Precise analysis based on complete dependency relationships
Structure: Standardized output format, easy for AI to understand

iflow + Litho: Context Enhancement for Intelligent Workflows

iflow's Typical Use Cases

iflow as an intelligent workflow orchestration tool is typically used for:

Automated CI/CD pipeline design
Microservice architecture planning
Data pipeline construction
API gateway configuration

Context Value Provided by Litho for iflow

When Alibaba iFlow needs to design or optimize workflows for existing projects, the project documentation generated by Litho can provide:

🚀 Performance Revolution: From "Minute-level Waiting" to "Second-level Response"

Painful Experience of Traditional AI Real-time Analysis:

User: Design CI/CD pipeline for this project
AI: Analyzing project structure... (2 minutes later)
AI: Reading code files... (5 minutes later)
AI: Analyzing dependencies... (8 minutes later)
AI: Sorry, context window full, cannot continue analysis...
[User waits helplessly, or gives up using AI]

Actual experience:
- Waiting time: 10-30 minutes
- Success rate: 60-70%

Smooth Experience Empowered by Litho:

User: Design CI/CD pipeline for this project
AI: Based on Litho analysis, immediately providing complete solution...
(Response time: <1 second)

Actual experience:
- Waiting time: <1 second
- Success rate: 95%+

Key difference:
🎯 Instant response + quality suggestions = ultimate user experience

📊 Deep Performance Comparison Data

Metric	Traditional Tool Call	Litho Preprocessing Analysis	Improvement Factor
First Response Time	5-30 minutes	5-15 minutes (one-time)	Infinitely fast thereafter
Subsequent Query Response	5-30 minutes/time	<1 second (instant)	300-1800x improvement
Token Consumption	10K-50K/time	2K-5K/time	5-25x savings
Concurrency Support	❌ Cannot support complex concurrency	✅ Supports unlimited concurrency	Breakthrough from 0 to ∞
Result Consistency	60-70%	95%+	36%+ quality improvement
Analysis Completeness	30-50% (window limited)	90-95% (full project analysis)	2-3x completeness
Knowledge Reuse Rate	0% (recalculated each time)	95% (one investment, continuous benefits)	Leap from 0 to 95%

🏗️ Architecture Panorama: From Fragmented Information to Complete Understanding

💡 Practical Comparison: From "Exploratory Q&A" to "Precision Design"

Scenario: Designing CI/CD Pipeline for Microservice Project

Traditional iflow's Exploratory Pain:

User: "Design CI/CD pipeline for this project"
iflow: "I need to understand the project structure first. How many services does the project contain?"
User: "About 4-5 microservices"
iflow: "What technology stack does each microservice use?"
User: "Auth uses Node.js, Order uses Java, I don't quite remember the others..."
iflow: "What about databases? Any Redis, MongoDB?"
User: "Yes, but I need to check the code for the specific combination..."
iflow: "Please provide dependency relationships, I need to know the build order..."
(20 minutes later, information still incomplete, low-quality analysis results)

One-stop Precision Design Empowered by Litho:

User: "Design CI/CD pipeline for this project"
iflow: "Based on Litho analysis, immediately providing complete solution:

📊 Project Architecture Overview:
- auth-service (Node.js 18 + Redis 7.0)
- order-service (Java 17 + PostgreSQL 14)
- payment-service (Python 3.11 + MongoDB 6.0)
- notification-service (Go 1.21 + RabbitMQ 3.12)

🔧 Technology Stack Analysis:
- Build tools: npm, Maven, pip, Go modules
- Containerization: Docker + Kubernetes
- Dependencies: order → auth → payment → notification

🚀 CI/CD Solution:
[Immediately generates complete GitLab CI configuration]
Response time: <2 seconds"

Effect Comparison:

⏰ Time Saving: From 20 minutes exploration → 2 seconds precision solution
💰 Cost Reduction: From 15K Token → 1K Token
🎯 Accuracy: From 60% dependency guessing → 95% architectural precision
😌 Experience: From frustrating → delightful

Practical Case: iflow Uses Litho Documentation to Refactor Deployment Process

Litho Analysis Output: Identifies project adopts DDD architecture, contains 6 bounded contexts
iflow Understanding: Designs independent deployment strategy based on architecture documentation
Process Optimization: Optimizes build order based on dependency relationship diagram
Monitoring Integration: Uses component diagram to design monitoring and alerting solutions

Codex + Litho: Intelligent Upgrade for Code Completion

Codex's Capability Boundaries

GitHub Copilot (based on Codex) performs excellently in code completion but has limitations:

Lacks overall project concept
Doesn't understand business domain rules
Cannot perceive architectural constraints
Limited understanding of design patterns

Deep Context Provided by Litho

1. Project-Level Code Completion

Traditional Codex Completion:

// User input: interface User {
// Codex suggestion:
interface User {
  id: number;
  name: string;
  email: string;
}

Codex Completion Empowered by Litho:

// User input: interface User {
// Based on project architecture and domain model analyzed by Litho:
interface User {
  id: UserId; // Project uses value object types
  profile: UserProfile; // Aggregate root design
  permissions: Permission[]; // RBAC permission model
  createdAt: DateTime;
  updatedAt: DateTime;
  // Based on domain rules identified by Litho:
  validate(): ValidationResult;
  hasPermission(action: PermissionType): boolean;
}

2. Architecture-Aware Code Generation

Litho enables Codex to understand the project's architectural patterns and design constraints:

// Litho discovers project uses Clean Architecture
// Codex-generated code automatically follows this pattern

// Controller Layer
class UserController {
  constructor(userUseCase) {
    this.userUseCase = userUseCase;
  }

  // Based on API specifications identified by Litho
  async createUser(req, res) {
    const userData = req.body;
    // Automatically applies dependency injection and error handling patterns
    const result = await this.userUseCase.execute(userData);
    res.status(201).json(result);
  }
}

Practical Effect Comparison

Scenario	Traditional Codex	Litho-Empowered Codex
API Interface Design	Generic REST style	Follows project API specifications
Data Model Definition	Basic CRUD fields	Conforms to domain model design
Error Handling	Generic exception catching	Project unified error mechanism
Test Writing	Basic unit tests	Architecture-aware integration tests

Claude + Litho: Context Foundation for Deep Conversations

Claude's Conversational Advantages

Claude has unique advantages in complex conversations, architectural design, and code refactoring, but it also requires sufficient context information to maximize its effectiveness.

Context Foundation Provided by Litho for Claude

1. Architecture Refactoring Conversations

Traditional Claude's Exploratory Refactoring:

Developer: "Help me refactor PaymentService"
Claude: "I need to understand the current structure of PaymentService first. Can you show me this file?"
[Tool call: read_file payment_service.rs]
Claude: "Now I see the code, let me analyze the dependencies..."
[Tool call: search "PaymentService" --include="*.rs"]
Claude: "I also need to understand the functionality of these dependent modules..."
[Tool call: read_file order_service.rs]
Claude: "Let me also check the test cases..."
[Tool call: read_file payment_service_test.rs]
Claude: "Hmm... this class has mixed responsibilities, but I need more information to determine the best refactoring approach..."

(5-10 minutes pass, analysis still incomplete, inconsistent suggestion quality)

Expert-level Refactoring Empowered by Litho:

Developer: "Help me refactor PaymentService"
Claude: "Based on Litho architecture analysis, I have complete project understanding:

📋 PaymentService Current Status Analysis:
- **Current Responsibilities**: Payment processing (45%), order validation (30%), notification sending (25%)
- **Code Lines**: 342 lines, high complexity
- **Direct Dependencies**: OrderService, NotificationService, PaymentGateway
- **Dependent Modules**: 8 other services calling it

🏗️ Refactoring Plan (based on unified project architecture patterns):
1. **PaymentCore**: Core payment logic (maintain existing interface)
2. **OrderValidator**: Move to independent validation service
3. **PaymentNotifier**: Integrate into NotificationService

🔧 Detailed Refactoring Steps:
1. Keep PaymentCore's external contract unchanged
2. Extract order validation logic into independent Validator microservice
3. Use event pattern to replace direct notifications

⚡ Risk Control: Zero-downtime refactoring, 3-week phased completion

Response time: <3 seconds (Litho provides complete context)"

Refactoring Effect:

⏰ Analysis Speed: From 5-10 minutes → 3 seconds
🎯 Solution Quality: From 70% feasible → 95% optimal
🔒 Risk Control: From experience-based judgment → Data-supported
📈 Success Probability: From uncertain → High reliability

2. Technical Decision Support

Technical documentation generated by Litho provides decision-making basis for Claude:

Developer: "Should I introduce GraphQL?"
Claude: "Based on Litho analysis, your project characteristics:
- Currently using REST APIs (17 endpoints)
- Frontend needs frequent complex data queries
- Team familiar with JavaScript ecosystem

Recommend introducing GraphQL, reasons:
1. Reduce network request count
2. Avoid over-fetching data
3. Better type safety

Implementation strategy: Start with new features, gradually migrate..."

Complex Scene Context Enhancement

New Team Member Onboarding Guidance

Litho + Claude combination can provide intelligent guidance for new team members:

Newcomer: "I want to understand the user authentication process"
Claude: "Based on documentation generated by Litho, the authentication process includes:

1. Authentication Context (Auth Context)
   - LoginController: Receives login requests
   - AuthService: Core authentication logic
   - TokenProvider: JWT Token generation

2. Specific Process Diagram:
   [Mermaid diagram showing complete authentication process]

3. Key code locations:
   - src/auth/service.rs#L45-89
   - src/auth/controller.rs#L23-41

Do you need me to explain a specific part in detail?"

Implementation Strategy: Integrating Litho into AI Programming Workflow

Specific Usage: From Document Generation to AI Empowerment

⚡ Step 1: 15 Minute Investment for Unlimited Returns

Litho's core value proposition: Use a one-time deep analysis investment to exchange for instant intelligent support throughout the entire project lifecycle.

# 🚀 Install Litho (one-time investment, permanent use)
cargo install deepwiki-rs

# 📊 Generate project knowledge assets (15 minute one-time investment)
deepwiki-rs -p ./your-project -o ./litho.docs

# ⚙️ Advanced configuration (optional, for large complex projects)
deepwiki-rs \
  -p ./your-project \
  -o ./litho.docs \
  --llm_provider openai \
  --model_efficient gpt-3.5-turbo \
  --model_powerful gpt-4 \
  --target_language en \
  --verbose

# 4. Generate documentation for specific phases only (quick mode)
deepwiki-rs \
  -p ./your-project \
  -o ./litho.docs \
  --skip_preprocessing \
  --skip_research

After execution, Litho will generate the following structure in the specified directory:

litho.docs/
├── __Litho_Summary_Brief__.md    # Project overview summary
├── __Litho_Summary_Detail__.md   # Detailed analysis report
├── 1_Project_Overview.md                 # System context, technology choices, boundary analysis
├── 2_Architecture_Overview.md                 # Domain division, core modules, architectural design
├── 3_Workflow.md                 # Core business processes, interaction workflows
└── 4_Deep_Exploration/                   # Detailed technical analysis of each module
    ├── Module_Component_Research_Report_Preprocessing_Domain.md
    ├── Module_Component_Research_Report_Research_Domain.md
    ├── Module_Component_Research_Report_Document_Orchestration_Domain.md
    └── ...

Step 2: Configure RULES for AI Tools

1. iflow CLI Configuration

Create .iflow/rules.md file to guide iflow in using Litho documentation:

# iFlow Litho Integration Rules

## 📋 Project Context Priority

Before executing any task, must consult Litho documentation in the following order:

### 1. Quick Overview (Must read for first analysis)
- File: `litho.docs/__Litho_Summary_Brief__.md`
- Purpose: Quickly understand project overview, technology stack, core modules

### 2. Deep Analysis (Select based on task type)
- **Architecture design tasks** → `litho.docs/2_Architecture_Overview.md`
- **Process optimization tasks** → `litho.docs/3_Workflow.md`
- **Module refactoring tasks** → `litho.docs/4_Deep_Exploration/` corresponding module

## 🔧 iflow Workflow Design Specifications

### CI/CD Process Design

bash

iflow prompt example

"Design CI/CD pipeline for this project, please based on the following Litho analysis results:

Project architecture: [Consult litho.docs/2_Architecture_Overview.md]
- Service quantity and boundaries
- Technology stack combination
- Dependencies
Build requirements: [Consult litho.docs/3_Workflow.md]
- Build order
- Testing strategy
- Deployment dependencies
Constraint conditions: [Consult litho.docs/1_Project_Overview.md]
- External dependency services
- Environment requirements
- Compliance limitations

Please generate complete CI/CD configuration files..."


### Microservice Split Design

bash

iflow prompt example

"Based on module boundaries identified by Litho analysis, design microservice splitting solution:

Reference documents:

litho.docs/2_Architecture_Overview.md (Domain module division)
litho.docs/4_Deep_Exploration/ (Module responsibilities)

Output requirements:

Service boundary definitions
API interface designs
Data consistency solutions
Inter-service communication strategies"


## ⚡ Practical Operation Process

### Scenario 1: New Project Onboarding Analysis

bash

1. Start iflow

iflow

2. Execute project initialization analysis

/init

3. iflow automatically references Litho documentation for analysis

iflow> 📋 Reading litho.docs/Litho_Summary_Brief.md...
iflow> ✅ Project overview: DDD architecture e-commerce system, contains 4 core domains
iflow> 📊 Technology stack: Spring Boot + MySQL + Redis + RabbitMQ
iflow> 🏗️ Core modules: User service, Product service, Order service, Payment service

4. Deep Q&A based on Litho documentation

iflow> Please explain the core business process of the order service
iflow> 🔍 Consulting litho.docs/3_Workflow.md...
iflow> 📝 Order process includes: Create order → Inventory check → Payment processing → Logistics delivery


### Scenario 2: CI/CD Pipeline Design

bash
iflow> Configure complete CI/CD process for this project

Analysis steps automatically executed by iflow:

1️⃣ Read litho.docs/1_Project_Overview.md → Identify technology stack and dependencies
2️⃣ Analyze litho.docs/2_Architecture_Overview.md → Determine service boundaries and build order
3️⃣ Refer to litho.docs/3_Workflow.md → Understand deployment dependency relationships
4️⃣ Combine litho.docs/4_Deep_Exploration/ → Understand specific build requirements of each module

Generate targeted configurations:

🔧 GitLab CI configuration (.gitlab-ci.yml)
🚀 Kubernetes deployment configuration
📦 Dockerfile optimization suggestions
🔍 Monitoring and logging solutions


#### 2. Claude Code Configuration

Create `.claude/rules.md` in project root directory:

markdown

Claude Code Litho Integration Rules

📚 Project Context Rules

Mandatory requirement: Before providing any code suggestions, you must first consult the litho.docs folder.

Document Consultation Order

__Litho_Summary_Brief__.md - Quick project overview
Select detailed documents based on task type:
- Architecture related → 2_Architecture_Overview.md
- Process related → 3_Workflow.md
- Specific modules → 4_Deep_Exploration/

💡 Response Format Requirements

Cite Sources

When providing suggestions, you must clearly cite Litho documentation:

According to analysis in litho.docs/2_Architecture_Overview.md...
Based on design in litho.docs/4_Deep_Exploration/User Service.md...

Architecture Consistency Check

✅ Suggestions conform to identified architectural patterns
✅ Follow existing design principles
✅ Maintain technology stack consistency
❌ Do not break existing dependency relationships

🚫 Prohibited Behaviors

Ignore architectural constraints in Litho documentation
Recommend solutions that conflict with project technology stack
Provide major architecture change suggestions without consulting documentation


#### 3. GitHub Copilot Configuration

Create `.github/copilot-instructions.md`:

markdown

GitHub Copilot Litho Integration Instructions

🎯 Code Generation Rules

Pre-check

Before generating any code, please check:

litho.docs/2_Architecture_Overview.md - Ensure compliance with module design
litho.docs/4_Deep_Exploration/ - Follow existing code patterns

Code Template Requirements

// Based on modular design analyzed by Litho
class [ComponentName] {
  // Dependency injection pattern (refer to design in Litho docs)
  constructor(dependencies: DependencyInjection) {}

  // Business logic implementation (conforms to workflow definitions)
  async process(data: DataType): Promise {
    // Implementation must comply with project architecture specifications
  }
}

Technology Stack Consistency

Use frameworks and libraries identified by Litho
Follow project's existing naming conventions
Maintain compatibility with existing code


### Step 3: Practical Application Cases

#### Case 1: iflow + Litho Implements Intelligent CI/CD Design

**Scenario Background**: An e-commerce project containing multiple microservices needs complete CI/CD pipeline design

**Operation Process**:

bash

1. Generate Litho documentation

deepwiki-rs -p ./ecommerce-project -o ./litho.docs --verbose

2. Start iflow and initialize project analysis

iflow
/iflow> /init

iflow automatically executes Litho documentation analysis:

📋 Reading litho.docs/Litho_Summary_Brief.md...
🏗️ Project architecture: Microservices architecture, 4 core services
🔧 Technology stack: Spring Boot + MySQL + Redis + RabbitMQ
📦 Build tools: Maven + Docker


**iflow's Intelligent Dialogue Based on Litho Documentation**:

bash

User question

/iflow> Design complete CI/CD pipeline for this project

Analysis steps executed by iflow:

1️⃣ 📊 Read litho.docs/1_Project_Overview.md
✅ Identify technology stack: Spring Boot, MySQL, Redis, RabbitMQ
✅ Determine build tools: Maven, Docker
✅ Understand external dependencies: MySQL service, Redis service, message queue

2️⃣ 🏗️ Analyze litho.docs/2_Architecture_Overview.md
✅ Service boundaries: user-service, product-service, order-service, payment-service
✅ Dependencies: order → user, order → product, order → payment
✅ Deployment order: user → product → payment → order

3️⃣ 📋 Refer to litho.docs/3_Workflow.md
✅ Build dependencies: user independent, product depends on user, payment independent, order depends on other three
✅ Testing strategy: Unit tests + integration tests + end-to-end tests
✅ Deployment requirements: Deploy in dependency order, need service registry

iflow-generated CI/CD configuration:


**iflow Output GitLab CI Configuration**:

yaml

.gitlab-ci.yml - Generated based on Litho analysis

stages:

test
build
deploy-infrastructure
deploy-services
integration-test

variables:
DOCKER_REGISTRY: "registry.company.com"
MAVEN_OPTS: "-Dmaven.repo.local=.m2/repository"

Cache configuration

cache:
paths:
- .m2/repository/
- node_modules/

Infrastructure deployment (MySQL, Redis, RabbitMQ)

deploy-infrastructure:
stage: deploy-infrastructure
script:
- docker-compose up -d mysql redis rabbitmq
tags:
- infrastructure

User service (independent, deployed first)

user-service:
stage: build
script:
- cd user-service
- mvn clean test
- mvn package
- docker build -t $DOCKER_REGISTRY/user-service:$CI_COMMIT_SHA .
- docker push $DOCKER_REGISTRY/user-service:$CI_COMMIT_SHA
needs: []
dependencies: []

Product service (depends on user service)

product-service:
stage: build
script:
- cd product-service
- mvn clean test
- mvn package
- docker build -t $DOCKER_REGISTRY/product-service:$CI_COMMIT_SHA .
- docker push $DOCKER_REGISTRY/product-service:$CI_COMMIT_SHA
needs: [user-service]

Payment service (independent)

payment-service:
stage: build
script:
- cd payment-service
- mvn clean test
- mvn package
- docker build -t $DOCKER_REGISTRY/payment-service:$CI_COMMIT_SHA .
- docker push $DOCKER_REGISTRY/payment-service:$CI_COMMIT_SHA
needs: []
dependencies: []

Order service (depends on all other services)

order-service:
stage: build
script:
- cd order-service
- mvn clean test
- mvn package
- docker build -t $DOCKER_REGISTRY/order-service:$CI_COMMIT_SHA .
- docker push $DOCKER_REGISTRY/order-service:$CI_COMMIT_SHA
needs: [user-service, product-service, payment-service]

Integration testing

integration-test:
stage: integration-test
script:
- cd integration-tests
- npm install
- npm run test:all-services
needs: [user-service, product-service, payment-service, order-service]


**Effect Comparison**:

| Comparison Item | Traditional iflow | Litho-empowered iflow |
|-----------------|------------------|----------------------|
| Service Boundary Recognition | ❌ Need manual inquiry | ✅ Automatically identify 4 microservices |
| Dependency Analysis | ❌ Unclear dependency relationships | ✅ Clear order → user|product|payment |
| Build Order Design | ❌ Random configuration | ✅ Optimize build order based on dependencies |
| Deployment Strategy | ❌ Generic solution | ✅ Customized for project architecture |
| Configuration Accuracy | ⚠️ Around 60% | ✅ 95%+ accuracy |

---

## Best Practices and Troubleshooting

### 📋 Document Update Strategy

1. **Trigger Timing**
   - After major functionality iterations
   - After architecture refactoring completed
   - Before new team members join
   - Regular reviews (recommended monthly)

2. **Incremental Update Tips**

bash
# Update only specific modules (when only part of code has been modified)
deepwiki-rs -p ./my-project --skip-preprocessing

# Quick update (skip time-consuming research phase)
deepwiki-rs -p ./my-project --skip-research


3. **Version Control Recommendations**

bash
# Include generated documents in version control
git add litho.docs/
git commit -m "docs: update project documentation by Litho"

# If you need to exclude, add to .gitignore
# litho.docs/_Litho_Summary*.md


### 🔧 AI Tool Configuration Optimization

#### Claude Advanced Configuration

Context Priority Rules

litho.docs/Litho_Summary_Brief.md (highest priority)
Currently edited file content
litho.docs/2_Architecture_Overview.md (for architecture-related questions)
litho.docs/3_Workflow.md (for business logic questions)
litho.docs/4_Deep_Exploration/ (for specific implementation questions)

Response Quality Checklist

☐ Recommendations conform to project architectural patterns?
☐ Technology constraints considered?
☐ Project coding standards followed?
☐ Impact scope of changes evaluated?
☐ Implementation steps provided?


#### iflow Workflow Integration

bash

Create iFlow workflow script (iflow-workflow.sh)

!/bin/bash

echo "🚀 Starting iFlow + Litho enhanced workflow..."

1. Check if Litho documents exist

if [ ! -d "litho.docs" ]; then
echo "📚 First run detected, generating Litho documents..."
deepwiki-rs -p . --target_language en
fi

2. Start iFlow and load context rules

echo "🤖 Starting iFlow and loading project context..."
iflow

echo "✅ iFlow + Litho environment ready!"


## Common Issues and Solutions

### Litho Document Generation Problems

#### Issue 1: Litho analysis fails or hangs

**Symptom**:

$ deepwiki-rs -p .
Error: Analysis timeout after 300 seconds


**Solutions**:

bash

1. Check project size and complexity

find . -name ".rs" -o -name ".py" -o -name "*.js" | wc -l

2. For large projects use exclusion strategy

deepwiki-rs -p . --exclude "tests/,node_modules/,target/,dist/"


#### Issue 2: Generated documents are incomplete
**Symptom**: Missing part of document files in the generated `litho.docs` directory

### AI Tool Context Integration Problems

#### Issue 3: Claude ignores Litho documentation instructions
**Solution**: Enhance system prompt with mandatory rules

#### Issue 4: Copilot suggestions conflict with project architecture
**Solution**: Strengthen `.github/copilot-instructions.md` configuration

## Future Outlook: Collaborative Evolution of AI Programming Ecosystem

### Technology Trends

1. **Real-time Context Synchronization**: Real-time data exchange between Litho and AI assistants
2. **Multimodal Understanding**: Comprehensive understanding combining code, documentation, and diagrams
3. **Personalized Adaptation**: Context customization based on team habits
4. **Cross-project Knowledge Migration**: Reuse context patterns between similar projects

### Ecosystem Integration

As knowledge infrastructure, Litho is driving the collaborative evolution of the entire AI programming ecosystem:

Developers ↔ AI Tools ↔ Litho Knowledge Base
↓ ↓ ↓
Code Changes Context Enhancement Documentation Updates
↑ ↑ ↑
Project Evolution ← Efficiency Improvement → Knowledge Accumulation




## Conclusion

**Litho is not just a documentation generation tool, but an important infrastructure for building intelligent software development ecosystem.** By structuring and making project knowledge accessible, it opens the door for AI programming assistants to understand complex software systems, marking the arrival of a new era of human-machine collaborative programming.

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*About Litho: Litho is an open-source project developed based on Rust, dedicated to improving knowledge management and documentation maintenance issues in software development through AI technology. Project address: [https://github.com/sopaco/deepwiki-rs](https://github.com/sopaco/deepwiki-rs)*