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tessl/maven-org-springframework-ai--spring-ai-spring-boot-autoconfigure

Spring AI Spring Boot Auto Configuration modules providing automatic setup for AI models, vector stores, MCP, and retry capabilities

Overview
Eval results
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troubleshooting.mddocs/reference/

Troubleshooting Guide

Common issues and solutions for Spring AI Spring Boot Auto Configuration.

Autoconfiguration Issues

Issue: Beans Not Created

Problem: Expected autoconfiguration beans are not available.

Diagnosis:

# Enable autoconfiguration debug logging
logging.level.org.springframework.boot.autoconfigure=DEBUG

Common Causes:

  1. Required classes not on classpath
  2. Property disabled autoconfiguration
  3. Conditional requirements not met
  4. Conflicting bean definitions

Solutions:

# Check autoconfiguration report
./mvnw spring-boot:run --debug

# Verify dependencies
./mvnw dependency:tree

# Check conditional evaluation
# Look for "did not match" in logs

Authentication Issues

Issue: API Key Errors

Problem: 401 Unauthorized or 403 Forbidden errors.

Solutions:

# Use environment variables
spring.ai.openai.api-key=${OPENAI_API_KEY}

# Verify no whitespace
# spring.ai.openai.api-key=sk-...  (no spaces)

# Check API key validity
# Test with curl or provider's playground

Issue: Azure OpenAI Authentication

Problem: Azure authentication failures.

Solutions:

# Verify endpoint format
spring.ai.azure.openai.endpoint=https://RESOURCE.openai.azure.com

# Use correct API key (not OpenAI key)
spring.ai.azure.openai.api-key=AZURE_KEY

# Or use Azure AD
# Configure via AzureOpenAIClientBuilderCustomizer

Rate Limiting

Issue: Frequent 429 Errors

Problem: Hitting rate limits frequently.

Solutions:

# Configure aggressive retry
spring.ai.retry.on-http-codes=429
spring.ai.retry.max-attempts=10
spring.ai.retry.backoff.initial-interval=5s
spring.ai.retry.backoff.multiplier=3

# Implement request queuing (see edge-cases.md)

Vector Store Issues

Issue: Schema Initialization Fails

Problem: Schema creation errors on startup.

Solutions:

# Check database permissions
# Ensure user has CREATE TABLE rights

# For existing tables
spring.ai.vectorstore.pgvector.initialize-schema=never

# For development
spring.ai.vectorstore.pgvector.initialize-schema=always

# Remove existing table first
spring.ai.vectorstore.pgvector.remove-existing-vector-store-table=true

Issue: Dimension Mismatch

Problem: Vector dimension mismatch errors.

Solutions:

# Ensure dimensions match embedding model
# OpenAI ada-002: 1536
spring.ai.vectorstore.pgvector.dimensions=1536

# Sentence transformers: 768
spring.ai.vectorstore.milvus.embedding-dimension=768

# Check embedding model documentation

MCP Issues

Issue: MCP Client Not Connecting

Problem: Cannot connect to MCP server.

Diagnosis:

# Enable debug logging
logging.level.org.springframework.ai.mcp=DEBUG
logging.level.io.modelcontextprotocol=DEBUG

Solutions:

# Verify command and args for stdio
spring.ai.mcp.client.stdio.connections.server.command=/usr/bin/node
spring.ai.mcp.client.stdio.connections.server.args[0]=/full/path/to/server.js

# Increase timeout
spring.ai.mcp.client.request-timeout=60s

# Check server is running (for SSE/HTTP)
# curl http://localhost:8080/sse

Performance Issues

Issue: Slow Response Times

Solutions:

# Use faster models
spring.ai.openai.chat.options.model=gpt-3.5-turbo

# Reduce max tokens
spring.ai.openai.chat.options.max-tokens=500

# Use streaming
# Implement: chatModel.stream(prompt)

# Enable connection pooling
spring.datasource.hikari.maximum-pool-size=20

Issue: High Memory Usage

Solutions:

  • Limit conversation history length
  • Enable TTL for chat memory
  • Use batch operations efficiently
  • Monitor heap usage

Native Image Issues

Issue: Build Failures

Solutions:

# Collect runtime hints
java -agentlib:native-image-agent=config-output-dir=src/main/resources/META-INF/native-image \
     -jar target/my-app.jar

# Add reflection configuration
# Create reflect-config.json

# Enable verbose build
./mvnw -Pnative native:compile -Dverbose

See Native Image Documentation →
See Edge Cases →

Getting Help

  1. Check Logs: Enable DEBUG logging for Spring AI packages
  2. Verify Configuration: Use --debug flag to see autoconfiguration report
  3. Test Dependencies: Verify all required classes on classpath
  4. Review Documentation: Check provider-specific documentation
  5. Check GitHub Issues: Spring AI project issues

Install with Tessl CLI

npx tessl i tessl/maven-org-springframework-ai--spring-ai-spring-boot-autoconfigure

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