feat : upgrade to spring boot 3.4.1 & polish #134
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WalkthroughThis pull request introduces modifications to a Spring AI-based RAG (Retrieval-Augmented Generation) application. The changes primarily focus on updating the Changes
Sequence DiagramsequenceDiagram
participant Client
participant AIChatService
participant ChatClientBuilder
participant VectorStore
participant OpenAI
Client->>AIChatService: Initialize with ChatClient.Builder
AIChatService->>ChatClientBuilder: Create aiClient
ChatClientBuilder-->>AIChatService: Configured ChatClient
Client->>AIChatService: Send prompt
AIChatService->>VectorStore: Retrieve context
AIChatService->>OpenAI: Call with prompt
OpenAI-->>AIChatService: Return response
AIChatService-->>Client: Return AI-generated content
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Actionable comments posted: 0
🔭 Outside diff range comments (1)
rag/rag-springai-openai-llm/src/main/resources/application.properties (1)
Line range hint
5-5: Secure the OpenAI API key configurationThe OpenAI API key is currently set to 'demo'. In a production environment, this should be externalized and secured.
Consider:
- Using environment variables:
${OPENAI_API_KEY}- Using a secure configuration service
- Adding this property to
.gitignoreand providing a template fileExample:
-spring.ai.openai.api-key=demo +spring.ai.openai.api-key=${OPENAI_API_KEY}
🧹 Nitpick comments (2)
rag/rag-springai-openai-llm/src/main/resources/application.properties (2)
12-14: Consider documenting the embedding model choiceThe configuration enables OpenAI embeddings with the
text-embedding-3-smallmodel, which is appropriate for RAG applications. However, consider adding a comment explaining why this model was chosen over alternatives (e.g., cost vs. performance trade-offs).Add a comment like:
spring.ai.openai.embedding.enabled=true +# text-embedding-3-small offers good balance between cost and performance for RAG spring.ai.openai.embedding.options.model=text-embedding-3-small
Line range hint
17-19: Consider schema initialization strategyThe
spring.ai.vectorstore.pgvector.initialize-schema=truesetting will automatically initialize the PgVector schema. While this is convenient for development, consider these points:
- For production environments, schema initialization should be handled through proper database migration tools
- This setting could potentially affect existing data if the schema already exists
Consider using a database migration tool like Flyway or Liquibase for schema management in production environments. Would you like me to provide an example migration script?
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⛔ Files ignored due to path filters (1)
rag/rag-springai-openai-llm/pom.xmlis excluded by!**/*.xml
📒 Files selected for processing (3)
rag/rag-springai-openai-llm/src/main/java/com/learning/ai/llmragwithspringai/service/AIChatService.java(2 hunks)rag/rag-springai-openai-llm/src/main/resources/application.properties(1 hunks)rag/rag-springai-openai-llm/src/test/java/com/learning/ai/llmragwithspringai/TestLlmRagWithSpringAiApplication.java(1 hunks)
🔇 Additional comments (7)
rag/rag-springai-openai-llm/src/main/java/com/learning/ai/llmragwithspringai/service/AIChatService.java (5)
6-6: Import usage is correct.
The newly added import statement for ChatClient is appropriate and aligns with the updated constructor signature.
41-42: Consider thread-safety of the cloned builder.
The builder pattern is a clean solution for creating aiClient. However, the clone() operation suggests multiple usages could occur. If you plan to share this chatClientBuilder among multiple components, verify that cloning does not introduce concurrency issues or shared mutable state that might affect subsequent builds. Otherwise, no immediate concerns are apparent.
50-50: Confirm the correctness of getText() usage.
Switching from Document::getContent to Document::getText is generally fine, assuming getText() is indeed the intended method for retrieving textual content. Validate that the returned value matches expectations, especially if you anticipate structured or formatted data.
57-57: Function-building approach is clear and concise.
Updating from the previous withFunction naming to function("currentDateFunction") is consistent with typical builder patterns and ensures clarity. This helps maintain a straightforward, fluent API style.
59-59: Improved read-flow with chained call.
Using aiClient.prompt(prompt).call().content() provides a clean, streamlined syntax. This makes the flow of prompt creation and response retrieval more readable. Ensure any exception handling or custom logic (e.g., fallback behavior) is still properly accounted for in this shortened chain.
rag/rag-springai-openai-llm/src/test/java/com/learning/ai/llmragwithspringai/TestLlmRagWithSpringAiApplication.java (1)
16-16: Verify compatibility with the updated Docker image version.
Upgrading to pgvector/pgvector:pg17 may introduce changes in the underlying PostgreSQL engine. Please confirm that there are no backward-incompatible differences and that any schema or data migration steps have been addressed.
✅ Verification successful
Docker image version mismatch detected between test and runtime environments
The verification reveals that while the test is using pgvector/pgvector:pg17, the actual runtime environment in docker-compose.yml is still using pgvector/pgvector:pg16. This version mismatch could lead to inconsistencies between test and production environments.
docker/docker-compose.yml: Update the image version frompg16topg17to maintain consistency- Update the version references in both README files to reflect the new version
Let's verify if there are any version-specific configurations or schema changes:
Coordinated version upgrade detected across the codebase
The verification reveals that the upgrade to pgvector/pgvector:pg17 is part of a coordinated update across the codebase. Multiple components are already using or being updated to use pg17:
embeddingstores/pgvector-springai/docker/docker-compose.yml: Using pg17embeddingstores/pgvector-langchain4j/docker/docker-compose.yml: Using pg17rag/rag-langchain4j-AllMiniLmL6V2-llm/docker/docker-compose.yml: Using pg17chatbot/chatbot-ollama-springai/docker/docker-compose.yml: Using pg17- Test configurations in multiple modules are already using pg17
Only one file still references pg16:
rag/rag-springai-openai-llm/docker/docker-compose.yml: Still using pg16
Let's verify if there are any version-specific schema changes:
Safe to upgrade - no version-specific schema dependencies found
The verification shows that the upgrade from pg16 to pg17 is safe for the following reasons:
- No version-specific SQL scripts or schema definitions were found in the codebase
- The pgvector schema initialization is handled automatically through Spring Boot properties (
initialize-schema=true) - Most of the codebase is already using pg17 in both test and runtime environments
- The configuration properties are version-agnostic and focus on generic features like dimensions and initialization
Action needed:
- Update
rag/rag-springai-openai-llm/docker/docker-compose.ymlto usepgvector/pgvector:pg17for consistency with the rest of the codebase
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Verify whether there are any migration steps needed for pg16 -> pg17
# 1) Search for references to the old Docker image or version-specific features
rg "pgvector/pgvector:pg16"
# 2) Check for sql scripts or migration scripts that reference version-based functionality
rg -A 5 "CREATE EXTENSION IF NOT EXISTS vector"
# 3) Inspect relevant test or config files that might rely on version 16
ast-grep --pattern $'class $_ {\n $$$\n}'
Length of output: 433
Script:
#!/bin/bash
# Check for any PostgreSQL or pgvector specific configurations
rg -l "postgresql" .
# Look for SQL files or database initialization scripts
fd -e sql
# Search for vector-related configurations
rg "vector" -A 5 -g "!*.md"
Length of output: 45526
Script:
#!/bin/bash
# Check for any SQL migration scripts or schema definitions
fd -e sql -e ddl
# Look for any pgvector extension or schema initialization code
rg -p "CREATE EXTENSION|CREATE TABLE.*vector" --type-add 'props:*.properties'
# Check for any version-specific configuration properties
rg "pgvector" --type-add 'props:*.properties' -g "*.properties"
Length of output: 1053
rag/rag-springai-openai-llm/src/main/resources/application.properties (1)
10-10: Verify the impact of removing responseFormat configuration
The removal of responseFormat=json_object configuration might affect how responses are handled. Please ensure this change is compatible with your response handling logic.
Run this script to check for JSON response handling in the codebase:
No description provided.