Java AI Chatbot
A Java AI chatbot is an application that allows users to communicate with an AI model through a Java-based application.
The simplest chatbot looks like:
User
↓
Java Application
↓
LLM
↓
AI Response
↓
User
A modern chatbot can be much more capable:
USER
│
▼
Java / Spring Boot
│
┌─────────────────┼─────────────────┐
│ │ │
▼ ▼ ▼
Chat Memory RAG Tools
│ │ │
└─────────────────┼─────────────────┘
▼
Chat Model
│
▼
Answer
With these components, the chatbot can remember a conversation, search your own documents, and call Java functions.
Spring AI currently provides ChatClient as the main fluent API for interacting with chat models and supports advisors for adding memory, RAG, and other reusable behaviors.
1. What Is a Java AI Chatbot?
A normal chatbot may simply follow predefined rules:
User:
Hello
Bot:
Hello! How can I help?
An AI chatbot sends the user's message to a language model:
User
↓
Java
↓
AI Model
↓
Generated Response
For example:
User:
Explain Java inheritance.
AI:
Inheritance allows one class to derive
properties and behavior from another class...
Java controls the application.
The LLM generates the natural-language response.
2. Java Does Not Become the AI
This distinction is important.
A Java application normally does not become the language model itself.
Instead:
Java
=
Application
LLM
=
Language Model
A chatbot combines both:
Java
+
LLM
=
AI Application
For example:
Spring Boot
↓
AI Service
↓
Ollama / OpenAI / Gemini / Claude
↓
Response
3. Basic Chatbot Architecture
The simplest architecture is:
Browser
↓
Spring Boot Controller
↓
Chat Service
↓
AI Model
↓
Response
For example:
POST /api/chat
Request:
{
"message": "What is polymorphism?"
}
Response:
{
"answer": "Polymorphism allows..."
}
4. Why Spring Boot?
Spring Boot is a natural choice for Java chatbot backends.
It provides:
REST APIs
Dependency Injection
Configuration
Security
Database Integration
WebSocket Support
Testing
Monitoring
Your chatbot can therefore become a normal enterprise Java application.
Architecture:
Web / Mobile
↓
Spring Boot
↓
AI Service
↓
LLM
5. Direct Java HTTP Chatbot
You do not need an AI framework just to create a chatbot.
A Java application can call an AI provider through HTTP.
Conceptually:
HttpClient client =
HttpClient.newHttpClient();
HttpRequest request =
HttpRequest.newBuilder()
.uri(URI.create("AI_ENDPOINT"))
.header("Content-Type", "application/json")
.POST(...)
.build();
The provider-specific request format depends on the AI service.
This approach is useful for understanding what happens underneath an AI framework.
6. Chatbot with Spring AI
For Spring-based applications, Spring AI provides ChatClient.
The architecture becomes:
Spring Boot
↓
ChatClient
↓
Chat Model
↓
AI Provider
The current Spring AI documentation shows a fluent ChatClient API for sending prompts and obtaining model responses.
A simple service can look like:
@Service
public class ChatService {
private final ChatClient chatClient;
public ChatService(ChatClient.Builder builder) {
this.chatClient = builder.build();
}
public String chat(String message) {
return chatClient
.prompt()
.user(message)
.call()
.content();
}
}
Then:
@RestController
@RequestMapping("/api/chat")
public class ChatController {
private final ChatService chatService;
public ChatController(ChatService chatService) {
this.chatService = chatService;
}
@PostMapping
public String chat(@RequestBody String message) {
return chatService.chat(message);
}
}
The flow is:
User
↓
POST /api/chat
↓
ChatController
↓
ChatService
↓
ChatClient
↓
LLM
↓
Response
7. What Makes a Chatbot "AI"?
A basic application can return fixed answers:
if message.equals("hello")
return "Hello";
That is rule-based logic.
An AI chatbot instead sends natural language to a language model:
User Message
↓
Chat Model
↓
Generated Response
The model can handle many forms of language without requiring a separate if condition for every question.
8. System Instructions
A chatbot often needs an application-defined role.
For example:
You are a Java programming assistant.
Explain technical concepts clearly.
Use Java examples when appropriate.
Then:
User:
What is an interface?
The chatbot follows the application instructions while answering.
This is useful for specialized bots:
Java Tutor
Customer Support Bot
HR Assistant
Interview Assistant
Product Assistant
Documentation Assistant
9. Chat History
A serious chatbot needs conversation context.
Consider:
User:
My name is Ravi.
AI:
Nice to meet you, Ravi.
User:
What is my name?
If the application sends only:
What is my name?
on the second request, the model may not know the earlier message.
The application needs to provide conversation context.
Spring AI's current documentation explicitly notes that chat-model APIs are stateless and that conversational history must be supplied on subsequent requests when you want the model to consider previous turns.
10. Chat Memory
Chat memory stores the messages that should be made available to the model.
Conceptually:
Conversation
│
├── User: My name is Ravi.
├── AI: Nice to meet you.
├── User: I am learning Java.
└── AI: Great!
Then another message arrives:
User:
What am I learning?
The application can provide the relevant conversation context.
11. Spring AI Chat Memory
Spring AI currently provides ChatMemory support for ChatClient.
One built-in approach is:
MessageChatMemoryAdvisor
It retrieves conversation history and adds it to the interaction with the model.
Conceptually:
MessageChatMemoryAdvisor.builder(chatMemory)
.build()
Then:
chatClient
.prompt()
.advisors(advisor)
.user(message)
.call()
.content();
The advisor takes care of adding the appropriate conversation memory.
12. Conversation ID
A chatbot normally needs to distinguish different conversations.
For example:
Conversation 1001
Conversation 1002
Conversation 1003
Spring AI's current memory integration uses a conversation identifier parameter for memory advisors. The current documentation states that ChatMemory.CONVERSATION_ID must be supplied for calls using these advisors.
Conceptually:
.param(
ChatMemory.CONVERSATION_ID,
conversationId
)
The architecture becomes:
User
↓
Conversation ID
↓
Java
↓
Chat Memory
↓
LLM
13. Why Multiple Users Need Separate Memory
Suppose:
User A:
My name is Arun.
User B:
My name is Priya.
They should not share the same conversation memory.
Bad architecture:
All users
↓
One Memory
Better:
User A
↓
Conversation A
↓
Memory A
User B
↓
Conversation B
↓
Memory B
LangChain4j also explicitly warns that a single shared ChatMemory instance does not work for multiple users and provides ChatMemoryProvider for separate memories.
14. Chat Memory vs Chat History
These concepts are related but not identical.
History is everything that happened in the conversation.
Memory is the information actually maintained or supplied to help the model behave as though it remembers.
LangChain4j's current documentation makes this distinction explicit: history represents what happened, while memory can evict, summarize, or otherwise transform information before presenting it to the model.
For example:
History:
100 messages
but:
Memory:
Most relevant 10 messages
This keeps the model context manageable.
15. Why We Cannot Keep Infinite Memory
LLMs have context limits.
Even when a model supports a large context window, sending more information can increase processing and latency.
Therefore a chatbot may use:
Recent Messages
+
Important Information
rather than every message ever exchanged.
LangChain4j's current MessageWindowChatMemory keeps a bounded number of recent messages, while more advanced memory strategies can summarize or transform older content.
16. Database Chat History
A production chatbot may store history in a database.
For example:
Conversations
----------------
Id
UserId
Title
CreatedDate
Messages
----------------
Id
ConversationId
Role
Content
CreatedDate
Architecture:
Browser
↓
Spring Boot
↓
Database
↓
Conversation Context
↓
LLM
This provides persistent conversations even after the application restarts.
Spring AI's current documentation lists repository options including JDBC, Cassandra, Neo4j, MongoDB, and Redis for chat-memory persistence.
17. Streaming Responses
A chatbot feels more natural when text appears progressively.
Without streaming:
User
↓
Wait
↓
Complete answer
With streaming:
User
↓
Text chunk
↓
Text chunk
↓
Text chunk
↓
Complete response
Spring AI's current ChatClient documentation supports streaming through its reactive stack.
A typical UI can therefore display:
Generating...
The...
Java...
class...
as the model produces output.
18. Chatbot + RAG
Now we can add the previous topic: RAG.
Suppose your chatbot should answer using:
Company Documents
Product Manuals
Technical Documentation
FAQ
The architecture becomes:
User
↓
Chatbot
↓
RAG Retrieval
↓
Relevant Documents
↓
LLM
↓
Answer
Spring AI currently provides advisors such as QuestionAnswerAdvisor for this style of retrieval-augmented interaction.
19. Chatbot + Memory + RAG
A more advanced chatbot combines both.
USER
│
▼
Spring Boot
│
┌──────────┴──────────┐
│ │
▼ ▼
Chat Memory RAG
│ │
▼ ▼
Conversation Context Relevant Documents
│ │
└──────────┬──────────┘
▼
LLM
│
▼
Answer
Spring AI's advisor system is designed to compose these kinds of reusable behaviors, and the current documentation shows combining MessageChatMemoryAdvisor with QuestionAnswerAdvisor.
20. Chatbot + Vector Database
RAG requires a retrieval system.
That may look like:
Documents
↓
Embeddings
↓
Vector Database
Question:
User Question
↓
Embedding
↓
Vector Search
↓
Relevant Chunks
Then:
Relevant Chunks
+
Conversation Context
+
User Question
↓
LLM
This produces a knowledge-aware chatbot.
21. Chatbot + Ollama
A local chatbot can use Ollama.
Architecture:
Browser
↓
Spring Boot
↓
Spring AI
↓
Ollama
↓
Local LLM
↓
Response
No cloud LLM provider is necessary for the inference portion when the model is running locally.
For example:
User:
Explain Java interfaces.
Java
↓
Ollama
↓
Local Model
↓
Answer
22. Chatbot + Ollama + RAG
You can make the entire AI pipeline local:
USER
│
▼
Spring Boot
│
┌────────┴────────┐
│ │
▼ ▼
Local Embeddings Memory
│
▼
Vector Database
│
▼
Relevant Context
│
▼
Ollama
│
▼
Local LLM
│
▼
Answer
This is a powerful architecture for private knowledge assistants.
23. Chatbot + Tools
Suppose your chatbot needs information that is not in documents.
For example:
User:
What is my order status?
The Java application may have:
getOrderStatus(orderId)
The chatbot can use a tool.
The flow is:
User
↓
LLM
↓
Tool Request
↓
Java Method
↓
Database
↓
Tool Result
↓
LLM
↓
Final Answer
24. Java Tool Example
Conceptually:
public OrderStatus getOrderStatus(
String orderId) {
return orderRepository
.findStatus(orderId);
}
The AI model can request the tool when appropriate.
LangChain4j's current AI Services API supports tools directly and can execute Java methods requested by the model.
25. AI Chatbot vs AI Agent
A chatbot generally follows:
Message
↓
LLM
↓
Response
A more agentic system can:
Understand goal
↓
Choose tool
↓
Execute tool
↓
Inspect result
↓
Choose next action
↓
Return answer
For example:
User:
Check my order and tell me whether it
has shipped.
Agent
↓
Get Order
↓
Check Shipping
↓
Return Result
The agent is coordinating multiple operations.
26. LangChain4j AI Services
LangChain4j provides a higher-level AiServices abstraction.
Instead of manually orchestrating every low-level operation, you can define a Java interface.
Conceptually:
interface Assistant {
String chat(String message);
}
Then LangChain4j creates the implementation around your configured model and components.
Its current documentation says AI Services can handle input formatting and output parsing and can incorporate chat memory, tools, and RAG.
27. Multi-User LangChain4j Chatbot
LangChain4j can use:
String chat(
@MemoryId int memoryId,
String message
);
The memory ID separates conversations.
For example:
Memory 1
→ User A
Memory 2
→ User B
Its current documentation recommends a ChatMemoryProvider for this multi-user scenario.
28. Chatbot Database Design
A practical database could contain:
Users
----------------
Id
Name
Email
Conversations
----------------
Id
UserId
Title
CreatedAt
Messages
----------------
Id
ConversationId
Role
Content
CreatedAt
Documents
----------------
Id
Name
Category
Version
And a vector store can separately hold embeddings for the document chunks.
29. Authentication
A production chatbot should normally identify the user.
Architecture:
User
↓
Login
↓
JWT / Session
↓
Chat API
↓
User's Conversation
↓
Memory / Database
↓
LLM
Authentication is important because:
User A
must not retrieve:
User B's conversation
or:
User B's private documents
30. Authorization
Authentication answers:
Who is this user?
Authorization answers:
What can this user access?
For a knowledge chatbot:
User
↓
Authorization
↓
Allowed Documents
↓
Vector Search
↓
RAG
Do not rely on the LLM to enforce access control.
Java and the data layer should enforce it.
31. Prompt Injection
RAG chatbots must also consider malicious or misleading instructions inside retrieved content.
For example, a document could contain text attempting to tell the AI:
Ignore previous instructions...
The chatbot should be designed so retrieved documents are treated as data rather than automatically trusted instructions.
A robust application should distinguish:
System/Application Instructions
User Message
Retrieved Context
Tool Results
and validate tool permissions independently.
32. Input Validation
A chatbot endpoint should validate incoming data.
For example:
if (message == null ||
message.isBlank()) {
throw new IllegalArgumentException(
"Message is required"
);
}
Also consider:
Maximum message length
Request size
Rate limits
Authentication
Abuse controls
33. Error Handling
AI calls can fail.
Possible failures include:
Model unavailable
Network error
Timeout
Rate limit
Invalid request
Context overflow
Vector database failure
Ollama not running
Your API should return a controlled response.
For example:
{
"error": "AI service is temporarily unavailable."
}
rather than exposing an internal exception stack trace.
34. Chatbot Response DTO
Instead of returning a raw model response, define your own API contract.
For example:
public class ChatResponse {
private String conversationId;
private String answer;
private List<SourceDto> sources;
}
This gives your frontend a stable format.
A RAG chatbot might return:
{
"conversationId": "1001",
"answer": "The leave policy provides...",
"sources": [
{
"name": "Employee Handbook.pdf",
"page": 18
}
]
}
35. Source Citations in RAG Chatbots
A good RAG chatbot can show where an answer came from.
For example:
Answer:
Employees receive 18 annual leave days.
Sources:
Employee Handbook.pdf — Page 18
Leave Policy.pdf — Page 4
Your vector records should therefore retain useful metadata:
Source
Page
Section
Document ID
Version
Java can return these alongside the generated answer.
36. Chatbot Conversation UI
A typical browser UI can look like:
---------------------------------------
Java AI Assistant
---------------------------------------
User:
What is dependency injection?
AI:
Dependency injection is...
User:
Give me a Java example.
AI:
Here is an example...
---------------------------------------
Type your message...
[Send]
---------------------------------------
The frontend might communicate with:
POST /api/chat
or use a streaming endpoint for progressively generated responses.
37. REST vs WebSocket vs Streaming HTTP
A simple chatbot can use:
REST
For example:
POST /api/chat
A real-time experience can use streaming.
For interactive applications, you may consider:
HTTP streaming
WebSocket
Server-Sent Events
The appropriate choice depends on your frontend and backend design.
38. Chatbot with Voice
Text is only one interface.
A voice chatbot can use:
Microphone
↓
Speech-to-Text
↓
Java
↓
LLM
↓
Text-to-Speech
↓
Speaker
The LLM is still the central reasoning/generation component.
Java coordinates the services.
39. Chatbot with Images
A multimodal chatbot can accept an image:
User
↓
Image + Text
↓
Multimodal Model
↓
Answer
For example:
User:
Explain this diagram.
Java receives the input and sends the appropriate multimodal request to the configured model.
40. Chatbot with Documents
Another common workflow:
Upload PDF
↓
Java
↓
Text Extraction
↓
Chunking
↓
Embeddings
↓
Vector Database
Then:
User Question
↓
RAG
↓
LLM
↓
Answer
This creates a document-aware chatbot.
41. Chatbot with Multiple Knowledge Bases
A larger system may contain:
Java Knowledge Base
Spring Knowledge Base
SQL Knowledge Base
Company Knowledge Base
Metadata can identify:
category = java
or:
category = company
The chatbot can then route queries to the appropriate knowledge source.
42. Chatbot Routing
A more advanced architecture:
User Question
↓
Router
┌────┼────┬────┐
▼ ▼ ▼ ▼
Java HR SQL General
│ │ │ │
└─────┴────┴──────┘
↓
LLM
This avoids searching every knowledge base for every question.
Advanced RAG frameworks provide routing components for this kind of architecture. LangChain4j currently documents QueryRouter and related retrieval components.
43. Chatbot Memory + Semantic Memory
Normal chat memory stores recent conversation messages.
Semantic memory can store important information as embeddings.
For example:
User:
I prefer Java examples.
The application can store that information for later semantic retrieval.
Then:
Future Question
↓
Semantic Memory Search
↓
Relevant User Preference
↓
LLM
This is a more advanced chatbot design.
44. Chatbot Performance
A request may involve:
Authentication
↓
Chat Memory
↓
Embedding
↓
Vector Search
↓
Prompt Construction
↓
LLM
↓
Response
Monitor:
Total latency
Embedding latency
Vector search latency
LLM latency
Token usage
Error rate
This helps identify bottlenecks.
45. Chatbot Cost
For cloud AI:
More requests
+
More tokens
=
More API usage
For local AI:
More requests
+
Larger models
=
More CPU/GPU/RAM usage
Therefore measure usage before scaling.
46. Local Chatbot Hardware
A local chatbot depends on:
Model size
Quantization
RAM
VRAM
CPU
GPU
Context size
Number of simultaneous users
A small local model can be practical for experimentation.
A larger model or many simultaneous users may require a dedicated AI server.
47. Hybrid Chatbot
A chatbot can also use local and cloud models.
Chatbot
│
┌─────────┴─────────┐
▼ ▼
Ollama Cloud AI
│ │
Local Model Hosted Model
For example:
Simple/private request
↓
Local model
Complex request
↓
Cloud model
Java can make the routing decision.
48. Provider-Abstraction Design
A useful Java design is:
public interface ChatService {
String chat(String message);
}
Then:
OllamaChatService
OpenAiChatService
GeminiChatService
ClaudeChatService
can all implement it.
Architecture:
ChatService
│
┌─────────────┼─────────────┐
▼ ▼ ▼
Ollama Gemini Claude
│ │ │
Local Cloud Cloud
This prevents provider-specific code from spreading through your entire application.
49. Spring AI Advisor Architecture
One particularly useful Spring AI concept is the Advisor.
An advisor can intercept and enhance AI interactions.
For example:
ChatClient
│
├── Memory Advisor
├── RAG Advisor
├── Logging Advisor
└── Other Advisors
↓
Chat Model
The current Spring AI documentation describes advisors as reusable components that can modify or enhance AI interactions.
The order of advisors matters because one advisor's changes can become input to the next.
50. Complete Spring AI Chatbot Architecture
A production-oriented application might look like:
USER
│
▼
Web / Mobile App
│
▼
Spring Boot API
│
┌───────────────┼───────────────┐
│ │ │
▼ ▼ ▼
Authentication Chat Memory RAG
│ │
│ ┌─────┴─────┐
│ ▼ ▼
│ Embedding Vector DB
│ │ │
│ └─────┬─────┘
│ │
└───────┬───────┘
▼
ChatClient
│
┌──────┴──────┐
▼ ▼
Chat Model Tools
│ │
▼ ▼
Ollama / Cloud Java Methods
│
▼
Answer
51. Complete Chatbot Request
Imagine:
User:
According to our company policy,
how many annual leave days do I have?
The application can perform:
1. Authenticate user
2. Load conversation memory
3. Search company documents
4. Apply user/document permissions
5. Retrieve relevant chunks
6. Build AI context
7. Call the LLM
8. Generate answer
9. Save the conversation
10. Return answer + sources
This is a real AI application rather than a simple prompt-and-response demo.
52. Java AI Chatbot Project Structure
A clean Spring Boot project might use:
src/main/java/com/example/chatbot
│
├── controller
│ └── ChatController.java
│
├── service
│ ├── ChatService.java
│ ├── MemoryService.java
│ ├── RagService.java
│ └── ToolService.java
│
├── ai
│ ├── ChatModelConfig.java
│ └── PromptConfig.java
│
├── memory
│ └── ConversationMemory.java
│
├── rag
│ ├── DocumentService.java
│ ├── EmbeddingService.java
│ └── VectorSearchService.java
│
├── tool
│ └── CustomerTools.java
│
├── dto
│ ├── ChatRequest.java
│ └── ChatResponse.java
│
├── entity
│ ├── Conversation.java
│ └── Message.java
│
└── repository
53. Beginner Version
Start much smaller.
Spring Boot
↓
ChatClient
↓
LLM
Example:
@Service
public class ChatService {
private final ChatClient chatClient;
public ChatService(ChatClient.Builder builder) {
this.chatClient = builder.build();
}
public String ask(String question) {
return chatClient
.prompt()
.user(question)
.call()
.content();
}
}
Then create:
POST /api/chat
and test:
What is Java inheritance?
54. Intermediate Version
Add:
Chat Memory
Architecture:
Spring Boot
↓
ChatClient
↓
Memory
↓
LLM
Then add:
Conversation ID
Database persistence
55. Advanced Version
Add:
RAG
Embeddings
Vector Database
Tools
Structured Output
Streaming
Security
Architecture:
User
↓
Spring Boot
↓
Memory + RAG + Tools
↓
ChatClient
↓
LLM
↓
Answer
56. Agent Version
Finally:
User
↓
AI Agent
├── Search Knowledge
├── Read Database
├── Call APIs
├── Use Tools
└── Generate Answer
LangChain4j's AI Services provide a high-level Java abstraction that can combine chat models with memory, RAG, and tools.
57. Recommended Learning Sequence
For Java developers, learn chatbot development in this order:
1. Java
↓
2. Spring Boot
↓
3. Basic LLM API
↓
4. Spring AI / LangChain4j
↓
5. Chatbot
↓
6. Chat Memory
↓
7. Database
↓
8. RAG
↓
9. Embeddings
↓
10. Vector Database
↓
11. Tool Calling
↓
12. AI Agents
This progression lets you understand each component before combining them.
58. Best Beginner Project
A very useful project is:
Java AI Study Assistant
Features:
Login
Chat
Conversation History
Question Answering
PDF Upload
RAG
Source Citations
Architecture:
Web UI
│
▼
Spring Boot
│
┌──────┼──────┐
▼ ▼ ▼
Memory RAG User Data
│
▼
Vector DB
│
▼
LLM
│
▼
Answer
59. More Advanced Project
You can then turn it into:
Java AI Technical Assistant
It can answer questions about:
Java
Spring Boot
SQL
REST APIs
Docker
Cloud
Internal Documentation
Add:
RAG
Tool Calling
Database Search
Code Generation
Source Citations
Conversation Memory
Now the chatbot becomes an AI assistant.
60. Final Architecture
A complete modern Java AI chatbot can be represented as:
USER
│
▼
Web / Mobile UI
│
▼
Spring Boot API
│
┌─────────────────┼─────────────────┐
│ │ │
▼ ▼ ▼
Authentication Chat Memory User Data
│
▼
ChatClient / AI Service
│
┌──────────────────┼──────────────────┐
│ │ │
▼ ▼ ▼
RAG Tools Prompt
│ │ │
┌─────┴─────┐ │ │
▼ ▼ ▼ │
Embedding Vector DB Java Functions │
│ │ │ │
└─────┬─────┘ │ │
└──────────────────┼──────────────────┘
▼
Chat Model
│
┌────────┴────────┐
▼ ▼
Ollama Cloud AI
│ │
└────────┬────────┘
▼
Answer
│
┌───────┴────────┐
▼ ▼
User Sources
The responsibilities are:
Java
→ Application logic
Spring Boot
→ API and backend
ChatClient / AI Service
→ AI interaction layer
Chat Memory
→ Conversation context
Embeddings
→ Semantic representation
Vector Database
→ Semantic retrieval
RAG
→ Knowledge retrieval + generation
Tools
→ Java functions / real-world actions
LLM
→ Natural-language generation
Ollama
→ Local model runtime
Cloud AI
→ Hosted model option
Conclusion
A Java AI chatbot starts with a very simple idea:
User
↓
Java
↓
LLM
↓
Response
But a production chatbot usually grows into:
Java
+
LLM
+
Chat Memory
+
Database
+
RAG
+
Embeddings
+
Vector Database
+
Tools
+
Security
+
Streaming
Spring AI currently provides ChatClient, chat-memory support, advisors, and RAG integrations that make this architecture easier to assemble in Spring Boot applications.
LangChain4j provides another Java-focused approach, with AI Services that can combine chat models, chat memory, tools, and RAG. Its documentation specifically supports stateful chatbots and per-user memory through memory IDs/providers.
A practical progression is:
Basic Chatbot
↓
Chat Memory
↓
Database
↓
RAG
↓
Embeddings
↓
Vector Database
↓
Tools
↓
AI Agent
Once you understand this architecture, the next step is Java AI Assistant, where the chatbot becomes more proactive and can use tools, retrieve information, maintain longer-term context, and perform useful tasks rather than simply answering questions.
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