Write a blog to talk about implementing RAG

[gkwan-ibm]

Based on the rag-db application, write a blog to talk about implementing RAG by using LangChain4J, Mongo Altas vector DB and Liberty

[gkwan-ibm]

• suggest the blog title to be
  • Building a Retrieval-Augmented Generation (RAG) application by using LangChain4j and Mongo Altas vector database
• suggest the blog description to be
  • Learn how to build RAG in Jakarta EE and MicroProfile applications using the LangChain4j library and Mongo Altas vector database on Open Liberty.
• because this blog focuses on RAG, no need to talk too much about LLMs. See the streaming chat and MCP blogs.
• In the introduction, let talk about different RAG implementations. Then, describe the blog focuses on RAG with database and explain why use Mongo Altas vector database. If possible, draw a diagram to explain how the application works
• no need to mention Model Builder
• no need to explain Assistant
• no need to fine tune the LangChain4j model
• no need to explain too much about WebSocket
• Suggest the following layout
  • Introduction
  • Try out the application (similar and simpler than the README.md)
  • How does the application work?
    • Building REST APIs to manage the vector database
      • no need to explain all APIs
      • focus on how to construct the vector and add to database
    • Retrieving data from the vector database
      • focus on how to retrieve data from the database and append it to the chat
  • Where to next?
  • Find out more
    • see the tools blog
    • mentions other blogs (that will be published before this blogs)

[gkwan-ibm]

• "since" -> "because"
• What is RAG (Retrieval-Augmented Generation)?
  • suggest to mention that there are different RAG techniques and point users to read https://www.ibm.com/think/topics/rag-techniques and/or https://docs.langchain4j.dev/tutorials/rag for more
  • state our post is focus on the technique of the retrieval data from vector database
• Prerequisites
  • "This guide uses Docker to run an instance of MongoDB Atlas."
    • rewrite this statement because it is not a guide
• Access the application
  • "... http://localhost:9081/ by trying out different prompts..."
    • better state that trying out without RAG data and provide the suggested questions
      • move the questions and chat diagram up
    • then ask users to initialize the database by using the http://localhost:9081/openapi/ui and run the certain REST APIs
    • then ask users to repeat the same questions and compare the results
• Connecting to MongoDB Atlas
  • "... in the form of: "mongodb://" user + ":" + password + "@" hostname + ":" + port + "/" dbName "?authSource=admin&directConnection=true" to produce ..." -> "... in the form of: "mongodb://<user>:<password>@<hostname>:<port>/<dbName>?authSource=admin&directConnection=true" to produce ..."
  • "...getDatabase(dbName) method" -> "...getDatabase(dbName) method"
    • make sure that all code mentions are quoted by `
    • if class or object, quote them and clarify what they are
      • e.g. MongoClient class , MongoClient object, MongoDatabase class

  • @Produces
        public MongoClient createMongo(){ 
    

    • code is not aligned
    • at right place, link the *.java to GH url
• Building REST APIs to manage the vector database
  • "See EmbeddingApplication.java", "See EmbeddingService.java", "See AtlasMongoDB.java." are no need
  • make sure that all code mentions are quoted by `
  • at right place, link the *.java to GH url
  • after the EmbeddingApplication, it will be clear to have subsections for each REST API
• Retrieving data from the vector database
  • make sure that all code mentions are quoted by `
  • at right place, link the *.java to GH url
  • See ... are no need
• Conclusion: Building AI Applications powered by RAG and Open Liberty
  • "A couple of ways ..."
    • rewording this phase because you mentioned 4 points