CAG (Cache-Augmented Generation) and RAG (Retrieval-Augmented Generation) are two methods for augmenting large language models (LLMs) with external knowledge to improve the accuracy and relevance of their responses. CAG preloads relevant data into the model's context, while RAG dynamically retrieves external knowledge at runtime. Context: I am building a Tour Package Recommendation Agent, wherein the AI Agent needs to search through the database (travel agent owned database of tours, activities, events, tickets, transfers etc) and help bundle and recomend the most suitable package as per the customer's request. System Capabilities: 1. Store large volume of Tours and Activities related content 2. Accurately match contents with destination and the type of activity (kids friendly, adventure etc..) 3. Build itineraries based on activities selected (smart routing and scheduling) What AI strategy would you choose?

If you are someone who has recently stepped into the AI world exploring the possibilities of getting a foothold in the game to make it big...but when digging a little deeper, you find the game has already begun and you are a little too late in the arena... Note: This poll is for someone who has seriously started looking into the AI related developments...and is here to seek help from the community. With awesome community members here, feel free to post you queris and seek guidance. We are always ready to guide... The question is...

Happy Monday Seamless fam! I want to introduce live calls every week, starting with one, and potentially moving forward with two per week. I want to continue with the 'AI Coffee Chat' series, most likely on Wednesdays or Fridays, where we discuss all things AI. Eventually, I am thinking about moving forward with live builds that will allow us to collaborate to build some nice automations together, while also providing technical support. Our last call was successful for being spontaneous, so I am really excited to see what it will look like with a planned event. We were able to bring together a mix of people: my good friend that is completely new to AI, an experienced and passionate Program Manager @Daniel Wada, and one of our newer but very respected professionals @Akil H. together for a conversation discussing our experiences within AI and our outlook for its future. Please let me know in the comments below what time zone you are in and when are your preferred hours. Also, if you cannot make it, I will be posting the live recordings :) I promise to do my best to find a time that best suits everyone 🤝🏼

Created a webscraper, that scraps all the tour agents details in a given country, saving them in spreadsheet, reading through the spreadsheet records, personalising email based on the bisuness type, and sending them a survey email (Survey on analysing the readiness to adopt AI Agent in their Business). Reason: Before diving deep into developing something I understood from my experience, wanted to get the feedback on what the market is really looking for, so I can navigate through the maze through data driven decision. What's next, Adding the Survey Link (getting it production ready), Extracting Survey Responses, Analysing and Updating the data in real-time with insights on the sentiments, perferences and readiness.

A work in progress flow (working on improving the package recommendation model) 🧠 AI-Powered Package Recommendation System – Workflow Documentation 1. Package Recommendation via Email (Customer Request Handling) This workflow handles incoming customer requests for travel packages via email, classifies the content, retrieves relevant packages using RAG (Retrieval-Augmented Generation), and sends a personalized response. 📌 Workflow Steps: 1. Email Trigger (IMAP)Watches for incoming emails in a specified mailbox. 2. Text ClassifierClassifies the email into one of several categories, such as: 3. Code NodeParses and prepares the request details (e.g., destination, duration, pax) from the email body for the AI Agent. 4. AI AgentOrchestrates the conversation using: 5. Vector Retrieval via PineconeSearches the Pinecone Vector Store using vector similarity based on the customer's email request. 6. Send EmailSends a personalized travel package recommendation email back to the customer. 2. Package Data Ingestion from Google Drive (RAG Preprocessing Flow) This workflow automates loading package content from PDFs, processing the text, and storing the information as embeddings in Pinecone for retrieval. 📌 Workflow Steps: 1. Manual Trigger (Test Button)Used to manually initiate the data ingestion pipeline. 2. Google Drive – File DownloadDownloads a batch of PDF files from a designated Google Drive folder containing travel package details. 3. Loop Over ItemsIterates through each file to extract and process the contents. 4. Default Data LoaderExtracts raw text from each PDF document. 5. Recursive Character Text SplitterSplits long package content into manageable chunks to ensure compatibility with embedding limits. 6. OpenAI EmbeddingsConverts each text chunk into vector embeddings. 7. Pinecone Vector StoreStores the embeddings under a unified namespace for later retrieval during customer queries. ✅ Summary These two workflows together create a fully automated AI Travel Package Recommendation Engine: