🎈 Introduction
With buzzwords like Generative AI (GenAI) echoing around, owing to the rise of models like ChatGPT and DALL-E, the AI landscape is changing rapidly.💫 Unlike its counterparts, GenAI has the prowess to tackle creativity-intensive tasks swiftly, like fabricating non-existent data, from making music, sketching art, conducting dialogues to even penning this article’s introduction.🖌️🎼🗨️🖊️
💼 Why Businesses Need GenAI?
A plethora of sectors have started utilizing GenAI technology. From generating custom clothing designs based on customer inputs, to Smart Chatbots that offer sales-like suggestions and aid in planning holiday trips, GenAI is enhancing user experiences, making them more efficient.👕👖🤖🎟️
🏭 How Businesses Can Implement Chat GenAI?
Businesses can leverage GenAI via the OpenAI API. But how to get a GenAI response based on company-specific data?🧐
One way is to use specific prompts to extract data from the supplied dataset. However, such approach often demands extensive context in each request, which can lead to larger token usage and increased costs as per OpenAI’s token to character ratio.💰🔍
For example:
prompt = "Company specific data"
🌩️ Alibaba Cloud’s GenAI Solution
At Alibaba Cloud, solutions like Large Language Model (LLM) come into play to address these issues. LLM is a potent Deep Learning algorithm capable of text recognition, summarization, translation, prediction, and generation based on its learning from large datasets.🤖📚
Businesses can leverage LLM with Alibaba Cloud’s AnalyticDB for efficient Vector Database Retrieval, allowing swift and accurate information extraction from vast databases.🏁🔍
🔗 Refer to this source for an illustrative image of Alibaba Cloud LLM + Vector Solution Architecture.
Steps involved include:
- Company data, in formats like .docx, .csv, .pdf, .pptx, .md, .txt etc., is segmented into several context-appropriate chunks.📄📚
- The chunked text undergoes a Text Embedding stage, where it’s converted into a vector and stored in AnalyticDB (ADBPG).🔄🔢
- Customer inquiries also undergo embedding but are not stored in ADBPG. Instead, a Vector Search is performed.🔍
- The Vector Search algorithm compares vector distances of words, identifying the most contextually relevant responses.🔬
- The appropriate context, along with the question, is uploaded to OpenAI as mentioned in the previous section.🔄
The combination of Open Source LLM Retrieval Plugins with AnalyticDB allows for finding the most relevant contexts for customer queries. As such, only necessary data gets uploaded to OpenAI’s API, rendering the service cost-effective.💰💡
With Alibaba Cloud’s LLM + AnalyticDB for Postgres, businesses can build their own dataset
based Generative AI chats, achieving cost-effectiveness and superior performance.🚀🎯
