How we create your business AI Chatbot
AI Chatbot using Multimodal Retrieval Augmented Generation (MM-RAG)
Six Sides link Large Language Models (LLMs) to your enterprise data, services, and workflows, unleashing potent AI capabilities for your business empowerment.
If you are a business, using a chatbot or any other AI agent will greatly speed up things over on the customer side, and if you choose to make the full use of the AI technology then it’ll have an even greater effect in your business workflow.
While out of the box chatbot solutions are readily available, building custom AI agents offers several distinct advantages for various companies. By developing proprietary solutions tailored to the specific Generative AI use cases they need covered, companies can ensure seamless integration with internal systems and databases. They can provide access to proprietary information to speed up the workflow of internal employees and enhance data security by keeping the custom agent locally hosted.
We can customize user experiences and functionalities to align with your brand and customer expectations, fostering deeper engagement and loyalty. A custom bot also brings greater flexibility and scalability in adapting to evolving business needs and technological advancement for long-term success in regards to AI.
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How it works
Data Preperation
Data Preparation is the first and foremost step for applying RAG effectively. This step involves uploading domain specific knowledge base in multiple formats such as pdfs, web pages, ppts, excel files, text files etc.. Cleaning and formatting the data by removing white spaces, special characters and other metadata is also performed. Splitting the text into tokens and manageable chunks for effective retrieval. Maintain control over the source documents that will be used by the bot which can be created for each topic or department.
Data Retrieval
Data retrieval is a process to efficiently fetch relevant information from a large dataset and real-time queries to support tasks like answering questions, generating text, or providing recommendations. It involves indexing and querying the datasets by organizing data and fetching data using search terms and queries. Convert documents into text and embeddings which are suitable for semantic search. This will help fine tune your own datasets and train your bot in specific domains.
Data Augmentation
Data augmentation is a technique used to increase the accuracy of the query results. It will help generate more training examples from existing data. This is particularly useful in machine learning and natural language processing (NLP) to improve model performance. This step involves creating of additional prompts and instructions along the most relevant ranked chunks of retrieval data as a single input to the LLM.
Data Generation
Data generation is a process of optimizing the output of an LLM based on different datasets. It focuses on generating different responses such as text, images and many other formats by incorporating retrieved information from your own data.
RAG Architecture
- 1
Domain specific private knowledge base content split and encoded into vector database
- 2
User sends a query through the chatbot application.
- 3
Middleware library tools ingest the prompt, analyze it and gather similar chunks of content.
- 4
Chunks of related content are ranked and then attached to the prompt.
- 5
The prompt, the user query and the related context are sent to the LLM to generate response.