Business QA Bot is an advanced Retrieval Augmented Generation (RAG) based Question-Answering bot designed specifically for business applications. This project showcases innovative techniques to optimize RAG models, enhancing performance, efficiency, and output quality for business-oriented queries.
Retrieval Augmented Generation (RAG) is a powerful approach in natural language processing that combines the strengths of large language models with the ability to access and leverage external knowledge. RAG models first retrieve relevant information from a knowledge base and then use this information to generate more accurate and contextually appropriate responses.
RAG-based QA bots offer several key advantages in business settings:
- Accurate and Up-to-date Information: By accessing current business data, these bots provide precise and timely answers to queries.
- Customization: They can be tailored to specific business domains and knowledge bases.
- Scalability: RAG bots can handle a growing volume of information and queries efficiently.
- Consistency: They ensure uniform responses across different customer or employee interactions.
- 24/7 Availability: Provide round-the-clock access to business information and support.
- Cost-effective: Reduce the workload on human support teams for routine queries.
- Data-driven Insights: Analysis of user queries can reveal valuable business intelligence.
- Efficient Data Collection: Utilizes Firecrawl API for comprehensive website content extraction.
- Optimized Chunk Size: Implements a 512-token chunk size for balanced and effective embeddings.
- Advanced LLM Integration: Leverages Gemini-1.5-Flash for expanded context handling and rapid inference.
- High-Speed Inference: Employs Groq LPU™ AI technology for near-instantaneous response generation.
- Google for the Gemini-1.5-Flash model
- Groq for their LPU™ AI inference technology
- Firecrawl for their efficient web crawling API
- Pinecone for the Vector Databse provider