AI Agent with RAG: Product Knowledge Base
Build intelligent product assistants with Retrieval-Augmented Generation (RAG). Use SimpleRAGRetrieve to create AI agents that search, analyze, and recommend products from a knowledge base with semantic understanding.
What is RAG (Retrieval-Augmented Generation)?
RAG enhances AI agents by combining the power of large language models with real-time information retrieval from your knowledge base
Knowledge Base
Store your product data, documentation, or any information in a vector database. RAG transforms this data into searchable embeddings that capture semantic meaning.
Intelligent Retrieval
When users ask questions, RAG finds the most relevant information using semantic search, understanding context and meaning rather than just matching keywords.
Augmented Responses
The AI agent uses retrieved information to generate accurate, contextual responses based on your actual data, reducing hallucinations and improving reliability.
How RAG Product Knowledge Works
Our Product Specialist agent uses SimpleRAGRetrieve to deliver intelligent product recommendations
Vector Store Initialization
Product data is embedded and stored in a vector database, enabling semantic search across product specifications, features, and descriptions. Each product becomes a searchable vector in the knowledge base.
Query Processing
The Product Specialist agent receives customer queries and uses SimpleRAGRetrieve to search the knowledge base with semantic understanding, capturing the intent behind questions.
Intelligent Retrieval
The RAG tool retrieves the top 4 most relevant products using similarity search, considering context and meaning rather than just keywords. It understands that "laptop for video editing" relates to processing power and storage.
Product Recommendation
Based on retrieved information, the agent provides detailed recommendations, comparing features and suggesting the best options for customer needs. It can handle complex queries like comparisons and specific use cases.
SimpleRAGRetrieve Features
- ✓Pre-loaded Data Support: Works with existing vector stores containing processed data
- ✓Flexible Vector Store Integration: Supports MemoryVectorStore, Pinecone, Weaviate, Qdrant, and all other LangChain-compatible vector databases
- ✓Customizable Components: Configure embeddings, retrieval options (k, searchType), and chunk sizes
- ✓Efficient Retrieval: Optimized for querying pre-existing knowledge bases with semantic search
- ✓Universal Embeddings Support: Compatible with all LangChain integrated embeddings including OpenAI, Cohere, HuggingFace, Google VertexAI, and more
Example Query
"I need a laptop for video editing and gaming. What do you recommend?"
The agent understands this requires high processing power, dedicated graphics, and ample storage, then retrieves and recommends the UltraBook Pro 15 with detailed specifications.
Technology Stack
Built with enterprise-grade tools and libraries
SimpleRAGRetrieve
KaibanJS RAG tool for intelligent retrieval
LangChain Embeddings
OpenAI, Cohere, HuggingFace, VertexAI & more
LangChain Integration
Vector stores and text splitting
Multiple Vector Stores
Memory, Pinecone, Weaviate, Qdrant
Real-World Use Cases
RAG-powered agents transform how businesses interact with their knowledge bases
E-commerce Product Support
Help customers find the right products by searching through thousands of items with natural language queries. Provide instant, accurate product recommendations based on specific needs.
Technical Documentation
Create AI assistants that answer questions about your product documentation, API references, or knowledge base articles. Reduce support tickets with instant, accurate answers.
Internal Knowledge Management
Enable employees to quickly find information from company policies, procedures, or internal wikis. Improve productivity with instant access to organizational knowledge.
Customer Support Automation
Build intelligent chatbots that provide accurate answers from your support documentation. Handle common queries automatically while escalating complex issues to human agents.
Healthcare Information Systems
Help healthcare professionals quickly access medical guidelines, drug information, or patient care protocols with semantic search capabilities.
Legal Document Search
Search through legal documents, case law, or contracts with natural language queries. Find relevant precedents and clauses instantly.
Implementation Highlights
Key features of this RAG implementation
Configuration Options
Sample Data Structure
See RAG Agents in Action
Watch how SimpleRAGRetrieve powers intelligent product recommendations
Interactive RAG Product Knowledge Demo
Experience the power of RAG-enabled AI agents. Try the interactive demo below to see how the Product Specialist uses SimpleRAGRetrieve to search and recommend products from the knowledge base. Ask questions like "What laptops do you have?" or "I need headphones for workouts" to see semantic search in action.
This demo showcases the collaborative AI agent workflow.Try the full version →
Learn More About SimpleRAGRetrieve
Dive deeper into the SimpleRAGRetrieve tool and RAG implementation
📚 Official Documentation
Read the complete SimpleRAGRetrieve documentation to learn about all available features, configuration options, and advanced use cases.
View Documentation→🔧 Key Features
- • Works with pre-loaded vector stores
- • Supports Pinecone, MemoryVectorStore, Weaviate, Qdrant & more
- • Customizable chunk sizes and retrieval options
- • All LangChain embeddings: OpenAI, Cohere, HuggingFace, VertexAI, etc.
- • Server-side execution support
- • Metadata filtering capabilities
💡 Pro Tip: Scaling to Production
While this demo uses MemoryVectorStore for simplicity, for production applications consider using persistent vector databases like Pinecone, Weaviate, or Qdrant. These provide better scalability, persistence, and advanced features like metadata filtering and hybrid search.
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