LlamaIndex Webinar - Build No-Code RAG with Flowise
AI Summary
Webinar Summary: FlowWise AI Integration with Llama Index
Introduction
- Host: Jerry
- Guest: Henry from FlowWise AI
- Topic: Building custom orchestration with LLMs using FlowWise AI
Overview of FlowWise AI
- Low code/no code, open-source visual tool
- Simplifies building applications with LLMs
- Includes application use cases and agent integration
Agenda
- Overview of FlowWise AI
- Demo application
- Q&A session
Henry’s Presentation
- Background: Henry’s experience at an investment brokerage firm
- Challenge: Fast-moving AI space and the need for expertise in AI applications
- Solution: Created FlowWise AI for non-experts to participate in LLM evolution
- Goal: Provide developer tools for data scientists and web developers
Demo of FlowWise AI
- Features drag and drop elements for custom orchestrations
- Provides templates for quick start and learning
- Integrates with Llama Index and other databases
- Allows creation of chat flows, tools, and agents
- Supports API keys for security
Specific Features Highlighted
- Text splitter for document processing
- Pinecone and embeddings for vector databases
- Metadata specification for precise data retrieval
- Query engine for document-based questions
- Context chat engine for human-like interactions and document retrieval
- Subquery engine for breaking down complex queries
Q&A Highlights
- Upcoming features include multimodal capabilities
- Credentials are stored encrypted in the user’s database
- FlowWise AI is primarily used locally by companies
- Users are mostly developers from non-tech industries
- Future plans include indexing pipelines, OpenAI agent integration, and improved observability
Conclusion
- FlowWise AI aims to make LLM application development accessible to a broader audience, including those without deep technical expertise in AI.