AI Agent (Bumblebee)
Bumblebee is the RAG Hybrid Agent within FlexInsightIQ—designed to adapt and transform on demand.
Overview
With the platform infrastructure and structured intelligence firmly in place, building a Hybrid RAG agent became the next logical step. Because structured intelligence was embedded at every level, the agent could tap into a rich metadata ecosystem with minimal additional overhead. The core challenge wasn’t in designing the RAG workflow itself but in creating a reference architecture that efficiently integrates all components—embeddings, storage, indexing, and contextualized query handling.
Key Goals:
Develop a Hybrid RAG AI agent capable of seamlessly navigating insights, dashboards, and SQL reporting without excessive resource consumption.
Complexity: High
Components
Bumblebee V1 - Completed
The first-generation Hybrid RAG agent, capable of understanding user context (Owner vs. Tenant) and intelligently routing requests. It could: - Retrieve existing SQL-generated reports - Redirect users to relevant dashboards, visualizations, and data capture tools - Provide high-level insights about FlexInsightIQ’s ecosystem
SOARL Summary
The goal was to leverage the deep structural intelligence built into the system, enabling effective RAG operations without requiring extensive context memory.
- A full Gen AI reference architecture needed to be built within the platform. - Implementing embeddings storage in Redis & FAISS and ensuring contextualized OpenAI model calls required additional orchestration. - Django had difficulty communicating with FAISS + Redis, necessitating an intermediary service to handle data movement.
Developed the full agent ecosystem, optimizing its ability to retrieve relevant data and insights efficiently.
Bumblebee V1 successfully executed end-to-end RAG processing, providing critical insights into building an AI agent.
- Developing a hybrid agent is an iterative process. - V1 was essential in laying the foundation, revealing that higher-quality pre-processing was necessary for better FAISS similarity search results. - This version became an invaluable live demonstration for clients, illustrating Hybrid RAG reference architecture in action.
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Bumblebee V2 (In Progress)
The second-generation Bumblebee agent introduces advanced pre-processing, refined embeddings, and structured indexing for higher accuracy and consistency.
SOARL Summary
Initial testing of embedding indexes and FAISS results revealed the need for a more sophisticated pre-processor to optimize queries and improve repeatability.
- Inconsistent similarity search results impacted LLM responses, even with temperature set to 0 and highly specific system prompts. - The agent required better structured indexes to ensure repeatable and predictable query outcomes.
- Moved query type classification to a pre-processing step rather than relying on the LLM. - Implemented index categorization (High - General, Medium - Links, Low - Specific) for targeted searches. - Adjusted index loading logic to prioritize relevant data based on the user’s account type.
- More consistent response quality and improved LLM interpretation. - By refining embeddings and categorizing indexes, the system guarantees higher accuracy in search matches, even when the match score is low.
- Breaking down indexes is key—precision in embeddings drastically improves search relevance. - Shifting pre-processing tasks to NLP-based logic rather than relying on LLMs ensures stability. - A robust dictionary of synonyms and structured guidelines enhances the agent’s response quality.
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Key Learnings
- A Hybrid RAG Agent must be iteratively refined—V1 laid the foundation, and V2 is optimizing retrieval quality. - Pre-processing is essential—LLM responses are unreliable without well-structured query classification and high-quality embeddings. - Index categorization significantly improves response accuracy—organizing indexes by query type ensures consistent and predictable results.
Demos
Final Thoughts
Building a Hybrid RAG AI agent isn’t just about retrieving insights—it’s about precision, efficiency, and adaptability. Bumblebee V1 provided a functional prototype, while Bumblebee V2 is focused on fine-tuning intelligence and retrieval accuracy. This journey showcases how structured intelligence, optimized embeddings, and pre-processing come together to build a truly effective AI-driven assistant. 🚀 More refinements to come—onward to V3!