Understanding Our RAG Pipeline
Our Retrieval-Augmented Generation (RAG) pipeline combines advanced AI with efficient data processing to provide personalized health insights.
Natural Language Processing
Our system uses advanced LLMs to understand and process user queries in natural language, allowing for intuitive interactions.
Dynamic SQL Generation
The LLM generates SQL queries based on the user's question and our database schema, enabling precise data retrieval.
Efficient Data Retrieval
Our SQLite database quickly executes the generated queries, fetching relevant health data for analysis.
Context-Aware Interpretation
The retrieved data is processed by the LLM, which interprets the results in the context of the user's query and health profile.
Personalized Response Generation
Finally, the system generates a natural language response, providing personalized insights and recommendations based on the analyzed data.
Advanced Technologies Powering Our System
Our RAG pipeline leverages cutting-edge technologies to provide accurate and context-aware responses:
LangChain
Orchestrates the flow of information between components, enabling seamless integration of various AI and data processing tasks.
LangGraph
Manages complex multi-step reasoning processes, allowing for sophisticated analysis and decision-making in health-related queries.
Vector Databases
Enable efficient similarity search for relevant information retrieval, enhancing the accuracy and speed of our health recommendations.
Large Language Models (LLMs)
Power natural language understanding and generation, allowing for intuitive interactions and personalized health insights.