How to build a simplified Corrective RAG assistant with Amazon Bedrock using LLMs, Embeddings model, Knowledge Bases for Amazon Bedrock, and Agents for Amazon Bedrock.
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Updated
May 22, 2024 - Jupyter Notebook
How to build a simplified Corrective RAG assistant with Amazon Bedrock using LLMs, Embeddings model, Knowledge Bases for Amazon Bedrock, and Agents for Amazon Bedrock.
adaptive rag, corrective rag and agentic rag examples using langgraph
React-based LLM frontend implementing CRAG & ToolCalls
Gradio based Chatbot for Medical Queries using CrewAI framework to showcase Corrective-RAG based AgenticAI in python v3.11.0
Successfully developed a Healthcare AI Clinical Decision Support System, leveraging LangGraph, GPT-4o-mini, and PubMed to deliver real-time patient risk stratification, evidence-based treatment recommendations, and personalized clinical road maps with integrated drug safety validations.
Production-grade Corrective RAG system in LangGraph — scores retrieved chunks before generation, falls back to web search when needed, and filters context to sentence level.
Context-aware tool for automated BDD test generation and execution using RAG, VectorDB, and LLaMA.
Example implementation of a Corrective-RAG workflow for agents using LangChain and LangGraph
Agentic RAG system with LangGraph, hybrid BM25+FAISS retrieval, cross-encoder reranking, Corrective RAG, FastAPI, RAGAs evaluation, and Docker deployment
Corrective RAG pipeline with LangChain + (Agentic Workflow) LangGraph. Intelligent document grading, query transformation & web search. Groq LLM, FAISS, Self_Query Retriever, FlashRank, FastAPI, LangSmith. Deployed on HF Spaces.
Advanced RAG using langgraph which uses websearch functionality to produce relevant documents.
🔍 Enhance your searches with Self-Corrective RAG, a system that optimizes queries and evaluates document relevance using LangGraph and Google Gemini.
local RAG (Retrieval-Augmented Generation) system engineered to handle massive, high-complexity technical documentation.
A CRAG agent crafted using Gemini, langgraph and python to audit the ML scripts before deploying in production level
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