Abhishek Vinod
AI Engineer building RAG systems, AI agents & automation
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Built a LangChain-based agentic application that turns a learning goal into a structured, personalised learning plan.
The application supports two execution modes:
⚡ ReAct Agent Mode
A tool-using agent dynamically selects custom tools for course lookup, completion-time calculation, and learning-plan generation.
🔍 Explain Mode — LCEL
A deterministic LCEL pipeline executes the planning workflow step-by-step, making the process more transparent, predictable, and easier to debug.
The project also uses Pydantic structured outputs, Groq, Python, Streamlit, and custom LangChain tools, with deterministic logic kept outside the LLM wherever possible.
This project was built to explore the practical engineering side of AI agents — tool calling, agent orchestration, structured outputs, deterministic workflows, and explainability.
The project is not currently deployed, but the complete source code and implementation are available on GitHub.
Tech: Python · LangChain · ReAct · LCEL · Groq · Pydantic · Streamlit
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Built a production-grade Deep Research RAG system for querying large business documents with grounded, source-cited answers.
The system combines dense retrieval, BM25 sparse retrieval, Reciprocal Rank Fusion (RRF), cross-encoder reranking, page-level deduplication, and grounded LLM generation.
The pipeline was evaluated using RAGAS, achieving 0.991 precision and 1.00 recall across the evaluation dataset.
Tech: Python, LangChain, ChromaDB, BM25, cross-encoder reranking, Groq, RAGAS.
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Deep Research RAG
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