Welcome to my LangChain learning repository — a hands-on journey through building with language models, tools, and agents.
This repo is structured around experiments, mini-projects, and notes that reflect key LangChain concepts.
I've started learning LangChain to build intelligent applications like chatbots, RAG pipelines, and LLM-based agents. This space documents my progress — with working code, notes, and observations.
| Folder | Description |
|---|---|
01_chat_models_prompts |
Build a simple LLM application with prompt templates and chat models |
02_semantic_search |
Build a semantic search engine over a PDF with document loaders, embedding models, and vector stores |
03_classification |
Classify text into categories or labels using chat models with structured outputs |
04_extraction |
Extract structured data from text and other unstructured media using chat models and few-shot examples |
05_build_a_chatbot |
Build a chatbot that incorporates memory |
06_build_an_agent |
Build an agent that interacts with external tools |
notes/ |
Concepts, comparisons, prompt experiments |
- LangChain
- Cohere LLM (https://cohere.com/)
- Chroma / FAISS Vector DB
- Python 3.10+
- Streamlit (optional for UI)
pip install -r requirements.txt
python main.py