benman1/generative_ai_with_langchain
Jupyter notebook-based code examples demonstrating how to build production LLM applications and advanced agents with LangChain and LangGraph.
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This repository contains Jupyter notebooks that walk through building production-ready LLM applications and advanced agents using Python, LangChain, and LangGraph. The material covers integrating with multiple LLM providers including OpenAI, Anthropic Claude, DeepSeek, and Hugging Face models, as well as deploying with tools like Ollama and llama.cpp. Topics include RAG, agent orchestration, prompt engineering, and enterprise deployment patterns.
Frequently asked
- What is benman1/generative_ai_with_langchain?
- Jupyter notebook-based code examples demonstrating how to build production LLM applications and advanced agents with LangChain and LangGraph.
- Is generative_ai_with_langchain open source?
- Yes — benman1/generative_ai_with_langchain is open source, released under the MIT license.
- What language is generative_ai_with_langchain written in?
- benman1/generative_ai_with_langchain is primarily written in Jupyter Notebook.
- How popular is generative_ai_with_langchain?
- benman1/generative_ai_with_langchain has 1.4k stars on GitHub.
- Where can I find generative_ai_with_langchain?
- benman1/generative_ai_with_langchain is on GitHub at https://github.com/benman1/generative_ai_with_langchain.