daveebbelaar/langchain-experiments resource

Health v2 · maintenance only

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Adoption not part of the score

1140 stars · 637 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Jupyter Notebook experiments demonstrating how to build LLM-powered applications with the LangChain library and OpenAI models. It includes examples such as creating a searchable database from YouTube transcripts, similarity search with FAISS, and building agents and chatbots.

Use cases

• learn how to build apps with langchain and llms
• create a searchable database from a youtube video transcript
• run similarity search over documents with faiss
• build a question-answering bot over custom text data
• experiment with langchain agents and memory
• prototype a slack bot powered by an llm

When to choose

• you are learning LangChain and want runnable example notebooks
• you want to prototype LLM apps like transcript search or chatbots quickly
• you need reference code for combining OpenAI models with custom data

When to avoid

• you need a production-ready, maintained library or application
• you want a stable API rather than evolving experiment code
• you do not want to depend on OpenAI and SerpAPI keys

Facets

learning-resource · maturity experimental

agent-framework rag chatbot llm-inference prompt-engineering search-engine large-language-models artificial-intelligence tutorials developer-tools python cross-platform langchain openai faiss jupyter-notebooks experiments youtube-transcripts slack-bot example-code ai-agents retrieval-augmented-generation

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