# daveebbelaar/langchain-experiments

Building Apps with LLMs

Repository: https://github.com/daveebbelaar/langchain-experiments
Canonical: https://ross.abutalabs.com/products/langchain-experiments
Homepage: https://datalumina.com
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: ai, langchain, langchain-python, python, slack-bot
Last push: 2024-02-11T11:23:23+00:00

## Health v2 (maintenance only)
Score: 30/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 87
- inputs: {"age_days": 1231, "days_push": 934, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1140, forks 637 (observed 2026-08-28T04:03:44.479307+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: experimental
- function: agent-framework, rag, chatbot, llm-inference, prompt-engineering, search-engine
- domain: large-language-models, artificial-intelligence, tutorials, developer-tools
- platform: python, cross-platform
- tags: langchain, openai, faiss, jupyter-notebooks, experiments, youtube-transcripts, slack-bot, example-code, ai-agents, retrieval-augmented-generation

## Member repositories
- daveebbelaar/langchain-experiments (main) score 30

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:44.479307+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:35:29.613001+00:00, confidence not recorded.
  - readme: https://github.com/daveebbelaar/langchain-experiments (fetched 2026-08-28T04:03:44.479307+00:00, sha 16257ca3d2a2)
  - homepage: https://datalumina.com (fetched 2026-08-29T12:40:41.742080+00:00, sha e936d2403752)
- Data as of 2026-08-30T08:39:29.467469+00:00.