# pinecone-io/examples

Jupyter Notebooks to help you get hands-on with Pinecone vector databases

Repository: https://github.com/pinecone-io/examples
Canonical: https://ross.abutalabs.com/products/pinecone-io-examples
Homepage: https://docs.pinecone.io
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: ai, jupyter-notebook, llm, python, semantic-search, vector-database, pinecone, rag, vector-search
Last push: 2026-08-14T16:12:34+00:00

## Health v2 (maintenance only)
Score: 76/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 97, release rhythm 35, longevity 100
- inputs: {"age_days": 2003, "days_push": 19, "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 3037, forks 1071 (observed 2026-08-28T04:07:39.167527+00:00)

## What it is
A collection of Jupyter Notebooks and sample applications from Pinecone for learning vector databases and common AI patterns like RAG and semantic search. It includes production-ready examples and learning-oriented notebooks maintained by Pinecone's Developer Advocacy team.

## Use cases
- learn how to use a vector database for semantic search
- build a RAG pipeline example with jupyter notebooks
- hands-on tutorials for llm applications with embeddings
- sample code for semantic search over documents
- learn vector search patterns for ai agents
- experiment with rag in google colab

## When to choose
- you want runnable notebooks to learn Pinecone and vector search concepts
- you need reference implementations of RAG and semantic search patterns
- you are evaluating how to integrate a vector database into an AI app

## When to avoid
- you need a production vector database service itself rather than examples
- you want a maintained SDK or library instead of educational notebooks
- you use a different vector database and don't want Pinecone-specific code

## Facets
- artifact type: learning-resource
- maturity: active
- function: vector-database, rag, search-engine, machine-learning, nlp
- domain: artificial-intelligence, large-language-models, databases, tutorials
- platform: python, jvm
- tags: jupyter-notebooks, pinecone, vector-search, semantic-search, sample-applications, developer-advocacy, llm, retrieval-augmented-generation, search

## Member repositories
- pinecone-io/examples (main) score 76

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:39.167527+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 2026-08-30T07:29:27.811980+00:00, confidence not recorded.
  - readme: https://github.com/pinecone-io/examples (fetched 2026-08-28T04:07:39.167527+00:00, sha ebe7e5070aa4)
  - homepage: https://docs.pinecone.io (fetched 2026-08-29T09:44:31.663583+00:00, sha 5d42c06f240c)
  - site_page: https://docs.pinecone.io/integrations/overview (fetched 2026-08-29T09:44:31.695338+00:00, sha dd7e007f0f68)
  - site_page: https://docs.pinecone.io/guides/get-started/quickstart (fetched 2026-08-29T09:44:31.697829+00:00, sha 782f6542752f)
  - site_page: https://www.pinecone.io (fetched 2026-08-29T09:44:31.692456+00:00, sha cde46239b446)
- Data as of 2026-08-30T08:39:29.467469+00:00.