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Pinecone

As explained in Software Testing with Generative AI

Pinecone is a managed cloud platform for creating and operating vector databases and similarity-search indexes.

Pinecone is a managed cloud platform for creating vector databases, called indexes, and supporting similarity-search workflows. Its Canopy framework connects this cloud setup to retrieval-augmented generation (RAG), reducing setup and coding while trading away some control. section 11.3.2

A Canopy-backed Pinecone setup requires:

  • Python 3.11 to install Canopy, followed by the canopy-sdk package. section 11.3.2
  • OpenAI and Pinecone API keys, plus a Pinecone account upgraded to a standard plan so Canopy can create the required index. section 11.3.2
  • A data directory that Canopy can upload into an index. section 11.3.2

Testing a custom RAG index

Upload custom data with Canopy, then query it to assess retrieval in a target context. A booking dataset can produce responses covering room availability, booking creation, booking retrieval, and booking summaries, with the response identifying the source data directory. section 11.3.3

export PINECONE_API_KEY="<PINECONE_API_KEY>"
export OPENAI_API_KEY="<OPENAI_API_KEY>"
export INDEX_NAME="<INDEX_NAME>"

Set the Pinecone API key, OpenAI API key, and index name as environment variables before using the framework. section 11.3.2

How Software Testing with Generative AI covers it

How deep it goes
Explains cloud indexes, installation, credentials, upgrading, data upload, and querying, while deferring lower-level implementation control.
Examples
Uses environment variables, Canopy upload commands, Pinecone indexes, and booking user stories as test data.
What it assumes
Assumes Python 3.11, OpenAI and Pinecone API keys, and a standard Pinecone account; Canopy handles most framework work.
What you can do after
Apply Pinecone through Canopy to create an index, upload custom data, and test retrieval against practical queries.
How it connects
Connects vector databases, retrieval-augmented generation, Canopy, API keys, indexes, and similarity search.

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This explainer was generated by Manning's knowledge graph from the text of Software Testing with Generative AI. The section links point to the parts of the book it draws on.