Refiner CLI allows you to store text as vectors in Pinecone and then search for similar text. It uses OpenAI to generate embeddings and then uses Pinecone to store and search for similar text.
Project description
refiner-cli
CLI for the Refiner python package convert and store text and metadata as vector embeddings. Embeddings are generated using OpenAI and stored as vectors in Pinecone. Stored embeddings can then be "queried" using the search
command. Matched embeddings contain contextually relavant metadata that can be used for AI chatbots, semnatic search APIs, and can also be used for training and tuning large language models.
Installation
pip install refiner-cli
OpenAI and Pinecone API Keys.
You'll need API keys for OpenAI and Pinecone.
Once you have your API keys, you can either set local ENV variables in a shell:
export PINECONE_API_KEY="API_KEY"
export PINECONE_ENVIRONMENT_NAME="ENV_NAME"
export OPENAI_API_KEY="API_KEY"
or you can create a .env
(dotenv) config file and pass it in with the --config-file
option.
Your .env file should follow key/value format:
PINECONE_API_KEY="API_KEY"
PINECONE_ENVIRONMENT_NAME="ENV_NAME"
OPENAI_API_KEY="API_KEY"
Help
The --help
option can be used to learn about the create
and search
commands.
refiner --help
refiner create --help
refiner search --help
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