This workshop contains a collection of labs that will get you hands-on with Pinecone for basic operations. If you would like more use case specific material please refer to Pinecone Examples. If you are interested in deep dives, refer to Pinecone Learning Center
This is a simple lab that has the basics for setting environment variables, installing dependencies and working with the pinecone client. This lab uses dummy data. This lab introduces the following operations concepts:
- Install the python pinecone grpc client
- Leverage environment variables for pinecone client initialization
- Index creation using a storage optimzed pod(s1)
- Generate and upsert vectors with meta-data
This lab introduces the following operations concepts:
- Index creation using the x2 pod size (a way to vertically scale your index)
- Use meta-data statistics to learn more about the index
- Index backup/restore using Collections
- Meta-data filter exclusions to guard against high cardinality
This lab also uses dummy data.
This lab introduces the following operations concepts:
- Load public datasets from Hugging Face
- Generate embeddings using an open source model(CLIP)
- Use training data labels as meta-data for your training data embeddings
- Query pinecone with a test image that is not included in the public data set
- Validate pinecone accuracy with test images that are included in the public data set
- Run a load test using public test data to validate accuracy, P50-P100 latency and QPS
If you encounter issues with python/pip the following commands might help clean up a broken dependency tree.
!pip freeze > requirements.txt
!cat requirements.txt | xargs -n 1 pip uninstall -y