This is the implementation of the Pinecone for MindsDB.

Pinecone is a vector database which is fully-managed, developer-friendly, and easily scalable.

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Prerequisites

Before proceeding, ensure the following prerequisites are met:

  1. Install MindsDB locally via Docker or use MindsDB Cloud.
  2. To connect Pinecone to MindsDB, install the required dependencies following this instruction.
  3. Install or ensure access to Pinecone.

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Implementation

This handler uses pinecone-client python library connect to a pinecone environment.

The required arguments to establish a connection are:

  • api_key: the API key that can be found in your pinecone account
  • environment: the environment name corresponding to the api_key

These optional arguments are used with CREATE statements:

  • dimension: dimensions of the vectors to be stored in the index (default=8)
  • metric: distance metric to be used for similarity search (default=‘cosine’)
  • pods: number of pods for the index to use, including replicas (default=1)
  • replicas: the number of replicas. replicas duplicate your index. they provide higher availability and throughput (default=1)
  • pod_type: the type of pod to use, refer to pinecone documentation (default=‘p1’)

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Limitations

  • DROP TABLE support
  • Support for namespaces
  • Display score/distance
  • Support for creating/reading sparse values
  • content column is not supported since it does not exist in Pinecone

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Usage

In order to make use of this handler and connect to an environment, use the following syntax:

CREATE DATABASE pinecone_dev
WITH ENGINE = "pinecone",
PARAMETERS = {
   "api_key": "...",
   "environment": "..."
};

You can query pinecone indexes (temp in the following examples) based on id or search_vector, but not both:

SELECT * from pinecone_dev.temp
WHERE id = "abc"
LIMIT 1

SELECT * from pinecone_dev.temp
WHERE search_vector = "[1,2,3,4,5,6,7,8]"

If you are using subqueries, make sure that the result is only a single row since the use of multiple search vectors is not allowed

SELECT * from pinecone_database.temp
WHERE search_vector = (
    SELECT embeddings FROM sqlitetesterdb.test WHERE id = 10
)

Optionally, you can filter based on metadata too:

SELECT * from pinecone_dev.temp
WHERE id = "abc" AND temp.metadata.hello < 100

You can delete records using id or metadata like so:

DELETE FROM pinecone_dev.temp
WHERE id = "abc"

Note that deletion through metadata is not supported in starter tier

DELETE FROM pinecone_dev.temp
WHERE temp.metadata.tbd = true

You can insert data into a new collection like so:

CREATE TABLE pinecone_dev.temp (
SELECT * FROM mysql_demo_db.temp LIMIT 10);

To update records, you can use insert statement. When there is a conflicting ID in pinecone index, the record is updated with new values. It might take a bit to see it reflected.

INSERT INTO pinecone_test.testtable (id,content,metadata,embeddings)
VALUES (
    'id1', 'this is a test', '{"test": "test"}', '[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]'
);
PGVectorWeaviate