lantern-pinecone 0.0.5

Pinecone compatiable client for Lantern

Lantern client compatible with Pinecone API

Install

pip install lantern-pinecone

Sync from Pinecone to Lantern

import lantern_pinecone
from getpass import getpass

lantern_pinecone.init('postgres://postgres@localhost:5432')

pinecone_ids = list(map(lambda x: str(x), range(100000)))

index = lantern_pinecone.create_from_pinecone(
        api_key=getpass("Pinecone API Key"),
        environment="us-east-1-aws",
        index_name="sift100k",
        namespace="",
        pinecone_ids=pinecone_ids,
        recreate=True,
        create_lantern_index=True)

index.describe_index_stats()

index.query(top_k=10, id='45500', namespace="")

NOTE: If you pass create_lantern_index=False only data will be copied under the table of your index name (in this example sift100k) and you can create an index later externally. Without the index most of the index operations will not be accessible via this client.

Extract Metadata Fields

When copying from Pinecone we create a table in this structure: sql (id TEXT, embedding REAL[], metadata jsonb) If you are planning to use the index with raw sql clients, you may want to extract metadata into separate columns, so you could have more complex/nice looking queries over your metadata fields. So if our metadata has this shape { "title": string, "description": string }, we can extract it using this query:

BEGIN;
ALTER TABLE sift100k
ADD COLUMN title TEXT,
ADD COLUMN description TEXT;

-- Update the new columns with data extracted from the JSONB column
UPDATE sift100k
SET
  title = metadata->>'title',
  description = metadata->>'description';


-- Optionally drop the metadata column
ALTER TABLE sift100k DROP COLUMN metadata;

COMMIT;

After doing this your index will most likely be uncomaptible with this python client, and you should use it via raw sql client like psycopg2

Index operations

import os
import lantern_pinecone
import pandas as pd

LANTERN_DB_URL = os.environ.get('LANTERN_DB_URL') or 'postgres://postgres@localhost:5432'
lantern_pinecone.init(LANTERN_DB_URL)

# Giving our index a name
index_name = "hello-lantern"

# Delete the index, if an index of the same name already exists
if index_name in lantern_pinecone.list_indexes():
    lantern_pinecone.delete_index(index_name)


import time

dimensions = 3
lantern_pinecone.create_index(name=index_name, dimension=dimensions, metric="cosine")
index = lantern_pinecone.Index(index_name=index_name)


df = pd.DataFrame(
    data={
        "id": ["A", "B"],
        "vector": [[1., 1., 1.], [1., 2., 3.]]
    })

# Insert vectors
index.upsert(vectors=zip(df.id, df.vector))

index.describe_index_stats()

index.query(
    vector=[2., 2., 2.],
    top_k=5,
    include_values=True) # returns top_k matches


lantern_pinecone.delete_index(index_name)