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Ingesting vectors with metadata

It's ingesting time! You'll be ingesting vectors, which is a list of dictionaries containing the vector values, IDs, and associated metadata. They're already been provided in a format that can be directly inserted into the index without further manipulation.

Here's another reminder about the structure of vectors.

vectors = [
    {
        "id": "0",
        "values": [0.025525547564029694, ..., 0.0188823901116848]
        "metadata": {"genre": "action", "year": 2024}
    },
        ...,
]

Cet exercice fait partie du cours

Vector Databases for Embeddings with Pinecone

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Instructions

  • Initialize the Pinecone connection with your API key.
  • Connect to your index called "datacamp-index".
  • Upsert vectors to the index.
  • Print the index's descriptive statistics.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Initialize the Pinecone client with your API key
pc = Pinecone(api_key="____")

# Connect to your index
index = pc.____("datacamp-index")

# Ingest the vectors and metadata
____

# Print the index statistics
print(____)
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