Pinecone:Docs

Pinecone Developer Reference

A curated index of Pinecone documentation. Fetch the relevant page(s) for the task at hand rather than relying on training data.

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NOTE TO AGENT

Please attempt to fetch the url listed when relevant. If you run into an error, please attempt to append ".md" to the url to retrieve the markdown version of the Docs page.

In case you need it: A full reference to ALL relevant URLs can be found here: https://docs.pinecone.io/llms.txt

Use this as a last resort if you cannot find the relevant page below.

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Getting Started

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Indexes

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Upsert & Data

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Search

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API & SDK Reference

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Production

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Data Formats

See references/data-formats.md for vector and record schemas.

Data Formats

Integrated Index Records

Used with upsert_records() (Python SDK) or upsert-records (MCP). Records are automatically embedded using the index's configured model.

JSON

[
  {
    "_id": "rec1",
    "chunk_text": "Your text content here.",
    "category": "example"
  },
  {
    "_id": "rec2",
    "chunk_text": "Another piece of text.",
    "category": "example"
  }
]

• _id — unique record identifier (required)
• The text field name must match the index's fieldMap (e.g. chunk_text if fieldMap: {text: "chunk_text"})
• All other fields are stored as metadata and can be used for filtering
• Do not nest extra fields under a metadata key — put them directly on the record

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Standard Index Vectors

Used with upsert() (Python SDK) or pc index vector upsert (CLI).

JSON (with `vectors` array)

{
  "vectors": [
    {
      "id": "vec1",
      "values": [0.1, 0.2, 0.3],
      "metadata": { "genre": "comedy", "year": 2021 }
    },
    {
      "id": "vec2",
      "values": [0.4, 0.5, 0.6],
      "metadata": { "genre": "drama", "year": 2019 }
    }
  ]
}

JSONL (one vector per line)

{"id": "vec1", "values": [0.1, 0.2, 0.3], "metadata": {"genre": "comedy"}}
{"id": "vec2", "values": [0.4, 0.5, 0.6], "metadata": {"genre": "drama"}}

• id — unique vector identifier (required)
• values — dense vector as float array, length must match index dimension (required)
• metadata — arbitrary key-value pairs for filtering (optional)

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Sparse Vectors

Used for keyword or hybrid search with sparse indexes.

{
  "id": "vec1",
  "values": [0.1, 0.2, 0.3],
  "sparse_values": {
    "indices": [10, 45, 316],
    "values": [0.5, 0.3, 0.8]
  },
  "metadata": { "genre": "comedy" }
}

• sparse_values.indices — non-zero dimension indices
• sparse_values.values — corresponding float values, same length as indices