Searches with metadata filters retrieve exactly the number of nearest-neighbor results that match the filters. For most cases, the search latency will be even lower than unfiltered searches.

Searches without metadata filters do not consider metadata. To combine keywords with semantic search, see sparse-dense embeddings.

For more background information on metadata filtering, see: The Missing WHERE Clause in Vector Search.

​
Supported metadata types

You can associate a metadata payload with each vector in an index, as key-value pairs in a JSON object where keys are strings and values are one of:

  • String
  • Number (integer or floating point, gets converted to a 64 bit floating point)
  • Booleans (true, false)
  • List of String

High cardinality consumes more memory: Pinecone indexes metadata to allow
for filtering. If the metadata contains many unique values — such as a unique
identifier for each vector — the index will consume significantly more
memory. Consider using selective metadata indexing to avoid indexing
high-cardinality metadata that is not needed for filtering.

Null metadata values are not supported. Instead of setting a key to hold a
null value, we recommend you remove that key from the metadata payload.

For example, the following would be valid metadata payloads:

JSON
{
    "genre": "action",
    "year": 2020,
    "length_hrs": 1.5
}

{
    "color": "blue",
    "fit": "straight",
    "price": 29.99,
    "is_jeans": true
}

​
Supported metadata size

Pinecone supports 40kb of metadata per vector.

​
Metadata query language

Pinecone’s filtering query language is based on MongoDB’s query and projection operators. We currently support a subset of those selectors.

The metadata filters can be combined with AND and OR:

  • $eq - Equal to (number, string, boolean)
  • $ne - Not equal to (number, string, boolean)
  • $gt - Greater than (number)
  • $gte - Greater than or equal to (number)
  • $lt - Less than (number)
  • $lte - Less than or equal to (number)
  • $in - In array (string or number)
  • $nin - Not in array (string or number)
  • $exists - Has the specified metadata field (boolean)

​
Using arrays of strings as metadata values or as metadata filters

A vector with metadata payload…

JSON
{ "genre": ["comedy", "documentary"] }

…means the "genre" takes on both values.

For example, queries with the following filters will match the vector:

JSON
{"genre":"comedy"}

{"genre": {"$in":["documentary","action"]}}

{"$and": [{"genre": "comedy"}, {"genre":"documentary"}]}

Queries with the following filter will not match the vector:

JSON
{ "$and": [{ "genre": "comedy" }, { "genre": "drama" }] }

And queries with the following filters will not match the vector because they are invalid. They will result in a query compilation error:

# INVALID QUERY:
{"genre": ["comedy", "documentary"]}

# INVALID QUERY:
{"genre": {"$eq": ["comedy", "documentary"]}}

​
Inserting metadata into an index

Metadata can be included in upsert requests as you insert your vectors.

For example, here’s how to insert vectors with metadata representing movies into an index:

import pinecone

pinecone.init(api_key="your-api-key", environment="us-west1-gcp")
index = pinecone.Index("example-index")

index.upsert([
    ("A", [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], {"genre": "comedy", "year": 2020}),
    ("B", [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2], {"genre": "documentary", "year": 2019}),
    ("C", [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3], {"genre": "comedy", "year": 2019}),
    ("D", [0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4], {"genre": "drama"}),
    ("E", [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], {"genre": "drama"})
])

​
Querying an index with metadata filters

Metadata filter expressions can be included with queries to limit the search to only vectors matching the filter expression.

For example, we can search the previous movies index for documentaries from the year 2019. This also uses the include_metadata flag so that vector metadata is included in the response.

For performance reasons, do not return vector data and metadata when
top_k>1000. Queries with top_k over 1000 should not contain
include_metadata=True or include_data=True.

  index.query(
      vector=[0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
      filter={
          "genre": {"$eq": "documentary"},
          "year": 2019
      },
      top_k=1,
      include_metadata=True
  )
  
  # Returns:
  # {'matches': [{'id': 'B',
  #               'metadata': {'genre': 'documentary', 'year': 2019.0},
  #               'score': 0.0800000429,
  #               'values': []}],
  #  'namespace': ''}

​
More example filter expressions

A comedy, documentary, or drama:

JSON
{
  "genre": { "$in": ["comedy", "documentary", "drama"] }
}
`
``
A drama from 2020:



```JSON JSON
{
  "genre": { "$eq": "drama" },
  "year": { "$gte": 2020 }
}

A drama from 2020 (equivalent to the previous example):

JSON
{
  "$and": [{ "genre": { "$eq": "drama" } }, { "year": { "$gte": 2020 } }]
}

A drama or a movie from 2020:

JSON
{
  "$or": [{ "genre": { "$eq": "drama" } }, { "year": { "$gte": 2020 } }]
}

​
Deleting vectors by metadata filter

To specify vectors to be deleted by metadata values, pass a metadata filter expression to the delete operation. This deletes all vectors matching the metadata filter expression.

Projects in the gcp-starter region do not support deleting by metadata.

Example

This example deletes all vectors with genre “documentary” and year 2019 from an index.

Python JavaScript curl

  index.delete(
      filter={
          "genre": {"$eq": "documentary"},
          "year": 2019
      }
  )

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