Understanding metadata

You can attach metadata key-value pairs to vectors in an index. When you query the index, you can then filter by metadata to ensure only relevant records are scanned.

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.

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Supported metadata types

Metadata payloads must be key-value pairs in a JSON object. Keys must be strings, and values can be one of the following data types:

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

For example, the following would be valid metadata payloads:

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

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

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Supported metadata size

Pinecone supports 40KB of metadata per vector.

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Metadata query language

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

For example, the following has a "genre" metadata field with a list of strings:

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

This means "genre" takes on both values, and requests with the following filters will match:

{"genre":"comedy"}

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

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

However, requests with the following filter will not match:

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

Additionally, requests with the following filters will not match because they are invalid. They will result in a compilation error:

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

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

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Manage high-cardinality in pod-based indexes

For pod-based indexes, Pinecone indexes all metadata by default. When metadata contains many unique values, pod-based indexes will consume significantly more memory, which can lead to performance issues, pod fullness, and a reduction in the number of possible vectors that fit per pod.

To avoid indexing high-cardinality metadata that is not needed for filtering, use selective metadata indexing, which lets you specify which fields need to be indexed and which do not, helping to reduce the overall cardinality of the metadata index while still ensuring that the necessary fields are able to be filtered.

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Considerations for serverless indexes

For each serverless index, Pinecone clusters records that are likely to be queried together. When you query a serverless index with a metadata filter, Pinecone first uses internal metadata statistics to exclude clusters that do not have records matching the filter and then chooses the most relevant remaining clusters.

Note the following considerations:

• When filtering by numeric metadata that cannot be ordered in a meaningful way (e.g., IDs as opposed to dates or prices), the chosen clusters may not be accurate. This is because the metadata statistics for each cluster reflect the min and max metadata values in the cluster, and min and max are not helpful when there is no meaningful order.

  In such cases, it is best to store the metadata as strings instead of numbers. When filtering by string metadata, the chosen clusters will be more accurate, with a low false-positive rate, because the string metadata statistics for each cluster reflect the actual string values, compressed for space-efficiency.

• When you use a highly selective metadata filter (i.e., a filter that rejects the vast majority of records in the index), the chosen clusters may not contain enough matching records to satisfy the designated top_k.

For more details about query execution, see Serverless architecture.

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Use metadata

The following operations support metadata:

• Query an index with metadata filters
• Insert metadata into an index
• Delete vectors by metadata filter

Pinecone Assistant also supports metadata filters. For more information, see Understanding Pinecone Assistant - Filter with metadata.