Cloud

pinecone

Available in: Cloud, Self-Managed

Inserts items into a Pinecone index.

  • Common

  • Advanced

outputs:
  label: ""
  pinecone:
    max_in_flight: 64
    batching:
      count: 0
      byte_size: 0
      period: ""
      check: ""
      processors: [] # No default (optional)
    host: "" # No default (required)
    api_key: "" # No default (required)
    operation: upsert-vectors
    id: "" # No default (required)
    vector_mapping: "" # No default (optional)
    metadata_mapping: "" # No default (optional)
outputs:
  label: ""
  pinecone:
    max_in_flight: 64
    batching:
      count: 0
      byte_size: 0
      period: ""
      check: ""
      processors: [] # No default (optional)
    host: "" # No default (required)
    api_key: "" # No default (required)
    operation: upsert-vectors
    namespace: ""
    id: "" # No default (required)
    vector_mapping: "" # No default (optional)
    metadata_mapping: "" # No default (optional)

Performance

This output benefits from sending multiple messages in flight in parallel for improved performance. You can tune the max number of in flight messages (or message batches) with the field max_in_flight.

This output benefits from sending messages as a batch for improved performance. Batches can be formed at both the input and output level. You can find out more in this doc.

Fields

api_key

The Pinecone API key.

This field contains sensitive information that usually shouldn’t be added to a configuration directly. For more information, see Manage Secrets before adding it to your configuration.

Type: string

batching

Allows you to configure a batching policy.

Type: object

# Examples:
batching:
  byte_size: 5000
  count: 0
  period: 1s

# ---

batching:
  count: 10
  period: 1s

# ---

batching:
  check: this.contains("END BATCH")
  count: 0
  period: 1m

batching.byte_size

An amount of bytes at which the batch should be flushed. If 0 disables size based batching.

Type: int

Default: 0

batching.check

A Bloblang query that should return a boolean value indicating whether a message should end a batch.

Type: string

Default: ""

# Examples:
check: this.type == "end_of_transaction"

batching.count

A number of messages at which the batch should be flushed. If 0 disables count based batching.

Type: int

Default: 0

batching.period

A period in which an incomplete batch should be flushed regardless of its size.

Type: string

Default: ""

# Examples:
period: 1s

# ---

period: 1m

# ---

period: 500ms

batching.processors[]

A list of processors to apply to a batch as it is flushed. This allows you to aggregate and archive the batch however you see fit. Please note that all resulting messages are flushed as a single batch, therefore splitting the batch into smaller batches using these processors is a no-op.

Type: processor

# Examples:
processors:
  - archive:
      format: concatenate


# ---

processors:
  - archive:
      format: lines


# ---

processors:
  - archive:
      format: json_array

host

The host for the Pinecone index.

Type: string

id

The ID for the index entry in Pinecone. This field supports interpolation functions.

Type: string

max_in_flight

The maximum number of messages to have in flight at a given time. Increase this to improve throughput.

Type: int

Default: 64

metadata_mapping

An optional mapping of message to metadata in the Pinecone index entry.

Type: string

# Examples:
metadata_mapping: root = @

# ---

metadata_mapping: root = metadata()

# ---

metadata_mapping: root = {"summary": this.summary, "foo": this.other_field}

namespace

The namespace to write to - writes to the default namespace by default. This field supports interpolation functions.

Type: string

Default: ""

operation

The operation to perform against the Pinecone index.

Type: string

Default: upsert-vectors

Options: update-vector, upsert-vectors, delete-vectors

vector_mapping

The mapping to extract out the vector from the document. The result must be a floating point array. Required if not a delete operation.

Type: string

# Examples:
vector_mapping: root = this.embeddings_vector

# ---

vector_mapping: root = [1.2, 0.5, 0.76]