The component definition and tasks are defined in the definition.yaml and tasks.yaml files respectively.
Setup
In order to communicate with Pinecone, the following connection details need to be
provided. You may specify them directly in a pipeline recipe as key-value pairs
within the component's setup block, or you can create a Connection from
the Integration Settings
page and reference the whole setup as setup: ${connection.<my-connection-id>}.
Field
Field ID
Type
Note
API Key (required)
api-key
string
Fill in your Pinecone AI API key. You can create an api key in Pinecone Console.
Pinecone Index URL
url
string
Fill in your Pinecone index URL. It is in the form.
Supported Tasks
Query
Retrieve the ids of the most similar items in a namespace, along with their similarity scores.
Input
Field ID
Type
Description
Task ID (required)
task
string
TASK_QUERY
ID
id
string
The unique ID of the vector to be used as a query vector. If present, the vector parameter will be ignored.
Vector (required)
vector
array[number]
An array of dimensions for the query vector.
Top K (required)
top-k
integer
The number of results to return for each query.
Namespace
namespace
string
The namespace to query.
Filter
filter
object
The filter to apply. You can use vector metadata to limit your search. See more details here.
Minimum Score
min-score
number
Exclude results whose score is below this value.
Include Metadata
include-metadata
boolean
Indicates whether metadata is included in the response as well as the IDs.
Include Values
include-values
boolean
Indicates whether vector values are included in the response.
A measure of similarity between this vector and the query vector. The higher the score, the more similar they are.
Values
values
array
Vector data values.
Upsert
Writes vectors into a namespace. If a new value is upserted for an existing vector id, it will overwrite the previous value. This task will be soon replaced by TASK_BATCH_UPSERT, which extends its functionality.
Input
Field ID
Type
Description
Task ID (required)
task
string
TASK_UPSERT
ID (required)
id
string
This is the vector's unique id.
Values (required)
values
array[number]
An array of dimensions for the vector to be saved.
Namespace
namespace
string
The namespace to query.
Metadata
metadata
object
The vector metadata.
Output
Field ID
Type
Description
Upserted Count
upserted-count
integer
Number of records modified or added.
Batch Upsert
Writes vectors into a namespace. If a new value is upserted for an existing vector ID, it will overwrite the previous value.
The vector metadata. This is a set of key-value pairs that can be used to store additional information about the vector. The values can have the following types: string, number, boolean, or array of strings.
Values
values
array
An array of dimensions for the vector to be saved.
Output
Field ID
Type
Description
Upserted Count
upserted-count
integer
Number of records modified or added.
Rerank
Rerank documents, such as text passages, according to their relevance to a query. The input is a list of documents and a query. The output is a list of documents, sorted by relevance to the query.
Input
Field ID
Type
Description
Task ID (required)
task
string
TASK_RERANK
Query (required)
query
string
The query to rerank the documents.
Documents (required)
documents
array[string]
The documents to rerank.
Top N
top-n
integer
The number of results to return sorted by relevance. Defaults to the number of inputs.
Output
Field ID
Type
Description
Reranked Documents.
documents
array[string]
Reranked documents.
Scores
scores
array[number]
The relevance score of the documents normalized between 0 and 1.
