Search

This plugin is currently in beta. While it is considered safe for use, please be aware that its API could change in ways that are not compatible with earlier versions in future releases, or it might become unsupported.

Search from an embedding store.

Performs a semantic search using a query string.

yaml
type: "io.kestra.plugin.ai.rag.Search"

Examples

Make a search query against an embedding store.

yaml
id: search_embeddings_flow
namespace: company.team

tasks:
  - id: ingest
    type: io.kestra.plugin.ai.rag.IngestDocument
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      modelName: gemini-embedding-exp-03-07
      apiKey: "{{ secret('GEMINI_API_KEY') }}"
    embeddings:
      type: io.kestra.plugin.ai.embeddings.KestraKVStore
    drop: true
    fromExternalURLs:
      - https://raw.githubusercontent.com/kestra-io/docs/refs/heads/main/content/blogs/release-0-22.md

  - id: search
    type: io.kestra.plugin.ai.rag.Search
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      modelName: gemini-embedding-exp-03-07
      apiKey: "{{ secret('GEMINI_API_KEY') }}"
    embeddings:
      type: io.kestra.plugin.ai.embeddings.KestraKVStore
    query: "Feature Highlights"
    maxResults: 5
    minScore: 0.5
    fetchType: FETCH

Properties

embeddings *Chroma Elasticsearch KestraKVStore Milvus MongoDBAtlas PGVector Pinecone Qdrant Weaviate

The embedding store provider

maxResults *integerstring

Maximum number of results to return

minScore *numberstring

Minimum similarity score

provider *AmazonBedrock Anthropic AzureOpenAI DeepSeek GoogleGemini GoogleVertexAI MistralAI Ollama OpenAI

The embedding model provider

query *string

Query string to search for

fetchType string

Default NONE

Possible Values

STOREFETCHFETCH_ONENONE

Outputs

results array

SubType string

List of matching text results

size integer

The count of the fetched or stored resources

uri string

Format uri

The output files URI in Kestra's internal storage

Only available when fetchType is set to STORE

Definitions

Azure OpenAI Model Provider

endpoint *string

API endpoint

The Azure OpenAI endpoint in the format: https://{resource}.openai.azure.com/

modelName *string

Model name

type *object

apiKey string

API Key

clientId string

Client ID

clientSecret string

Client secret

serviceVersion string

API version

tenantId string

Tenant ID

PGVector Embedding Store

database *string

The database name

host *string

The database server host

password *string

The database password

port *integerstring

The database server port

table *string

The table to store embeddings in

type *object

user *string

The database user

useIndex booleanstring

Default false

Whether to use use an IVFFlat index

An IVFFlat index divides vectors into lists, and then searches a subset of those lists closest to the query vector. It has faster build times and uses less memory than HNSW but has lower query performance (in terms of speed-recall tradeoff).

Qdrant Embedding Store

apiKey *string

The API key

collectionName *string

The collection name

host *string

The database server host

port *integerstring

The database server port

type *object

Google VertexAI Model Provider

endpoint *string

Endpoint URL

location *string

Project location

modelName *string

Model name

project *string

Project ID

type *object

Google Gemini Model Provider

apiKey *string

API Key

modelName *string

Model name

type *object

MongoDB Atlas Embedding Store

collectionName *string

The collection name

host *string

The host

indexName *string

The index name

scheme *string

The scheme (e.g. mongodb+srv)

type *object

createIndex booleanstring

Create the index

database string

The database

metadataFieldNames array

SubType string

The metadata field names

options object

The connection string options

password string

The password

username string

The username

Mistral AI Model Provider

apiKey *string

API Key

modelName *string

Model name

type *object

baseUrl string

API base URL

In-memory Embedding Store that then store its serialization form as a Kestra K/V pair

type *object

kvName string

Default {{flow.id}}-embedding-store

The name of the K/V entry to use

Chroma Embedding Store

baseUrl *string

The database base URL

collectionName *string

The collection name

type *object

io.kestra.plugin.ai.embeddings.Elasticsearch-ElasticsearchConnection-BasicAuth

password string

Basic auth password.

username string

Basic auth username.

Milvus Embedding Store

token *string

The token

type *object

autoFlushOnDelete booleanstring

Whether to auto flush on delete

autoFlushOnInsert booleanstring

Whether to auto flush on insert

collectionName string

The collection name

If there is no such collection yet, it will be created automatically. Default value: "default".

consistencyLevel string

The consistency level

databaseName string

The database name

If not provided, the default database will be used.

host string

The host

Default value: "localhost"

idFieldName string

The id field name

indexType string

The index type

metadataFieldName string

The metadata field name

metricType string

The metric type

password string

The password

If user authentication and TLS is enabled, this parameter is required. See: https://milvus.io/docs/authenticate.md

port integerstring

The port

Default value: "19530"

retrieveEmbeddingsOnSearch booleanstring

Whether to retrieve embeddings on search

textFieldName string

The text field name

uri string

The uri

username string

The username

If user authentication and TLS is enabled, this parameter is required. See: https://milvus.io/docs/authenticate.md

vectorFieldName string

The vector field name

Deepseek Model Provider

apiKey *string

API Key

modelName *string

Model name

type *object

baseUrl string

Default https://api.deepseek.com/v1

API base URL

Pinecone Embedding Store

apiKey *string

The API key

cloud *string

The cloud provider

index *string

The index

region *string

The cloud provider region

type *object

namespace string

The namespace (default will be used if not provided)

Anthropic AI Model Provider

apiKey *string

API Key

modelName *string

Model name

type *object

Weaviate Embedding Store

apiKey *string

Weaviate API key

Your Weaviate API key. Not required for local deployment.

host *string

Weaviate host

The host, e.g. "ai-4jw7ufd9.weaviate.network" of cluster URL. Find in under Details of your Weaviate cluster.

type *object

avoidDups booleanstring

Weaviate avoid dups

If true (default), then WeaviateEmbeddingStore will generate a hashed ID based on provided text segment, which avoids duplicated entries in DB. If false, then random ID will be generated.

consistencyLevel string

Possible Values

ONEQUORUMALL

Weaviate consistency level

Consistency level: ONE, QUORUM (default) or ALL.

grpcPort integerstring

gRPC port if used

metadataFieldName string

Weaviate metadata field name

The name of the metadata field to store. If not provided, will default to "_metadata".

metadataKeys array

SubType string

Weaviate metadata keys

The list of metadata keys to store. If not provided, will default to an empty list.

objectClass string

Weaviate object class

The object class you want to store, e.g. "MyGreatClass". Must start from an uppercase letter. If not provided, will default to "Default".

port integerstring

Weaviate port

The port, e.g. 8080. This parameter is optional.

scheme string

Weaviate scheme

The scheme, e.g. "https" of cluster URL. Find in under Details of your Weaviate cluster.

securedGrpc booleanstring

The gRPC connection is secured

useGrpcForInserts booleanstring

Use gRPC for inserts

Use GRPC instead of HTTP for batch inserts only. You still need HTTP configured for search.

Ollama Model Provider

endpoint *string

Model endpoint

modelName *string

Model name

type *object

OpenAI Model Provider

apiKey *string

API Key

modelName *string

Model name

type *object

baseUrl string

API base URL

io.kestra.plugin.ai.embeddings.Elasticsearch-ElasticsearchConnection

hosts *array

SubType string

Min items 1

List of HTTP ElasticSearch servers.

Must be an URI like https://elasticsearch.com: 9200 with scheme and port.

basicAuth Elasticsearch-ElasticsearchConnection-BasicAuth

Basic auth configuration.

headers array

SubType string

List of HTTP headers to be send on every request.

Must be a string with key value separated with : , ex: Authorization: Token XYZ.

pathPrefix string

Sets the path's prefix for every request used by the HTTP client.

For example, if this is set to /my/path, then any client request will become /my/path/ + endpoint. In essence, every request's endpoint is prefixed by this pathPrefix. The path prefix is useful for when ElasticSearch is behind a proxy that provides a base path or a proxy that requires all paths to start with '/'; it is not intended for other purposes and it should not be supplied in other scenarios.

strictDeprecationMode booleanstring

Whether the REST client should return any response containing at least one warning header as a failure.

trustAllSsl booleanstring

Trust all SSL CA certificates.

Use this if the server is using a self signed SSL certificate.

Elasticsearch Embedding Store

connection *Elasticsearch-ElasticsearchConnection

indexName *string

The name of the index to store embeddings

type *object

Amazon Bedrock Model Provider

accessKeyId *string

AWS Access Key ID

modelName *string

Model name

secretAccessKey *string

AWS Secret Access Key

type *object

modelType string

Default COHERE

Possible Values

COHERETITAN

Amazon Bedrock Embedding Model Type

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