AI IngestDocument

AI IngestDocument
Certified
Ingest documents into embeddings

Loads text from local path, internal storage, URLs, or inline docs; splits with the chosen splitter; then writes embeddings to the configured store. drop=true clears the store first. Default splitter is absent; provide one for chunking.

yaml
type: io.kestra.plugin.ai.rag.IngestDocument

Examples

Ingest documents into a KV embedding store. WARNING: the KV embedding store is for quick prototyping only; it stores embedding vectors in a KV store and loads them all into memory.

yaml
id: document_ingestion
namespace: company.ai

tasks:
  - id: ingest
    type: io.kestra.plugin.ai.rag.IngestDocument
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      modelName: gemini-embedding-001
      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-0-24.md

Properties

embeddings *

Embedding store provider

Definitions

Store embeddings in Chroma

Connects to a Chroma HTTP instance using cosine distance; targets the given collection and drops it when drop=true.

Example

Ingest documents into a Chroma embedding store

yaml
id: document_ingestion
namespace: company.ai

tasks:
  - id: ingest
    type: io.kestra.plugin.ai.rag.IngestDocument
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      modelName: gemini-embedding-001
      apiKey: "{{ secret('GEMINI_API_KEY') }}"
    embeddings:
      type: io.kestra.plugin.ai.embeddings.Chroma
      baseUrl: http://localhost:8000
      collectionName: embeddings
    fromExternalURLs:
      - https://raw.githubusercontent.com/kestra-io/docs/refs/heads/main/content/blogs/release-0-24.md

baseUrl*string

The database base URL

collectionName*string

The collection name

type*object

Possible Values

io.kestra.plugin.ai.embeddings.Chromaio.kestra.plugin.langchain4j.embeddings.Chroma

Store embeddings in Elasticsearch

Targets an Elasticsearch 8.15+ cluster using the provided hosts/index; when drop=true the index is deleted. Supports basic auth, custom headers, path prefix, and trust-all TLS for self-signed certs.

Example

Ingest documents into an Elasticsearch embedding store (requires Elasticsearch 8.15+)

yaml
id: document_ingestion
namespace: company.ai

tasks:
  - id: ingest
    type: io.kestra.plugin.ai.rag.IngestDocument
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      modelName: gemini-embedding-001
      apiKey: "{{ secret('GEMINI_API_KEY') }}"
    embeddings:
      type: io.kestra.plugin.ai.embeddings.Elasticsearch
      connection:
        hosts:
          - http://localhost:9200
    fromExternalURLs:
      - https://raw.githubusercontent.com/kestra-io/docs/refs/heads/main/content/blogs/release-0-24.md

connection*

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

hosts*array

SubTypestring

Min items1

List of HTTP Elasticsearch servers

Must be a URI like https://example.com: 9200 with scheme and port

basicAuth

Basic authorization configuration

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

passwordstring

Basic authorization password

usernamestring

Basic authorization username

headersarray

SubTypestring

List of HTTP headers to be sent with every request

Each item is a key: value string, e.g., Authorization: Token XYZ

pathPrefixstring

Path prefix for all HTTP requests

If set to /my/path, each client request becomes /my/path/ + endpoint. Useful when Elasticsearch is behind a proxy providing a base path; do not use otherwise.

strictDeprecationModebooleanstring

Treat responses with deprecation warnings as failures

targetServerVersionintegerstring

Default8

Target Elasticsearch server major version

Major version used for compatible-with media-type headers (Accept and Content-Type). The bundled elasticsearch-java 9.x client defaults to compatible-with=9, which Elasticsearch 8 rejects. Set to 8 when targeting an Elasticsearch 8 cluster (the default), or 9 for Elasticsearch 9.

trustAllSslbooleanstring

Trust all SSL CA certificates

Use this if the server uses a self-signed SSL certificate. WARNING: enabling this disables both certificate chain validation and hostname verification, exposing connections to man-in-the-middle attacks. Prefer supplying a custom CA certificate instead. Use only in trusted, controlled environments.

indexName*string

The name of the index to store embeddings

type*object

Possible Values

io.kestra.plugin.ai.embeddings.Elasticsearchio.kestra.plugin.langchain4j.embeddings.Elasticsearch

Prototype embeddings in Kestra KV

Stores embeddings in-memory and persists them to Kestra namespace KV on completion. Suitable for small demos; not scalable. drop=true discards any previously saved KV snapshot.

Example

Ingest documents into a KV embedding store.\nWARNING: the KestraKVStore embeddings are for quick prototyping only; since they are stored in a KV store and loaded into memory, this won't scale with large numbers of documents.

yaml
id: document_ingestion
namespace: company.ai

tasks:
  - id: ingest
    type: io.kestra.plugin.ai.rag.IngestDocument
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      modelName: gemini-embedding-001
      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-0-24.md

type*object

Possible Values

io.kestra.plugin.ai.embeddings.KestraKVStoreio.kestra.plugin.langchain4j.embeddings.KestraKVStore

kvNamestring

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

The name of the KV pair to use

Store embeddings in MariaDB

Persists embeddings to a MariaDB table; create/drop behavior is controlled by createTable and drop. Metadata defaults to COMBINED_JSON unless COLUMN_PER_KEY is configured with column/index definitions. Requires valid JDBC URL and credentials.

Example

Ingest documents into a MariaDB embedding store

yaml
id: document_ingestion
namespace: company.ai

tasks:
  - id: ingest
    type: io.kestra.plugin.ai.rag.IngestDocument
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      modelName: gemini-embedding-001
      apiKey: "{{ secret('GEMINI_API_KEY') }}"
    embeddings:
      type: io.kestra.plugin.ai.embeddings.MariaDB
      username: "{{ secret('MARIADB_USERNAME') }}"
      password: "{{ secret('MARIADB_PASSWORD') }}"
      databaseUrl: "{{ secret('MARIADB_DATABASE_URL') }}"
      tableName: embeddings
      fieldName: id
    fromExternalURLs:
      - https://raw.githubusercontent.com/kestra-io/docs/refs/heads/main/content/blogs/release-0-24.md

createTable*booleanstring

Whether to create the table if it doesn't exist

databaseUrl*string

Database URL of the MariaDB database (e.g., jdbc: mariadb://host: port/dbname)

fieldName*string

Name of the column used as the unique ID in the database

password*string

The password

tableName*string

Name of the table where embeddings will be stored

type*object

username*string

The username

columnDefinitionsarray

SubTypestring

Metadata Column Definitions

List of SQL column definitions for metadata fields (e.g., 'text TEXT', 'source TEXT'). Required only when using COLUMN_PER_KEY storage mode.

indexesarray

SubTypestring

Metadata Index Definitions

List of SQL index definitions for metadata columns (e.g., 'INDEX idx_text (text)'). Used only with COLUMN_PER_KEY storage mode.

metadataStorageModestring

DefaultCOLUMN_PER_KEY

Metadata Storage Mode

Determines how metadata is stored: - COLUMN_PER_KEY: Use individual columns for each metadata field (requires columnDefinitions and indexes). - COMBINED_JSON (default): Store metadata as a JSON object in a single column. If columnDefinitions and indexes are provided, COLUMN_PER_KEY must be used.

Store embeddings in Milvus

Connects via URI or host/port with token-based auth; creates the target collection if missing. Metric/index/consistency options map to Milvus settings; drop=true clears the collection. Use defaults for host=localhost, port=19530, secure gRPC unless overridden.

Example

Ingest documents into a Milvus embedding store

yaml
id: document_ingestion
namespace: company.ai

tasks:
  - id: ingest
    type: io.kestra.plugin.ai.rag.IngestDocument
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      modelName: gemini-embedding-001
      apiKey: "{{ secret('GEMINI_API_KEY') }}"
    embeddings:
      type: io.kestra.plugin.ai.embeddings.Milvus
      # Use either `uri` or `host`/`port`:
      # For gRPC (typical): milvus://localhost:19530
      # For HTTP: http://localhost:9091
      uri: "http://localhost:19200"
      token: "{{ secret('MILVUS_TOKEN') }}"  # omit if auth is disabled
      collectionName: embeddings
    fromExternalURLs:
      - https://raw.githubusercontent.com/kestra-io/docs/refs/heads/main/content/blogs/release-0-24.md

token*string

Token

Milvus auth token. Required if authentication is enabled; omit for local deployments without auth.

type*object

Possible Values

io.kestra.plugin.ai.embeddings.Milvusio.kestra.plugin.langchain4j.embeddings.Milvus

autoFlushOnDeletebooleanstring

Auto flush on delete

If true, flush after delete operations.

autoFlushOnInsertbooleanstring

Auto flush on insert

If true, flush after insert operations. Setting it to false can improve throughput.

collectionNamestring

Collection name

Target collection. Created automatically if it does not exist. Default: "default".

consistencyLevelstring

Consistency level

Read/write consistency level. Common values include STRONG, BOUNDED, or EVENTUALLY (depends on client/version).

databaseNamestring

Database name

Logical database to use. If not provided, the default database is used.

hoststring

Host

Milvus host name (used when uri is not set). Default: "localhost".

idFieldNamestring

ID field name

Field name for document IDs. Default depends on collection schema.

indexTypestring

Index type

Vector index type (e.g., IVF_FLAT, IVF_SQ8, HNSW). Depends on Milvus deployment and dataset.

metadataFieldNamestring

Metadata field name

Field name for metadata. Default depends on collection schema.

metricTypestring

Metric type

Similarity metric (e.g., L2, IP, COSINE). Should match the embedding provider’s expected metric.

passwordstring

Password

Required when authentication/TLS is enabled. See https://milvus.io/docs/authenticate.md

portintegerstring

Port

Milvus port (used when uri is not set). Typical: 19530 (gRPC) or 9091 (HTTP). Default: 19530.

retrieveEmbeddingsOnSearchbooleanstring

Retrieve embeddings on search

If true, return stored embeddings along with matches. Default: false.

textFieldNamestring

Text field name

Field name for original text. Default depends on collection schema.

uristring

URI

Connection URI. Use either uri OR host/port (not both). Examples:

gRPC (typical): "milvus://host: 19530"
HTTP: "http://host: 9091"

usernamestring

Username

Required when authentication/TLS is enabled. See https://milvus.io/docs/authenticate.md

vectorFieldNamestring

Vector field name

Field name for the embedding vector. Must match the index definition and embedding dimensionality.

Store embeddings in MongoDB Atlas

Uses MongoDB Atlas vector search with the provided collection/index; can optionally create the index and wait for readiness (up to 1 minute). Supply scheme/host and credentials; drop=true removes stored vectors.

Example

Ingest documents into a MongoDB Atlas embedding store

yaml
id: document_ingestion
namespace: company.ai

tasks:
  - id: ingest
    type: io.kestra.plugin.ai.rag.IngestDocument
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      modelName: gemini-embedding-001
      apiKey: "{{ secret('GEMINI_API_KEY') }}"
    embeddings:
      type: io.kestra.plugin.ai.embeddings.MongoDBAtlas
      username: "{{ secret('MONGODB_ATLAS_USERNAME') }}"
      password: "{{ secret('MONGODB_ATLAS_PASSWORD') }}"
      host: "{{ secret('MONGODB_ATLAS_HOST') }}"
      database: "{{ secret('MONGODB_ATLAS_DATABASE') }}"
      collectionName: embeddings
      indexName: embeddings
    fromExternalURLs:
      - https://raw.githubusercontent.com/kestra-io/docs/refs/heads/main/content/blogs/release-0-24.md

collectionName*string

The collection name

host*string

The host

indexName*string

The index name

scheme*string

The scheme (e.g., mongodb+srv)

type*object

Possible Values

io.kestra.plugin.ai.embeddings.MongoDBAtlasio.kestra.plugin.langchain4j.embeddings.MongoDBAtlas

createIndexbooleanstring

Create the index

databasestring

The database

metadataFieldNamesarray

SubTypestring

The metadata field names

optionsobject

The connection string options

passwordstring

The password

usernamestring

The username

Store embeddings with pgvector

Uses the PostgreSQL pgvector extension to persist embeddings in the given table. drop=true recreates the table; optional IVF index (useIndex) defaults to false. Ensure pgvector extension is installed and the user can create tables/indexes.

Example

Ingest documents into a PGVector embedding store

yaml
id: document_ingestion
namespace: company.ai

tasks:
  - id: ingest
    type: io.kestra.plugin.ai.rag.IngestDocument
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      modelName: gemini-embedding-001
      apiKey: "{{ secret('GEMINI_API_KEY') }}"
    embeddings:
      type: io.kestra.plugin.ai.embeddings.PGVector
      host: localhost
      port: 5432
      user: "{{ secret('POSTGRES_USER') }}"
      password: "{{ secret('POSTGRES_PASSWORD') }}"
      database: postgres
      table: embeddings
    fromExternalURLs:
      - https://raw.githubusercontent.com/kestra-io/docs/refs/heads/main/content/blogs/release-0-24.md

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

Possible Values

io.kestra.plugin.ai.embeddings.PGVectorio.kestra.plugin.langchain4j.embeddings.PGVector

user*string

The database user

useIndexbooleanstring

Defaultfalse

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).

Store embeddings in Pinecone

Creates or connects to a serverless Pinecone index in the given cloud/region; namespace defaults to Pinecone’s default. Requires an API key; drop=true clears the index contents.

Example

Ingest documents into a Pinecone embedding store

yaml
id: document_ingestion
namespace: company.ai

tasks:
  - id: ingest
    type: io.kestra.plugin.ai.rag.IngestDocument
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      modelName: gemini-embedding-001
      apiKey: "{{ secret('GEMINI_API_KEY') }}"
    embeddings:
      type: io.kestra.plugin.ai.embeddings.Pinecone
      apiKey: "{{ secret('PINECONE_API_KEY') }}"
      cloud: AWS
      region: us-east-1
      index: embeddings
    fromExternalURLs:
      - https://raw.githubusercontent.com/kestra-io/docs/refs/heads/main/content/blogs/release-0-24.md

apiKey*string

The API key

cloud*string

The cloud provider

index*string

The index

region*string

The cloud provider region

type*object

Possible Values

io.kestra.plugin.ai.embeddings.Pineconeio.kestra.plugin.langchain4j.embeddings.Pinecone

namespacestring

The namespace (default will be used if not provided)

Store embeddings in Qdrant

Uses the Qdrant gRPC client with API key, host, and port. Targets the specified collection; drop=true removes its contents. Distance metric follows the Qdrant collection defaults.

Example

Ingest documents into a Qdrant embedding store

yaml
id: document_ingestion
namespace: company.ai

tasks:
  - id: ingest
    type: io.kestra.plugin.ai.rag.IngestDocument
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      modelName: gemini-embedding-001
      apiKey: "{{ secret('GEMINI_API_KEY') }}"
    embeddings:
      type: io.kestra.plugin.ai.embeddings.Qdrant
      apiKey: "{{ secret('QDRANT_API_KEY') }}"
      host: localhost
      port: 6334
      collectionName: embeddings
    fromExternalURLs:
      - https://raw.githubusercontent.com/kestra-io/docs/refs/heads/main/content/blogs/release-0-24.md

apiKey*string

The API key

collectionName*string

The collection name

host*string

The database server host

port*integerstring

The database server port

type*object

Possible Values

io.kestra.plugin.ai.embeddings.Qdrantio.kestra.plugin.langchain4j.embeddings.Qdrant

Store embeddings in Redis

Backs an embedding index with Redis (jedis). Uses indexName (defaults to embedding-index); drop=true clears stored vectors. Ensure Redis deployment supports vector search modules for production use.

Example

Ingest documents into a Redis embedding store

yaml
id: document_ingestion
namespace: company.ai

tasks:
  - id: ingest
    type: io.kestra.plugin.ai.rag.IngestDocument
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      modelName: gemini-embedding-001
      apiKey: my_api_key
    embeddings:
      type: io.kestra.plugin.ai.embeddings.Redis
      host: localhost
      port: 6379
      indexName: embeddings
    fromExternalURLs:
      - https://raw.githubusercontent.com/kestra-io/docs/refs/heads/main/content/blogs/release-0-24.md

host*string

The database server host

port*integerstring

The database server port

type*object

indexNamestring

Defaultembedding-index

The index name

Store embeddings in Alibaba Tablestore

Connects to Tablestore using access keys and writes embeddings with cosine distance. Uses the configured instance/endpoint; metadata schemas are optional. drop=true is not supported—manage cleanup in Tablestore.

Example

Ingest documents into a Tablestore embedding store

yaml
id: document_ingestion
namespace: company.ai

tasks:
  - id: ingest
    type: io.kestra.plugin.ai.rag.IngestDocument
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      modelName: gemini-embedding-001
      apiKey: "{{ secret('GEMINI_API_KEY') }}"
    embeddings:
      type: io.kestra.plugin.ai.embeddings.Tablestore
      endpoint:  "{{ secret('TABLESTORE_ENDPOINT') }}"
      instanceName:  "{{ secret('TABLESTORE_INSTANCE_NAME') }}"
      accessKeyId:  "{{ secret('TABLESTORE_ACCESS_KEY_ID') }}"
      accessKeySecret:  "{{ secret('TABLESTORE_ACCESS_KEY_SECRET') }}"
    fromExternalURLs:
      - https://raw.githubusercontent.com/kestra-io/docs/refs/heads/main/content/blogs/release-0-24.md

accessKeyId*string

Access Key ID

The access key ID used for authentication with the database.

accessKeySecret*string

Access Key Secret

The access key secret used for authentication with the database.

endpoint*string

Endpoint URL

The base URL for the Tablestore database endpoint.

instanceName*string

Instance Name

The name of the Tablestore database instance.

type*object

metadataSchemaListarray

Metadata Schema List

Optional list of metadata field schemas for the collection.

com.alicloud.openservices.tablestore.model.search.FieldSchema

analyzerstring

Possible Values

SingleWordMaxWordMinWordSplitFuzzy

analyzerParameter

com.alicloud.openservices.tablestore.model.search.analysis.AnalyzerParameter

dateFormatsarray

SubTypestring

enableHighlightingboolean

enableSortAndAggboolean

fieldNamestring

fieldTypestring

Possible Values

LONGDOUBLEBOOLEANKEYWORDTEXTNESTEDGEO_POINTDATEVECTORFUZZY_KEYWORDIPJSONUNKNOWN

indexboolean

indexOptionsstring

Possible Values

DOCSFREQSPOSITIONSOFFSETS

isArrayboolean

jsonTypestring

Possible Values

FLATTENNESTED

sourceFieldNamesarray

SubTypestring

storeboolean

subFieldSchemasarray

com.alicloud.openservices.tablestore.model.search.FieldSchema

analyzerstring

Possible Values

SingleWordMaxWordMinWordSplitFuzzy

analyzerParameter

dateFormatsarray

SubTypestring

enableHighlightingboolean

enableSortAndAggboolean

fieldNamestring

fieldTypestring

Possible Values

LONGDOUBLEBOOLEANKEYWORDTEXTNESTEDGEO_POINTDATEVECTORFUZZY_KEYWORDIPJSONUNKNOWN

indexboolean

indexOptionsstring

Possible Values

DOCSFREQSPOSITIONSOFFSETS

isArrayboolean

jsonTypestring

Possible Values

FLATTENNESTED

sourceFieldNamesarray

SubTypestring

storeboolean

subFieldSchemasarray

vectorOptions

com.alicloud.openservices.tablestore.model.search.vector.VectorOptions

dataTypestring

dimensioninteger

metricTypestring

Possible Values

EUCLIDEANCOSINEDOT_PRODUCT

Store embeddings in Weaviate

Connects to a Weaviate cluster (HTTP + optional gRPC) using the given host/scheme. Defaults: objectClass "Default", avoidDups true, consistency QUORUM. Provide API key when auth is enabled; drop=true clears the class contents.

Example

Ingest documents into a Weaviate embedding store

yaml
id: document_ingestion
namespace: company.ai

tasks:
  - id: ingest
    type: io.kestra.plugin.ai.rag.IngestDocument
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      modelName: gemini-embedding-001
      apiKey: "{{ secret('GEMINI_API_KEY') }}"
    embeddings:
      type: io.kestra.plugin.ai.embeddings.Weaviate
      apiKey: "{{ secret('WEAVIATE_API_KEY') }}"   # omit for local/no-auth
      scheme: https                                 # http | https
      host: your-cluster-id.weaviate.network        # no protocol
      # port: 443                                   # optional; usually omit
    drop: true
    fromExternalURLs:
      - https://raw.githubusercontent.com/kestra-io/docs/refs/heads/main/content/blogs/release-0-24.md

apiKey*string

API key

Weaviate API key. Omit for local deployments without auth.

host*string

Host

Cluster host name without protocol, e.g., "abc123.weaviate.network".

type*object

Possible Values

io.kestra.plugin.ai.embeddings.Weaviateio.kestra.plugin.langchain4j.embeddings.Weaviate

avoidDupsbooleanstring

Avoid duplicates

If true (default), a hash-based ID is derived from each text segment to prevent duplicates. If false, a random ID is used.

consistencyLevelstring

Possible Values

ONEQUORUMALL

Consistency level

Write consistency: ONE, QUORUM (default), or ALL.

grpcPortintegerstring

gRPC port

Port for gRPC if enabled (e.g., 50051).

metadataFieldNamestring

Metadata field name

Field used to store metadata. Defaults to "_metadata" if not set.

metadataKeysarray

SubTypestring

Metadata keys

The list of metadata keys to store - if not provided, it will default to an empty list.

objectClassstring

Object class

Weaviate class to store objects in (must start with an uppercase letter). Defaults to "Default" if not set.

portintegerstring

Port

Optional port (e.g., 443 for https, 80 for http). Leave unset to use provider defaults.

schemestring

Scheme

Cluster scheme: "https" (recommended) or "http".

securedGrpcbooleanstring

Secure gRPC

Whether the gRPC connection is secured (TLS).

useGrpcForInsertsbooleanstring

Use gRPC for batch inserts

If true, use gRPC for batch inserts. HTTP remains required for search operations.

provider *

Language model provider

Must be configured with an embedding model.

Definitions

Use Amazon Bedrock models

Invokes Bedrock-hosted chat/embedding models with AWS credentials. Supports standard AWS region/auth settings; ensure model IDs match your Bedrock region and account access.

Example

Chat completion with OpenAI

yaml
id: chat_completion
namespace: company.ai

inputs:
  - id: prompt
    type: STRING

tasks:
  - id: chat_completion
    type: io.kestra.plugin.ai.completion.ChatCompletion
    provider:
      type: io.kestra.plugin.ai.provider.AmazonBedrock
      accessKeyId: "{{ secret('AWS_ACCESS_KEY') }}"
      secretAccessKey: "{{ secret('AWS_SECRET_KEY') }}"
      modelName: anthropic.claude-3-sonnet-20240229-v1:0
      thinkingBudgetTokens: 1024
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

accessKeyId*string

AWS Access Key ID

modelName*string

Model name

secretAccessKey*string

AWS Secret Access Key

type*object

Possible Values

io.kestra.plugin.ai.provider.AmazonBedrockio.kestra.plugin.langchain4j.provider.AmazonBedrock

baseUrlstring

Base URL

Custom base URL to override the default endpoint (useful for local tests, WireMock, or enterprise gateways).

caPemstring

CA PEM certificate content

CA certificate as text, used to verify SSL/TLS connections when using custom endpoints.

clientPemstring

Client PEM certificate content

PEM client certificate as text, used to authenticate the connection to enterprise AI endpoints.

modelTypestring

DefaultCOHERE

Possible Values

COHERETITAN

Amazon Bedrock Embedding Model Type

Use Anthropic Claude models

Provides Claude chat models only; embeddings and images are unsupported. Enforces Anthropic rules (no seed/responseFormat). Thinking mode requires max_tokens > thinking.budget_tokens; set API key and optional base URL.

Example

Chat completion with Anthropic

yaml
id: chat_completion
namespace: company.ai

inputs:
  - id: prompt
    type: STRING

tasks:
  - id: chat_completion
    type: io.kestra.plugin.ai.completion.ChatCompletion
    provider:
      type: io.kestra.plugin.ai.provider.Anthropic
      apiKey: "{{ secret('ANTHROPIC_API_KEY') }}"
      modelName: claude-3-haiku-20240307
    configuration:
      thinkingEnabled: true
      thinkingBudgetTokens: 1024
      returnThinking: false
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

apiKey*string

API Key

modelName*string

Model name

type*object

Possible Values

io.kestra.plugin.ai.provider.Anthropicio.kestra.plugin.langchain4j.provider.Anthropic

baseUrlstring

Base URL

Custom base URL to override the default endpoint (useful for local tests, WireMock, or enterprise gateways).

caPemstring

CA PEM certificate content

CA certificate as text, used to verify SSL/TLS connections when using custom endpoints.

clientPemstring

Client PEM certificate content

PEM client certificate as text, used to authenticate the connection to enterprise AI endpoints.

maxTokensintegerstring

Maximum Tokens

Specifies the maximum number of tokens that the model is allowed to generate in its response.

Use Azure OpenAI deployments

Targets Azure-hosted OpenAI models via the resource endpoint and deployment name. Supports API key or AAD client credentials; set apiVersion when required by the deployment.

Example

Chat completion with Azure OpenAI

yaml
id: chat_completion
namespace: company.ai

inputs:
  - id: prompt
    type: STRING

tasks:
  - id: chat_completion
    type: io.kestra.plugin.ai.completion.ChatCompletion
    provider:
      type: io.kestra.plugin.ai.provider.AzureOpenAI
      apiKey: "{{ secret('AZURE_API_KEY') }}"
      endpoint: https://your-resource.openai.azure.com/
      modelName: anthropic.claude-3-sonnet-20240229-v1:0
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

endpoint*string

API endpoint

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

modelName*string

Model name

type*object

Possible Values

io.kestra.plugin.ai.provider.AzureOpenAIio.kestra.plugin.langchain4j.provider.AzureOpenAI

apiKeystring

API Key

baseUrlstring

Base URL

Custom base URL to override the default endpoint (useful for local tests, WireMock, or enterprise gateways).

caPemstring

CA PEM certificate content

CA certificate as text, used to verify SSL/TLS connections when using custom endpoints.

clientIdstring

Client ID

clientPemstring

Client PEM certificate content

PEM client certificate as text, used to authenticate the connection to enterprise AI endpoints.

clientSecretstring

Client secret

serviceVersionstring

API version

tenantIdstring

Tenant ID

Use DashScope (Qwen) models

Calls Alibaba Cloud DashScope for Qwen chat/embeddings/images with API key. Some params (timeouts, retries, stop, maxTokens) map directly to DashScope limits.

Example

Chat completion with DashScope (Qwen)

yaml
id: chat_completion
namespace: company.ai

inputs:
  - id: prompt
    type: STRING

tasks:
  - id: chat_completion
    type: io.kestra.plugin.ai.completion.ChatCompletion
    provider:
      type: io.kestra.plugin.ai.provider.DashScope
      apiKey: "{{ secret('DASHSCOPE_API_KEY') }}"
      modelName: qwen-plus
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

apiKey*string

API Key

modelName*string

Model name

type*object

baseUrlstring

Defaulthttps://dashscope-intl.aliyuncs.com/api/v1

API base URL

If you use a model in the China (Beijing) region, you need to replace the URL with: https://dashscope.aliyuncs.com/api/v1,
otherwise use the Singapore region of: "https://dashscope-intl.aliyuncs.com/api/v1.
The default value is computed based on the system timezone.

caPemstring

CA PEM certificate content

CA certificate as text, used to verify SSL/TLS connections when using custom endpoints.

clientPemstring

Client PEM certificate content

PEM client certificate as text, used to authenticate the connection to enterprise AI endpoints.

enableSearchbooleanstring

Whether the model uses Internet search results for reference when generating text or not

maxTokensintegerstring

The maximum number of tokens returned by this request

repetitionPenaltynumberstring

Repetition in a continuous sequence during model generation

Increasing repetition_penalty reduces the repetition in model generation,
1.0 means no penalty. Value range: (0, +inf)

Use DeepSeek models

Connects to DeepSeek’s OpenAI-compatible endpoint with API key and model name for chat/embedding tasks.

Example

Chat completion with DeepSeek

yaml
id: chat_completion
namespace: company.ai

inputs:
  - id: prompt
    type: STRING

tasks:
  - id: chat_completion
    type: io.kestra.plugin.ai.completion.ChatCompletion
    provider:
      type: io.kestra.plugin.ai.provider.DeepSeek
      apiKey: "{{ secret('DEEPSEEK_API_KEY') }}"
      modelName: deepseek-chat
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

apiKey*string

API Key

modelName*string

Model name

type*object

Possible Values

io.kestra.plugin.ai.provider.DeepSeekio.kestra.plugin.langchain4j.provider.DeepSeek

baseUrlstring

Defaulthttps://api.deepseek.com/v1

API base URL

caPemstring

CA PEM certificate content

CA certificate as text, used to verify SSL/TLS connections when using custom endpoints.

clientPemstring

Client PEM certificate content

PEM client certificate as text, used to authenticate the connection to enterprise AI endpoints.

Use GitHub Models via Azure AI Inference

Calls GitHub Models through the Azure AI Inference API with a GitHub token. Supports chat and embeddings; response format options map to Azure AI Inference capabilities.

Example

Chat completion with GitHub Models

yaml
id: chat_completion
namespace: company.ai

inputs:
  - id: prompt
    type: STRING

tasks:
  - id: chat_completion
    type: io.kestra.plugin.ai.completion.ChatCompletion
    provider:
      type: io.kestra.plugin.ai.provider.GitHubModels
      gitHubToken: "{{ secret('GITHUB_TOKEN') }}"
      modelName: gpt-4o-mini
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely.
      - type: USER
        content: "{{ inputs.prompt }}"

gitHubToken*string

GitHub Token

Personal Access Token (PAT) used to access GitHub Models.

modelName*string

Model name

type*object

baseUrlstring

Base URL

Custom base URL to override the default endpoint (useful for local tests, WireMock, or enterprise gateways).

caPemstring

CA PEM certificate content

CA certificate as text, used to verify SSL/TLS connections when using custom endpoints.

clientPemstring

Client PEM certificate content

PEM client certificate as text, used to authenticate the connection to enterprise AI endpoints.

Use Google Gemini models

Supports Gemini chat, embeddings, and images. Tools do not support JSON Schema anyOf, and tools cannot be combined with responseFormat; configure either but not both.

Thinking models (e.g. gemini-3.5-flash) attach a thought_signature to every function-call part. This provider automatically captures those signatures (returnThinking defaults to true) and re-attaches them to the conversation history for every follow-up request (sendThinking is always enabled), preventing the 400 INVALID_ARGUMENT – Function call is missing a thought_signature error.

In addition, thinking is disabled by default (thinkingBudget = 0) to reduce token usage, unless thinkingEnabled: true or thinkingBudgetTokens > 0 is explicitly set.

Example

Chat completion with Google Gemini

yaml
id: chat_completion
namespace: company.ai

inputs:
  - id: prompt
    type: STRING

tasks:
  - id: chat_completion
    type: io.kestra.plugin.ai.completion.ChatCompletion
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      apiKey: "{{ secret('GOOGLE_API_KEY') }}"
      modelName: gemini-2.5-flash
    configuration:
      thinkingEnabled: true
      thinkingBudgetTokens: 1024
      returnThinking: true
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

Chat completion with Google Gemini with a local base URL + PEM certificates

yaml
id: chat_completion
namespace: company.ai

inputs:
  - id: prompt
    type: STRING

tasks:
  - id: chat_completion
    type: io.kestra.plugin.ai.completion.ChatCompletion
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      modelName: gemini-2.5-flash
      clientPem: "{{ secret('CLIENT_PEM') }}"
      caPem: "{{ secret('CA_PEM') }}"
      baseUrl: "https://internal.gemini.company.com/endpoint"
    configuration:
      thinkingEnabled: true
      thinkingBudgetTokens: 1024
      returnThinking: true
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

modelName*string

Model name

type*object

Possible Values

io.kestra.plugin.ai.provider.GoogleGeminiio.kestra.plugin.langchain4j.provider.GoogleGemini

apiKeystring

API Key

Required unless certificate-based authentication is configured with clientPem (optionally with caPem).

baseUrlstring

Base URL

Custom base URL to override the default endpoint (useful for local tests, WireMock, or enterprise gateways).

caPemstring

CA PEM certificate content

CA certificate as text, used to verify SSL/TLS connections when using custom endpoints.

clientPemstring

Client PEM certificate content

PEM client certificate as text, used to authenticate the connection to enterprise AI endpoints.

embeddingModelConfiguration

The configuration for embeddingModel

io.kestra.plugin.ai.provider.GoogleGemini-EmbeddingModelConfiguration

maxRetriesintegerstring

Maximum number of retries for failed requests

outputDimensionalityintegerstring

Used to specify output embedding size

If set, output embeddings will be truncated to the size specified.

taskTypestring

Possible Values

RETRIEVAL_QUERYRETRIEVAL_DOCUMENTSEMANTIC_SIMILARITYCLASSIFICATIONCLUSTERINGQUESTION_ANSWERINGFACT_VERIFICATION

Used to convey intended downstream application to help the model produce better embeddings.

timeoutstring

Timeout in seconds for each request

titleMetadataKeystring

The headline or name of the document (passed to the model as metadata).

If set, this help improving retrieval quality by providing context for a document.

Use Google Vertex AI models

Calls Vertex AI Gemini chat, embeddings, or images using project, location, and endpoint settings. Requires GCP credentials; ensure response formats are supported by the selected model/region.

Example

Chat completion with Google Vertex AI

yaml
id: chat_completion
namespace: company.ai

inputs:
  - id: prompt
    type: STRING

tasks:
  - id: chat_completion
    type: io.kestra.plugin.ai.completion.ChatCompletion
    provider:
      type: io.kestra.plugin.ai.provider.GoogleVertexAI
      endpoint: your-vertex-ai-endpoint
      location: your-google-cloud-region
      project: your-google-cloud-project-id
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

endpoint*string

Endpoint URL

location*string

Project location

modelName*string

Model name

project*string

Project ID

type*object

Possible Values

io.kestra.plugin.ai.provider.GoogleVertexAIio.kestra.plugin.langchain4j.provider.GoogleVertexAI

baseUrlstring

Base URL

Custom base URL to override the default endpoint (useful for local tests, WireMock, or enterprise gateways).

caPemstring

CA PEM certificate content

CA certificate as text, used to verify SSL/TLS connections when using custom endpoints.

clientPemstring

Client PEM certificate content

PEM client certificate as text, used to authenticate the connection to enterprise AI endpoints.

Use Hugging Face Inference endpoints

Routes requests to Hugging Face Inference Endpoints via the OpenAI-compatible gateway (default router.huggingface.co). Requires an API token and deployment model name.

Example

Chat completion with HuggingFace

yaml
id: chat_completion
namespace: company.ai

inputs:
  - id: prompt
    type: STRING

tasks:
  - id: chat_completion
    type: io.kestra.plugin.ai.completion.ChatCompletion
    provider:
      type: io.kestra.plugin.ai.provider.HuggingFace
      apiKey: "{{ secret('HUGGING_FACE_API_KEY') }}"
      modelName: HuggingFaceTB/SmolLM3-3B:hf-inference
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

apiKey*string

API Key

modelName*string

Model name

type*object

baseUrlstring

Defaulthttps://router.huggingface.co/v1

API base URL

caPemstring

CA PEM certificate content

CA certificate as text, used to verify SSL/TLS connections when using custom endpoints.

clientPemstring

Client PEM certificate content

PEM client certificate as text, used to authenticate the connection to enterprise AI endpoints.

Use LocalAI OpenAI-compatible server

Targets a self-hosted LocalAI instance via its OpenAI-compatible API for chat/embeddings/images. Set baseUrl if your server is not on the default.

Example

Chat completion with LocalAI

yaml
id: chat_completion
namespace: company.ai

inputs:
  - id: prompt
    type: STRING

tasks:
  - id: chat_completion
    type: io.kestra.plugin.ai.ChatCompletion
    provider:
      type: io.kestra.plugin.ai.provider.LocalAI
      modelName: gemma-3-1b-it
      baseUrl: http://localhost:8080/v1
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

baseUrl*string

API base URL

modelName*string

Model name

type*object

Possible Values

io.kestra.plugin.ai.provider.LocalAIio.kestra.plugin.langchain4j.provider.LocalAI

caPemstring

CA PEM certificate content

CA certificate as text, used to verify SSL/TLS connections when using custom endpoints.

clientPemstring

Client PEM certificate content

PEM client certificate as text, used to authenticate the connection to enterprise AI endpoints.

Use Mistral models

Calls Mistral chat/embedding APIs with an API key. topK is not supported; chat configuration must respect model limits.

Example

Chat completion with Mistral AI

yaml
id: chat_completion
namespace: company.ai

inputs:
  - id: prompt
    type: STRING

tasks:
  - id: chat_completion
    type: io.kestra.plugin.ai.completion.ChatCompletion
    provider:
      type: io.kestra.plugin.ai.provider.MistralAI
      apiKey: "{{ secret('MISTRAL_API_KEY') }}"
      modelName: mistral:7b
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

apiKey*string

API Key

modelName*string

Model name

type*object

Possible Values

io.kestra.plugin.ai.provider.MistralAIio.kestra.plugin.langchain4j.provider.MistralAI

baseUrlstring

Base URL

Custom base URL to override the default endpoint (useful for local tests, WireMock, or enterprise gateways).

caPemstring

CA PEM certificate content

CA certificate as text, used to verify SSL/TLS connections when using custom endpoints.

clientPemstring

Client PEM certificate content

PEM client certificate as text, used to authenticate the connection to enterprise AI endpoints.

Use OCI Generative AI models

Calls Oracle Cloud GenAI chat/embedding models with compartment OCID and region. Auth is handled via OCI SDK provider (config file or instance principals). Ensure the model is permitted in the chosen compartment.

Example

Chat completion with OciGenAI

yaml
id: chat_completion
namespace: company.ai

inputs:
  - id: prompt
    type: STRING

tasks:
  - id: chat_completion
    type: io.kestra.plugin.ai.ChatCompletion
    provider:
      type: io.kestra.plugin.ai.provider.OciGenAI
      region: "{{ secret('OCI_GENAI_MODEL_REGION_PROPERTY') }}"
      compartmentId: "{{ secret('OCI_GENAI_COMPARTMENT_ID_PROPERTY') }}"
      authProvider: "{{ secret('OCI_GENAI_CONFIG_PROFILE_PROPERTY') }}"
      modelName: oracle.chat.gpt-3.5
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

compartmentId*string

OCID of OCI Compartment with the model

modelName*string

Model name

region*string

OCI Region to connect the client to

type*object

authProviderstring

OCI SDK Authentication provider

baseUrlstring

Base URL

Custom base URL to override the default endpoint (useful for local tests, WireMock, or enterprise gateways).

caPemstring

CA PEM certificate content

CA certificate as text, used to verify SSL/TLS connections when using custom endpoints.

clientPemstring

Client PEM certificate content

PEM client certificate as text, used to authenticate the connection to enterprise AI endpoints.

Use local Ollama models

Calls an Ollama server for chat/embeddings using the given endpoint and model name. Ideal for self-hosted/local models; ensure the Ollama daemon is reachable.

Example

Chat completion with Ollama

yaml
id: chat_completion
namespace: company.ai

inputs:
  - id: prompt
    type: STRING

tasks:
  - id: chat_completion
    type: io.kestra.plugin.ai.completion.ChatCompletion
    provider:
      type: io.kestra.plugin.ai.provider.Ollama
      modelName: llama3
      endpoint: http://localhost:11434
    configuration:
      thinkingEnabled: true
      returnThinking: true
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

endpoint*string

Model endpoint

modelName*string

Model name

type*object

Possible Values

io.kestra.plugin.ai.provider.Ollamaio.kestra.plugin.langchain4j.provider.Ollama

baseUrlstring

Base URL

Custom base URL to override the default endpoint (useful for local tests, WireMock, or enterprise gateways).

caPemstring

CA PEM certificate content

CA certificate as text, used to verify SSL/TLS connections when using custom endpoints.

clientPemstring

Client PEM certificate content

PEM client certificate as text, used to authenticate the connection to enterprise AI endpoints.

Use OpenAI models

Connects to OpenAI-compatible endpoints (defaults to api.openai.com) for chat, embeddings, or images. Requires API key; override baseUrl for Azure-compatible or proxy setups.

Example

Chat completion with OpenAI

yaml
id: chat_completion
namespace: company.ai

inputs:
  - id: prompt
    type: STRING

tasks:
  - id: chat_completion
    type: io.kestra.plugin.ai.completion.ChatCompletion
    provider:
      type: io.kestra.plugin.ai.provider.OpenAI
      apiKey: "{{ secret('OPENAI_API_KEY') }}"
      modelName: gpt-5-mini
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

apiKey*string

API Key

modelName*string

Model name

type*object

Possible Values

io.kestra.plugin.ai.provider.OpenAIio.kestra.plugin.langchain4j.provider.OpenAI

baseUrlstring

Defaulthttps://api.openai.com/v1

API base URL

caPemstring

CA PEM certificate content

CA certificate as text, used to verify SSL/TLS connections when using custom endpoints.

clientPemstring

Client PEM certificate content

PEM client certificate as text, used to authenticate the connection to enterprise AI endpoints.

Use OpenRouter models

Routes requests through OpenRouter’s multi-model API using your API key. Supports chat/embedding/image models exposed by OpenRouter; honor provider-specific safety/usage limits.

Example

Chat completion with OpenRouter

yaml
id: chat_completion
namespace: company.ai

inputs:
  - id: prompt
    type: STRING

tasks:
  - id: chat_completion
    type: io.kestra.plugin.ai.completion.ChatCompletion
    provider:
      type: io.kestra.plugin.ai.provider.OpenRouter
      apiKey: "{{ secret('OPENROUTER_API_KEY') }}"
      baseUrl: https://openrouter.ai/api/v1
      modelName: x-ai/grok-beta
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

apiKey*string

API Key

modelName*string

Model name

type*object

Possible Values

io.kestra.plugin.ai.provider.OpenRouterio.kestra.plugin.langchain4j.provider.OpenRouter

baseUrlstring

Base URL

Custom base URL to override the default endpoint (useful for local tests, WireMock, or enterprise gateways).

caPemstring

CA PEM certificate content

CA certificate as text, used to verify SSL/TLS connections when using custom endpoints.

clientPemstring

Client PEM certificate content

PEM client certificate as text, used to authenticate the connection to enterprise AI endpoints.

Use IBM watsonx.ai models

Calls IBM watsonx.ai chat/embedding endpoints with API key and project ID. Ensure the selected model ID is available in the configured project.

Example

Chat completion with Watsonx AI

yaml
id: chat_completion
namespace: company.ai

inputs:
  - id: prompt
    type: STRING

tasks:
  - id: chat_completion
    type: io.kestra.plugin.ai.completion.ChatCompletion
    provider:
      type: io.kestra.plugin.ai.provider.WatsonxAI
      apiKey: "{{ secret('WATSONX_API_KEY') }}"
      projectId: "{{ secret('WATSONX_PROJECT_ID') }}"
      modelName: ibm/granite-3-3-8b-instruct
      baseUrl : "https://api.eu-de.dataplatform.cloud.ibm.com/wx"
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

apiKey*string

API Key

modelName*string

Model name

projectId*string

Project Id

type*object

baseUrlstring

Base URL

Custom base URL to override the default endpoint (useful for local tests, WireMock, or enterprise gateways).

caPemstring

CA PEM certificate content

CA certificate as text, used to verify SSL/TLS connections when using custom endpoints.

clientPemstring

Client PEM certificate content

PEM client certificate as text, used to authenticate the connection to enterprise AI endpoints.

Use Cloudflare Workers AI models

Invokes Workers AI chat, embedding, and image models using account ID and API key. Ensure the selected model is available in your account/region.

Example

Chat completion with WorkersAI

yaml
id: chat_completion
namespace: company.ai

inputs:
  - id: prompt
    type: STRING

tasks:
  - id: chat_completion
    type: io.kestra.plugin.ai.ChatCompletion
    provider:
      type: io.kestra.plugin.ai.provider.WorkersAI
      accountId: "{{ secret('WORKERS_AI_ACCOUNT_ID') }}"
      apiKey: "{{ secret('WORKERS_AI_API_KEY') }}"
      modelName: @cf/meta/llama-2-7b-chat-fp16
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

accountId*string

Account Identifier

Unique identifier assigned to an account

apiKey*string

API Key

modelName*string

Model name

type*object

Possible Values

io.kestra.plugin.ai.provider.WorkersAIio.kestra.plugin.langchain4j.provider.WorkersAI

baseUrlstring

Base URL

Custom base URL to override the default endpoint (useful for local tests, WireMock, or enterprise gateways).

caPemstring

CA PEM certificate content

CA certificate as text, used to verify SSL/TLS connections when using custom endpoints.

clientPemstring

Client PEM certificate content

PEM client certificate as text, used to authenticate the connection to enterprise AI endpoints.

Use ZhiPu AI models

Calls ZhiPu’s OpenAI-compatible chat/embedding/image APIs with API key and model name. Supports stop tokens, retry count, and max tokens per request.

Example

Chat completion with ZhiPu AI

yaml
id: chat_completion
namespace: company.ai

inputs:
  - id: prompt
    type: STRING

tasks:
  - id: chat_completion
    type: io.kestra.plugin.ai.ChatCompletion
    provider:
      type: io.kestra.plugin.ai.provider.ZhiPuAI
      apiKey: "{{ secret('ZHIPU_API_KEY') }}"
      modelName: glm-4.5-flash
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

apiKey*string

API Key

modelName*string

Model name

type*object

baseUrlstring

Defaulthttps://open.bigmodel.cn/

API base URL

The base URL for ZhiPu API (defaults to https://open.bigmodel.cn/)

caPemstring

CA PEM certificate content

CA certificate as text, used to verify SSL/TLS connections when using custom endpoints.

clientPemstring

Client PEM certificate content

PEM client certificate as text, used to authenticate the connection to enterprise AI endpoints.

maxRetriesintegerstring

The maximum retry times to request

maxTokenintegerstring

The maximum number of tokens returned by this request

stopsarray

SubTypestring

With the stop parameter, the model will automatically stop generating text when it is about to contain the specified string or token_id

bulkSize integerstring

Default500

Bulk ingestion size

Maximum number of documents sent per ingestion request.

documentSplitter

Document splitter

Definitions

io.kestra.plugin.ai.rag.IngestDocument-DocumentSplitter

maxOverlapSizeInChars*integer

Maximum overlap size (characters). Only full sentences are considered for overlap.

maxSegmentSizeInChars*integer

Maximum segment size (characters)

splitterstring

DefaultRECURSIVE

Possible Values

RECURSIVEPARAGRAPHLINESENTENCEWORD

DocumentSplitter type

Recommended: RECURSIVE for generic text. It splits into paragraphs first and fits as many as possible into a single TextSegment. If paragraphs are too long, they are recursively split into lines, then sentences, then words, then characters until they fit into a segment.

drop booleanstring

Defaultfalse

Drop the store before ingestion (useful for testing)

fromDocuments array

List of inline documents

Definitions

io.kestra.plugin.ai.rag.IngestDocument-InlineDocument

content*string

Document content

metadataobject

Document metadata

fromExternalURLs array

SubTypestring

List of document URLs from external sources

fromInternalURIs array

SubTypestring

List of internal storage URIs for documents

Pebble expression referencing an Internal Storage URI e.g. {{ outputs.mytask.uri }}.

fromPath string

Path in the task working directory containing documents to ingest

Each document in the directory will be ingested into the embedding store. Ingestion is recursive and protected against path traversal (CWE-22).

metadata object

Additional metadata to add to all ingested documents

pluginDefaultsRef string

Reference (ref) of the pluginDefaults to apply to this task.

Outputs

embeddingStoreOutputs object

Additional outputs from the embedding store

ingestedDocuments integer

Number of ingested documents

inputTokenCount integer

Input token count

outputTokenCount integer

Output token count

totalTokenCount integer

Total token count

Metrics

ai.embedding.store.calls counter

Unitcalls

Number of times an embedding store is used, tagged by store class name

ai.provider.calls counter

Unitcalls

Number of times an embedding model is obtained from a provider, tagged by provider class name

indexed.documents counter

Unitrecords

Number of indexed documents

input.token.count counter

Unittoken

Large Language Model (LLM) input token count

output.token.count counter

Unittoken

Large Language Model (LLM) output token count

total.token.count counter

Unittoken

Large Language Model (LLM) total token count

Weaviate

Tasks that manage and query vectors in Weaviate.

DataAI

Timefold

Timefold plugin for Kestra

DataAI

AI IngestDocumentCertifiedIngest documents into embeddings

AI IngestDocument

More Plugins in this Category

Weaviate

Timefold

AI IngestDocument
Certified
Ingest documents into embeddings