ChatCompletion

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.

Create a Retrieval Augmented Generation (RAG) pipeline.

yaml
type: "io.kestra.plugin.langchain4j.rag.ChatCompletion"

Examples

Chat with your data using Retrieval Augmented Generation (RAG). This flow will index documents and use the RAG Chat task to interact with your data using natural language prompts. The flow contrasts prompts to LLM with and without RAG. The Chat with RAG retrieves embeddings stored in the KV Store and provides a response grounded in data rather than hallucinating. WARNING: the KV embedding store is for quick prototyping only, as it stores the embedding vectors in Kestra's KV store an loads them all into memory.

yaml
id: rag
namespace: company.team

tasks:
  - id: ingest
    type: io.kestra.plugin.langchain4j.rag.IngestDocument
    provider:
      type: io.kestra.plugin.langchain4j.provider.GoogleGemini
      modelName: gemini-embedding-exp-03-07
      apiKey: "{{ secret('GEMINI_API_KEY') }}"
    embeddings:
      type: io.kestra.plugin.langchain4j.embeddings.KestraKVStore
    drop: true
    fromExternalURLs:
      - https://raw.githubusercontent.com/kestra-io/docs/refs/heads/main/content/blogs/release-0-22.md

  - id: chat_without_rag
    type: io.kestra.plugin.langchain4j.ChatCompletion
    provider:
      type: io.kestra.plugin.langchain4j.provider.GoogleGemini
      modelName: gemini-2.0-flash
      apiKey: "{{ secret('GEMINI_API_KEY') }}"
    messages:
    - type: user
      content: Which features were released in Kestra 0.22?

  - id: chat_with_rag
    type: io.kestra.plugin.langchain4j.rag.ChatCompletion
    chatProvider:
      type: io.kestra.plugin.langchain4j.provider.GoogleGemini
      modelName: gemini-2.0-flash
      apiKey: "{{ secret('GEMINI_API_KEY') }}"
    embeddingProvider:
      type: io.kestra.plugin.langchain4j.provider.GoogleGemini
      modelName: gemini-embedding-exp-03-07
      apiKey: "{{ secret('GEMINI_API_KEY') }}"
    embeddings:
      type: io.kestra.plugin.langchain4j.embeddings.KestraKVStore
    prompt: Which features were released in Kestra 0.22?

Chat with your data using Retrieval Augmented Generation (RAG) and a WebSearch content retriever. The Chat with RAG retrieves contents from a WebSearch client and provides a response grounded in data rather than hallucinating.

yaml
id: rag
namespace: company.team

tasks:
  - id: chat_with_rag_and_websearch_content_retriever
    type: io.kestra.plugin.langchain4j.rag.ChatCompletion
    chatProvider:
      type: io.kestra.plugin.langchain4j.provider.GoogleGemini
      modelName: gemini-2.0-flash
      apiKey: "{{ secret('GEMINI_API_KEY') }}"
    contentRetrievers:
    - type: io.kestra.plugin.langchain4j.retriever.GoogleCustomWebSearch
      apiKey: "{{ secret('GOOGLE_SEARCH_API_KEY') }}"
      csi: "{{ secret('GOOGLE_SEARCH_CSI') }}"
    prompt: What is the latest release of Kestra?

Chat with your data using Retrieval Augmented Generation (RAG) and an additional WebSearch tool. This flow will index documents and use the RAG Chat task to interact with your data using natural language prompts. The flow contrasts prompts to LLM with and without RAG. The Chat with RAG retrieves embeddings stored in the KV Store and provides a response grounded in data rather than hallucinating. It may also include results from a web search engine if using the provided tool. WARNING: the KV embedding store is for quick prototyping only, as it stores the embedding vectors in Kestra's KV store an loads them all into memory.

yaml
id: rag
namespace: company.team

tasks:
  - id: ingest
    type: io.kestra.plugin.langchain4j.rag.IngestDocument
    provider:
      type: io.kestra.plugin.langchain4j.provider.GoogleGemini
      modelName: gemini-embedding-exp-03-07
      apiKey: "{{ secret('GEMINI_API_KEY') }}"
    embeddings:
      type: io.kestra.plugin.langchain4j.embeddings.KestraKVStore
    drop: true
    fromExternalURLs:
      - https://raw.githubusercontent.com/kestra-io/docs/refs/heads/main/content/blogs/release-0-22.md

  - id: chat_with_rag_and_tool
    type: io.kestra.plugin.langchain4j.rag.ChatCompletion
    chatProvider:
      type: io.kestra.plugin.langchain4j.provider.GoogleGemini
      modelName: gemini-2.0-flash
      apiKey: "{{ secret('GEMINI_API_KEY') }}"
    embeddingProvider:
      type: io.kestra.plugin.langchain4j.provider.GoogleGemini
      modelName: gemini-embedding-exp-03-07
      apiKey: "{{ secret('GEMINI_API_KEY') }}"
    embeddings:
      type: io.kestra.plugin.langchain4j.embeddings.KestraKVStore
    tools:
    - type: io.kestra.plugin.langchain4j.tool.GoogleCustomWebSearch
      apiKey: "{{ secret('GOOGLE_SEARCH_API_KEY') }}"
      csi: "{{ secret('GOOGLE_SEARCH_CSI') }}"
    prompt: What is the latest release of Kestra?

Properties

chatProvider *AmazonBedrock Anthropic AzureOpenAI DeepSeek GoogleGemini GoogleVertexAI MistralAI Ollama OpenAI

Chat Model Provider

prompt *string

Text prompt

The input prompt for the language model

chatConfiguration ChatConfiguration

Default {}

Chat configuration

contentRetrieverConfiguration ChatCompletion-ContentRetrieverConfiguration

Default

{
  "maxResults": 3,
  "minScore": 0
}

Content Retriever Configuration

contentRetrievers array

Additional content retrievers

Some content retrievers like WebSearch can be used also as tools, but using them as content retrievers will make them always used whereas tools are only used when the LLM decided to.

embeddingProvider AmazonBedrock Anthropic AzureOpenAI DeepSeek GoogleGemini GoogleVertexAI MistralAI Ollama OpenAI

Embedding Store Model Provider

Optional, if not set, the embedding model will be created by the chatModelProvider. In this case, be sure that the chatModelProvider supports embeddings.

embeddings Elasticsearch KestraKVStore PGVector

Embedding Store Provider

Optional if at least one contentRetrievers is provided

tools array

Tools that the LLM may use to augment its response

Outputs

completion string

Generated text completion

The result of the text completion

finishReason string

Possible Values

STOPLENGTHTOOL_EXECUTIONCONTENT_FILTEROTHER

Finish reason

tokenUsage TokenUsage

Token usage

Definitions

io.kestra.plugin.langchain4j.rag.ChatCompletion-ContentRetrieverConfiguration

maxResults integer

Default 3

The maximum number of results from the embedding store.

minScore number

Default 0

The minimum score, ranging from 0 to 1 (inclusive). Only embeddings with a score >= minScore will be returned.

Google VertexAI Model Provider

endpoint *string

Endpoint URL

location *string

Project location

modelName *string

Model name

project *string

Project ID

type *object

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

Deepseek Model Provider

apiKey *string

API Key

modelName *string

Model name

type *object

baseUrl string

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

API base URL

WebSearch content retriever for Tavily Search

apiKey *string

API Key

type *object

maxResults integerstring

Default 3

Maximum number of results to return

io.kestra.plugin.langchain4j.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.

Anthropic AI Model Provider

apiKey *string

API Key

modelName *string

Model name

type *object

OpenAI Model Provider

apiKey *string

API Key

modelName *string

Model name

type *object

baseUrl string

API base URL

WebSearch tool for Tavily Search

apiKey *string

API Key

type *object

Ollama Model Provider

endpoint *string

Model endpoint

modelName *string

Model name

type *object

io.kestra.plugin.langchain4j.embeddings.Elasticsearch-ElasticsearchConnection-BasicAuth

password string

Basic auth password.

username string

Basic auth username.

io.kestra.plugin.langchain4j.domain.ChatConfiguration

seed integerstring

seed

temperature numberstring

Temperature

topK integerstring

topK

topP numberstring

topP

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

Google Gemini Model Provider

apiKey *string

API Key

modelName *string

Model name

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

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).

Mistral AI Model Provider

apiKey *string

API Key

modelName *string

Model name

type *object

baseUrl string

API base URL

WebSearch content retriever for Google Custom Search

apiKey *string

API Key

csi *string

API Key

type *object

maxResults integerstring

Default 3

Maximum number of results to return

Model Context Protocol (MCP) HTTP client tool

command *array

SubType string

The MCP client command, as a list of command parts.

type *object

environment object

SubType string

Environment variables

WebSearch tool for Google Custom Search

apiKey *string

API Key

csi *string

API Key

type *object

Model Context Protocol (MCP) HTTP client tool

sseUrl *string

SSE URL to the MCP server

type *object

timeout string

Format duration

Connection timeout

io.kestra.plugin.langchain4j.domain.TokenUsage

inputTokenCount integer

outputTokenCount integer

totalTokenCount integer

Elasticsearch Embedding Store

connection *Elasticsearch-ElasticsearchConnection

indexName *string

The name of the index to store embeddings

type *object

​Chat​Completion

ChatCompletion