Google Cloud ChatCompletion
CertifiedGenerate chat completions with Vertex AI
Google Cloud ChatCompletion
Generate chat completions with Vertex AI
Produces chat-style responses using the configured Vertex model and generation parameters.
type: io.kestra.plugin.gcp.vertexai.ChatCompletionExamples
Chat completion using the Vertex AI Gemini API
id: gcp_vertexai_chat_completion
namespace: company.team
tasks:
- id: chat_completion
type: io.kestra.plugin.gcp.vertexai.ChatCompletion
region: us-central1
projectId: my-project
context: I love jokes that talk about sport
messages:
- author: user
content: Please tell me a joke
Properties
messages *Requiredarray
1Chat messages
Messages appear in chronological order: oldest first, newest last. When the history of messages causes the input to exceed the maximum length, the oldest messages are removed until the entire prompt is within the allowed limit.
io.kestra.plugin.gcp.vertexai.ChatCompletion-Message
This property is not used anymore since migration to Gemini LLM
region *Requiredstring
Region
Vertex AI region used for the API endpoint
history array
Conversation history provided to the model
Messages appear in chronological order: oldest first, newest last. When the history of messages causes the input to exceed the maximum length, the oldest messages are removed until the entire prompt is within the allowed limit.
io.kestra.plugin.gcp.vertexai.ChatCompletion-Message
This property is not used anymore since migration to Gemini LLM
impersonatedServiceAccount string
The GCP service account to impersonate
modelId string
gemini-proModel ID
Vertex model name (e.g., gemini-1.5-flash); defaults to gemini-pro
parameters Non-dynamic
{
"temperature": 0.2,
"maxOutputTokens": 128,
"topK": 40,
"topP": 0.95
}Model parameters
Temperature/topK/topP/maxOutputTokens generation settings
io.kestra.plugin.gcp.vertexai.AbstractGenerativeAi-ModelParameter
128>= 1<= 1024Maximum number of tokens that can be generated in the response
Specify a lower value for shorter responses and a higher value for longer responses. A token may be smaller than a word. A token is approximately four characters. 100 tokens correspond to roughly 60-80 words.
0.2> <= 1Temperature used for sampling during the response generation, which occurs when topP and topK are applied
Temperature controls the degree of randomness in token selection. Lower temperatures are good for prompts that require a more deterministic and less open-ended or creative response, while higher temperatures can lead to more diverse or creative results. A temperature of 0 is deterministic: the highest probability response is always selected. For most use cases, try starting with a temperature of 0.2.
40>= 1<= 40Top-k changes how the model selects tokens for output
A top-k of 1 means the selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a top-k of 3 means that the next token is selected from among the 3 most probable tokens (using temperature). For each token selection step, the top K tokens with the highest probabilities are sampled. Then tokens are further filtered based on topP with the final token selected using temperature sampling. Specify a lower value for less random responses and a higher value for more random responses.
0.95> <= 1Top-p changes how the model selects tokens for output
Tokens are selected from most K (see topK parameter) probable to least until the sum of their probabilities equals the top-p value. For example, if tokens A, B, and C have a probability of 0.3, 0.2, and 0.1 and the top-p value is 0.5, then the model will select either A or B as the next token (using temperature) and doesn't consider C. The default top-p value is 0.95. Specify a lower value for less random responses and a higher value for more random responses.
pluginDefaultsRef Non-dynamicstring
Reference (ref) of the pluginDefaults to apply to this task.
projectId string
The GCP project ID
scopes array
["https://www.googleapis.com/auth/cloud-platform"]The GCP scopes to be used
serviceAccount string
The GCP service account
Outputs
predictions array
List of text predictions made by the model
io.kestra.plugin.gcp.vertexai.AbstractGenerativeAi-Prediction
io.kestra.plugin.gcp.vertexai.AbstractGenerativeAi-CitationMetadata
io.kestra.plugin.gcp.vertexai.AbstractGenerativeAi-Citation
io.kestra.plugin.gcp.vertexai.AbstractGenerativeAi-SafetyAttributes
Metrics
candidate.token.count counter
Number of tokens in the candidate response.
prompt.token.count counter
Number of tokens in the prompt.
serialized.size counter
bytesSize of the serialized metadata.
total.token.count counter
Total number of tokens (prompt + candidate).
