ChatCompletion
Chat completion using the Vertex AI for Google's Gemini large language models (LLM).
See Generative AI quickstart using the Vertex AI API for more information.
type: "io.kestra.plugin.gcp.vertexai.ChatCompletion"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
The GCP region.
For backward compatibility, since migration to Gemini LLM this property will be the first message to be send to the chat.
The GCP service account to impersonate.
The model parameters.
The GCP project ID.
The GCP scopes to be used.
The GCP service account.
Maximum 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.
Temperature 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.
Top-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.
Top-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.