import app from "../../google_palm_api.app.mjs";
import constants from "../../common/constants.mjs";
export default {
key: "google_palm_api-chat",
name: "Chat",
description: "Chat using Google PaLM. [See the docs here](https://developers.generativeai.google/api/python/google/generativeai/chat)",
version: "0.0.2",
type: "action",
props: {
app,
promptText: {
type: "string",
label: "Prompt Text",
description: "The text to be used as a prompt for the chat",
},
previousMessages: {
type: "string[]",
label: "Previous Messages",
description: "The previous messages in the chat. If provided, will override the chat history",
optional: true,
},
temperature: {
type: "string",
label: "Temperature",
description: `The temperature to use for the chat. Values can range from [0.0,1.0], inclusive.
A value closer to 1.0 will produce responses that are more varied and creative, while a value closer to 0.0 will typically result in more straightforward responses from the model.
Defaults to \`0.5\``,
optional: true,
},
context: {
type: "string",
label: "Context",
description: "Text that should be provided to the model first, to ground the response",
optional: true,
},
candidateCount: {
type: "integer",
label: "Candidate Count",
description: "The maximum number of generated response messages to return. This value must be between [1, 8], inclusive. If unset, this will default to 1. Note: Only unique candidates are returned. Higher temperatures are more likely to produce unique candidates. Setting temperature=0.0 will always return 1 candidate regardless of the candidate_count.",
optional: true,
default: 1,
min: 1,
max: 8,
},
topK: {
type: "string",
label: "Top K",
description: "The API uses combined nucleus and top-k sampling. top_k sets the maximum number of tokens to sample from on each step.",
optional: true,
},
topP: {
type: "string",
label: "Top P",
description: " The API uses combined nucleus and top-k sampling. top_p configures the nucleus sampling. It sets the maximum cumulative probability of tokens to sample from. For example, if the sorted probabilities are [0.5, 0.2, 0.1, 0.1, 0.05, 0.05] a top_p of 0.8 will sample as [0.625, 0.25, 0.125, 0, 0, 0]. Typical values are in the [0.9, 1.0] range.",
optional: true,
},
maxOutputTokens: {
type: "integer",
label: "Max Output Tokens",
description: "Maximum number of tokens to include in a candidate. Must be greater than zero. If unset, will default to 64.",
optional: true,
},
stopSequences: {
type: "string",
label: "Stop Sequences",
description: "A set of up to 5 character sequences that will stop output generation. If specified, the API will stop at the first appearance of a stop sequence. The stop sequence will not be included as part of the response.",
optional: true,
},
harmCategories: {
type: "string[]",
label: "Harm Categories",
description: "To set safety settings, select the harm categories to set a threshold for",
optional: true,
options() {
return constants.HARM_CATEGORIES.map(({
value, label,
}) => ({
value,
label,
}));
},
reloadProps: true,
},
},
async additionalProps() {
const props = {};
if (!this.harmCategories?.length) {
return props;
}
for (const category of this.harmCategories) {
props[`${category}_threshold`] = {
type: "string",
label: `${category} - Harm Block Threshold`,
description: `Select the harm block threshold to set for the category ${category}`,
options: this.getThresholdOptions(),
};
}
return props;
},
methods: {
getThresholdOptions() {
return constants.HARM_BLOCK_THRESHOLD.map(({
value, label,
}) => ({
value,
label,
}));
},
async chat({
promptText,
previousMessages,
temperature,
context,
candidateCount,
topK,
topP,
maxOutputTokens,
stopSequences,
safetySettings,
}) {
return this.app.chat({
temperature,
prompt: {
context,
messages: [
...previousMessages.map((message) => ({
content: message,
})),
{
content: promptText,
},
],
},
candidate_count: candidateCount,
top_k: topK
? +topK
: undefined,
top_p: topP
? +topP
: undefined,
max_output_tokens: maxOutputTokens,
stop_sequences: stopSequences,
safety_settings: safetySettings,
});
},
},
async run({ $ }) {
const safetySettings = [];
if (this.harmCategories?.length) {
for (const category of this.harmCategories) {
const threshold = constants.HARM_BLOCK_THRESHOLD.find(({ value }) => value === this[`${category}_threshold`]);
safetySettings.push({
category: (constants.HARM_CATEGORIES.find(({ value }) => value === category)).numValue,
threshold: threshold?.numValue,
});
}
}
const response = await this.chat({
promptText: this.promptText,
previousMessages: this.previousMessages || [],
temperature: parseFloat(this.temperature || "0.5"),
context: this.context,
candidteaCount: this.candidateCount,
topK: this.topK,
topP: this.topP,
maxOutputTokens: this.maxOutputTokens,
stopSequences: this.stopSequences,
safetySettings,
});
$.export("$summary", "Successfully received response from Google PaLM");
return response;
},
};
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