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@@ -135,64 +135,9 @@ export enum AgentStrategy {
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react = 'react',
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}
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-export type CompletionParams = {
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- /** Maximum number of tokens in the answer message returned by Completion */
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- max_tokens: number
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- /**
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- * A number between 0 and 2.
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- * The larger the number, the more random the result;
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- * otherwise, the more deterministic.
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- * When in use, choose either `temperature` or `top_p`.
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- * Default is 1.
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- */
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- temperature: number
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- /**
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- * Represents the proportion of probability mass samples to take,
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- * e.g., 0.1 means taking the top 10% probability mass samples.
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- * The determinism between the samples is basically consistent.
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- * Among these results, the `top_p` probability mass results are taken.
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- * When in use, choose either `temperature` or `top_p`.
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- * Default is 1.
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- */
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- top_p: number
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- /** When enabled, the Completion Text will concatenate the Prompt content together and return it. */
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- echo: boolean
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- /**
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- * Specify up to 4 to automatically stop generating before the text specified in `stop`.
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- * Suitable for use in chat mode.
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- * For example, specify "Q" and "A",
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- * and provide some Q&A examples as context,
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- * and the model will give out in Q&A format and stop generating before Q&A.
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- */
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- stop: string[]
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- /**
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- * A number between -2.0 and 2.0.
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- * The larger the value, the less the model will repeat topics and the more it will provide new topics.
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- */
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- presence_penalty: number
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- /**
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- * A number between -2.0 and 2.0.
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- * A lower setting will make the model appear less cultured,
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- * always repeating expressions.
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- * The difference between `frequency_penalty` and `presence_penalty`
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- * is that `frequency_penalty` penalizes a word based on its frequency in the training data,
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- * while `presence_penalty` penalizes a word based on its occurrence in the input text.
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- */
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- frequency_penalty: number
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-}
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/**
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* Model configuration. The backend type.
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*/
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-export type Model = {
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- /** LLM provider, e.g., OPENAI */
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- provider: string
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- /** Model name, e.g, gpt-3.5.turbo */
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- name: string
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- mode: ModelModeType
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- /** Default Completion call parameters */
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- completion_params: CompletionParams
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-}
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-
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export type ModelConfig = {
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opening_statement: string
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suggested_questions?: string[]
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@@ -229,7 +174,59 @@ export type ModelConfig = {
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strategy?: AgentStrategy
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tools: ToolItem[]
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}
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- model: Model
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+ model: {
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+ /** LLM provider, e.g., OPENAI */
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+ provider: string
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+ /** Model name, e.g, gpt-3.5.turbo */
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+ name: string
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+ mode: ModelModeType
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+ /** Default Completion call parameters */
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+ completion_params: {
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+ /** Maximum number of tokens in the answer message returned by Completion */
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+ max_tokens: number
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+ /**
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+ * A number between 0 and 2.
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+ * The larger the number, the more random the result;
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+ * otherwise, the more deterministic.
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+ * When in use, choose either `temperature` or `top_p`.
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+ * Default is 1.
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+ */
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+ temperature: number
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+ /**
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+ * Represents the proportion of probability mass samples to take,
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+ * e.g., 0.1 means taking the top 10% probability mass samples.
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+ * The determinism between the samples is basically consistent.
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+ * Among these results, the `top_p` probability mass results are taken.
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+ * When in use, choose either `temperature` or `top_p`.
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+ * Default is 1.
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+ */
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+ top_p: number
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+ /** When enabled, the Completion Text will concatenate the Prompt content together and return it. */
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+ echo: boolean
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+ /**
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+ * Specify up to 4 to automatically stop generating before the text specified in `stop`.
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+ * Suitable for use in chat mode.
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+ * For example, specify "Q" and "A",
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+ * and provide some Q&A examples as context,
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+ * and the model will give out in Q&A format and stop generating before Q&A.
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+ */
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+ stop: string[]
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+ /**
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+ * A number between -2.0 and 2.0.
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+ * The larger the value, the less the model will repeat topics and the more it will provide new topics.
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+ */
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+ presence_penalty: number
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+ /**
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+ * A number between -2.0 and 2.0.
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+ * A lower setting will make the model appear less cultured,
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+ * always repeating expressions.
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+ * The difference between `frequency_penalty` and `presence_penalty`
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+ * is that `frequency_penalty` penalizes a word based on its frequency in the training data,
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+ * while `presence_penalty` penalizes a word based on its occurrence in the input text.
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+ */
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+ frequency_penalty: number
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+ }
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+ }
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dataset_configs: DatasetConfigs
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file_upload?: {
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image: VisionSettings
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