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@hypermode/models-as - npm Package Compare versions

Comparing version 0.1.6 to 0.2.0

models/experimental/classification.ts
models/experimental/embeddings.ts

484

models/openai/chat.ts
import { Model } from "../..";
// Reference: https://platform.openai.com/docs/api-reference/chat
/**
* Provides input and output types that conform to the OpenAI Chat API.
*
* Reference: https://platform.openai.com/docs/api-reference/chat
*/
export class ChatModel extends Model<ChatInput, ChatOutput> {
/**
* Creates an input object for the OpenAI Chat API.
*
* @param messages: An array of messages to send to the chat model.
* @returns An input object that can be passed to the `invoke` method.
*/
createInput(messages: Message[]): ChatInput {

@@ -12,9 +21,31 @@ const model = this.info.fullName;

/**
* The input object for the OpenAI Chat API.
*/
@json
class ChatInput {
/**
* The name of the model to use for the chat.
* Must be the exact string expected by the model provider.
* For example, "gpt-3.5-turbo".
*
* @remarks
* This field is automatically set by the `createInput` method when creating this object.
* It does not need to be set manually.
*/
model!: string;
/**
* An array of messages to send to the chat model.
*/
messages!: Message[];
/**
* Number between `-2.0` and `2.0`.
*
* Positive values penalize new tokens based on their existing frequency in the text so far,
* decreasing the model's likelihood to repeat the same line verbatim.
*
* @default 0.0
*/
@alias("frequency_penalty")

@@ -24,3 +55,10 @@ @omitif("this.frequencyPenalty == 0.0")

/**
* Modifies the likelihood of specified tokens appearing in the completion.
*
* Accepts an object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100.
* Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model,
* but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban
* or exclusive selection of the relevant token.
*/
@alias("logit_bias")

@@ -30,7 +68,16 @@ @omitnull()

/**
* Whether to return log probabilities of the output tokens or not,
*
* If true, returns the log probabilities of each output token returned in the content of message.
*
* @default false
*/
@omitif("this.logprobs == false")
logprobs: bool = false;
/**
* An integer between 0 and 20 specifying the number of most likely tokens to return at each token position,
* each with an associated log probability. `logprobs` must be set to `true` if this parameter is used.
*/
@alias("top_logprobs")

@@ -40,3 +87,7 @@ @omitif("this.logprobs == false")

/**
* The maximum number of tokens to generate in the chat completion.
*
* @default 4096
*/
@alias("max_tokens")

@@ -46,7 +97,17 @@ @omitif("this.maxTokens == 4096")

/**
* The number of completions to generate for each prompt.
*/
@omitif("this.n == 1")
n: i32 = 1;
/**
*
* Number between `-2.0` and `2.0`.
*
* Positive values penalize new tokens based on whether they appear in the text so far,
* increasing the model's likelihood to talk about new topics.
*
* @default 0.0
*/
@alias("presence_penalty")

@@ -56,3 +117,9 @@ @omitif("this.presencePenalty == 0.0")

/**
* Specifies the format for the response.
*
* If set to `ResponseFormat.Json`, the response will be a JSON object.
*
* @default ResponseFormat.Text
*/
@alias("response_format")

@@ -62,7 +129,21 @@ @omitif("this.responseFormat.type == 'text'")

/**
* If specified, the model will make a best effort to sample deterministically, such that
* repeated requests with the same seed and parameters should return the same result.
* Determinism is not guaranteed, and you should use the `systemFingerprint` response
* parameter to monitor changes in the backend.
*/
@omitif("this.seed == -1")
seed: i32 = -1; // TODO: make this an `i32 | null` when supported
/**
* Specifies the latency tier to use for processing the request.
*/
@alias("service_tier")
@omitnull()
serviceTier: ServiceTier | null = null;
/**
* Up to 4 sequences where the API will stop generating further tokens.
*/
@omitnull()

@@ -77,6 +158,23 @@ stop: string[] | null = null;

/**
* A number between `0.0` and `2.0` that controls the sampling temperature.
*
* Higher values like `0.8` will make the output more random, while lower values like `0.2` will make
* it more focused and deterministic.
*
* We generally recommend altering this or `topP` but not both.
*
* @default 1.0
*/
@omitif("this.temperature == 1.0")
temperature: f64 = 1.0;
/**
* An alternative to sampling with temperature, called nucleus sampling, where the model
* considers the results of the tokens with `topP` probability mass.
*
* For example, `0.1` means only the tokens comprising the top 10% probability mass are considered.
*
* We generally recommend altering this or `temperature` but not both.
*/
@alias("top_p")

@@ -86,12 +184,28 @@ @omitif("this.topP == 1.0")

/**
* A list of tools the model may call. Currently, only functions are supported as a tool.
* Use this to provide a list of functions the model may generate JSON inputs for.
* A max of 128 functions are supported.
*/
@omitnull()
tools: Tool[] | null = null;
/**
* Controls which (if any) tool is called by the model.
* - `"none"` means the model will not call any tool and instead generates a message.
* - `"auto"` means the model can pick between generating a message or calling one or more tools.
* - `"required"` means the model must call one or more tools.
* - Specifying a particular tool via `{"type": "function", "function": {"name": "my_function"}}` forces the model to call that tool.
*
* `none` is the default when no tools are present. `auto` is the default if tools are present.
*/
@alias("tool_choice")
@omitnull()
toolChoice: string | null = null; // TODO: verify this works
toolChoice: string | null = null; // TODO: Make this work with a custom tool object
/**
* Whether to enable parallel function calling during tool use.
*
* @default true
*/
@alias("parallel_tool_calls")

@@ -101,3 +215,7 @@ @omitif("this.parallelToolCalls == true || !this.tools || this.tools!.length == 0")

/**
* The user ID to associate with the request.
* If not specified, the request will be anonymous.
* See https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids
*/
@omitnull()

@@ -107,24 +225,102 @@ user: string | null = null;

/**
* The OpenAI service tier used to process the request.
*/
// eslint-disable-next-line @typescript-eslint/no-namespace
export namespace ServiceTier {
/**
* The OpenAI system will utilize scale tier credits until they are exhausted.
*/
export const Auto = "auto";
/**
* The request will be processed using the default OpenAI service tier with a lower
* uptime SLA and no latency guarantee.
*/
export const Default = "default";
}
export type ServiceTier = string;
/**
* The output object for the OpenAI Chat API.
*/
@json
class ChatOutput {
/**
* A unique identifier for the chat completion.
*/
id!: string;
/**
* The name of the output object type returned by the API.
* Always `"chat.completion"`.
*/
object!: string;
/**
* A list of chat completion choices. Can be more than one if `n` is greater than 1 in the input options.
*/
choices!: Choice[];
created!: i32; // unix timestamp seconds
/**
* The Unix timestamp (in seconds) of when the chat completion was created.
*/
created!: i32;
/**
* The model used for the chat completion.
* In most cases, this will match the requested `model` field in the input.
*/
model!: string;
/**
* The service tier used for processing the request.
*
* This field is only included if the `serviceTier` parameter is specified in the request.
*/
@alias("service_tier")
serviceTier: string | null = null;
/**
* This fingerprint represents the OpenAI backend configuration that the model runs with.
*
* Can be used in conjunction with the seed request parameter to understand when backend changes
* have been made that might impact determinism.
*/
@alias("system_fingerprint")
systemFingerprint!: string;
/**
* The usage statistics for the request.
*/
usage!: Usage;
}
/**
* An object specifying the format that the model must output.
*/
@json
export class ResponseFormat {
type!: string;
/**
* The type of response format. Must be one of `"text"` or `"json_object"`.
*/
readonly type!: string;
/**
* Instructs the model to output the response as a JSON object.
*
* @remarks
* You must also instruct the model to produce JSON yourself via a system or user message.
*
* Additionally, if you need an array you must ask for an object that wraps the array,
* because the model will not reliably produce arrays directly (ie., there is no `json_array` option).
*/
static Json: ResponseFormat = { type: "json_object" };
/**
* Instructs the model to output the response as a plain text string.
*
* @remarks
* This is the default response format.
*/
static Text: ResponseFormat = { type: "text" };

@@ -141,18 +337,43 @@ }

/**
* A tool object that the model may call.
*/
@json
export class Tool {
/**
* The type of the tool. Currently, only `"function"` is supported.
*
* @default "function"
*/
type: string = "function";
/**
* The definition of the function.
*/
function: FunctionDefinition = new FunctionDefinition();
}
/**
* The definition of a function that can be called by the model.
*/
@json
export class FunctionDefinition {
/**
* The name of the function to be called.
*
* Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
*/
name!: string;
/**
* An optional description of what the function does, used by the model to choose when and how to call the function.
*/
@omitnull()
description: string | null = null;
/**
* The parameters the functions accepts, described as a JSON Schema object.
*
* See https://platform.openai.com/docs/guides/function-calling
*/
@omitnull()

@@ -162,29 +383,65 @@ parameters: string | null = null; // TODO: verify this works

/**
* A tool call object that the model may generate.
*/
@json
export class ToolCall {
/**
* The ID of the tool call.
*/
id!: string;
/**
* The type of the tool. Currently, only `"function"` is supported.
*
* @default "function"
*/
type: string = "function";
/**
* The function that the model called.
*/
function!: FunctionCall;
}
/**
* A function call object that the model may generate.
*/
@json
export class FunctionCall {
/**
* The name of the function to call.
*/
name!: string;
/**
* The arguments to call the function with, as generated by the model in JSON format.
*
* @remarks
* Note that the model does not always generate valid JSON, and may hallucinate parameters not
* defined by your function schema. Validate the arguments in your code before calling your function.
*/
arguments!: string;
}
/**
* The usage statistics for the request.
*/
@json
class Usage {
/**
* The number of completion tokens used in the response.
*/
@alias("completion_tokens")
completionTokens!: i32;
/**
* The number of prompt tokens used in the request.
*/
@alias("prompt_tokens")
promptTokens!: i32;
/**
* The total number of tokens used in the request and response.
*/
@alias("total_tokens")

@@ -194,30 +451,75 @@ totalTokens!: i32;

/**
* A completion choice object returned in the response.
*/
@json
class Choice {
/**
* The reason the model stopped generating tokens.
*
* Possible values are:
* - `"stop"` if the model hit a natural stop point or a provided stop sequence
* - `"length"` if the maximum number of tokens specified in the request was reached
* - `"content_filter"` if content was omitted due to a flag from our content filters
* - `"tool_calls"` if the model called a tool
*/
@alias("finish_reason")
finishReason!: string;
/**
* The index of the choice in the list of choices.
*/
index!: i32;
/**
* A chat completion message generated by the model.
*/
message!: CompletionMessage;
/**
* Log probability information for the choice.
*/
logprobs!: Logprobs | null;
}
/**
* Log probability information for a choice.
*/
@json
class Logprobs {
/**
* A list of message content tokens with log probability information.
*/
content: LogprobsContent[] | null = null;
}
/**
* Log probability information for a message content token.
*/
@json
class LogprobsContent {
/**
* The token.
*/
token!: string;
/**
* The log probability of this token, if it is within the top 20 most likely tokens.
* Otherwise, the value `-9999.0` is used to signify that the token is very unlikely.
*/
logprob!: f64;
/**
* A list of integers representing the UTF-8 bytes representation of the token.
*
* Useful in instances where characters are represented by multiple tokens and their byte
* representations must be combined to generate the correct text representation.
* Can be null if there is no bytes representation for the token.
*/
bytes!: u8[] | null; // TODO: verify this works
/**
* List of the most likely tokens and their log probability, at this token position.
* In rare cases, there may be fewer than the number of requested `topLogprobs` returned.
*/
@alias("top_logprobs")

@@ -227,13 +529,38 @@ topLogprobs!: TopLogprobsContent[]; // TODO: verify this works

/**
* Log probability information for the most likely tokens at a given position.
*/
@json
class TopLogprobsContent {
/**
* The token.
*/
token!: string;
/**
* The log probability of this token, if it is within the top 20 most likely tokens.
* Otherwise, the value `-9999.0` is used to signify that the token is very unlikely.
*/
logprob!: f64;
/**
* A list of integers representing the UTF-8 bytes representation of the token.
* Useful in instances where characters are represented by multiple tokens and their byte
* representations must be combined to generate the correct text representation.
* Can be null if there is no bytes representation for the token.
*/
bytes!: u8[] | null; // TODO: verify this works
}
/**
* A message object that can be sent to the chat model.
*/
@json
abstract class Message {
/**
* Creates a new message object.
*
* @param role The role of the author of this message.
* @param content The contents of the message.
*/
constructor(role: string, content: string) {

@@ -247,2 +574,6 @@ this._role = role;

protected _role: string;
/**
* The role of the author of this message.
*/
get role(): string {

@@ -252,8 +583,18 @@ return this._role;

/**
* The contents of the message.
*/
content: string;
}
/**
* A system message.
*/
@json
export class SystemMessage extends Message {
/**
* Creates a new system message object.
*
* @param content The contents of the message.
*/
constructor(content: string) {

@@ -263,3 +604,6 @@ super("system", content);

/**
* An optional name for the participant.
* Provides the model information to differentiate between participants of the same role.
*/
@omitnull()

@@ -269,5 +613,12 @@ name: string | null = null;

/**
* A user message.
*/
@json
export class UserMessage extends Message {
/**
* Creates a new user message object.
*
* @param content The contents of the message.
*/
constructor(content: string) {

@@ -277,3 +628,6 @@ super("user", content);

/**
* An optional name for the participant.
* Provides the model information to differentiate between participants of the same role.
*/
@omitnull()

@@ -283,5 +637,12 @@ name: string | null = null;

/**
* An assistant message.
*/
@json
export class AssistantMessage extends Message {
/**
* Creates a new assistant message object.
*
* @param content The contents of the message.
*/
constructor(content: string) {

@@ -291,7 +652,12 @@ super("assistant", content);

/**
* An optional name for the participant.
* Provides the model information to differentiate between participants of the same role.
*/
@omitnull()
name: string | null = null;
/**
* The tool calls generated by the model, such as function calls.
*/
@alias("tool_calls")

@@ -302,10 +668,20 @@ @omitif("this.toolCalls.length == 0")

/**
* A tool message.
*/
@json
export class ToolMessage extends Message {
constructor(content: string) {
/**
* Creates a new tool message object.
*
* @param content The contents of the message.
*/
constructor(content: string, toolCallId: string) {
super("tool", content);
this.toolCallId = toolCallId;
}
/**
* Tool call that this message is responding to.
*/
@alias("tool_call_id")

@@ -315,5 +691,13 @@ toolCallId!: string;

/**
* A chat completion message generated by the model.
*/
@json
class CompletionMessage extends Message {
/**
* Creates a new completion message object.
*
* @param role The role of the author of this message.
* @param content The contents of the message.
*/
constructor(role: string, content: string) {

@@ -323,5 +707,7 @@ super(role, content);

/**
* The tool calls generated by the model, such as function calls.
*/
@alias("tool_calls")
toolCalls: ToolCall[] = [];
}

168

models/openai/embeddings.ts
import { Model } from "../..";
// Reference: https://platform.openai.com/docs/api-reference/embeddings
/**
* Provides input and output types that conform to the OpenAI Embeddings API.
*
* Reference: https://platform.openai.com/docs/api-reference/embeddings
*/
export class EmbeddingsModel extends Model<EmbeddingsInput, EmbeddingsOutput> {
createInput(text: string): EmbeddingsInput {
/**
* Creates an input object for the OpenAI Embeddings API.
*
* @param content The input content to vectorize. Can be any of:
* - A string representing the text to vectorize.
* - An array of strings representing multiple texts to vectorize.
* - An array of integers representing pre-tokenized text to vectorize.
* - An array of arrays of integers representing multiple pre-tokenized texts to vectorize.
*
* @returns An input object that can be passed to the `invoke` method.
*
* @remarks
* The input content must not exceed the maximum token limit of the model.
*/
createInput<T>(content: T): EmbeddingsInput {
const model = this.info.fullName;
return <EmbeddingsInput>{ model, input: text };
switch (idof<T>()) {
case idof<string>():
case idof<string[]>():
case idof<i64[]>():
case idof<i32[]>():
case idof<i16[]>():
case idof<i8[]>():
case idof<u64[]>():
case idof<u32[]>():
case idof<u16[]>():
case idof<u8[]>():
case idof<i64[][]>():
case idof<i32[][]>():
case idof<i16[][]>():
case idof<i8[][]>():
case idof<u64[][]>():
case idof<u32[][]>():
case idof<u16[][]>():
case idof<u8[][]>():
return <TypedEmbeddingsInput<T>>{ model, input: content };
}
throw new Error("Unsupported input content type.");
}
}
/**
* The input object for the OpenAI Embeddings API.
*/
@json
class EmbeddingsInput {
input!: string; // todo: support other types of input (arrays, etc.)
/**
* The name of the model to use for the embeddings.
* Must be the exact string expected by the model provider.
* For example, "text-embedding-3-small".
*
* @remarks
* This field is automatically set by the `createInput` method when creating this object.
* It does not need to be set manually.
*/
model!: string;
/**
* The encoding format for the output embeddings.
*
* @default EncodingFormat.Float
*
* @remarks
* Currently only `EncodingFormat.Float` is supported.
*/
@alias("encoding_format")
@omitif("this.encodingFormat == 'float'")
encodingFormat: string = EncodingFormat.Float;
@omitif("this.encodingFormat.type == 'float'")
encodingFormat: EncodingFormat = EncodingFormat.Float;
/**
* The maximum number of dimensions for the output embeddings.
* If not specified, the model's default number of dimensions will be used.
*/
@omitif("this.dimensions == -1")
dimensions: i32 = -1; // TODO: make this an `i32 | null` when supported
/**
* The user ID to associate with the request.
* If not specified, the request will be anonymous.
* See https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids
*/
@omitnull()

@@ -31,38 +96,97 @@ user: string | null = null;

/**
* The input object for the OpenAI Embeddings API.
*/
@json
class TypedEmbeddingsInput<T> extends EmbeddingsInput {
/**
* The input content to vectorize.
*/
input!: T;
}
/**
* The output object for the OpenAI Embeddings API.
*/
@json
class EmbeddingsOutput {
/**
* The name of the output object type returned by the API.
* Always `"list"`.
*/
object!: string;
/**
* The name of the model used to generate the embeddings.
* In most cases, this will match the requested `model` field in the input.
*/
model!: string;
/**
* The usage statistics for the request.
*/
usage!: Usage;
/**
* The output vector embeddings data.
*/
data!: Embedding[];
}
/**
* The encoding format for the output embeddings.
*/
// eslint-disable-next-line @typescript-eslint/no-namespace
export namespace EncodingFormat {
/**
* The output embeddings are encoded as an array of floating-point numbers.
*/
export const Float = "float";
@json
export class EncodingFormat {
type: string = "float";
static Float: EncodingFormat = { type: "float" };
static Base64: EncodingFormat = { type: "base64" };
/**
* The output embeddings are encoded as a base64-encoded string,
* containing an binary representation of an array of floating-point numbers.
*
* @remarks
* This format is currently not supported through this interface.
*/
export const Base64 = "base64";
}
export type EncodingFormat = string;
/**
* The output vector embeddings data.
*/
@json
class Embedding {
/**
* The name of the output object type returned by the API.
* Always `"embedding"`.
*/
object!: string;
/**
* The index of the input text that corresponds to this embedding.
* Used when requesting embeddings for multiple texts.
*/
index!: i32;
embedding!: f64[];
embedding!: f32[]; // TODO: support `f32[] | string` based on input encoding format
}
/**
* The usage statistics for the request.
*/
@json
class Usage {
/**
* The number of prompt tokens used in the request.
*/
@alias("prompt_tokens")
promptTokens!: i32;
/**
* The total number of tokens used in the request.
*/
@alias("total_tokens")
totalTokens!: i32;
}

14

package.json
{
"name": "@hypermode/models-as",
"version": "0.1.6",
"version": "0.2.0",
"description": "Hypermode Model Interface Library for AssemblyScript",

@@ -17,13 +17,13 @@ "author": "Hypermode, Inc.",

"dependencies": {
"json-as": "^0.9.6"
"json-as": "^0.9.8"
},
"devDependencies": {
"@types/node": "^20.14.5",
"@typescript-eslint/eslint-plugin": "^7.13.1",
"@typescript-eslint/parser": "^7.13.1",
"assemblyscript": "^0.27.27",
"@types/node": "^20.14.8",
"@typescript-eslint/eslint-plugin": "^7.14.1",
"@typescript-eslint/parser": "^7.14.1",
"assemblyscript": "^0.27.28",
"assemblyscript-prettier": "^3.0.1",
"eslint": "^8.57.0",
"prettier": "^3.3.2",
"typescript": "^5.4.5",
"typescript": "^5.5.2",
"visitor-as": "^0.11.4"

@@ -30,0 +30,0 @@ },

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