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| 8d5059534a |
@@ -0,0 +1,181 @@
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import { z } from "zod";
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import {
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OPENAI_OUTPUT_MAX,
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OpenAIV1ChatCompletionSchema,
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flattenOpenAIMessageContent,
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} from "./openai";
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import { APIFormatTransformer } from ".";
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// https://docs.cohere.com/reference/chat
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export const CohereV1ChatSchema = z
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.object({
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message: z.string(),
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model: z.string().default("command-r-plus"),
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stream: z.boolean().default(false).optional(),
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preamble: z.string().optional(),
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chat_history: z
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.array(
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// Either a message from a chat participant, or a past tool call
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z.union([
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z.object({
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role: z.enum(["CHATBOT", "SYSTEM", "USER"]),
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message: z.string(),
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tool_calls: z
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.array(z.object({ name: z.string(), parameters: z.any() }))
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.optional(),
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}),
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z.object({
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role: z.enum(["TOOL"]),
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tool_results: z.array(
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z.object({
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call: z.object({ name: z.string(), parameters: z.any() }),
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outputs: z.array(z.any()),
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})
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),
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}),
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])
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)
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.optional(),
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// Don't allow conversation_id as it causes calls to be stateful and we don't
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// offer guarantees about which key a user's request will be routed to.
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conversation_id: z.literal(undefined).optional(),
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prompt_truncation: z
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.enum(["AUTO", "AUTO_PRESERVE_ORDER", "OFF"])
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.optional(),
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/*
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Supporting RAG is complex because documents can be arbitrary size and have
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to have embeddings generated, which incurs a cost that is not trivial to
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estimate. We don't support it for now.
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connectors: z
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.array(
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z.object({
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id: z.string(),
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user_access_token: z.string().optional(),
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continue_on_failure: z.boolean().default(false).optional(),
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options: z.any().optional(),
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})
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)
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.optional(),
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search_queries_only: z.boolean().default(false).optional(),
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documents: z
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.array(
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z.object({
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id: z.string().optional(),
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title: z.string().optional(),
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text: z.string(),
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_excludes: z.array(z.string()).optional(),
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})
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)
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.optional(),
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citation_quality: z.enum(["accurate", "fast"]).optional(),
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*/
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temperature: z.number().default(0.3).optional(),
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max_tokens: z
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.number()
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.int()
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.nullish()
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.default(Math.min(OPENAI_OUTPUT_MAX, 4096))
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.transform((v) => Math.min(v ?? OPENAI_OUTPUT_MAX, OPENAI_OUTPUT_MAX)),
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max_input_tokens: z.number().int().optional(),
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k: z.number().int().min(0).max(500).default(0).optional(),
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p: z.number().min(0.01).max(0.99).default(0.75).optional(),
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seed: z.number().int().optional(),
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stop_sequences: z.array(z.string()).max(5).optional(),
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frequency_penalty: z.number().min(0).max(1).default(0).optional(),
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presence_penalty: z.number().min(0).max(1).default(0).optional(),
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tools: z
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.array(
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z.object({
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name: z.string(),
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description: z.string(),
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parameter_definitions: z.record(
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z.object({
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description: z.string().optional(),
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type: z.string(),
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required: z.boolean().optional().default(false),
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})
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),
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})
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)
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.optional(),
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tool_results: z
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.array(
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z.object({
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call: z.object({
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name: z.string(),
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parameters: z.record(z.any()),
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}),
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outputs: z.array(z.record(z.any())),
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})
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)
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.optional(),
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// We always force single step to avoid stateful calls or expensive multi-step
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// generations when tools are involved.
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force_single_step: z.literal(true).default(true).optional(),
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})
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.strip();
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export type CohereChatMessage = NonNullable<
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z.infer<typeof CohereV1ChatSchema>["chat_history"]
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>[number];
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export function flattenCohereMessageContent(
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message: CohereChatMessage
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): string {
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return message.role === "TOOL"
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? message.tool_results.map((r) => r.outputs[0].text).join("\n")
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: message.message;
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}
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export const transformOpenAIToCohere: APIFormatTransformer<
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typeof CohereV1ChatSchema
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> = async (req) => {
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const { body } = req;
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const result = OpenAIV1ChatCompletionSchema.safeParse({
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...body,
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model: "gpt-3.5-turbo",
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});
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if (!result.success) {
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req.log.warn(
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{ issues: result.error.issues, body },
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"Invalid OpenAI-to-Cohere request"
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);
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throw result.error;
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}
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const { messages, ...rest } = result.data;
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// Final OAI message becomes the `message` field in Cohere
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const message = messages[messages.length - 1];
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// If the first message has system role, use it as preamble.
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const hasSystemPreamble = messages[0]?.role === "system";
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const preamble = hasSystemPreamble
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? flattenOpenAIMessageContent(messages[0].content)
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: undefined;
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const chatHistory = messages.slice(0, -1).map((m) => {
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const role: Exclude<CohereChatMessage["role"], "TOOL"> =
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m.role === "assistant"
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? "CHATBOT"
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: m.role === "system"
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? "SYSTEM"
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: "USER";
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const content = flattenOpenAIMessageContent(m.content);
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const message = m.name ? `${m.name}: ${content}` : content;
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return { role, message };
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});
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return {
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model: rest.model,
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preamble,
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chat_history: chatHistory,
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message: flattenOpenAIMessageContent(message.content),
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stop_sequences:
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typeof rest.stop === "string" ? [rest.stop] : rest.stop ?? undefined,
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max_tokens: rest.max_tokens,
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temperature: rest.temperature,
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p: rest.top_p,
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frequency_penalty: rest.frequency_penalty,
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presence_penalty: rest.presence_penalty,
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seed: rest.seed,
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stream: rest.stream,
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};
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};
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@@ -22,6 +22,7 @@ import {
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transformOpenAIToGoogleAI,
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} from "./google-ai";
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import { MistralAIV1ChatCompletionsSchema } from "./mistral-ai";
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import { CohereV1ChatSchema, transformOpenAIToCohere } from "./cohere";
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export { OpenAIChatMessage } from "./openai";
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export {
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@@ -33,15 +34,29 @@ export {
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export { GoogleAIChatMessage } from "./google-ai";
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export { MistralAIChatMessage } from "./mistral-ai";
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/** Represents a pair of API formats that can be transformed between. */
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type APIPair = `${APIFormat}->${APIFormat}`;
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/** Represents a map of API format pairs to transformer functions. */
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type TransformerMap = {
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[key in APIPair]?: APIFormatTransformer<any>;
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};
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/**
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* Represents a transformer function that takes a Request and returns a Promise
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* resolving to a value of the specified Zod schema type.
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*
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* @template Z The Zod schema type to transform the request into (from api-schemas).
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* @param req The incoming Request to transform.
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* @returns A Promise resolving to the transformed request body.
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*/
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export type APIFormatTransformer<Z extends z.ZodType<any, any>> = (
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req: Request
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) => Promise<z.infer<Z>>;
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/**
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* Specifies possible translations between API formats and the corresponding
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* transformer functions to apply them.
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*/
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export const API_REQUEST_TRANSFORMERS: TransformerMap = {
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"anthropic-text->anthropic-chat": transformAnthropicTextToAnthropicChat,
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"openai->anthropic-chat": transformOpenAIToAnthropicChat,
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@@ -49,8 +64,12 @@ export const API_REQUEST_TRANSFORMERS: TransformerMap = {
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"openai->openai-text": transformOpenAIToOpenAIText,
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"openai->openai-image": transformOpenAIToOpenAIImage,
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"openai->google-ai": transformOpenAIToGoogleAI,
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"openai->cohere-chat": transformOpenAIToCohere,
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};
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/**
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* Specifies the schema for each API format to validate incoming requests.
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*/
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export const API_REQUEST_VALIDATORS: Record<APIFormat, z.ZodSchema<any>> = {
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"anthropic-chat": AnthropicV1MessagesSchema,
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"anthropic-text": AnthropicV1TextSchema,
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@@ -59,4 +78,5 @@ export const API_REQUEST_VALIDATORS: Record<APIFormat, z.ZodSchema<any>> = {
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"openai-image": OpenAIV1ImagesGenerationSchema,
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"google-ai": GoogleAIV1GenerateContentSchema,
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"mistral-ai": MistralAIV1ChatCompletionsSchema,
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"cohere-chat": CohereV1ChatSchema,
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};
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@@ -9,7 +9,8 @@ export type APIFormat =
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| "anthropic-chat" // Anthropic's newer messages array format
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| "anthropic-text" // Legacy flat string prompt format
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| "google-ai"
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| "mistral-ai";
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| "mistral-ai"
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| "cohere-chat";
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export interface Key {
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/** The API key itself. Never log this, use `hash` instead. */
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+18
-2
@@ -14,7 +14,8 @@ export type LLMService =
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| "google-ai"
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| "mistral-ai"
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| "aws"
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| "azure";
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| "azure"
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| "cohere";
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export type OpenAIModelFamily =
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| "turbo"
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@@ -32,13 +33,15 @@ export type MistralAIModelFamily =
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| "mistral-large";
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export type AwsBedrockModelFamily = "aws-claude" | "aws-claude-opus";
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export type AzureOpenAIModelFamily = `azure-${OpenAIModelFamily}`;
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export type CohereModelFamily = "command-r" | "command-r-plus";
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export type ModelFamily =
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| OpenAIModelFamily
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| AnthropicModelFamily
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| GoogleAIModelFamily
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| MistralAIModelFamily
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| AwsBedrockModelFamily
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| AzureOpenAIModelFamily;
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| AzureOpenAIModelFamily
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| CohereModelFamily;
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export const MODEL_FAMILIES = (<A extends readonly ModelFamily[]>(
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arr: A & ([ModelFamily] extends [A[number]] ? unknown : never)
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@@ -64,6 +67,8 @@ export const MODEL_FAMILIES = (<A extends readonly ModelFamily[]>(
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"azure-gpt4-turbo",
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"azure-gpt4o",
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"azure-dall-e",
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"command-r",
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"command-r-plus",
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] as const);
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export const LLM_SERVICES = (<A extends readonly LLMService[]>(
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@@ -75,6 +80,7 @@ export const LLM_SERVICES = (<A extends readonly LLMService[]>(
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"mistral-ai",
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"aws",
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"azure",
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"cohere",
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] as const);
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export const OPENAI_MODEL_FAMILY_MAP: { [regex: string]: OpenAIModelFamily } = {
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@@ -116,6 +122,8 @@ export const MODEL_FAMILY_SERVICE: {
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"mistral-small": "mistral-ai",
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"mistral-medium": "mistral-ai",
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"mistral-large": "mistral-ai",
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"command-r": "cohere",
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"command-r-plus": "cohere",
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};
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export const IMAGE_GEN_MODELS: ModelFamily[] = ["dall-e", "azure-dall-e"];
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@@ -181,6 +189,11 @@ export function getAzureOpenAIModelFamily(
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return defaultFamily;
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}
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export function getCohereModelFamily(model: string): CohereModelFamily {
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if (model.includes("plus")) return "command-r-plus";
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return "command-r";
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}
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export function assertIsKnownModelFamily(
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modelFamily: string
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): asserts modelFamily is ModelFamily {
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@@ -220,6 +233,9 @@ export function getModelFamilyForRequest(req: Request): ModelFamily {
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case "mistral-ai":
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modelFamily = getMistralAIModelFamily(model);
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break;
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case "cohere-chat":
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modelFamily = getCohereModelFamily(model);
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break;
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default:
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assertNever(req.outboundApi);
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}
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