feat: anthropic endpoint

.env

@@ -9,6 +9,7 @@ COOKIE_NAME=hf-chat
 HF_TOKEN=#hf_<token> from from https://huggingface.co/settings/token
 HF_API_ROOT=https://api-inference.huggingface.co/models
 OPENAI_API_KEY=#your openai api key here
+ANTHROPIC_API_KEY=#your anthropic api key here
 
 HF_ACCESS_TOKEN=#LEGACY! Use HF_TOKEN instead
 

README.md

@@ -459,6 +459,51 @@ MODELS=`[
 ]`
 ```
 
+#### Anthropic
+
+We also support Anthropic models through the official SDK. You may provide your API key via the `ANTHROPIC_API_KEY` env variable, or alternatively, through the `endpoints.apiKey` as per the following example.
+
+```
+MODELS=`[
+ {
+ "name": "claude-3-sonnet-20240229",
+ "displayName": "Claude 3 Sonnet",
+ "description": "Ideal balance of intelligence and speed",
+ "parameters": {
+ "max_new_tokens": 4096,
+ },
+ "endpoints": [
+ {
+ "type": "anthropic",
+ // optionals
+ "apiKey": "sk-ant-...",
+ "baseURL": "https://api.anthropic.com",
+ defaultHeaders: {},
+ defaultQuery: {}
+ }
+ ]
+ },
+ {
+ "name": "claude-3-opus-20240229",
+ "displayName": "Claude 3 Opus",
+ "description": "Most powerful model for highly complex tasks",
+ "parameters": {
+ "max_new_tokens": 4096
+ },
+ "endpoints": [
+ {
+ "type": "anthropic",
+ // optionals
+ "apiKey": "sk-ant-...",
+ "baseURL": "https://api.anthropic.com",
+ defaultHeaders: {},
+ defaultQuery: {}
+ }
+ ]
+ }
+]`
+```
+
 #### Amazon
 
 You can also specify your Amazon SageMaker instance as an endpoint for chat-ui. The config goes like this:

package.json

@@ -81,6 +81,7 @@
  "zod": "^3.22.3"
  },
  "optionalDependencies": {
+ "@anthropic-ai/sdk": "^0.17.1",
  "aws4fetch": "^1.0.17",
  "openai": "^4.14.2"
  }

src/lib/server/endpoints/anthropic/endpointAnthropic.ts

@@ -0,0 +1,95 @@
+import { z } from "zod";
+import { ANTHROPIC_API_KEY } from "$env/static/private";
+import type { Endpoint } from "../endpoints";
+import type { TextGenerationStreamOutput } from "@huggingface/inference";
+
+export const endpointAnthropicParametersSchema = z.object({
+ weight: z.number().int().positive().default(1),
+ model: z.any(),
+ type: z.literal("anthropic"),
+ baseURL: z.string().url().default("https://api.anthropic.com"),
+ apiKey: z.string().default(ANTHROPIC_API_KEY ?? "sk-"),
+ defaultHeaders: z.record(z.string()).optional(),
+ defaultQuery: z.record(z.string()).optional(),
+});
+
+export async function endpointAnthropic(
+ input: z.input<typeof endpointAnthropicParametersSchema>
+): Promise<Endpoint> {
+ const { baseURL, apiKey, model, defaultHeaders, defaultQuery } =
+ endpointAnthropicParametersSchema.parse(input);
+ let Anthropic;
+ try {
+ Anthropic = (await import("@anthropic-ai/sdk")).default;
+ } catch (e) {
+ throw new Error("Failed to import @anthropic-ai/sdk", { cause: e });
+ }
+
+ const anthropic = new Anthropic({
+ apiKey,
+ baseURL,
+ defaultHeaders,
+ defaultQuery,
+ });
+
+ return async ({ messages, preprompt }) => {
+ let system = preprompt;
+ if (messages?.[0]?.from === "system") {
+ system = messages[0].content;
+ }
+
+ const messagesFormatted = messages
+ .filter((message) => message.from !== "system")
+ .map((message) => ({
+ role: message.from,
+ content: message.content,
+ })) as unknown as {
+ role: "user" | "assistant";
+ content: string;
+ }[];
+
+ let tokenId = 0;
+ return (async function* () {
+ const stream = anthropic.messages.stream({
+ model: model.id ?? model.name,
+ messages: messagesFormatted,
+ max_tokens: model.parameters?.max_new_tokens,
+ temperature: model.parameters?.temperature,
+ top_p: model.parameters?.top_p,
+ top_k: model.parameters?.top_k,
+ stop_sequences: model.parameters?.stop,
+ system,
+ });
+ while (true) {
+ const result = await Promise.race([stream.emitted("text"), stream.emitted("end")]);
+
+ // Stream end
+ if (result === undefined) {
+ yield {
+ token: {
+ id: tokenId++,
+ text: "",
+ logprob: 0,
+ special: true,
+ },
+ generated_text: await stream.finalText(),
+ details: null,
+ } satisfies TextGenerationStreamOutput;
+ return;
+ }
+
+ // Text delta
+ yield {
+ token: {
+ id: tokenId++,
+ text: result as unknown as string,
+ special: false,
+ logprob: 0,
+ },
+ generated_text: null,
+ details: null,
+ } satisfies TextGenerationStreamOutput;
+ }
+ })();
+ };
+}

src/lib/server/endpoints/endpoints.ts

@@ -6,6 +6,10 @@ import endpointAws, { endpointAwsParametersSchema } from "./aws/endpointAws";
 import { endpointOAIParametersSchema, endpointOai } from "./openai/endpointOai";
 import endpointLlamacpp, { endpointLlamacppParametersSchema } from "./llamacpp/endpointLlamacpp";
 import endpointOllama, { endpointOllamaParametersSchema } from "./ollama/endpointOllama";
+import {
+ endpointAnthropic,
+ endpointAnthropicParametersSchema,
+} from "./anthropic/endpointAnthropic";
 
 // parameters passed when generating text
 export interface EndpointParameters {
@@ -28,13 +32,15 @@ export type EndpointGenerator<T extends CommonEndpoint> = (parameters: T) => End
 // list of all endpoint generators
 export const endpoints = {
  tgi: endpointTgi,
+ anthropic: endpointAnthropic,
  aws: endpointAws,
  openai: endpointOai,
  llamacpp: endpointLlamacpp,
  ollama: endpointOllama,
 };
 
 export const endpointSchema = z.discriminatedUnion("type", [
+ endpointAnthropicParametersSchema,
  endpointAwsParametersSchema,
  endpointOAIParametersSchema,
  endpointTgiParametersSchema,

src/lib/server/models.ts

@@ -109,6 +109,8 @@ const addEndpoint = (m: Awaited<ReturnType<typeof processModel>>) => ({
  switch (args.type) {
  case "tgi":
  return endpoints.tgi(args);
+ case "anthropic":
+ return endpoints.anthropic(args);
  case "aws":
  return await endpoints.aws(args);
  case "openai":