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import { Ollama } from "ollama";
import OpenAI from "openai";
import serverConfig from "@hoarder/shared/config";
import logger from "@hoarder/shared/logger";
export interface InferenceResponse {
response: string;
totalTokens: number | undefined;
}
export interface InferenceClient {
inferFromText(prompt: string): Promise<InferenceResponse>;
inferFromImage(
prompt: string,
contentType: string,
image: string,
): Promise<InferenceResponse>;
}
export class InferenceClientFactory {
static build(): InferenceClient | null {
if (serverConfig.inference.openAIApiKey) {
return new OpenAIInferenceClient();
}
if (serverConfig.inference.ollamaBaseUrl) {
return new OllamaInferenceClient();
}
return null;
}
}
class OpenAIInferenceClient implements InferenceClient {
openAI: OpenAI;
constructor() {
this.openAI = new OpenAI({
apiKey: serverConfig.inference.openAIApiKey,
baseURL: serverConfig.inference.openAIBaseUrl,
});
}
async inferFromText(prompt: string): Promise<InferenceResponse> {
const chatCompletion = await this.openAI.chat.completions.create({
messages: [{ role: "system", content: prompt }],
model: serverConfig.inference.textModel,
response_format: { type: "json_object" },
});
const response = chatCompletion.choices[0].message.content;
if (!response) {
throw new Error(`Got no message content from OpenAI`);
}
return { response, totalTokens: chatCompletion.usage?.total_tokens };
}
async inferFromImage(
prompt: string,
contentType: string,
image: string,
): Promise<InferenceResponse> {
const chatCompletion = await this.openAI.chat.completions.create({
model: serverConfig.inference.imageModel,
messages: [
{
role: "user",
content: [
{ type: "text", text: prompt },
{
type: "image_url",
image_url: {
url: `data:${contentType};base64,${image}`,
detail: "low",
},
},
],
},
],
max_tokens: 2000,
});
const response = chatCompletion.choices[0].message.content;
if (!response) {
throw new Error(`Got no message content from OpenAI`);
}
return { response, totalTokens: chatCompletion.usage?.total_tokens };
}
}
class OllamaInferenceClient implements InferenceClient {
ollama: Ollama;
constructor() {
this.ollama = new Ollama({
host: serverConfig.inference.ollamaBaseUrl,
});
}
async runModel(model: string, prompt: string, image?: string) {
const chatCompletion = await this.ollama.chat({
model: model,
format: "json",
stream: true,
messages: [
{ role: "user", content: prompt, images: image ? [image] : undefined },
],
});
let totalTokens = 0;
let response = "";
try {
for await (const part of chatCompletion) {
response += part.message.content;
if (!isNaN(part.eval_count)) {
totalTokens += part.eval_count;
}
if (!isNaN(part.prompt_eval_count)) {
totalTokens += part.prompt_eval_count;
}
}
} catch (e) {
// There seem to be some bug in ollama where you can get some successfull response, but still throw an error.
// Using stream + accumulating the response so far is a workaround.
// https://github.com/ollama/ollama-js/issues/72
totalTokens = NaN;
logger.warn(`Got an exception from ollama, will still attempt to deserialize the response we got so far: ${e}`)
}
return { response, totalTokens };
}
async inferFromText(prompt: string): Promise<InferenceResponse> {
return await this.runModel(serverConfig.inference.textModel, prompt);
}
async inferFromImage(
prompt: string,
_contentType: string,
image: string,
): Promise<InferenceResponse> {
return await this.runModel(serverConfig.inference.imageModel, prompt, image);
}
}
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