fix: Attempt to increase the reliability of the ollama inference

apps/workers/inference.ts

@@ -2,6 +2,7 @@ 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;
@@ -96,30 +97,48 @@ class OllamaInferenceClient implements InferenceClient {
     });
   }
 
-  async inferFromText(prompt: string): Promise<InferenceResponse> {
+  async runModel(model: string, prompt: string, image?: string) {
     const chatCompletion = await this.ollama.chat({
-      model: serverConfig.inference.textModel,
+      model: model,
       format: "json",
-      messages: [{ role: "system", content: prompt }],
+      stream: true,
+      messages: [
+        { role: "user", content: prompt, images: image ? [image] : undefined },
+      ],
     });
 
-    const response = chatCompletion.message.content;
+    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 };
+  }
 
-    return { response, totalTokens: chatCompletion.eval_count };
+  async inferFromText(prompt: string): Promise<InferenceResponse> {
+    return await this.runModel(serverConfig.inference.textModel, prompt);
   }
 
   async inferFromImage(
     prompt: string,
     _contentType: string,
     image: string,
   ): Promise<InferenceResponse> {
-    const chatCompletion = await this.ollama.chat({
-      model: serverConfig.inference.imageModel,
-      format: "json",
-      messages: [{ role: "user", content: prompt, images: [`${image}`] }],
-    });
-
-    const response = chatCompletion.message.content;
-    return { response, totalTokens: chatCompletion.eval_count };
+    return await this.runModel(serverConfig.inference.imageModel, prompt, image);
   }
 }

apps/workers/openaiWorker.ts

@@ -14,7 +14,7 @@ import {
   zOpenAIRequestSchema,
 } from "@hoarder/shared/queues";
 
-import { InferenceClientFactory, InferenceClient } from "./inference";
+import { InferenceClient, InferenceClientFactory } from "./inference";
 
 const openAIResponseSchema = z.object({
   tags: z.array(z.string()),
@@ -36,7 +36,7 @@ async function attemptMarkTaggingStatus(
       })
       .where(eq(bookmarks.id, request.bookmarkId));
   } catch (e) {
-    console.log(`Something went wrong when marking the tagging status: ${e}`);
+    logger.error(`Something went wrong when marking the tagging status: ${e}`);
   }
 }
 
@@ -196,8 +196,9 @@ async function inferTags(
 
     return tags;
   } catch (e) {
+    const responseSneak = response.response.substr(0, 20);
     throw new Error(
-      `[inference][${jobId}] Failed to parse JSON response from inference client: ${e}`,
+      `[inference][${jobId}] The model ignored our prompt and didn't respond with the expected JSON: ${JSON.stringify(e)}. Here's a sneak peak from the response: ${responseSneak}`,
     );
   }
 }
@@ -285,6 +286,10 @@ async function runOpenAI(job: Job<ZOpenAIRequest, void>) {
     );
   }
 
+  logger.info(
+    `[inference][${jobId}] Starting an inference job for bookmark with id "${bookmark.id}"`,
+  );
+
   const tags = await inferTags(jobId, bookmark, inferenceClient);
 
   await connectTags(bookmarkId, tags, bookmark.userId);

docs/docs/02-installation.md

@@ -55,7 +55,7 @@ Learn more about the costs of using openai [here](/openai).
 
     - Make sure ollama is running.
     - Set the `OLLAMA_BASE_URL` env variable to the address of the ollama API.
-    - Set `INFERENCE_TEXT_MODEL` to the model you want to use for text inference in ollama (for example: `llama2`)
+    - Set `INFERENCE_TEXT_MODEL` to the model you want to use for text inference in ollama (for example: `mistral`)
     - Set `INFERENCE_IMAGE_MODEL` to the model you want to use for image inference in ollama (for example: `llava`)
     - Make sure that you `ollama pull`-ed the models that you want to use.
 

packages/shared/queues.ts

@@ -1,5 +1,6 @@
 import { Queue } from "bullmq";
 import { z } from "zod";
+
 import serverConfig from "./config";
 
 export const queueConnectionDetails = {
@@ -27,6 +28,13 @@ export type ZOpenAIRequest = z.infer<typeof zOpenAIRequestSchema>;
 
 export const OpenAIQueue = new Queue<ZOpenAIRequest, void>("openai_queue", {
   connection: queueConnectionDetails,
+  defaultJobOptions: {
+    attempts: 3,
+    backoff: {
+      type: "exponential",
+      delay: 500,
+    },
+  },
 });
 
 // Search Indexing Worker