Refactor: Simplify embedding model selection and docker compose commands

Co-authored-by: msaug <[email protected]>

Author: cursoragent

CLAUDE.md

@@ -52,12 +52,7 @@ uv run pytest -k "test_name"   # Run specific test
 
 ```bash
 docker compose up postgres backend  # Start main services
-
-# Recommended: run ingester so compose exits when done
-docker compose up --abort-on-container-exit --exit-code-from ingester ingester
-
-# Alternative: run Postgres in background, then run a one-shot ingester container
-docker compose up -d postgres && docker compose run --rm ingester
+docker compose up ingester         # Run documentation ingestion
 ```
 
 ## Architecture Overview

ingesters/src/generateEmbeddings.ts

@@ -85,24 +85,17 @@ async function setupVectorStore(): Promise<VectorStore> {
     // Get database configuration
     const dbConfig = getVectorDbConfig();
 
-    // Try Gemini first, then OpenAI as fallback
-    const geminiModels = await loadGeminiEmbeddingsModels();
-    const openaiModels = await loadOpenAIEmbeddingsModels();
-    const embeddingModel: Embeddings | undefined =
-      (geminiModels['Gemini embedding 001'] as unknown as Embeddings) ||
-      (openaiModels['Text embedding 3 large'] as unknown as Embeddings) ||
-      (openaiModels['Text embedding 3 small'] as unknown as Embeddings);
+    const embeddingModels = await loadGeminiEmbeddingsModels();
+    const embeddingModel = embeddingModels['Gemini embedding 001'];
 
     if (!embeddingModel) {
-      throw new Error(
-        'No embedding model configured. Set GEMINI_API_KEY or OPENAI_API_KEY.',
-      );
+      throw new Error('Text embedding 3 large model not found');
     }
 
     // Initialize vector store
     vectorStore = await VectorStore.getInstance(
       dbConfig,
-      embeddingModel,
+      embeddingModel as unknown as Embeddings,
     );
     logger.info('VectorStore initialized successfully');
     return vectorStore;