From 8e4565dacb8e0b843c01abd766cddb2f67ebc8cc Mon Sep 17 00:00:00 2001
From: Martin Mose Facondini <me@martinmose.com>
Date: Thu, 23 Apr 2026 23:48:41 +0200
Subject: [PATCH] feat: add OPENAI_BASE_URL and OPENAI_EMBEDDING_MODEL env var
 support

Allow using OpenAI-compatible APIs (e.g. LiteLLM) for embeddings by reading
OPENAI_BASE_URL (defaults to https://api.openai.com/v1) and
OPENAI_EMBEDDING_MODEL (defaults to text-embedding-3-small) from environment
variables instead of hardcoding them.

This enables self-hosted deployments to use any OpenAI-compatible embeddings
provider.
---
 convex/api.ts        | 5 +++--
 convex/embeddings.ts | 7 ++++---
 convex/search.ts     | 7 ++++---
 3 files changed, 11 insertions(+), 8 deletions(-)

diff --git a/convex/api.ts b/convex/api.ts
index 458b9eb..cb5ff59 100644
--- a/convex/api.ts
+++ b/convex/api.ts
@@ -504,14 +504,15 @@ export const semanticSearch = internalAction({
     const apiKey = process.env.OPENAI_API_KEY;
     if (!apiKey) throw new Error("OPENAI_API_KEY not set");
 
-    const response = await fetch("https://api.openai.com/v1/embeddings", {
+    const baseUrl = process.env.OPENAI_BASE_URL || "https://api.openai.com/v1";
+    const response = await fetch(`${baseUrl}/embeddings`, {
       method: "POST",
       headers: {
         Authorization: `Bearer ${apiKey}`,
         "Content-Type": "application/json",
       },
       body: JSON.stringify({
-        model: "text-embedding-3-small",
+        model: process.env.OPENAI_EMBEDDING_MODEL || "text-embedding-3-small",
         input: query.slice(0, 8000),
       }),
     });
diff --git a/convex/embeddings.ts b/convex/embeddings.ts
index d512f6f..d042279 100644
--- a/convex/embeddings.ts
+++ b/convex/embeddings.ts
@@ -14,19 +14,20 @@ function hashText(text: string): string {
   return hash.toString(16);
 }
 
-// Generate embedding via OpenAI
+// Generate embedding via OpenAI-compatible API
 async function embed(text: string): Promise<number[]> {
   const apiKey = process.env.OPENAI_API_KEY;
   if (!apiKey) throw new Error("OPENAI_API_KEY not set");
 
-  const response = await fetch("https://api.openai.com/v1/embeddings", {
+  const baseUrl = process.env.OPENAI_BASE_URL || "https://api.openai.com/v1";
+  const response = await fetch(`${baseUrl}/embeddings`, {
     method: "POST",
     headers: {
       Authorization: `Bearer ${apiKey}`,
       "Content-Type": "application/json",
     },
     body: JSON.stringify({
-      model: "text-embedding-3-small",
+      model: process.env.OPENAI_EMBEDDING_MODEL || "text-embedding-3-small",
       input: text.slice(0, 8000),
     }),
   });
diff --git a/convex/search.ts b/convex/search.ts
index 7fec5bc..e806d93 100644
--- a/convex/search.ts
+++ b/convex/search.ts
@@ -571,19 +571,20 @@ export const loadSessionsFromEmbeddings = internalQuery({
   },
 });
 
-// Helper: generate embedding via OpenAI
+// Helper: generate embedding via OpenAI-compatible API
 async function generateEmbedding(text: string): Promise<number[]> {
   const apiKey = process.env.OPENAI_API_KEY;
   if (!apiKey) throw new Error("OPENAI_API_KEY not set");
 
-  const response = await fetch("https://api.openai.com/v1/embeddings", {
+  const baseUrl = process.env.OPENAI_BASE_URL || "https://api.openai.com/v1";
+  const response = await fetch(`${baseUrl}/embeddings`, {
     method: "POST",
     headers: {
       Authorization: `Bearer ${apiKey}`,
       "Content-Type": "application/json",
     },
     body: JSON.stringify({
-      model: "text-embedding-3-small",
+      model: process.env.OPENAI_EMBEDDING_MODEL || "text-embedding-3-small",
       input: text.slice(0, 8000),
     }),
   });