fix: parse query params from base_url in OpenAI embeddings client (#735)

* fix: parse query params from base_url in OpenAI embeddings client

The OpenAI-compatible LLM provider already parses query parameters
(e.g. ?api-version=xxx for Azure OpenAI) from the base_url and passes
them as default_query to the OpenAI client. However, the OpenAI
embeddings provider did not do this, causing Azure OpenAI embeddings
to fail with 404 errors at runtime.

This applies the same URL parsing logic from the LLM provider to the
embeddings provider, enabling Azure OpenAI embeddings to work correctly.

* ci: add workflow to build fork Docker image

* ci: add slim image build (no local models)

* ci: remove fork build workflow per review request

---------

Co-authored-by: Antoine Khater <ak@ptgroup.eu>

hindsight-api-slim/hindsight_api/engine/embeddings.py

@@ -13,6 +13,7 @@ import logging
 import os
 import warnings
 from abc import ABC, abstractmethod
+from urllib.parse import parse_qs, urlparse, urlunparse
 
 import httpx
 
@@ -426,9 +427,19 @@ class OpenAIEmbeddings(Embeddings):
         logger.info(f"Embeddings: initializing OpenAI provider with model {self.model}{base_url_msg}")
 
         # Build client kwargs, only including base_url if set (for Azure or custom endpoints)
+        # Parse query parameters from base_url (e.g. ?api-version=xxx for Azure OpenAI)
+        # and pass them as default_query so they're included in every request.
         client_kwargs = {"api_key": self.api_key, "max_retries": self.max_retries}
         if self.base_url:
-            client_kwargs["base_url"] = self.base_url
+            parsed = urlparse(self.base_url)
+            if parsed.query:
+                clean_url = urlunparse(parsed._replace(query=""))
+                client_kwargs["base_url"] = clean_url
+                default_query = {k: v[0] for k, v in parse_qs(parsed.query).items()}
+                client_kwargs["default_query"] = default_query
+                self.base_url = clean_url
+            else:
+                client_kwargs["base_url"] = self.base_url
         self._client = OpenAI(**client_kwargs)
 
         # Try to get dimension from known models, otherwise do a test embedding