diff --git a/paperqa/docs.py b/paperqa/docs.py
index 06203ff0f..f20c936f5 100644
--- a/paperqa/docs.py
+++ b/paperqa/docs.py
@@ -403,36 +403,47 @@ def add_texts(
         loop = get_loop()
         return loop.run_until_complete(self.aadd_texts(texts, doc))
 
-    async def aadd_texts(
-        self,
-        texts: list[Text],
-        doc: Doc,
-    ) -> bool:
-        """Add chunked texts to the collection. This is useful if you have already chunked the texts yourself.
+    async def aadd_texts(self, texts: list[Text], doc: Doc) -> bool:
+        """
+        Add chunked texts to the collection.
 
-        Returns True if the document was added, False if it was already in the collection.
+        NOTE: this is useful if you have already chunked the texts yourself.
+
+        Returns:
+            True if the doc was added, otherwise False if already in the collection.
         """
         if doc.dockey in self.docs:
             return False
-        if len(texts) == 0:
+        if not texts:
             raise ValueError("No texts to add.")
-        if doc.docname in self.docnames:
-            new_docname = self._get_unique_name(doc.docname)
-            for t in texts:
-                t.name = t.name.replace(doc.docname, new_docname)
-            doc.docname = new_docname
-        if texts[0].embedding is None:
-            text_embeddings = await self.texts_index.embedding_model.embed_documents(
-                self._embedding_client, [t.text for t in texts]
+        # 1. Calculate text embeddings if not already present, but don't set them into
+        # the texts until we've set up the Doc's embedding, so callers can retry upon
+        # OpenAI rate limit errors
+        text_embeddings: list[list[float]] | None = (
+            await self.texts_index.embedding_model.embed_documents(
+                self._embedding_client, texts=[t.text for t in texts]
             )
-            for i, t in enumerate(texts):
-                t.embedding = text_embeddings[i]
+            if texts[0].embedding is None
+            else None
+        )
+        # 2. Set the Doc's embedding to be the Doc's citation embedded
         if doc.embedding is None:
             doc.embedding = (
                 await self.docs_index.embedding_model.embed_documents(
-                    self._embedding_client, [doc.citation]
+                    self._embedding_client, texts=[doc.citation]
                 )
             )[0]
+        # 3. Now we can set the text embeddings
+        if text_embeddings is not None:
+            for t, t_embedding in zip(texts, text_embeddings, strict=True):
+                t.embedding = t_embedding
+        # 4. Update texts and the Doc's name
+        if doc.docname in self.docnames:
+            new_docname = self._get_unique_name(doc.docname)
+            for t in texts:
+                t.name = t.name.replace(doc.docname, new_docname)
+            doc.docname = new_docname
+        # 5. Index remaining updates
         if not self.jit_texts_index:
             self.texts_index.add_texts_and_embeddings(texts)
         self.docs_index.add_texts_and_embeddings([doc])