update async_add method to follow pinecone-client example

llama_index/vector_stores/pinecone.py

@@ -33,6 +33,8 @@
 
 DEFAULT_BATCH_SIZE = 200
 
+SEM_MAX_CONCURRENT = 10
+
 _logger = logging.getLogger(__name__)
 
 
@@ -98,6 +100,14 @@ def _to_pinecone_filter(standard_filters: MetadataFilters) -> dict:
     return filters
 
 
+async def async_upload(index, vectors, batch_size, semaphore):
+    async def send_batch(batch):
+        async with semaphore:
+            return await asyncio.to_thread(index.upsert, batch, async_req=True)
+
+    await asyncio.gather(*[send_batch(chunk) for chunk in iter_batch(vectors, size=batch_size)])
+
+
 import_err_msg = (
     "`pinecone` package not found, please run `pip install pinecone-client`"
 )
@@ -224,39 +234,42 @@ def from_params(
     def class_name(cls) -> str:
         return "PinconeVectorStore"
 
-    def add(
-        self,
-        nodes: List[BaseNode],
-    ) -> List[str]:
-        """Add nodes to index.
-
-        Args:
-            nodes: List[BaseNode]: list of nodes with embeddings
-
-        """
-        ids = []
+    def _prepare_entries_for_upsert(self, nodes: List[BaseNode]) -> List[Dict]:
         entries = []
         for node in nodes:
-            node_id = node.node_id
-
             metadata = node_to_metadata_dict(
                 node, remove_text=False, flat_metadata=self.flat_metadata
             )
 
             entry = {
-                ID_KEY: node_id,
+                ID_KEY: node.node_id,
                 VECTOR_KEY: node.get_embedding(),
                 METADATA_KEY: metadata,
             }
-            if self.add_sparse_vector and self._tokenizer is not None:
+
+            if self.add_sparse_vector:
                 sparse_vector = generate_sparse_vectors(
                     [node.get_content(metadata_mode=MetadataMode.EMBED)],
                     self._tokenizer,
                 )[0]
                 entry[SPARSE_VECTOR_KEY] = sparse_vector
 
-            ids.append(node_id)
             entries.append(entry)
+
+        return entries
+
+    def add(
+        self,
+        nodes: List[BaseNode],
+    ) -> List[str]:
+        """Add nodes to index.
+
+        Args:
+            nodes: List[BaseNode]: list of nodes with embeddings
+
+        """
+
+        entries = self._prepare_entries_for_upsert(nodes)
 
         [
             self._pinecone_index.upsert(
@@ -266,7 +279,7 @@ def add(
             for batch in iter_batch(entries, self.batch_size)
         ]
 
-        return ids
+        return [entry[ID_KEY] for entry in entries]
 
     async def async_add(
         self,
@@ -280,7 +293,14 @@ async def async_add(
         Returns:
             List[str]: List of IDs of the added documents.
         """
-        return await asyncio.to_thread(self.add, nodes)  # type: ignore
+
+        entries = self._prepare_entries_for_upsert(nodes)
+
+        semaphore = asyncio.Semaphore(SEM_MAX_CONCURRENT)
+        await async_upload(self._pinecone_index, entries, DEFAULT_BATCH_SIZE, semaphore)
+
+        return [entry[ID_KEY] for entry in entries]
+
 
     def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
         """