add async_add implementation and async upsert to Pinecone vector store

llama_index/indices/vector_store/base.py

@@ -145,7 +145,7 @@ async def _async_add_nodes_to_index(
             return
 
         nodes = await self._aget_node_with_embedding(nodes, show_progress)
-        new_ids = self._vector_store.add(nodes)
+        new_ids = await self._vector_store.async_add(nodes)
 
         # if the vector store doesn't store text, we need to add the nodes to the
         # index struct and document store

llama_index/vector_stores/pinecone.py

@@ -8,7 +8,8 @@
 from collections import Counter
 from functools import partial
 from typing import Any, Callable, Dict, List, Optional, cast
-
+import asyncio
+from llama_index.utils import iter_batch
 from llama_index.bridge.pydantic import PrivateAttr
 from llama_index.schema import BaseNode, MetadataMode, TextNode
 from llama_index.vector_stores.types import (
@@ -30,7 +31,7 @@
 SPARSE_VECTOR_KEY = "sparse_values"
 METADATA_KEY = "metadata"
 
-DEFAULT_BATCH_SIZE = 100
+DEFAULT_BATCH_SIZE = 200
 
 _logger = logging.getLogger(__name__)
 
@@ -170,7 +171,7 @@ def __init__(
 
         if tokenizer is None and add_sparse_vector:
             tokenizer = get_default_tokenizer()
-        self._tokenizer = tokenizer
+        self._tokenizer = tokenizer  # type: ignore
 
         super().__init__(
             index_name=index_name,
@@ -256,14 +257,31 @@ def add(
 
             ids.append(node_id)
             entries.append(entry)
-        self._pinecone_index.upsert(
-            entries,
-            namespace=self.namespace,
-            batch_size=self.batch_size,
-            **self.insert_kwargs,
-        )
+
+        [
+            self._pinecone_index.upsert(
+                vectors=batch,
+                async_req=True,
+            )
+            for batch in iter_batch(entries, self.batch_size)
+        ]
+
         return ids
 
+    async def async_add(
+        self,
+        nodes: List[BaseNode],
+    ) -> List[str]:
+        """Asynchronously add a list of embedding results to the collection.
+
+        Args:
+            nodes (List[BaseNode]): Embedding results to add.
+
+        Returns:
+            List[str]: List of IDs of the added documents.
+        """
+        return await asyncio.to_thread(self.add, nodes)  # type: ignore
+
     def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
         """
         Delete nodes using with ref_doc_id.

tests/indices/vector_store/utils.py

@@ -12,19 +12,19 @@
 class MockPineconeIndex:
     def __init__(self) -> None:
         """Mock pinecone index."""
-        self._tuples: List[Dict[str, Any]] = []
+        self._vectors: List[Dict[str, Any]] = []
 
-    def upsert(self, tuples: List[Dict[str, Any]], **kwargs: Any) -> None:
+    def upsert(self, vectors: List[Dict[str, Any]], **kwargs: Any) -> None:
         """Mock upsert."""
-        self._tuples.extend(tuples)
+        self._vectors.extend(vectors)
 
     def delete(self, ids: List[str]) -> None:
         """Mock delete."""
-        new_tuples = []
-        for tup in self._tuples:
-            if tup["id"] not in ids:
-                new_tuples.append(tup)
-        self._tuples = new_tuples
+        new_vectors = []
+        for vec in self._vectors:
+            if vec["id"] not in ids:
+                new_vectors.append(vec)
+        self._vectors = new_vectors
 
     def query(
         self,
@@ -38,7 +38,7 @@ def query(
     ) -> Any:
         """Mock query."""
         # index_mat is n x k
-        index_mat = np.array([tup["values"] for tup in self._tuples])
+        index_mat = np.array([tup["values"] for tup in self._vectors])
         query_vec = np.array(vector)[np.newaxis, :]
 
         # compute distances
@@ -49,10 +49,10 @@ def query(
 
         matches = []
         for index in indices:
-            tup = self._tuples[index]
+            vec = self._vectors[index]
             match = MagicMock()
-            match.metadata = tup["metadata"]
-            match.id = tup["id"]
+            match.metadata = vec["metadata"]
+            match.id = vec["id"]
             matches.append(match)
 
         response = MagicMock()