Update Pinecone Upsert method usage (langchain-ai#7358)

Description: Refactor the upsert method in the Pinecone class to allow
for additional keyword arguments. This change adds flexibility and
extensibility to the method, allowing for future modifications or
enhancements. The upsert method now accepts the `**kwargs` parameter,
which can be used to pass any additional arguments to the Pinecone
index. This change has been made in both the `upsert` method in the
`Pinecone` class and the `upsert` method in the
`similarity_search_with_score` class method. Falls in line with the
usage of the upsert method in
[Pinecone-Python-Client](https://github.com/pinecone-io/pinecone-python-client/blob/4640c4cf27b1ab935e76929c46d230dea9fe5506/pinecone/index.py#L73)
Issue: [This feature request in Pinecone
Repo](pinecone-io/pinecone-python-client#184)

Maintainer responsibilities:
  - General / Misc / if you don't know who to tag: @baskaryan
  - Memory: @hwchase17

---------

Co-authored-by: kwesi <[email protected]>
Co-authored-by: Bagatur <[email protected]>
Co-authored-by: Lance Martin <[email protected]>

langchain/vectorstores/pinecone.py

@@ -94,7 +94,9 @@ def add_texts(
             metadata[self._text_key] = text
             docs.append((ids[i], embedding, metadata))
         # upsert to Pinecone
-        self._index.upsert(vectors=docs, namespace=namespace, batch_size=batch_size)
+        self._index.upsert(
+            vectors=docs, namespace=namespace, batch_size=batch_size, **kwargs
+        )
         return ids
 
     def similarity_search_with_score(
@@ -274,6 +276,7 @@ def from_texts(
         text_key: str = "text",
         index_name: Optional[str] = None,
         namespace: Optional[str] = None,
+        upsert_kwargs: Optional[dict] = None,
         **kwargs: Any,
     ) -> Pinecone:
         """Construct Pinecone wrapper from raw documents.
@@ -346,8 +349,9 @@ def from_texts(
             to_upsert = zip(ids_batch, embeds, metadata)
 
             # upsert to Pinecone
-            index.upsert(vectors=list(to_upsert), namespace=namespace)
-        return cls(index, embedding.embed_query, text_key, namespace)
+            _upsert_kwargs = upsert_kwargs or {}
+            index.upsert(vectors=list(to_upsert), namespace=namespace, **_upsert_kwargs)
+        return cls(index, embedding.embed_query, text_key, namespace, **kwargs)
 
     @classmethod
     def from_existing_index(