diff --git a/docs/docs/integrations/providers/pinecone.mdx b/docs/docs/integrations/providers/pinecone.mdx
index e6cf0b87e2c..4275c20d2bc 100644
--- a/docs/docs/integrations/providers/pinecone.mdx
+++ b/docs/docs/integrations/providers/pinecone.mdx
@@ -29,11 +29,44 @@ For a more detailed walkthrough of the Pinecone vectorstore, see [this notebook]
 
 ### Sparse Vector store
 
+LangChain's `PineconeSparseVectorStore` enables sparse retrieval using Pinecone's sparse English model. It maps text to sparse vectors and supports adding documents and similarity search.
+
 ```python
 from langchain_pinecone import PineconeSparseVectorStore
+
+# Initialize sparse vector store
+vector_store = PineconeSparseVectorStore(
+    index=my_index,
+    embedding_model="pinecone-sparse-english-v0"
+)
+# Add documents
+vector_store.add_documents(documents)
+# Query
+results = vector_store.similarity_search("your query", k=3)
 ```
 
-For a more detailed walkthrough of the Pinecone vectorstore, see [this notebook](/docs/integrations/vectorstores/pinecone_sparse)
+For a more detailed walkthrough, see the [Pinecone Sparse Vector Store notebook](/docs/integrations/vectorstores/pinecone_sparse).
+
+### Sparse Embedding
+
+LangChain's `PineconeSparseEmbeddings` provides sparse embedding generation using Pinecone's `pinecone-sparse-english-v0` model.
+
+```python
+from langchain_pinecone.embeddings import PineconeSparseEmbeddings
+
+# Initialize sparse embeddings
+sparse_embeddings = PineconeSparseEmbeddings(
+    model="pinecone-sparse-english-v0"
+)
+# Embed a single query (returns SparseValues)
+query_embedding = sparse_embeddings.embed_query("sample text")
+
+# Embed multiple documents (returns list of SparseValues)
+docs = ["Document 1 content", "Document 2 content"]
+doc_embeddings = sparse_embeddings.embed_documents(docs)
+```
+
+For more detailed usage, see the [Pinecone Sparse Embeddings notebook](/docs/integrations/vectorstores/pinecone_sparse).
 
 
 ## Retrievers