> ## Documentation Index > Fetch the complete documentation index at: https://docs.langchain.com/llms.txt > Use this file to discover all available pages before exploring further. # Pinecone (Sparse) integration > Integrate with the Pinecone (Sparse) vector store using LangChain Python. > [Pinecone](https://docs.pinecone.io/docs/overview) is a vector database with broad functionality. This notebook shows how to use functionality related to the `Pinecone` vector database. ## Setup To use the `PineconeSparseVectorStore` you first need to install the partner package, as well as the other packages used throughout this notebook. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -qU "langchain-pinecone==0.2.5" ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} WARNING: pinecone 6.0.2 does not provide the extra 'async' ``` ### Credentials Create a new Pinecone account, or sign into your existing one, and create an API key to use in this notebook. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import os from getpass import getpass from pinecone import Pinecone # get API key at app.pinecone.io os.environ["PINECONE_API_KEY"] = os.getenv("PINECONE_API_KEY") or getpass( "Enter your Pinecone API key: " ) # initialize client pc = Pinecone() ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} Enter your Pinecone API key: ·········· ``` ## Initialization Before initializing our vector store, let's connect to a Pinecone index. If one named index\_name doesn't exist, it will be created. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from pinecone import AwsRegion, CloudProvider, Metric, ServerlessSpec index_name = "langchain-sparse-vector-search" # change if desired model_name = "pinecone-sparse-english-v0" if not pc.has_index(index_name): pc.create_index_for_model( name=index_name, cloud=CloudProvider.AWS, region=AwsRegion.US_EAST_1, embed={ "model": model_name, "field_map": {"text": "chunk_text"}, "metric": Metric.DOTPRODUCT, }, ) index = pc.Index(index_name) print(f"Index `{index_name}` host: {index.config.host}") ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} Index `langchain-sparse-vector-search` host: https://langchain-sparse-vector-search-yrrgefy.svc.aped-4627-b74a.pinecone.io ``` For our sparse embedding model we use [`pinecone-sparse-english-v0`](https://docs.pinecone.io/models/pinecone-sparse-english-v0), we initialize it like so: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_pinecone.embeddings import PineconeSparseEmbeddings sparse_embeddings = PineconeSparseEmbeddings(model=model_name) ``` Now that our Pinecone index and embedding model are both ready, we can initialize our sparse vector store in LangChain: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_pinecone import PineconeSparseVectorStore vector_store = PineconeSparseVectorStore(index=index, embedding=sparse_embeddings) ``` ## Manage vector store Once you have created your vector store, we can interact with it by adding and deleting different items. ### Add items to vector store We can add items to our vector store by using the `add_documents` function. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from uuid import uuid4 from langchain_core.documents import Document documents = [ Document( page_content="I had chocolate chip pancakes and scrambled eggs for breakfast this morning.", metadata={"source": "social"}, ), Document( page_content="The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees.", metadata={"source": "news"}, ), Document( page_content="Building an exciting new project with LangChain - come check it out!", metadata={"source": "social"}, ), Document( page_content="Robbers broke into the city bank and stole $1 million in cash.", metadata={"source": "news"}, ), Document( page_content="Wow! That was an amazing movie. I can't wait to see it again.", metadata={"source": "social"}, ), Document( page_content="Is the new iPhone worth the price? Read this review to find out.", metadata={"source": "website"}, ), Document( page_content="The top 10 soccer players in the world right now.", metadata={"source": "website"}, ), Document( page_content="LangGraph is the best framework for building stateful, agentic applications!", metadata={"source": "social"}, ), Document( page_content="The stock market is down 500 points today due to fears of a recession.", metadata={"source": "news"}, ), Document( page_content="I have a bad feeling I am going to get deleted :(", metadata={"source": "social"}, ), ] uuids = [str(uuid4()) for _ in range(len(documents))] vector_store.add_documents(documents=documents, ids=uuids) ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} ['95b598af-c3dc-4a8a-bdb7-5d21283e5a86', '838614a5-5635-4efd-9ac3-5237a37a542b', '093fd11f-c85b-4c83-83f0-117df64ff442', 'fb3ba32f-f802-410a-ad79-56f7bce938fe', '75cde9bf-7e91-4f06-8bae-c824dab16a08', '9de8f769-d604-4e56-b677-ee333cbc8e34', 'f5f4ae97-88e6-4669-bcf7-87072bb08550', 'f9f82811-187c-4b25-85b5-7a42b4da3bff', 'ce45957c-e8fc-41ef-819b-1bd52b6fc815', '66cacc6f-b8e2-441b-9f7f-468788aad88f'] ``` ### Delete items from vector store We can delete records from our vector store using the `delete` method, providing it with a list of document IDs to delete. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} vector_store.delete(ids=[uuids[-1]]) ``` ## Query vector store Once we have loaded our documents into the vector store we're most likely ready to begin querying. There are various method for doing this in LangChain. First, we'll see how to perform a simple vector search by querying our `vector_store` directly via the `similarity_search` method: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} results = vector_store.similarity_search("I'm building a new LangChain project!", k=3) for res in results: print(f"* {res.page_content} [{res.metadata}]") ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} * Building an exciting new project with LangChain - come check it out! [{'source': 'social'}] * Building an exciting new project with LangChain - come check it out! [{'source': 'social'}] * LangGraph is the best framework for building stateful, agentic applications! [{'source': 'social'}] ``` We can also add [metadata filtering](https://docs.pinecone.io/guides/data/understanding-metadata#metadata-query-language) to our query to limit our search based on various criteria. Let's try a simple filter to limit our search to include only records with `source=="social"`: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} results = vector_store.similarity_search( "I'm building a new LangChain project!", k=3, filter={"source": "social"}, ) for res in results: print(f"* {res.page_content} [{res.metadata}]") ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} * Building an exciting new project with LangChain - come check it out! [{'source': 'social'}] * Building an exciting new project with LangChain - come check it out! [{'source': 'social'}] * LangGraph is the best framework for building stateful, agentic applications! [{'source': 'social'}] ``` When comparing these results, we can see that our first query returned a different record from the `"website"` source. In our latter, filtered, query—this is no longer the case. ### Similarity search and scores We can also search while returning the similarity score in a list of `(document, score)` tuples. Where the `document` is a LangChain [`Document`](https://reference.langchain.com/python/langchain-core/documents/base/Document) object containing our text content and metadata. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} results = vector_store.similarity_search_with_score( "I'm building a new LangChain project!", k=3, filter={"source": "social"} ) for doc, score in results: print(f"[SIM={score:3f}] {doc.page_content} [{doc.metadata}]") ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} [SIM=12.959961] Building an exciting new project with LangChain - come check it out! [{'source': 'social'}] [SIM=12.959961] Building an exciting new project with LangChain - come check it out! [{'source': 'social'}] [SIM=1.942383] LangGraph is the best framework for building stateful, agentic applications! [{'source': 'social'}] ``` ### As a retriever In our chains and agents we'll often use the vector store as a `VectorStoreRetriever`. To create that, we use the `as_retriever` method: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} retriever = vector_store.as_retriever( search_type="similarity_score_threshold", search_kwargs={"k": 3, "score_threshold": 0.5}, ) retriever ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} VectorStoreRetriever(tags=['PineconeSparseVectorStore', 'PineconeSparseEmbeddings'], vectorstore=, search_type='similarity_score_threshold', search_kwargs={'k': 3, 'score_threshold': 0.5}) ``` We can now query our retriever using the `invoke` method: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} retriever.invoke( input="I'm building a new LangChain project!", filter={"source": "social"} ) ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} /usr/local/lib/python3.11/dist-packages/langchain_core/vectorstores/base.py:1082: UserWarning: Relevance scores must be between 0 and 1, got [(Document(id='093fd11f-c85b-4c83-83f0-117df64ff442', metadata={'source': 'social'}, page_content='Building an exciting new project with LangChain - come check it out!'), 6.97998045), (Document(id='54f8f645-9f77-4aab-b9fa-709fd91ae3b3', metadata={'source': 'social'}, page_content='Building an exciting new project with LangChain - come check it out!'), 6.97998045), (Document(id='f9f82811-187c-4b25-85b5-7a42b4da3bff', metadata={'source': 'social'}, page_content='LangGraph is the best framework for building stateful, agentic applications!'), 1.471191405)] self.vectorstore.similarity_search_with_relevance_scores( ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} [Document(id='093fd11f-c85b-4c83-83f0-117df64ff442', metadata={'source': 'social'}, page_content='Building an exciting new project with LangChain - come check it out!'), Document(id='54f8f645-9f77-4aab-b9fa-709fd91ae3b3', metadata={'source': 'social'}, page_content='Building an exciting new project with LangChain - come check it out!'), Document(id='f9f82811-187c-4b25-85b5-7a42b4da3bff', metadata={'source': 'social'}, page_content='LangGraph is the best framework for building stateful, agentic applications!')] ``` ## Usage for retrieval-augmented generation For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections: * [Retrieval docs](/oss/python/langchain/retrieval) * [Build a RAG app with LangChain](/oss/python/langchain/rag) * [Agentic RAG](/oss/python/langgraph/agentic-rag) *** ## API reference For detailed documentation of all features and configurations head to the API reference: * [PineconeSparseVectorStore](https://reference.langchain.com/python/langchain-pinecone/vectorstores_sparse/PineconeSparseVectorStore) * [Sparse Embeddings](https://reference.langchain.com/python/langchain-pinecone/embeddings/PineconeSparseEmbeddings) ***
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