---
title: Pinecone Vector Store - Hybrid Search
 | LlamaIndex OSS Documentation
---

If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

```
%pip install llama-index-vector-stores-pinecone "transformers[torch]"
```

#### Creating a Pinecone Index

```
from pinecone import Pinecone, ServerlessSpec
```

```
import os


os.environ["PINECONE_API_KEY"] = "..."
os.environ["OPENAI_API_KEY"] = "sk-..."


api_key = os.environ["PINECONE_API_KEY"]


pc = Pinecone(api_key=api_key)
```

```
# delete if needed
pc.delete_index("quickstart")
```

```
# dimensions are for text-embedding-ada-002
# NOTE: needs dotproduct for hybrid search


pc.create_index(
    name="quickstart",
    dimension=1536,
    metric="dotproduct",
    spec=ServerlessSpec(cloud="aws", region="us-east-1"),
)


# If you need to create a PodBased Pinecone index, you could alternatively do this:
#
# from pinecone import Pinecone, PodSpec
#
# pc = Pinecone(api_key='xxx')
#
# pc.create_index(
#    name='my-index',
#    dimension=1536,
#    metric='cosine',
#    spec=PodSpec(
#      environment='us-east1-gcp',
#      pod_type='p1.x1',
#      pods=1
#    )
# )
#
```

```
pinecone_index = pc.Index("quickstart")
```

Download Data

```
!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'
```

#### Load documents, build the PineconeVectorStore

When `add_sparse_vector=True`, the `PineconeVectorStore` will compute sparse vectors for each document.

By default, it is using simple token frequency for the sparse vectors. But, you can also specify a custom sparse embedding model.

```
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.pinecone import PineconeVectorStore
from IPython.display import Markdown, display
```

```
# load documents
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
```

```
# set add_sparse_vector=True to compute sparse vectors during upsert
from llama_index.core import StorageContext


if "OPENAI_API_KEY" not in os.environ:
    raise EnvironmentError(f"Environment variable OPENAI_API_KEY is not set")


vector_store = PineconeVectorStore(
    pinecone_index=pinecone_index,
    add_sparse_vector=True,
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)
```

```
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
  - Avoid using `tokenizers` before the fork if possible
  - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)






Upserted vectors:   0%|          | 0/22 [00:00<?, ?it/s]
```

#### Query Index

May need to wait a minute or two for the index to be ready

```
# set Logging to DEBUG for more detailed outputs
query_engine = index.as_query_engine(vector_store_query_mode="hybrid")
response = query_engine.query("What happened at Viaweb?")
```

```
display(Markdown(f"<b>{response}</b>"))
```

**Paul Graham started Viaweb because he needed money. As the company grew, he realized he didn’t want to run a big company and decided to build a subset of the vision as an open source project. Eventually, Viaweb was bought by Yahoo in the summer of 1998, which was a huge relief for Paul Graham.**

## Changing the sparse embedding model

```
%pip install llama-index-sparse-embeddings-fastembed
```

```
# Clear the vector store
vector_store.clear()
```

```
from llama_index.sparse_embeddings.fastembed import FastEmbedSparseEmbedding


sparse_embedding_model = FastEmbedSparseEmbedding(
    model_name="prithivida/Splade_PP_en_v1"
)


vector_store = PineconeVectorStore(
    pinecone_index=pinecone_index,
    add_sparse_vector=True,
    sparse_embedding_model=sparse_embedding_model,
)
```

```
Fetching 5 files:   0%|          | 0/5 [00:00<?, ?it/s]
```

```
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)
```

```
Upserted vectors:   0%|          | 0/22 [00:00<?, ?it/s]
```

Wait a mininute for things to upload..

```
response = query_engine.query("What happened at Viaweb?")
display(Markdown(f"<b>{response}</b>"))
```

**Paul Graham started Viaweb because he needed money. He recruited a team to work on building software and services, with a focus on creating an application builder and network infrastructure. However, halfway through the summer, Paul realized he didn’t want to run a big company and decided to shift his focus to building a subset of the project as an open source project. This led to the development of a new dialect of Lisp called Arc. Ultimately, Viaweb was sold to Yahoo in the summer of 1998, providing relief to Paul Graham and allowing him to transition to a new phase in his life.**