Build knowledgeable AI
Pinecone serverless lets you deliver remarkable GenAI applications faster.
Pinecone is the leading vector database that helps power AI for the world's best companies
At Cisco, we’re not only integrating generative AI capabilities throughout products for our customers, we’re also enabling our employees with the most cutting-edge technologies like Pinecone. By leveraging Pinecone’s industry-leading vector database, our enterprise platform team built an AI assistant that accurately and securely searches through millions of our documents to support our multiple orgs across Cisco.
Sujith Joseph
Principal Engineer, Enterprise AI & Search at Cisco
Start and scale seamlessly
Create an account and your first index in 30 seconds, then upload a few vector embeddings from any model… or a few billion.
Perform low-latency vector search to retrieve relevant data for search, RAG, recommendation, detection, and other applications.
Pinecone is serverless so you never have to worry about managing or scaling the database.
from pinecone import Pinecone, ServerlessSpec
# Create a serverless index
# "dimension" needs to match the dimensions of the vectors you upsert
pc = Pinecone(api_key="YOUR_API_KEY")
pc.create_index(name="products", dimension=1536,
spec=ServerlessSpec(cloud='aws', region='us-east-1')
)
# Target the index
index = pc.Index("products")
# Mock vector and metadata objects (you would bring your own)
vector = [0.010, 2.34,...] # len(vector) = 1536
metadata = {"id": 3056, "description": "Networked neural adapter"}
# Upsert your vector(s)
index.upsert(
vectors=[
{"id": "some_id", "values": vector, "metadata": metadata}
]
)
Search and scale seamlessly
Perform low-latency vector search to retrieve relevant data for search, RAG, recommendation, detection, and other applications.
Quickstart Guide# Mock vectorized search query (vectorize with LLM of choice)
query = [0.13, 0.45, 1.34, ...] # len(query) = 1536, same as the indexed vectors
# Send query with (optional) filter to index and get back 1 result (top_k=1)
index.query(
vector=query,
filter={"description": {"$eq": "Networked neural adapter"}},
top_k=1
)
More relevant results make better applications
Filter by
metadata
Combine vector search with familiar metadata filters to get just the results you want.
Find
context
Fast and accurate vector search over all your data.
Update in
real time
As your data changes, the Pinecone index is updated in realtime to provide the freshest results.
Make (the right)
keywords matter
Combine vector search with keyword boosting for the best of both worlds (hybrid search).
30k+
organizations
96%
recall*
51ms
query latency (p95)*
Part of the developer-favorite AI stack
Use Pinecone with your favorite cloud provider, data sources, models, frameworks, and more.
Data source
Embedding model
Pinecone Vector Database
Search application
Join the movement
Join a growing community of 400,000+ ambitious developers building the next generation of applications with Pinecone.
Events
Learn and connect with your peers, in person and online.
Secure and Enterprise-ready
Meet security and operational requirements to bring AI products to market faster.
Secure
Control your data and know it’s safe. Pinecone is SOC 2 and HIPAA certified.
Reliable
Powering mission-critical applications of all sizes, with support SLAs and observability.
Cloud-native
Fully managed in the cloud of your choice. Also available via marketplaces: AWS, Azure, GCP.
Start building knowledgeable AI now
Create your first index for free, then upgrade and pay as you go when you're ready to scale, or talk to sales.