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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

Customers

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.

Quickstart Guide >

Docs

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

Docs

# 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
)