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Perform fast, approximate nearest neighbor search across billions of embeddings.
Organize vectors and filter queries using metadata and namespaces.
Handles billions of vectors with automatic sharding and replication.
Integrates with LLMs to retrieve relevant context for grounded generation.
Use Python, Node.js, or REST APIs to manage indexes and run queries.
Define your vector index with dimensions, metric type, and metadata schema.
Use models like OpenAI, Cohere, or Hugging Face to convert data into vectors.
Insert vectors into Pinecone with optional metadata and namespace tags.
Search for nearest neighbors using vector similarity and metadata filters.
Use retrieved context to enhance LLM responses in chatbots and agents.
import pinecone
import openai
pinecone.init(api_key="YOUR_API_KEY", environment="us-west1-gcp")
index = pinecone.Index("my-index")
query = "What is vector search?"
embedding = openai.Embedding.create(input=query, model="text-embedding-ada-002")["data"][0]["embedding"]
results = index.query(vector=embedding, top_k=5, include_metadata=True)
print(results)Retrieve relevant context for LLMs to generate grounded responses.
Search documents, FAQs, or transcripts using meaning-based similarity.
Suggest products, content, or users based on vector proximity.
Tailor experiences using user embeddings and behavioral vectors.
Identify anomalies by comparing transaction vectors to known patterns.
Explore Pinecone’s ecosystem and find the tools, platforms, and docs to accelerate your workflow.
Common questions about Pinecone’s capabilities, usage, and ecosystem.