Build AI-Powered Search with Pinecone

Vector databases store embeddings—numerical representations of data (text, images, audio) that capture semantic meaning. Unlike keyword search, vector search finds conceptually similar items. Essential for RAG (AI chatbots with knowledge bases), semantic search, recommendations, and any AI application requiring similarity matching.

1) Create an index with appropriate dimension (e.g., 1536 for OpenAI ada-002). 2) Chunk your documents and generate embeddings using OpenAI/Cohere. 3) Upsert vectors with metadata. 4) On query, embed the question, search Pinecone for relevant chunks, pass to LLM with context. LangChain/LlamaIndex simplify this pattern.

OpenAI text-embedding-3-small (1536 dim) offers best cost/quality for most use cases. text-embedding-3-large for maximum accuracy. Cohere embed-v3 is competitive for multilingual. For on-prem, sentence-transformers models work well. Match dimension to your index configuration.

Use metadata filters to reduce search space. Create namespaces for logical data separation. Choose appropriate pod type for your workload (s1 for accuracy, p1/p2 for throughput). Batch upserts for large ingestion. Use sparse-dense hybrid search for keyword + semantic matching. Monitor query latency in console.

Pods: dedicated resources, consistent performance, pay for always-on capacity. Best for high-throughput, latency-sensitive workloads. Serverless: pay-per-query, auto-scaling from zero, lower cost for variable workloads. Best for development, low-traffic production, and cost-sensitive applications.

Chunk documents intelligently (e.g., 512 tokens with overlap) to preserve context. Batch upserts (100-1000 vectors per call). Use async ingestion for large datasets. Store chunk text in metadata for retrieval. Consider preprocessing pipelines with tools like Unstructured.io for PDFs

Yes, Pinecone supports real-time upserts and deletes with immediate queryability. Upsert latency is typically under 1 second. For high-volume streaming, batch updates to reduce API calls. Use namespaces for logical isolation if updates are frequent in specific domains.

Pinecone supports hybrid search with sparse-dense vectors. Generate dense embeddings for semantic meaning and sparse vectors for keyword matching (BM25). Query with both for combined results. Alternatively, filter by metadata keywords, then rank by vector similarity.

Namespaces partition vectors within an index. Use for: multi-tenancy (one namespace per customer), data versioning, A/B testing different embeddings, or logical separation (one per document type). Queries are scoped to a namespace, improving performance and isolation without multiple indexes.

Use Pinecone Console for index stats, query latency, and usage metrics. Enable request logging for debugging. Track embedding quality with relevance testing. Monitor upsert success rates. For production, integrate with your observability stack via API metrics or export logs.

Pinecone supports up to 20,000 dimensions per vector. However, most embeddings use 768-1536 dimensions. Higher dimensions increase storage and query costs without proportional accuracy gains. Match your index dimension to your embedding model's output.

pgvector is great for small-scale (millions of vectors) with existing PostgreSQL. Pinecone excels at scale (billions), offers managed infrastructure, and provides purpose-built performance. Choose pgvector for simplicity and SQL integration; Pinecone for production AI applications requiring dedicated vector infrastructure.