#embeddings

NVIDIA's Nemotron 3.5 Embed, Rerank and Parse search PDFs as page images. Which document failures each layer fixes, what the cards claim, when text is enough.

Evaluate multilingual AI search with matched questions and documents. Test cross-language retrieval, names and mixed-language queries before launch.

Diagnose weak AI search before rebuilding an index. Separate missing candidates from poor ranking, test a reranker and plan a controlled model migration.

Cohere Embed 5 comes in Pro and Fast versions with 128K context. How the two compare on price, speed and use cases, and when to switch embedding models.

A practical playbook for chunking documents in RAG pipelines, comparing fixed, semantic, recursive, and late-chunking with retrieval-quality benchmarks.

Build a self-hosted retrieval-augmented chatbot: pgvector schema, embedding pipeline, semantic search, and a streaming Next.js answer endpoint inside.

Definitions for embeddings, ANN indexes, distance metrics, hybrid search, and reranking, with source references and a worked retrieval evaluation example.

Pinecone, Qdrant, Weaviate, Milvus, Chroma, pgvector, Vertex Vector, Vespa compared on latency, cost, hybrid search, metadata filtering, managed-service.

Developer guide to Perplexity's Agent API platform. Four APIs for search, agents, embeddings, and sandboxed code execution with enterprise controls.

Build RAG applications with vector databases: Pinecone, Weaviate, Qdrant comparison. Embedding strategies, indexing, and production deployment.