#Vector Search

Articles about Vector Search — exploring patterns, best practices, and real-world implementations in production systems.

3 posts tagged with vector search. ← All posts

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Pratik Dhanave · ·13 min read

Retrieval-Augmented Generation

Wire the embedding client, vector store, and chat client from the last five posts into one working RAG pipeline in Go — ingest and chunk documents, retrieve the top matches for a question, inject them as grounded context, and generate a cited answer, all from scratch.

Assemble embeddings and vector search into a working RAG pipeline in Go: chunk documents, embed and store them, retrieve the top-k for a query, augment the prompt with grounded context (and cite sources), then generate — a baseline end-to-end Answer() built from scratch.

#AI Engineering#RAG#Go#Vector Search#Backend Engineering#Embeddings#Vector Databases#Prompt Engineering
Pratik Dhanave · ·16 min read

Vector Search from Scratch

Build a working in-memory vector store and exact k-nearest-neighbor search in Go by hand — no vector database — then understand precisely what HNSW, FAISS, and pgvector optimize when brute force finally runs out of road.

Build an in-memory vector store and exact k-NN search in Go by hand: a VectorStore with Add and Search, top-k selection with container/heap, normalize-on-insert, an honest look at when brute force is right, and when ANN (HNSW, FAISS, pgvector) earns its keep.

#AI Engineering#Vector Search#Go#Embeddings#Backend Engineering#Vector Databases
Pratik Dhanave · ·13 min read

Embeddings

Turn text into a `[]float32` that places meaning in space — what an embedding is, cosine similarity implemented by hand in Go, calling an OpenAI-compatible /embeddings endpoint with net/http, and a worked pairwise-similarity example that scores related sentences higher.

Turn text into a []float32 that places meaning in space — what an embedding is, cosine similarity implemented by hand in Go, calling an OpenAI-compatible /embeddings endpoint with net/http, and a worked pairwise-similarity example that scores related sentences higher.

#AI Engineering#Embeddings#Go#Vector Search#Backend Engineering#Vector Databases

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.