--- title: "#vector-embeddings articles | Hashnode" description: "Explore 312 articles tagged #vector-embeddings on Hashnode. Tutorials, guides, and how-tos from the community." url: "https://hashnode.com/tag/vector-embeddings" --- Tag # #vector-embeddings 312 posts · 13 followers [Asfaq Leeon](https://hashnode.com/@leeonscoding) · [Oct 9](https://hashnode.com/posts/embeddings-vector-search-rag-from-scratch-with-node-js-and-postgresql-pgvector/6ac886790b56f76134c18b6f) ### [Embeddings, Vector Search & RAG from Scratch with Node.js and PostgreSQL (pgvector)](https://leeonscoding.hashnode.dev/embeddings-vector-search-rag-from-scratch-with-node-js-and-postgresql-pgvector?utm_source=hashnode&utm_medium=feed) Learn the building blocks before you touch LangChain 13 min read · [leeonscoding.hashnode.dev](https://leeonscoding.hashnode.dev/embeddings-vector-search-rag-from-scratch-with-node-js-and-postgresql-pgvector?utm_source=hashnode&utm_medium=feed) [3](https://hashnode.com/posts/embeddings-vector-search-rag-from-scratch-with-node-js-and-postgresql-pgvector/6ac886790b56f76134c18b6f) [Priya Singh](https://hashnode.com/@Itspri) · [Sep 27](https://hashnode.com/posts/first-time-implementing-rag-repo-recall/6ab94506370d0dda69635739) ### [First Time Implementing RAG](https://priyasinghdev.hashnode.dev/first-time-implementing-rag-repo-recall?utm_source=hashnode&utm_medium=feed) How I built Repo Recall, a full-stack app that lets you chat with your GitHub repos. Lessons on RAG, embeddings, pgvector, and adm-zip. 4 min read · [priyasinghdev.hashnode.dev](https://priyasinghdev.hashnode.dev/first-time-implementing-rag-repo-recall?utm_source=hashnode&utm_medium=feed) [Mihir Inamdar](https://hashnode.com/@inamdarmihir) · [Sep 24](https://hashnode.com/posts/fastembed-vs-openai-embedding-cost-qdrant-docs/6ab49039726418bbd9da7867) ### [I embedded all of Qdrant's docs with FastEmbed. The API would have cost 2 cents.](https://aihive.hashnode.dev/fastembed-vs-openai-embedding-cost-qdrant-docs?utm_source=hashnode&utm_medium=feed) 5,084 chunks, 1.2 million tokens, 24 minutes on a 2 vCPU box. What running embeddings locally next to Qdrant actually costs, with every number labeled. 9 min read · [aihive.hashnode.dev](https://aihive.hashnode.dev/fastembed-vs-openai-embedding-cost-qdrant-docs?utm_source=hashnode&utm_medium=feed) [Niran Pravithana](https://hashnode.com/@marketdx) · [Sep 19](https://hashnode.com/posts/mcp-optimization-round-trip-resolver-jev-3-en/6aae2a0d5835e5dac5ff8b9f) ### [Cutting MCP Round-Trips with Jev (Part 3)](https://marketiv.hashnode.dev/mcp-optimization-round-trip-resolver-jev-3-en?utm_source=hashnode&utm_medium=feed) MCP's resolvers ping-pong top-k lists to the LLM every query — slow and costly. We tried Jev to pick server-side, cutting round-trips and cost ~4×. 11 min read · [marketiv.hashnode.dev](https://marketiv.hashnode.dev/mcp-optimization-round-trip-resolver-jev-3-en?utm_source=hashnode&utm_medium=feed) [2](https://hashnode.com/posts/mcp-optimization-round-trip-resolver-jev-3-en/6aae2a0d5835e5dac5ff8b9f) [Sachin Nandanwar](https://hashnode.com/@SachinNandanwar) · [Aug 26](https://hashnode.com/posts/rag-for-microsoft-ai-agent-through-textsearchprovider-and-redis/6a8e69cedeab3ab2cc6fd4ac) ### [RAG for Microsoft AI Agent through TextSearchProvider and Redis](https://www.azureguru.net/rag-for-microsoft-ai-agent-through-textsearchprovider-and-redis?utm_source=hashnode&utm_medium=feed) Implement RAG in Microsoft Agent Framework using Redis and TextSearchProvider for vector search, embeddings and semantic retrieval 12 min read · [azureguru.net](https://www.azureguru.net/rag-for-microsoft-ai-agent-through-textsearchprovider-and-redis?utm_source=hashnode&utm_medium=feed) [1](https://hashnode.com/posts/rag-for-microsoft-ai-agent-through-textsearchprovider-and-redis/6a8e69cedeab3ab2cc6fd4ac) [Nahid Mahmud](https://hashnode.com/@nahidmahmud555) · [Aug 12](https://hashnode.com/posts/deep-dive-into-vector-search-ann-hnsw-and-production-optimization/6a7cb4ef1bcd94806af3bb5e) ### [Deep Dive into Vector Search: ANN, HNSW, and Production Optimization](https://nahid-mahmud555.hashnode.dev/deep-dive-into-vector-search-ann-hnsw-and-production-optimization?utm_source=hashnode&utm_medium=feed) Learn core vector search concepts, ANN, IVF, HNSW indexing, and optimization strategies for high-performance AI systems 3 min read · [nahid-mahmud555.hashnode.dev](https://nahid-mahmud555.hashnode.dev/deep-dive-into-vector-search-ann-hnsw-and-production-optimization?utm_source=hashnode&utm_medium=feed) [Nahid Mahmud](https://hashnode.com/@nahidmahmud555) · [Aug 10](https://hashnode.com/posts/how-computers-read-and-match-text-a-quick-vector-search-experiment/6a7a091c73751fab6d035591) ### [How Computers "Read" and Match Text: A Quick Vector Search Experiment](https://nahid-mahmud555.hashnode.dev/how-computers-read-and-match-text-a-quick-vector-search-experiment?utm_source=hashnode&utm_medium=feed) Learn how Vector Search works under the hood by building a simple semantic search engine in Python using embeddings and cosine distance. 3 min read · [nahid-mahmud555.hashnode.dev](https://nahid-mahmud555.hashnode.dev/how-computers-read-and-match-text-a-quick-vector-search-experiment?utm_source=hashnode&utm_medium=feed) [Gaurav Kumar](https://hashnode.com/@gauravbytes) · [Aug 3](https://hashnode.com/posts/how-to-evaluate-rag-retrieval-a-practical-guide-to-precision-recall-mrr-map-and-ndcg/6a70e915ac23f68b04cb65b6) ### [How to Evaluate RAG Retrieval: A Practical Guide to Precision, Recall, MRR, MAP, and NDCG](https://gauravbytes.dev/how-to-evaluate-rag-retrieval-a-practical-guide-to-precision-recall-mrr-map-and-ndcg?utm_source=hashnode&utm_medium=feed) You implemented a search algorithm in your RAG system and it's returning data without any error, and you think that's it, you're done. You're wrong. That mindset works fine for a normal system — an AP 14 min read · [gauravbytes.dev](https://gauravbytes.dev/how-to-evaluate-rag-retrieval-a-practical-guide-to-precision-recall-mrr-map-and-ndcg?utm_source=hashnode&utm_medium=feed) [3](https://hashnode.com/posts/how-to-evaluate-rag-retrieval-a-practical-guide-to-precision-recall-mrr-map-and-ndcg/6a70e915ac23f68b04cb65b6) [Aditya Biranje](https://hashnode.com/@AdityaBiranje) · [Jul 23](https://hashnode.com/posts/designing-the-ingestion-layer-of-a-production-rag-system/6a6281ce6d3f3aafddb25818) ### [Designing the Ingestion Layer of a Production RAG System](https://adityabiranje210.hashnode.dev/designing-the-ingestion-layer-of-a-production-rag-system?utm_source=hashnode&utm_medium=feed) How a modular parsing, chunking, and embedding pipeline turns PDFs, HTML, DOCX, PPTX, and TXT files into queryable vectors in Qdrant — and where it still breaks. 27 min read · [adityabiranje210.hashnode.dev](https://adityabiranje210.hashnode.dev/designing-the-ingestion-layer-of-a-production-rag-system?utm_source=hashnode&utm_medium=feed) [3](https://hashnode.com/posts/designing-the-ingestion-layer-of-a-production-rag-system/6a6281ce6d3f3aafddb25818) [Avanish Garg](https://hashnode.com/@avanish-garg) · [Jul 22](https://hashnode.com/posts/ai-illusion/6a60f4db3d52fa66fa31f8da) ### [AI is an Illusion: Decoding the Greatest Magic Trick in Tech](https://avanish-garg.hashnode.dev/ai-illusion?utm_source=hashnode&utm_medium=feed) The most beautiful high-dimensional calculus ever mistaken for a mind, which apparently now runs the world! WELCOME TO THE CURIOSITY CRUNCH — EPISODE 04: A series where we take the technical phenome 6 min read · [avanish-garg.hashnode.dev](https://avanish-garg.hashnode.dev/ai-illusion?utm_source=hashnode&utm_medium=feed)