{"id":10,"date":"2026-08-12T08:34:42","date_gmt":"2026-08-12T08:34:42","guid":{"rendered":"http:\/\/corpolearn.wp\/software\/\/production-rag-pipelines-vector-databases-llms\/"},"modified":"2026-08-12T08:34:42","modified_gmt":"2026-08-12T08:34:42","slug":"production-rag-pipelines-vector-databases-llms","status":"publish","type":"post","link":"https:\/\/corpolearn.com\/software\/production-rag-pipelines-vector-databases-llms\/","title":{"rendered":"Building Production-Ready RAG Pipelines with Vector Databases &#038; LLMs"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Retrieval-Augmented Generation bridges LLMs with proprietary enterprise knowledge repositories, eliminating hallucinations and ensuring factual precision.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Python Vector Similarity Query Example<\/h2>\n\n\n\n<pre class=\"wp-block-code\"><code>from openai import OpenAI\nimport qdrant_client\n\nclient = OpenAI()\nqdrant = qdrant_client.QdrantClient(host=\"localhost\", port=6333)\n\ndef query_rag_pipeline(user_query: str):\n    # 1. Generate Query Vector Embedding\n    emb_res = client.embeddings.create(\n        input=user_query,\n        model=\"text-embedding-3-small\"\n    )\n    query_vector = emb_res.data[0].embedding\n\n    # 2. Vector Search Top K Documents\n    hits = qdrant.search(\n        collection_name=\"engineering_kb\",\n        query_vector=query_vector,\n        limit=5\n    )\n    \n    context = \"n\".join([hit.payload[\"text\"] for hit in hits])\n    return context<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Architect Retrieval-Augmented Generation (RAG) applications using LangChain, Pinecone\/Qdrant, OpenAI embeddings, and semantic re-ranking.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_cl_workflow_state":"","_cl_content_brief":"","_cl_target_topic":"","footnotes":""},"categories":[8],"tags":[],"class_list":["post-10","post","type-post","status-publish","format-standard","hentry","category-ai-ml-engineering"],"_links":{"self":[{"href":"https:\/\/corpolearn.com\/software\/wp-json\/wp\/v2\/posts\/10","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/corpolearn.com\/software\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/corpolearn.com\/software\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/corpolearn.com\/software\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/corpolearn.com\/software\/wp-json\/wp\/v2\/comments?post=10"}],"version-history":[{"count":0,"href":"https:\/\/corpolearn.com\/software\/wp-json\/wp\/v2\/posts\/10\/revisions"}],"wp:attachment":[{"href":"https:\/\/corpolearn.com\/software\/wp-json\/wp\/v2\/media?parent=10"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/corpolearn.com\/software\/wp-json\/wp\/v2\/categories?post=10"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/corpolearn.com\/software\/wp-json\/wp\/v2\/tags?post=10"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}