{"id":1105,"date":"2026-06-15T18:14:22","date_gmt":"2026-06-15T18:14:22","guid":{"rendered":"https:\/\/convly.ai\/how-to-build-a-rag-pipeline-2026\/"},"modified":"2026-06-15T18:17:48","modified_gmt":"2026-06-15T18:17:48","slug":"how-to-build-a-rag-pipeline-2026","status":"publish","type":"post","link":"https:\/\/convly.ai\/fr\/how-to-build-a-rag-pipeline-2026\/","title":{"rendered":"How to Build a RAG Pipeline in 2026 (Step by Step)"},"content":{"rendered":"<p>Retrieval-augmented generation stopped being a research curiosity years ago. In 2026 it is the default way to put an LLM in front of your own documents without paying to fine-tune a model or risking it confidently inventing answers. The pattern is simple to describe and full of sharp edges to implement: find the right text, hand it to the model, and let the model write the answer.<\/p>\n<p>This is a build guide, not a survey. By the end you will know exactly which components a working RAG pipeline needs in 2026, which specific tools and model versions to reach for, and a minimal code sketch you can run locally or against an API. We verified every version number, price, and benchmark below against current sources \u2014 because the worst RAG bug is the one you copy from a blog post written for last year&#8217;s libraries.<\/p>\n<div class=\"convly-tldr\">\n<h3>Principaux enseignements<\/h3>\n<ul>\n<li><strong>Six stages, in order:<\/strong> chunk, embed, store, retrieve, rerank, generate. Skip the reranker and your top results are noticeably worse; skip evaluation and you&#8217;ll never know.<\/li>\n<li><strong>Boring chunking wins.<\/strong> Recursive splitting at ~512 tokens with 10\u201320% overlap beat fancy semantic chunking (69% vs 54% accuracy) in a 2026 benchmark. Start there.<\/li>\n<li><strong>Embeddings:<\/strong> nomic-embed-text (768 dims, free, local) for prototypes; OpenAI text-embedding-3-large ($0.13\/1M tokens, 3072 dims) or Voyage-3.5 for quality at scale.<\/li>\n<li><strong>Vector DB:<\/strong> pgvector if you already run Postgres; Qdrant v1.18 (Apache 2.0, Rust) when you need fast filtered search; Chroma for quick local work.<\/li>\n<li><strong>Frameworks:<\/strong> LangChain 1.x (LangGraph runtime) for agentic flows, LlamaIndex 0.14.x for retrieval-heavy apps \u2014 and you can run a useful pipeline in ~40 lines without either.<\/li>\n<li><strong>Add a reranker.<\/strong> Cohere Rerank 3.5 ($2 per 1,000 searches) or open-source BGE-reranker-v2-m3 (free, ~50\u2013100ms on GPU) cheaply lifts top-k relevance.<\/li>\n<\/ul>\n<\/div>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_84 counter-flat ez-toc-counter ez-toc-container-direction\">\n<label for=\"ez-toc-cssicon-toggle-item-6a307bdf874be\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #000000;color:#000000\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #000000;color:#000000\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewbox=\"0 0 24 24\" version=\"1.2\" baseprofile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6a307bdf874be\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/convly.ai\/fr\/how-to-build-a-rag-pipeline-2026\/#How_a_RAG_pipeline_actually_works\" >How a RAG pipeline actually works<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/convly.ai\/fr\/how-to-build-a-rag-pipeline-2026\/#Step_1_Chunk_your_documents\" >Step 1: Chunk your documents<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/fr\/how-to-build-a-rag-pipeline-2026\/#Step_2_Choose_an_embedding_model\" >Step 2: Choose an embedding model<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/fr\/how-to-build-a-rag-pipeline-2026\/#Step_3_Store_vectors_in_a_vector_database\" >Step 3: Store vectors in a vector database<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/fr\/how-to-build-a-rag-pipeline-2026\/#Step_4_Retrieve_and_rerank\" >Step 4: Retrieve and rerank<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/fr\/how-to-build-a-rag-pipeline-2026\/#Step_5_Augment_the_prompt_and_generate\" >Step 5: Augment the prompt and generate<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/convly.ai\/fr\/how-to-build-a-rag-pipeline-2026\/#Step_6_A_minimal_code_sketch\" >Step 6: A minimal code sketch<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/convly.ai\/fr\/how-to-build-a-rag-pipeline-2026\/#Step_7_Evaluate_%E2%80%94_dont_skip_this\" >Step 7: Evaluate \u2014 don&#8217;t skip this<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/convly.ai\/fr\/how-to-build-a-rag-pipeline-2026\/#FAQ\" >FAQ<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/convly.ai\/fr\/how-to-build-a-rag-pipeline-2026\/#Bottom_line\" >R\u00e9sultat<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/convly.ai\/fr\/how-to-build-a-rag-pipeline-2026\/#Related_articles\" >Articles connexes<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"How_a_RAG_pipeline_actually_works\"><\/span>How a RAG pipeline actually works<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>A RAG system has two phases. <strong>Indexing<\/strong> happens once (or whenever your documents change): you split source files into chunks, convert each chunk to a vector with an embedding model, and store those vectors in a database. <strong>Querying<\/strong> happens on every request: you embed the user&#8217;s question, find the most similar chunks, optionally rerank them, paste the best ones into a prompt, and call an LLM.<\/p>\n<p>That is the whole idea. The engineering is in the details \u2014 chunk size, which embedding model, how many results to retrieve, whether to rerank, and how you measure if any of it works. If you want the conceptual background before building, our <a href=\"\/fr\/rag-retrieval-augmented-generation-explained\/\">RAG explainer<\/a> covers the theory; this piece is about wiring it up. And if you&#8217;re still deciding between RAG and customizing the model itself, the <a href=\"\/fr\/fine-tuning-vs-rag\/\">fine-tuning vs RAG comparison<\/a> is the right place to start \u2014 for most teams feeding private, changing data to an LLM, RAG is the cheaper and more maintainable answer.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Step_1_Chunk_your_documents\"><\/span>Step 1: Chunk your documents<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Embedding models have a context limit and, more importantly, lose precision on long passages. So you split documents into chunks. The 2026 consensus, backed by benchmarks rather than vibes, is unglamorous: use a recursive character splitter targeting roughly <strong>512 tokens with 10\u201320% overlap<\/strong> (50\u2013100 tokens).<\/p>\n<p>A February 2026 evaluation across 50 real documents found that naive recursive splitting at 512 tokens scored 69% retrieval accuracy, while semantic chunking \u2014 which tries to split on meaning boundaries \u2014 scored only 54%. The reason is mundane: semantic chunking produced fragments averaging 43 tokens, too small to give the model enough context to answer. Meanwhile a separate January 2026 study using SPLADE retrieval found overlap added indexing cost with no measurable benefit on its dataset. The honest takeaway: start with fixed-size recursive chunks, and only reach for semantic or page-level chunking if your evaluation metrics prove you need it on your specific documents.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Step_2_Choose_an_embedding_model\"><\/span>Step 2: Choose an embedding model<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>This is the most consequential decision in the pipeline, and the gap between options is real. Here are the choices worth considering in mid-2026, with verified numbers.<\/p>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Mod\u00e8le<\/th>\n<th>Dimensions<\/th>\n<th>Context<\/th>\n<th>Price \/ 1M tokens<\/th>\n<th>Notes<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>nomic-embed-text v1.5<\/td>\n<td>768 (MRL 64\u2013768)<\/td>\n<td>8,192<\/td>\n<td>Free (local)<\/td>\n<td>274MB; the default local pick<\/td>\n<\/tr>\n<tr>\n<td>mxbai-embed-large<\/td>\n<td>1024<\/td>\n<td>512<\/td>\n<td>Free (local)<\/td>\n<td>670MB; higher quality, short context<\/td>\n<\/tr>\n<tr>\n<td>BGE-M3<\/td>\n<td>1024 + sparse<\/td>\n<td>8,192<\/td>\n<td>Free (local)<\/td>\n<td>MIT license, 100+ languages<\/td>\n<\/tr>\n<tr>\n<td>OpenAI text-embedding-3-small<\/td>\n<td>1536<\/td>\n<td>8,191<\/td>\n<td>$0.02<\/td>\n<td>Cheap API baseline<\/td>\n<\/tr>\n<tr>\n<td>OpenAI text-embedding-3-large<\/td>\n<td>3072<\/td>\n<td>8,191<\/td>\n<td>$0.13<\/td>\n<td>$0.065 via Batch API<\/td>\n<\/tr>\n<tr>\n<td>Voyage-3.5<\/td>\n<td>2048 (MRL 256\u20132048)<\/td>\n<td>32,000<\/td>\n<td>$0.06<\/td>\n<td>Beats 3-large by ~8% on retrieval<\/td>\n<\/tr>\n<tr>\n<td>Gemini Embedding<\/td>\n<td>3072<\/td>\n<td>\u2014<\/td>\n<td>API<\/td>\n<td>Tops MTEB v2 (~68.3)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>For a prototype, start local with <strong>nomic-embed-text<\/strong> \u2014 it&#8217;s fast, free, fits on a 16GB laptop, and reportedly beats OpenAI&#8217;s older <code>text-embedding-ada-002<\/code>. For production, the open-source field has genuinely caught up: BGE-M3 is the MIT-licensed workhorse most self-hosted stacks default to, while Voyage-3.5 and Gemini Embedding lead the managed-API benchmarks. The one rule that matters: <strong>whatever you embed your documents with, you must embed your queries with the same model.<\/strong> Mixing models silently destroys retrieval.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Step_3_Store_vectors_in_a_vector_database\"><\/span>Step 3: Store vectors in a vector database<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Once you have embeddings, they need to live somewhere that supports fast nearest-neighbor search. You have three sensible tiers in 2026.<\/p>\n<div class=\"convly-procons\">\n<div class=\"pros\">\n<h4>Reach for these<\/h4>\n<ul>\n<li><strong>pgvector 0.8<\/strong> if you already run Postgres. With an HNSW index it serves single-digit-to-low-double-digit-millisecond p95 latency at 1M vectors. Version 0.8 added iterative scans so filtered queries return enough results. No new infrastructure.<\/li>\n<li><strong>Qdrant v1.18<\/strong> (Apache 2.0, Rust) when filtering matters. Its ACORN algorithm (added in 1.16) tackles the classic &#8220;filter kills my recall&#8221; problem by widening the HNSW search under restrictive filters, and is among the strongest options for filtered search. One Docker command to self-host.<\/li>\n<li><strong>Chroma<\/strong> for local prototyping. Best developer experience, embedded mode, zero ops \u2014 perfect until you outgrow it.<\/li>\n<\/ul>\n<\/div>\n<div class=\"cons\">\n<h4>Watch out for<\/h4>\n<ul>\n<li>Managed services bill by usage and surprise people: at 100M vectors, Pinecone can run $5,000+\/month versus a far cheaper self-hosted Qdrant or pgvector on your own VMs. Audit before you scale.<\/li>\n<li>HNSW index builds are slow at scale, and the index can hit ~8GB for 1M vectors at 1536 dims (use halfvec to roughly halve that).<\/li>\n<li>Storage hardware dominates throughput: the same pgvector setup did ~410 QPS on cloud SSD versus 2,150 QPS on NVMe.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<p>A deeper breakdown lives in our <a href=\"\/fr\/what-is-a-vector-database-2026\/\">vector database guide<\/a>, but for most teams the decision tree is short: already on Postgres \u2192 pgvector; need heavy filtering or billions of vectors \u2192 Qdrant or Milvus; just experimenting \u2192 Chroma.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Step_4_Retrieve_and_rerank\"><\/span>Step 4: Retrieve and rerank<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Retrieval itself is one call: embed the query, ask the database for the top-k nearest chunks (k of 20\u201350 is typical). But raw vector similarity is a blunt instrument. A <strong>reranker<\/strong> \u2014 a cross-encoder that scores each query-document pair individually \u2014 re-sorts those candidates and surfaces the genuinely relevant ones before they reach the model.<\/p>\n<p>The standard pattern: retrieve top 50 with your bi-encoder, rerank, keep the top 5\u201310. Cohere Rerank 3.5 costs $0.002 per search ($2 per 1,000) and typically adds on the order of 100\u2013300ms of latency. If you have a GPU and want zero per-query cost, the open-source <strong>BGE-reranker-v2-m3<\/strong> runs in ~50\u2013100ms and supports multilingual content. Reranking is one of the highest-leverage, lowest-effort upgrades you can make \u2014 most pipelines that &#8220;retrieve garbage&#8221; are missing this step.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Step_5_Augment_the_prompt_and_generate\"><\/span>Step 5: Augment the prompt and generate<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Now assemble the prompt: a short system instruction telling the model to answer only from the supplied context, the reranked chunks, and the user&#8217;s question. Then call your LLM.<\/p>\n<p>For the generation model you can go local or API. Locally via <a href=\"\/fr\/what-is-ollama-complete-guide-2026\/\">Ollama<\/a>, the 2026 sweet spot is an 8B-class model \u2014 Qwen3 8B or Llama 3.1 8B at Q4_K_M quantization \u2014 which fits in 8\u201312GB of VRAM and runs at 40+ tokens\/second on a modern GPU. Qwen3 14B (~8\u20139GB at Q4) is a strong step up with a 128K context window for stuffing in more retrieved text. For a hosted, higher-ceiling option, a frontier API model works well; our <a href=\"\/fr\/build-ai-chatbot-claude-api\/\">Claude API chatbot tutorial<\/a> walks through that path end to end. A useful reminder from practitioners: for RAG, retrieval quality usually matters more than model size \u2014 clean chunks plus a good embedder plus a small LLM beats a huge model fed bad context.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Step_6_A_minimal_code_sketch\"><\/span>Step 6: A minimal code sketch<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Here is a complete local pipeline using LangChain 1.x, Chroma, and Ollama. It indexes a document and answers a question \u2014 no API keys required.<\/p>\n<pre><code class=\"language-python\"># pip install langchain langchain-community langchain-chroma langchain-ollama\nfrom langchain_community.document_loaders import TextLoader\nfrom langchain_text_splitters import RecursiveCharacterTextSplitter\nfrom langchain_ollama import OllamaEmbeddings, ChatOllama\nfrom langchain_chroma import Chroma\n\n# 1. Load + chunk (~512 tokens, ~15% overlap; sizes are in characters)\ndocs = TextLoader(&quot;handbook.txt&quot;).load()\nchunks = RecursiveCharacterTextSplitter(\n    chunk_size=2000, chunk_overlap=300\n).split_documents(docs)\n\n# 2. Embed + 3. Store\nembeddings = OllamaEmbeddings(model=&quot;nomic-embed-text&quot;)\nstore = Chroma.from_documents(chunks, embeddings)\n\n# 4. Retrieve (top 4)\nretriever = store.as_retriever(search_kwargs={&quot;k&quot;: 4})\n\n# 5. Augment + generate\nllm = ChatOllama(model=&quot;qwen3:8b&quot;)\nquestion = &quot;What is the refund window?&quot;\ncontext = &quot;nn&quot;.join(d.page_content for d in retriever.invoke(question))\nprompt = (f&quot;Answer using ONLY the context. If it's not there, say so.nn&quot;\n          f&quot;Context:n{context}nnQuestion: {question}&quot;)\nprint(llm.invoke(prompt).content)\n<\/code><\/pre>\n<p>That&#8217;s the whole loop. To add reranking, insert a <code>ContextualCompressionRetriever<\/code> with a cross-encoder between steps 4 and 5. With LlamaIndex 0.14.x the same flow is typically less code thanks to its purpose-built retrieval abstractions \u2014 it&#8217;s the better choice for retrieval-heavy apps, while LangChain&#8217;s LangGraph runtime shines when you need stateful, multi-step agents. (Choosing an orchestration layer is its own topic; see our <a href=\"\/fr\/best-ai-agent-frameworks-2026\/\">AI agent frameworks comparison<\/a>.)<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Step_7_Evaluate_%E2%80%94_dont_skip_this\"><\/span>Step 7: Evaluate \u2014 don&#8217;t skip this<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The difference between a demo and a product is measurement. The standard tool is <strong>RAGAS<\/strong>, which scores faithfulness (did the answer stick to the context?), context precision, and context recall using an LLM as judge. Build a small set of 20\u201350 question-answer pairs from your real documents and run it on every change.<\/p>\n<p>This is also how you make every earlier decision honestly. Should you switch to semantic chunking? Add a reranker? Bump k from 4 to 8? Don&#8217;t guess \u2014 change one variable, rerun RAGAS, and keep the change only if the numbers improve. Without this loop you&#8217;re tuning blind.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"FAQ\"><\/span>FAQ<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>How much does it cost to run a RAG pipeline?<\/h3>\n<p>Almost free to prototype. With local Ollama embeddings, Chroma, and a local LLM, your only cost is electricity. At scale, the main bills are the vector DB (a self-hosted Qdrant or pgvector instance on your own VM is dramatically cheaper than managed offerings, which can exceed $5,000\/month at 100M vectors) and, if you use APIs, embeddings (OpenAI text-embedding-3-large is $0.13 per million tokens) plus generation calls.<\/p>\n<h3>Do I need a vector database, or can I use a regular one?<\/h3>\n<p>You need vector search, but not necessarily a dedicated product. pgvector adds it to PostgreSQL and handles 1M vectors at low p95 latency (single-digit ms on NVMe, higher on cloud SSD), so if you already run Postgres you can avoid new infrastructure entirely. Reach for a dedicated DB like Qdrant when you need heavy metadata filtering or billions of vectors.<\/p>\n<h3>What chunk size should I use?<\/h3>\n<p>Start at roughly 512 tokens with 10\u201320% overlap using a recursive splitter. A 2026 benchmark found this beat semantic chunking 69% to 54% on retrieval accuracy. Only move to more sophisticated chunking if your evaluation metrics show it helps on your specific documents.<\/p>\n<h3>Is a reranker actually necessary?<\/h3>\n<p>Not to get something working, but it&#8217;s one of the cheapest quality upgrades available. Retrieve a wide set (top 50), rerank with Cohere Rerank 3.5 or open-source BGE-reranker-v2-m3, and keep the top 5\u201310. Most pipelines that surface irrelevant chunks are simply missing this step.<\/p>\n<h3>Can I build RAG without LangChain or LlamaIndex?<\/h3>\n<p>Yes. The core loop \u2014 embed, search, prompt, generate \u2014 is about 40 lines of plain Python calling your embedding model, vector DB client, and LLM directly. Frameworks save time on loaders, rerankers, and agentic orchestration, but they&#8217;re optional, and a from-scratch build gives you full control over every step.<\/p>\n<h3>Should I use a local model or an API for generation?<\/h3>\n<p>Local (via Ollama, with an 8B model on 8\u201312GB of VRAM) is great for privacy, cost control, and offline use. An API gives you a higher quality ceiling and zero ops. Many teams prototype locally to iterate cheaply, then choose per-deployment based on data-sensitivity and budget.<\/p>\n<h3>How do I keep the index fresh as documents change?<\/h3>\n<p>Re-embed and upsert only what changed rather than rebuilding everything. Track a content hash or modified-date per source document, and on update delete the old chunks for that document and insert new ones. Most vector DBs support upserts and deletes by metadata filter, which makes incremental updates straightforward.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Bottom_line\"><\/span>R\u00e9sultat<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Building a RAG pipeline in 2026 is genuinely approachable: six stages, a handful of mature tools, and roughly 40 lines of code to a working prototype. The traps are not in the architecture \u2014 they&#8217;re in the defaults. Use boring 512-token chunks, match your query and document embedders, add a reranker, and never tune without RAGAS in the loop. Start local and free with nomic-embed-text, Chroma, and an 8B Ollama model; graduate individual components to pgvector, Qdrant, Voyage, or a frontier API only when your evaluation numbers \u2014 not a blog post \u2014 tell you to. Get the retrieval right and a small model will carry you surprisingly far.<\/p>\n<p><!--related-block--><\/p>\n<div class=\"convly-related\">\n<h2><span class=\"ez-toc-section\" id=\"Related_articles\"><\/span>Articles connexes<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><a href=\"https:\/\/convly.ai\/fr\/how-to-run-llama-3-locally-on-snapdragon-8-gen-4\/\">Comment faire fonctionner Llama 3 en local sur Snapdragon 8 Gen 4 (\u00e9tape par \u00e9tape, 2026)<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/fr\/90-day-ai-engineer-path\/\">De z\u00e9ro \u00e0 ing\u00e9nieur en IA : Votre parcours d'apprentissage en 90 jours<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/fr\/ai-resume-screener-tutorial\/\">Cr\u00e9er un filtrage de CV par l'IA (Tutoriel complet)<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/fr\/local-llm-ollama-setup\/\">Mise en place de votre premier programme local d'\u00e9ducation et de formation tout au long de la vie avec Ollama<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>A practical, code-first walkthrough of building a retrieval-augmented generation pipeline in 2026 \u2014 from embeddings and chunking to vector storage, reranking, and generation, with verified tool versions and honest notes on what actually 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