{"id":345293,"date":"2025-12-15T13:08:12","date_gmt":"2025-12-15T13:08:12","guid":{"rendered":"https:\/\/peraltafinancing.com\/analytics\/a-guide-to-automated-rag-pipeline\/"},"modified":"2025-12-15T13:08:12","modified_gmt":"2025-12-15T13:08:12","slug":"a-guide-to-automated-rag-pipeline","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=345293","title":{"rendered":"A Guide to Automated RAG Pipeline"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div id=\"article-start\">\n<p>Retrieval-Augmented Generation (RAG) has become the go-to method for building reliable AI applications that depend on external data, since it helps overcome LLM limitations, cuts down hallucinations, and delivers expert-level responses grounded in trusted sources. As interest in RAG rises, so does the need for tools that make it easier to explore, test, and refine different RAG strategies. AutoRAG is one of the newer solutions built for this purpose, automating much of the development workflow so you can experiment with configurations, evaluate pipelines, and pinpoint what works best for your use case. <\/p>\n<p>In this guide we\u2019ll cover how AutoRAG works, and how to create an end-to-end RAG application with this technology.<\/p>\n<h2 class=\"wp-block-heading\" id=\"h-what-is-retrieval-augmented-generation-rag\">What is Retrieval-Augmented Generation (RAG)?<\/h2>\n<p>The two main pieces of <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2023\/09\/retrieval-augmented-generation-rag-in-ai\/\" target=\"_blank\" rel=\"noreferrer noopener\">RAG<\/a> are The Retriever and the Generator. Together these pieces make up the Pipeline of RAG that allows you to get accurate answers to complex queries. The generator will read the context that the retriever found and generates a response based on that.\u00a0<\/p>\n<p>RAG is an approach that combines searches for external data with Generative Models to improve accuracy by providing reliable references for model-generated answers. Some applications of RAG include Chatbots, Knowledge Assistants, Analytics Solutions and Enterprise Q&amp;A Systems.\u00a0<\/p>\n<h2 class=\"wp-block-heading\" id=\"h-key-components-of-rag\">Key Components of RAG<\/h2>\n<p>There are several building blocks that make RAG successful and affect the ability of RAG to provide accurate information. These include The Retriever, The Embedding Model and The Generator.\u00a0\u00a0<\/p>\n<ul class=\"wp-block-list\">\n<li><strong>Retriever:<\/strong><em> <\/em>The first step in RAG is to index the documents using the retriever, then the retriever will search the index for the relevant chunks of documents.\u00a0 The method for searching the\u00a0<\/li>\n<li><strong>Embedding Model:<\/strong><em> <\/em>Indexes can be based on the similarity of the chunks of the documents through various methods such as Similarity Search, Vector Embedding or Hybrid Retrieval.\u00a0<\/li>\n<li><strong>Generator (LLM):<\/strong><em> <\/em>The generator must rely on accurate context in order to produce an accurate response.\u00a0 This is why it is necessary to utilize the best possible version of the context in order to generate an accurate response.\u00a0<\/li>\n<\/ul>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img fetchpriority=\"high\" decoding=\"async\" width=\"901\" height=\"489\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/12\/chart-7.webp\" alt=\"A conceptual RAG pipeline\" class=\"wp-image-247711\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/12\/chart-7.webp 901w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/12\/chart-7-300x163.webp 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/12\/chart-7-768x417.webp 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/12\/chart-7-150x81.webp 150w\" sizes=\"(max-width: 901px) 100vw, 901px\"\/><figcaption class=\"wp-element-caption\">A conceptual RAG pipeline<\/figcaption><\/figure>\n<\/div>\n<h2 class=\"wp-block-heading\" id=\"h-what-is-autorag-and-when-to-use-it\">What is AutoRAG and When to Use It<\/h2>\n<p><a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2025\/02\/autorag\/\" target=\"_blank\" rel=\"noreferrer noopener\">AutoRAG<\/a> is a framework that allows you to create, evaluate, and optimize multiple RAG pipelines quickly. It helps developers quickly test multiple design choices without needing to write custom evaluation scripts. When teams want to compare different retrieval methods, embedding models, chunking strategies, or generations, they use AutoRAG.\u00a0<\/p>\n<p>With AutoRAG, developers can quickly iterate through many different RAG configurations because it automatically runs tests and provides an evaluation of retrieval accuracy and best configuration for generating pipelines. When you need systematic evaluation or want to test a large number of pipeline configurations, you should use AutoRAG.\u00a0<\/p>\n<h3 class=\"wp-block-heading\" id=\"h-what-problems-autorag-solve\">What Problems AutoRAG Solve?<\/h3>\n<p>AutoRAG addresses many of the challenges that make RAG development time-consuming. As RAG projects become increasingly complex, these challenges become even more significant.\u00a0<\/p>\n<ul class=\"wp-block-list\">\n<li><em>Pipeline Exploration: <\/em>It enables experimentation across a variety of similarity metrics, chunking sizes, embedding models, and retrievers without manual code.\u00a0<\/li>\n<li><em>Evaluation: <\/em>AutoRAG includes evaluation metrics that include retrieval accuracy, citation correctness, and answer quality, making it easy to assess the effectiveness of <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2023\/10\/rag-pipeline-with-the-llama-index\/\" target=\"_blank\" rel=\"noreferrer noopener\">RAG pipelines<\/a>.\u00a0<\/li>\n<li><em>Optimization: <\/em>AutoRAG identifies the most effective configurations, which enables teams to make the best decision possible for the configuration of their data and use case.<\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\" id=\"h-preparing-to-build-prerequisites-amp-setup\">Preparing to Build: Prerequisites &amp; Setup<\/h2>\n<p>Set up a development environment suitable for using AutoRAG to develop an application based on RAG. That means you\u2019ll need to establish Python, dependencies, data, and find out what credentials you need from your LLM or embedding model provider.\u00a0<\/p>\n<p>You must have a well-defined <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2021\/05\/introduction-to-python-programming-beginners-guide\/\" target=\"_blank\" rel=\"noreferrer noopener\">Python<\/a> environment as well as several additional dependencies to be able to successfully run AutoRAG. If you have an unclean or improperly defined environment, you may experience conflicts within your dependencies, and your experiments with RAG will not run smoothly.\u00a0<\/p>\n<ol class=\"wp-block-list\">\n<li>Python Setup\u00a0<\/li>\n<\/ol>\n<pre class=\"wp-block-code\"><code>python3 -m venv autorag-venv\u00a0\n\nsource autorag-venv\/bin\/activate\u00a0\n\npython -m pip install --upgrade pip<\/code><\/pre>\n<ul class=\"wp-block-list\">\n<li>Python 3.10 or higher\u00a0<\/li>\n<li>A virtual environment using venv or conda\u00a0<\/li>\n<li>pip for package installation\u00a0<\/li>\n<\/ul>\n<ol start=\"2\" class=\"wp-block-list\">\n<li>Core Dependencies\u00a0<\/li>\n<\/ol>\n<p>Install the following packages as required by AutoRAG:\u00a0<\/p>\n<pre class=\"wp-block-code\"><code>pip install AutoRAG pandas langchain sentence-transformers faiss-cpu\u00a0<\/code><\/pre>\n<ul class=\"wp-block-list\">\n<li>autorag\u00a0<\/li>\n<li>pandas\u00a0<\/li>\n<li>sentence-transformers\u00a0<\/li>\n<li>faiss-cpu or another vector store backend\u00a0<\/li>\n<li><a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2024\/06\/langchain-guide\/\" target=\"_blank\" rel=\"noreferrer noopener\">langchain<\/a> or langchain-community for LLM integrations\u00a0<\/li>\n<\/ul>\n<ol start=\"3\" class=\"wp-block-list\">\n<li>Export LLM \/ embedding keys\u00a0<\/li>\n<\/ol>\n<pre class=\"wp-block-code\"><code>export OPENAI_API_KEY=\"sk-...\"\u00a0<\/code><\/pre>\n<p>If you are using someother providers, set their <code>env<\/code> vars similarly.<\/p>\n<h2 class=\"wp-block-heading\" id=\"h-building-a-rag-application-with-autorag\">Building a RAG Application with AutoRAG<\/h2>\n<p>To create an application based on RAG using AutoRAG and a knowledge base, proceed through the following main steps.\u00a0<\/p>\n<ul class=\"wp-block-list\">\n<li><strong>Indexing and Embedding:<\/strong> Each chunk of information will be converted into an embedding and stored in a vector database.\u00a0<\/li>\n<li><strong>Retrieval and Pipeline Experimenting:<\/strong> Create a QA evaluation set if necessary, then run AutoRAG to experiment with different RAG pipelines to determine which produces the best results.\u00a0<\/li>\n<li><strong>Deployment:<\/strong> Use the resulting RAG pipeline to respond to user enquiries.\u00a0<\/li>\n<\/ul>\n<h3 class=\"wp-block-heading\" id=\"h-data-ingestion-and-preprocessing\">Data Ingestion and Preprocessing<\/h3>\n<p>Firstly, we\u2019ll load the raw documents:<\/p>\n<pre class=\"wp-block-code\"><code>import json\nimport os\nfrom pathlib import Path\nimport PyPDF2\n\ndef parse_pdf(pdf_path=\"data\/raw_docs\/attention.pdf\", out_path=\"data\/parsed.jsonl\"):\n    os.makedirs(\"data\", exist_ok=True)\n\n    with open(pdf_path, \"rb\") as f, open(out_path, \"w\", encoding=\"utf-8\") as fout:\n        reader = PyPDF2.PdfReader(f)\n\n        for i, page in enumerate(reader.pages):\n            text = page.extract_text() or \"\"\n\n            fout.write(json.dumps({\n                \"doc_id\": \"attention.pdf\",\n                \"page\": i + 1,\n                \"content\": text,\n                \"source\": \"attention.pdf\",\n                \"title\": \"Attention Is All You Need\"\n            }) + \"\\n\")\n\n    print(\"Parsed PDF \u2192 data\/parsed.jsonl\")\n\n\nparse_pdf()<\/code><\/pre>\n<p>The following method would babe used for splitting and chunking documents:<\/p>\n<pre class=\"wp-block-code\"><code>import json\n\ndef chunk_text(text, chunk_size=600, overlap=60):\n    words = text.split()\n    i = 0\n    chunks = []\n\n    while i &lt; len(words):\n        chunk = words[i:i + chunk_size]\n        chunks.append(\" \".join(chunk))\n\n        if i + chunk_size &gt;= len(words):\n            break\n\n        i += chunk_size - overlap\n\n    return chunks\n\n\ndef create_corpus(parsed=\"data\/parsed.jsonl\", out=\"data\/corpus.jsonl\"):\n    with open(parsed, \"r\") as fin, open(out, \"w\") as fout:\n        for line in fin:\n            row = json.loads(line)\n            chs = chunk_text(row[\"content\"])\n\n            for idx, c in enumerate(chs):\n                fout.write(json.dumps({\n                    \"id\": f\"{row['doc_id']}_p{row['page']}_c{idx}\",\n                    \"doc_id\": row[\"doc_id\"],\n                    \"page\": row[\"page\"],\n                    \"chunk_id\": idx,\n                    \"content\": c,\n                    \"source\": row[\"source\"],\n                    \"title\": row[\"title\"]\n                }) + \"\\n\")\n\n    print(\"Created corpus \u2192 data\/corpus.jsonl\")\n\n\ncreate_corpus()<\/code><\/pre>\n<p>The following code normalises metadata:<\/p>\n<pre class=\"wp-block-code\"><code>import json\n\ndef normalize_metadata(path=\"data\/corpus.jsonl\"):\n    rows = []\n\n    with open(path) as fin:\n        for line in fin:\n            obj = json.loads(line)\n\n            obj.setdefault(\"source\", \"attention.pdf\")\n            obj.setdefault(\"title\", \"Attention Is All You Need\")\n\n            rows.append(obj)\n\n    with open(path, \"w\") as fout:\n        for r in rows:\n            fout.write(json.dumps(r) + \"\\n\")\n\n    print(\"Metadata normalized.\")\n\nnormalize_metadata()<\/code><\/pre>\n<h4 class=\"wp-block-heading\" id=\"h-indexing-embedding-storage-setup\">Indexing \/ Embedding + Storage Setup<\/h4>\n<p>Now, we\u2019d be using the embedding model:<\/p>\n<pre class=\"wp-block-code\"><code>import json\nimport numpy as np\nimport faiss\nfrom sentence_transformers import SentenceTransformer\nimport os\n\ndef build_faiss_index(corpus=\"data\/corpus.jsonl\", index_dir=\"data\/faiss_index\"):\n    os.makedirs(index_dir, exist_ok=True)\n\n    texts, ids = [], []\n\n    with open(corpus) as f:\n        for line in f:\n            row = json.loads(line)\n            texts.append(row[\"content\"])\n            ids.append(row[\"id\"])\n\n    model = SentenceTransformer(\"all-MiniLM-L6-v2\")\n    embeddings = model.encode(texts, convert_to_numpy=True)\n\n    faiss.normalize_L2(embeddings)\n\n    index = faiss.IndexFlatIP(embeddings.shape[1])\n    index.add(embeddings)\n\n    faiss.write_index(index, f\"{index_dir}\/index.faiss\")\n    json.dump(ids, open(f\"{index_dir}\/ids.json\", \"w\"))\n\n    print(\"Stored FAISS index at\", index_dir)\n\n\nbuild_faiss_index()<\/code><\/pre>\n<p><strong>Vector store or database setup:\u00a0<\/strong><\/p>\n<pre class=\"wp-block-code\"><code>vectordb: faiss_local\nindex_dir: data\/faiss_index\nembedding_module: sentence_transformers\/all-MiniLM-L6-v2<\/code><\/pre>\n<h2 class=\"wp-block-heading\" id=\"h-retrieval-and-rag-pipeline-experimentation-with-autorag\">Retrieval and RAG Pipeline Experimentation with AutoRAG<\/h2>\n<p>Firstly, we\u2019ll be creating an QA Evaluation Dataset:<\/p>\n<pre class=\"wp-block-code\"><code>import pandas as pd\nfrom autorag.data.qa.schema import Raw, Corpus\nfrom autorag.data.qa.sample import random_single_hop\nfrom autorag.data.qa.query.llama_gen_query import factoid_query_gen\nfrom autorag.data.qa.generation_gt.llama_index_gen_gt import make_basic_gen_gt\nfrom llama_index.llms.openai import OpenAI\n\ndef create_qa():\n    raw_df = pd.read_parquet(\"data\/parsed.parquet\")\n    corpus_df = pd.read_parquet(\"data\/corpus.parquet\")\n\n    raw_inst = Raw(raw_df)\n    corpus_inst = Corpus(corpus_df, raw_inst)\n\n    llm = OpenAI()\n\n    sampled = corpus_inst.sample(random_single_hop, n=140)\n    sampled = sampled.make_retrieval_gt_contents()\n    sampled = sampled.batch_apply(factoid_query_gen, llm=llm)\n    sampled = sampled.batch_apply(make_basic_gen_gt, llm=llm)\n\n    sampled.to_parquet(\"data\/qa.parquet\", index=False)\n\n    print(\"Created data\/qa.parquet\")\n\ncreate_qa()<\/code><\/pre>\n<p><strong>Running AutoRAG evaluation:<\/strong><\/p>\n<p><em>configs\/default_rag_config.yaml<\/em><\/p>\n<pre class=\"wp-block-code\"><code>node_lines:\u00a0\n\n- node_line_name: retrieve_node_line\u00a0\n\nnodes:\u00a0\n\n- node_type: semantic_retrieval\u00a0\n\ntop_k: 3\u00a0\n\nmodules:\u00a0\n\n- module_type: vectordb\u00a0\n\nvectordb: faiss_local\u00a0\n\nindex_dir: data\/faiss_index\u00a0\n\nembedding_module: sentence_transformers\/all-MiniLM-L6-v2\u00a0\n\n- node_line_name: post_retrieve_node_line\u00a0\n\nnodes:\u00a0\n\n- node_type: prompt_maker\u00a0\n\nmodules:\u00a0\n\n- module_type: fstring\u00a0\n\nprompt: |\u00a0\n\nUse the passages to answer the question.\u00a0\n\nQuestion: {query}\u00a0\n\nPassages: {retrieved_contents}\u00a0\n\nAnswer:\u00a0\n\n- node_type: generator\u00a0\n\nmodules:\u00a0\n\n- module_type: openai_llm\u00a0\n\nllm: gpt-4o-mini\u00a0\n\nbatch: 8<\/code><\/pre>\n<p>Use the following code for running the evaluator: <\/p>\n<pre class=\"wp-block-code\"><code>from autorag.evaluator import Evaluator\u00a0\n\nevaluator = Evaluator(\u00a0\n\nqa_data_path=\"data\/qa.parquet\",\u00a0\n\ncorpus_data_path=\"data\/corpus.parquet\"\u00a0\n\n)\u00a0\n\nevaluator.start_trial(\"configs\/default_rag_config.yaml\")<\/code><\/pre>\n<p><strong>Running the RAG:\u00a0<\/strong><\/p>\n<p><strong>User Query:<\/strong> <em>\u201cWhat is multi-head attention in the Transformer model?\u201d<\/em><\/p>\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"901\" height=\"192\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/12\/chart-8.webp\" alt=\"Running the RAG\" class=\"wp-image-247712\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/12\/chart-8.webp 901w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/12\/chart-8-300x64.webp 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/12\/chart-8-768x164.webp 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/12\/chart-8-150x32.webp 150w\" sizes=\"auto, (max-width: 901px) 100vw, 901px\"\/><\/figure>\n<h2 class=\"wp-block-heading\" id=\"h-best-practices-amp-recommendations\">Best Practices &amp; Recommendations<\/h2>\n<p>Building a robust RAG application involves careful planning. Here are some best practices:\u00a0<\/p>\n<ul class=\"wp-block-list\">\n<li><strong>Keep original text with embeddings:<\/strong> You should always keep the inode content of any chunk or reference to the inode content along with the embeddings for that content chunk \u2013 do not depend solely upon the embeddings.\u00a0<\/li>\n<li><strong>Chunk sensibly:<\/strong> Chunking is done in way that preserves meaning at the boundaries of chunks when being processed; therefore, you should use the same tokenization and the same size for all chunks of content (e.g., 300 to 512 tokens) and maintain overlap of chunks (e.g., 50 overlapping tokens).\u00a0<\/li>\n<li><strong>Cache embeddings:<\/strong> Cache identical embeddings for all content when sending a repeated query or when processing large amounts of content.\u00a0<\/li>\n<li><strong>Monitor quality:<\/strong> Monitor retrieval and generation performance using standard metrics; for example: AutoRAG provides standard metrics for assessing retrieval F1, recall, and correct answering; you can use these metrics to identify any problems with the performance of your RAG system.\u00a0<\/li>\n<li><strong>Secure your keys:<\/strong> Protect your API keys or model tokens; do not hard-code your keys into your application; instead, make use of environment variables or secure vaults.\u00a0<\/li>\n<\/ul>\n<p>By following the above guidelines, development teams will be able to build robust and reliable RAG applications that take advantage of AutoRAG\u2019s automation and yield high-quality results.\u00a0<\/p>\n<h2 class=\"wp-block-heading\" id=\"h-conclusion\">Conclusion<\/h2>\n<p>The design of a RAG application consists of multiple components between data processing and retrieval techniques. AutoRAG enables developers to automate the experimental phase and evaluation processes to make the creation of RAG applications simpler. With AutoRAG, developers can quickly experiment with various pipeline designs and launch a superior RAG application based on conclusive data.\u00a0<\/p>\n<p>By following the instructions provided within this document, users will be able to produce a dependable, accurate, and ready-to-use application with the implementation of optimal methods. Utilizing AutoRAG\u2019s optimization capabilities and incorporating established best practices, teams have a greater opportunity to create the most beneficial AI experience while reducing the need for time-consuming manual configuration.\u00a0<\/p>\n<h2 class=\"wp-block-heading\" id=\"h-frequently-asked-questions\">Frequently Asked Questions<\/h2>\n<div class=\"schema-faq wp-block-yoast-faq-block\">\n<div class=\"schema-faq-section\" id=\"faq-question-1765277736139\"><strong class=\"schema-faq-question\">Q1. What is AutoRAG used for?<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. AutoRAG automates RAG pipeline exploration, evaluation, and optimization. It helps developers identify the best configuration for retrieval and generation tasks.\u00a0<\/p>\n<\/p><\/div>\n<div class=\"schema-faq-section\" id=\"faq-question-1765277746629\"><strong class=\"schema-faq-question\">Q2. Do I need large datasets to use AutoRAG?<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. No, AutoRAG works with small and large datasets. However, more data improves evaluation accuracy and retrieval performance.\u00a0<\/p>\n<\/p><\/div>\n<div class=\"schema-faq-section\" id=\"faq-question-1765277754341\"><strong class=\"schema-faq-question\">Q3. Which embedding model should I choose?\u00a0<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. The choice depends on your use case. Lightweight models like MiniLM offer speed, while models like BGE or Jina provide higher semantic accuracy.\u00a0<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"border-top py-3 author-info my-4\">\n<div class=\"author-card d-flex align-items-center\">\n<div class=\"flex-shrink-0 overflow-hidden\">\n                                    <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/author\/vipin355333\/\" class=\"text-decoration-none active-avatar\"><br \/>\n                                                                       <img decoding=\"async\" src=\"https:\/\/av-eks-lekhak.s3.amazonaws.com\/media\/lekhak-profile-images\/converted_image_q6dapDN.webp\" width=\"48\" height=\"48\" alt=\"Vipin Vashisth\" loading=\"lazy\" class=\"rounded-circle\"\/><\/p>\n<p>                                <\/a>\n                                <\/div>\n<\/p><\/div>\n<p>Hello! I&#8217;m Vipin, a passionate data science and machine learning enthusiast with a strong foundation in data analysis, machine learning algorithms, and programming. I have hands-on experience in building models, managing messy data, and solving real-world problems. My goal is to apply data-driven insights to create practical solutions that drive results. I&#8217;m eager to contribute my skills in a collaborative environment while continuing to learn and grow in the fields of Data Science, Machine Learning, and NLP.<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<p><h4 class=\"fs-24 text-dark\">Login to continue reading and enjoy expert-curated content.<\/h4>\n<p>                        <button class=\"btn btn-primary mx-auto d-table\" data-bs-toggle=\"modal\" data-bs-target=\"#loginModal\" id=\"readMoreBtn\">Keep Reading for Free<\/button>\n                    <\/p>\n\n","protected":false},"excerpt":{"rendered":"<p>Retrieval-Augmented Generation (RAG) has become the go-to method for building reliable AI applications that depend on external data, since it helps overcome LLM limitations, cuts down hallucinations, and delivers expert-level responses grounded in trusted sources. As interest in RAG rises, so does the need for tools that make it easier to explore, test, and refine [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":345294,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12033],"tags":[16382,2059,24977,32726],"dealstore":[],"offerexpiration":[],"class_list":["post-345293","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","tag-automated","tag-guide","tag-pipeline","tag-rag"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>A Guide to Automated RAG Pipeline - Som2ny Network<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fivemor.com\/?p=345293\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"A Guide to Automated RAG Pipeline - Som2ny Network\" \/>\n<meta property=\"og:description\" content=\"Retrieval-Augmented Generation (RAG) has become the go-to method for building reliable AI applications that depend on external data, since it helps overcome LLM limitations, cuts down hallucinations, and delivers expert-level responses grounded in trusted sources. 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