{"id":6488,"date":"2026-08-04T13:34:58","date_gmt":"2026-08-04T11:34:58","guid":{"rendered":"https:\/\/www.hostx.ro\/kb\/"},"modified":"2026-08-04T13:35:13","modified_gmt":"2026-08-04T11:35:13","password":"","slug":"cum-construiesc-un-chatbot-pe-baza-documentelor-mele-rag-in-flowise","status":"publish","type":"docs","link":"https:\/\/www.hostx.ro\/kb\/cum-construiesc-un-chatbot-pe-baza-documentelor-mele-rag-in-flowise\/","title":{"rendered":"Cum construiesc un chatbot pe baza documentelor mele (RAG) \u00een Flowise?"},"content":{"rendered":"<p><strong>RAG<\/strong> (Retrieval-Augmented Generation) \u00ee\u021bi permite s\u0103 faci un chatbot care r\u0103spunde <strong>pe baza documentelor tale<\/strong> (PDF-uri, pagini, manuale), nu doar din cuno\u0219tin\u021bele generale ale modelului.<\/p>\n<h2>Ce componente \u00ee\u021bi trebuie<\/h2>\n<ul>\n<li>Un <strong>Document Loader<\/strong> (\u00eencarc\u0103 fi\u0219ierele: PDF, text, site)<\/li>\n<li>Un <strong>Text Splitter<\/strong> (\u00eemparte textul \u00een fragmente)<\/li>\n<li>Un nod de <strong>Embeddings<\/strong> (transform\u0103 fragmentele \u00een vectori)<\/li>\n<li>Un <strong>Vector Store<\/strong> (stocheaz\u0103 vectorii \u2014 de exemplu \u00een memorie, Chroma, Qdrant)<\/li>\n<li>Un <strong>Retrieval QA Chain<\/strong> (leag\u0103 c\u0103utarea \u00een vectori de model)<\/li>\n<\/ul>\n<h2>1. \u00cencarc\u0103 documentele<\/h2>\n<p>Adaug\u0103 un Document Loader potrivit (de exemplu <em>PDF File<\/em>) \u0219i \u00eencarc\u0103 fi\u0219ierele. Leag\u0103-l de un Text Splitter care taie textul \u00een buc\u0103\u021bi de c\u00e2teva sute de caractere.<\/p>\n<h2>2. Creeaz\u0103 embeddings \u0219i vector store<\/h2>\n<p>Conecteaz\u0103 splitter-ul la un nod de <strong>Embeddings<\/strong> (OpenAI sau Ollama) \u0219i apoi la un <strong>Vector Store<\/strong>. Aici fragmentele devin vectori c\u0103utabili.<\/p>\n<h2>3. Leag\u0103 la un Retrieval QA Chain<\/h2>\n<p>Adaug\u0103 un <strong>Conversational Retrieval QA Chain<\/strong>. Conecteaz\u0103 la el: modelul de chat, vector store-ul (ca retriever) \u0219i, op\u021bional, memoria. Acum, la fiecare \u00eentrebare, Flowise caut\u0103 fragmentele relevante \u0219i le trimite modelului \u00eempreun\u0103 cu \u00eentrebarea.<\/p>\n<h2>4. Testeaz\u0103<\/h2>\n<p>Deschide chat-ul \u0219i pune o \u00eentrebare al c\u0103rei r\u0103spuns se afl\u0103 \u00een documente. Modelul ar trebui s\u0103 r\u0103spund\u0103 folosind informa\u021bia din fi\u0219ierele tale. Dac\u0103 r\u0103spunde \u201enu \u0219tiu&#8221;, verific\u0103 dac\u0103 documentele au fost indexate corect.<\/p>\n<div class=\"note\">\n<p><strong>Sfat:<\/strong> Pentru informa\u021bii care se modific\u0103 des, folose\u0219te un <strong>Document Store<\/strong> persistent (Chroma\/Qdrant), ca s\u0103 nu reindexezi la fiecare pornire. Fragmentele prea mari sau prea mici scad calitatea r\u0103spunsurilor \u2014 experimenteaz\u0103 cu dimensiunea.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>RAG (Retrieval-Augmented Generation) \u00ee\u021bi permite s\u0103 faci un chatbot care r\u0103spunde pe baza documentelor tale (PDF-uri, pagini, manuale), nu doar din cuno\u0219tin\u021bele generale ale modelului. Ce componente \u00ee\u021bi trebuie Un Document Loader (\u00eencarc\u0103 fi\u0219ierele: PDF, text, site) Un Text Splitter (\u00eemparte textul \u00een fragmente) Un nod de Embeddings (transform\u0103 fragmentele \u00een vectori) Un Vector Store &#8230; <a title=\"Cum construiesc un chatbot pe baza documentelor mele (RAG) \u00een Flowise?\" class=\"read-more\" href=\"https:\/\/www.hostx.ro\/kb\/cum-construiesc-un-chatbot-pe-baza-documentelor-mele-rag-in-flowise\/\" aria-label=\"Cite\u0219te mai multe despre Cum construiesc un chatbot pe baza documentelor mele (RAG) \u00een Flowise?\">Cite\u0219te mai mult<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","template":"","meta":{"inline_featured_image":false,"whmcs_kb_id":0,"footnotes":""},"doc_category":[496],"doc_tag":[262,371,407,363,511,452],"class_list":["post-6488","docs","type-docs","status-publish","hentry","doc_category-flowise","doc_tag-api","doc_tag-aplicatii-web","doc_tag-content-management","doc_tag-debugging","doc_tag-flowise","doc_tag-integrare"],"year_month":"2026-08","word_count":256,"total_views":"2","reactions":{"happy":"0","normal":"0","sad":"0"},"author_info":{"name":"benyhill","author_nicename":"benyhill","author_url":"https:\/\/www.hostx.ro\/kb\/author\/benyhill\/"},"doc_category_info":[{"term_name":"Flowise","term_url":"https:\/\/www.hostx.ro\/kb\/docs-category\/flowise\/"}],"doc_tag_info":[{"term_name":"API","term_url":"https:\/\/www.hostx.ro\/kb\/docs-tag\/api\/"},{"term_name":"aplica\u021bii web","term_url":"https:\/\/www.hostx.ro\/kb\/docs-tag\/aplicatii-web\/"},{"term_name":"content management","term_url":"https:\/\/www.hostx.ro\/kb\/docs-tag\/content-management\/"},{"term_name":"debugging","term_url":"https:\/\/www.hostx.ro\/kb\/docs-tag\/debugging\/"},{"term_name":"Flowise","term_url":"https:\/\/www.hostx.ro\/kb\/docs-tag\/flowise\/"},{"term_name":"integrare","term_url":"https:\/\/www.hostx.ro\/kb\/docs-tag\/integrare\/"}],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.0 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Chatbot RAG \u00een Flowise cu documente proprii - Centrul de suport HostX<\/title>\n<meta name=\"description\" content=\"Construie\u0219te un chatbot RAG \u00een Flowise care r\u0103spunde pe baza documentelor tale (PDF-uri, manuale). 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