{"id":19,"date":"2026-09-07T11:49:44","date_gmt":"2026-09-07T11:49:44","guid":{"rendered":"http:\/\/127.0.0.1:8080\/index.php\/2026\/09\/07\/xay-dung-he-thong-rag-retrieval-augmented-generation-voi-vector-database-va-llms\/"},"modified":"2026-09-07T11:49:44","modified_gmt":"2026-09-07T11:49:44","slug":"xay-dung-he-thong-rag-retrieval-augmented-generation-voi-vector-database-va-llms","status":"publish","type":"post","link":"https:\/\/zunliz.com\/index.php\/2026\/09\/07\/xay-dung-he-thong-rag-retrieval-augmented-generation-voi-vector-database-va-llms\/","title":{"rendered":"X\u00e2y d\u1ef1ng H\u1ec7 th\u1ed1ng RAG (Retrieval-Augmented Generation) v\u1edbi Vector Database v\u00e0 LLMs"},"content":{"rendered":"

M\u1eb7c d\u00f9 c\u00e1c m\u00f4 h\u00ecnh ng\u00f4n ng\u1eef l\u1edbn (LLMs) s\u1edf h\u1eefu l\u01b0\u1ee3ng tri th\u1ee9c kh\u1ed5ng l\u1ed3, ch\u00fang v\u1eabn m\u1eafc ph\u1ea3i hai gi\u1edbi h\u1ea1n l\u1edbn: hi\u1ec7n t\u01b0\u1ee3ng “\u1ea3o gi\u00e1c” (Hallucination) v\u00e0 thi\u1ebfu th\u00f4ng tin d\u1eef li\u1ec7u n\u1ed9i b\u1ed9\/th\u1eddi gian th\u1ef1c. K\u1ef9 thu\u1eadt RAG (Retrieval-Augmented Generation)<\/strong> ch\u00ednh l\u00e0 gi\u1ea3i ph\u00e1p h\u00e0ng \u0111\u1ea7u \u0111\u1ec3 kh\u1eafc ph\u1ee5c tri\u1ec7t \u0111\u1ec3 v\u1ea5n \u0111\u1ec1 n\u00e0y.<\/p>\n\n\n

1. Nguy\u00ean l\u00fd Ho\u1ea1t \u0111\u1ed9ng c\u1ee7a RAG Pipeline<\/h2>\n\n

H\u1ec7 th\u1ed1ng RAG ho\u1ea1t \u0111\u1ed9ng theo quy tr\u00ecnh 3 giai \u0111o\u1ea1n c\u1ed1t l\u00f5i:<\/p>\n

    \n
  1. Indexing (Ch\u1ec9 m\u1ee5c h\u00f3a):<\/strong> T\u00e0i li\u1ec7u v\u0103n b\u1ea3n (PDF, Docs, Markdown) \u0111\u01b0\u1ee3c b\u00f3c t\u00e1ch th\u00e0nh c\u00e1c \u0111o\u1ea1n nh\u1ecf (Chunks) c\u00f3 \u0111\u1ed9 d\u00e0i kho\u1ea3ng 500-1000 tokens, sau \u0111\u00f3 chuy\u1ec3n \u0111\u1ed5i th\u00e0nh c\u00e1c Vector s\u1ed1 h\u1ecdc nhi\u1ec1u chi\u1ec1u th\u00f4ng qua Embedding Model (nh\u01b0 text-embedding-3-small<\/code>).<\/li>\n
  2. Retrieval (Truy v\u1ea5n t\u01b0\u01a1ng \u0111\u1ed3ng):<\/strong> Khi ng\u01b0\u1eddi d\u00f9ng \u0111\u1eb7t c\u00e2u h\u1ecfi, c\u00e2u h\u1ecfi c\u0169ng \u0111\u01b0\u1ee3c m\u00e3 h\u00f3a th\u00e0nh Vector. C\u01a1 s\u1edf d\u1eef li\u1ec7u Vector (ChromaDB, Pinecone, Qdrant) s\u1ebd t\u00ecm ki\u1ebfm c\u00e1c \u0111o\u1ea1n v\u0103n b\u1ea3n c\u00f3 \u0111\u1ed9 t\u01b0\u01a1ng \u0111\u1ed3ng cosine (Cosine Similarity) cao nh\u1ea5t v\u1edbi c\u00e2u h\u1ecfi.<\/li>\n
  3. Generation (Sinh ph\u1ea3n h\u1ed3i):<\/strong> C\u00e1c \u0111o\u1ea1n th\u00f4ng tin t\u00ecm \u0111\u01b0\u1ee3c k\u1ebft h\u1ee3p c\u00f9ng c\u00e2u h\u1ecfi g\u1ed1c \u0111\u01b0\u1ee3c \u0111\u01b0a v\u00e0o Prompt g\u1eedi \u0111\u1ebfn LLM \u0111\u1ec3 m\u00f4 h\u00ecnh t\u1ed5ng h\u1ee3p c\u00e2u tr\u1ea3 l\u1eddi ch\u00ednh x\u00e1c 100% d\u1ef1a tr\u00ean t\u00e0i li\u1ec7u cung c\u1ea5p.<\/li>\n<\/ol>\n\n\n

    2. M\u00e3 ngu\u1ed3n Tri\u1ec3n khai RAG v\u1edbi Python & LangChain<\/h2>\n\n

    \u0110o\u1ea1n m\u00e3 ho\u00e0n ch\u1ec9nh d\u01b0\u1edbi \u0111\u00e2y minh h\u1ecda vi\u1ec7c n\u1ea1p t\u00e0i li\u1ec7u v\u00e0 th\u1ef1c hi\u1ec7n h\u1ecfi \u0111\u00e1p th\u00f4ng minh:<\/p>\n\n

    import os\nfrom langchain_community.document_loaders import TextLoader\nfrom langchain_text_splitters import RecursiveCharacterTextSplitter\nfrom langchain_community.vectorstores import Chroma\nfrom langchain_openai import OpenAIEmbeddings, ChatOpenAI\nfrom langchain.chains import create_retrieval_chain\nfrom langchain.chains.combine_documents import create_stuff_documents_chain\nfrom langchain_core.prompts import ChatPromptTemplate\n\n# 1. N\u1ea1p v\u00e0 ph\u00e2n \u0111o\u1ea1n t\u00e0i li\u1ec7u\nloader = TextLoader(\"knowledge_base.txt\", encoding=\"utf-8\")\ndocs = loader.load()\ntext_splitter = RecursiveCharacterTextSplitter(chunk_size=600, chunk_overlap=100)\nsplits = text_splitter.split_documents(docs)\n\n# 2. T\u1ea1o Vector Store v\u1edbi Chroma\nvectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())\nretriever = vectorstore.as_retriever(search_kwargs={\"k\": 3})\n\n# 3. Thi\u1ebft l\u1eadp LLM v\u00e0 Prompt template\nllm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0.1)\nsystem_prompt = (\n    \"B\u1ea1n l\u00e0 tr\u1ee3 l\u00fd AI chuy\u00ean nghi\u1ec7p. H\u00e3y s\u1eed d\u1ee5ng c\u00e1c \u0111o\u1ea1n ng\u1eef c\u1ea3nh sau \u0111\u00e2y \u0111\u1ec3 tr\u1ea3 l\u1eddi c\u00e2u h\u1ecfi: \"\n    \"nn{context}nn\"\n    \"N\u1ebfu th\u00f4ng tin kh\u00f4ng c\u00f3 trong t\u00e0i li\u1ec7u, h\u00e3y th\u00e0nh th\u1eadt tr\u1ea3 l\u1eddi l\u00e0 b\u1ea1n kh\u00f4ng bi\u1ebft.\"\n)\nprompt = ChatPromptTemplate.from_messages([\n    (\"system\", system_prompt),\n    (\"human\", \"{input}\"),\n])\n\n# 4. Th\u1ef1c thi chu\u1ed7i truy v\u1ea5n RAG\nquestion_answer_chain = create_stuff_documents_chain(llm, prompt)\nrag_chain = create_retrieval_chain(retriever, question_answer_chain)\n\nresponse = rag_chain.invoke({\"input\": \"Quy tr\u00ecnh b\u1ea3o h\u00e0nh s\u1ea3n ph\u1ea9m di\u1ec5n ra nh\u01b0 th\u1ebf n\u00e0o?\"})\nprint(\"Ph\u1ea3n h\u1ed3i:\", response[\"answer\"])<\/code><\/pre>\n\n\n
    3. C\u00e1c Chi\u1ebfn l\u01b0\u1ee3c T\u1ed1i \u01b0u RAG N\u00e2ng cao<\/h2>\n\n
      \n  
    • Hybrid Search:<\/strong> K\u1ebft h\u1ee3p gi\u1eefa t\u00ecm ki\u1ebfm t\u1eeb kh\u00f3a truy\u1ec1n th\u1ed1ng (BM25) v\u00e0 t\u00ecm ki\u1ebfm ng\u1eef ngh\u0129a (Dense Vector) \u0111\u1ec3 t\u1ed1i \u01b0u \u0111\u1ed9 ch\u00ednh x\u00e1c cho c\u00e1c thu\u1eadt ng\u1eef chuy\u00ean ng\u00e0nh.<\/li>\n  
    • Reranking:<\/strong> S\u1eed d\u1ee5ng m\u00f4 h\u00ecnh Cohere Rerank ho\u1eb7c Cross-Encoder \u0111\u1ec3 s\u1eafp x\u1ebfp l\u1ea1i th\u1ee9 t\u1ef1 \u01b0u ti\u00ean c\u1ee7a c\u00e1c k\u1ebft qu\u1ea3 tr\u01b0\u1edbc khi \u0111\u01b0a v\u00e0o ng\u1eef c\u1ea3nh LLM.<\/li>\n<\/ul>","protected":false},"excerpt":{"rendered":"
      M\u1eb7c d\u00f9 c\u00e1c m\u00f4 h\u00ecnh ng\u00f4n ng\u1eef l\u1edbn (LLMs) s\u1edf h\u1eefu l\u01b0\u1ee3ng tri th\u1ee9c kh\u1ed5ng l\u1ed3, ch\u00fang v\u1eabn m\u1eafc ph\u1ea3i hai gi\u1edbi h\u1ea1n l\u1edbn: hi\u1ec7n t\u01b0\u1ee3ng “\u1ea3o gi\u00e1c” (Hallucination) v\u00e0 thi\u1ebfu th\u00f4ng tin d\u1eef li\u1ec7u n\u1ed9i b\u1ed9\/th\u1eddi gian th\u1ef1c. K\u1ef9 thu\u1eadt RAG (Retrieval-Augmented Generation) ch\u00ednh l\u00e0 gi\u1ea3i ph\u00e1p h\u00e0ng \u0111\u1ea7u \u0111\u1ec3 kh\u1eafc ph\u1ee5c tri\u1ec7t \u0111\u1ec3 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3,4],"tags":[],"class_list":["post-19","post","type-post","status-publish","format-standard","hentry","category-cong-nghe-lap-trinh","category-tri-tue-nhan-tao"],"_links":{"self":[{"href":"https:\/\/zunliz.com\/index.php\/wp-json\/wp\/v2\/posts\/19","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/zunliz.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/zunliz.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/zunliz.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/zunliz.com\/index.php\/wp-json\/wp\/v2\/comments?post=19"}],"version-history":[{"count":0,"href":"https:\/\/zunliz.com\/index.php\/wp-json\/wp\/v2\/posts\/19\/revisions"}],"wp:attachment":[{"href":"https:\/\/zunliz.com\/index.php\/wp-json\/wp\/v2\/media?parent=19"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/zunliz.com\/index.php\/wp-json\/wp\/v2\/categories?post=19"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/zunliz.com\/index.php\/wp-json\/wp\/v2\/tags?post=19"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}