{"id":34944,"date":"2025-08-04T16:52:37","date_gmt":"2025-08-04T08:52:37","guid":{"rendered":"https:\/\/www.kdjingpai.com\/?p=34944"},"modified":"2025-08-04T16:52:37","modified_gmt":"2025-08-04T08:52:37","slug":"raglight","status":"publish","type":"post","link":"https:\/\/www.kdjingpai.com\/ja\/raglight\/","title":{"rendered":"RAGLight\uff1a\u8f7b\u91cf\u7ea7\u68c0\u7d22\u589e\u5f3a\u751f\u6210Python\u5e93"},"content":{"rendered":"<p>RAGLight \u662f\u4e00\u4e2a\u8f7b\u91cf\u7ea7\u3001\u6a21\u5757\u5316\u7684 Python \u5e93\uff0c\u4e13\u4e3a\u5b9e\u73b0\u68c0\u7d22\u589e\u5f3a\u751f\u6210\uff08RAG\uff09\u8bbe\u8ba1\u3002\u5b83\u901a\u8fc7\u7ed3\u5408\u6587\u6863\u68c0\u7d22\u548c\u81ea\u7136\u8bed\u8a00\u751f\u6210\uff0c\u63d0\u5347\u5927\u8bed\u8a00\u6a21\u578b\uff08LLM\uff09\u7684\u4e0a\u4e0b\u6587\u7406\u89e3\u80fd\u529b\u3002RAGLight \u652f\u6301\u591a\u79cd\u8bed\u8a00\u6a21\u578b\u3001\u5d4c\u5165\u6a21\u578b\u548c\u5411\u91cf\u5b58\u50a8\uff0c\u9002\u5408\u5f00\u53d1\u8005\u5feb\u901f\u6784\u5efa\u4e0a\u4e0b\u6587\u611f\u77e5\u7684 AI \u5e94\u7528\u3002\u5b83\u7684\u8bbe\u8ba1\u6ce8\u91cd\u7b80\u5355\u6027\u548c\u7075\u6d3b\u6027\uff0c\u7528\u6237\u53ef\u4ee5\u8f7b\u677e\u96c6\u6210\u672c\u5730\u6587\u4ef6\u5939\u6216 GitHub \u4ed3\u5e93\u4e2d\u7684\u6570\u636e\uff0c\u751f\u6210\u51c6\u786e\u7684\u56de\u7b54\u3002RAGLight \u5b8c\u5168\u5f00\u6e90\uff0c\u8fd0\u884c\u65f6\u9700\u914d\u5408 <a href=\"https:\/\/www.kdjingpai.com\/ollama\/\">Ollama<\/a> \u6216 LMStudio\uff0c\u652f\u6301\u672c\u5730\u5316\u90e8\u7f72\uff0c\u9002\u5408\u5bf9\u9690\u79c1\u548c\u6210\u672c\u654f\u611f\u7684\u9879\u76ee\u3002<\/p>\n<dl><\/dl>\n<dl>\n<dt><img decoding=\"async\" src=\"https:\/\/www.kdjingpai.com\/wp-content\/uploads\/2025\/08\/4c3facf4924c622-scaled.jpeg\" alt=\"\" \/><\/dt>\n<\/dl>\n<p>&nbsp;<\/p>\n<h2>\u529f\u80fd\u5217\u8868<\/h2>\n<ul>\n<li>\u652f\u6301\u591a\u79cd\u6570\u636e\u6e90\uff1a\u53ef\u4ece\u672c\u5730\u6587\u4ef6\u5939\uff08\u5982 PDF\u3001\u6587\u672c\u6587\u4ef6\uff09\u6216 GitHub \u4ed3\u5e93\u5bfc\u5165\u77e5\u8bc6\u5e93\u3002<\/li>\n<li>\u6a21\u5757\u5316 <a href=\"https:\/\/www.kdjingpai.com\/rag\/\">RAG<\/a> \u7ba1\u9053\uff1a\u7ed3\u5408\u6587\u6863\u68c0\u7d22\u548c\u8bed\u8a00\u751f\u6210\uff0c\u652f\u6301\u6807\u51c6 RAG\u3001Agentic RAG \u548c RAT\uff08\u68c0\u7d22\u589e\u5f3a\u601d\u8003\uff09\u6a21\u5f0f\u3002<\/li>\n<li>\u7075\u6d3b\u7684\u6a21\u578b\u96c6\u6210\uff1a\u652f\u6301 Ollama \u548c LMStudio \u7684\u591a\u79cd\u5927\u8bed\u8a00\u6a21\u578b\uff0c\u5982\u00a0<code>llama3<\/code>\u3002<\/li>\n<li>\u9ad8\u6548\u5411\u91cf\u5b58\u50a8\uff1a\u4f7f\u7528 Chroma \u6216 HuggingFace \u5d4c\u5165\u6a21\u578b\u751f\u6210\u6587\u6863\u5411\u91cf\uff0c\u652f\u6301\u5feb\u901f\u76f8\u4f3c\u6027\u641c\u7d22\u3002<\/li>\n<li>\u81ea\u5b9a\u4e49\u914d\u7f6e\uff1a\u5141\u8bb8\u7528\u6237\u8c03\u6574\u5d4c\u5165\u6a21\u578b\u3001\u5411\u91cf\u5b58\u50a8\u8def\u5f84\u548c\u68c0\u7d22\u53c2\u6570\uff08\u5982\u00a0<code>k<\/code>\u00a0\u503c\uff09\u3002<\/li>\n<li>\u6587\u6863\u5904\u7406\u81ea\u52a8\u5316\uff1a\u81ea\u52a8\u4ece\u6307\u5b9a\u6e90\u63d0\u53d6\u548c\u7d22\u5f15\u6587\u6863\u5185\u5bb9\uff0c\u7b80\u5316\u77e5\u8bc6\u5e93\u6784\u5efa\u3002<\/li>\n<\/ul>\n<h2>\u4f7f\u7528\u5e2e\u52a9<\/h2>\n<h3>\u5b89\u88c5\u6d41\u7a0b<\/h3>\n<p>RAGLight \u7684\u5b89\u88c5\u548c\u4f7f\u7528\u9700\u8981 Python \u73af\u5883\u548c\u8fd0\u884c\u4e2d\u7684 Ollama \u6216 LMStudio\u3002\u4ee5\u4e0b\u662f\u8be6\u7ec6\u6b65\u9aa4\uff1a<\/p>\n<ol>\n<li><strong>\u5b89\u88c5 Python \u548c\u4f9d\u8d56\u9879<\/strong><br \/>\n\u786e\u4fdd\u7cfb\u7edf\u5df2\u5b89\u88c5 Python 3.8 \u6216\u66f4\u9ad8\u7248\u672c\u3002\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5 RAGLight\uff1a<\/p>\n<pre><code>pip install raglight\r\n<\/code><\/pre>\n<p>\u5982\u679c\u4f7f\u7528 HuggingFace \u5d4c\u5165\u6a21\u578b\uff0c\u9700\u989d\u5916\u5b89\u88c5\u76f8\u5173\u4f9d\u8d56\uff1a<\/p>\n<pre><code>pip install sentence-transformers\r\n<\/code><\/pre>\n<\/li>\n<li><strong>\u5b89\u88c5\u5e76\u8fd0\u884c Ollama \u6216 LMStudio<\/strong>\n<ul>\n<li>\u4e0b\u8f7d\u5e76\u5b89\u88c5 Ollama\uff08https:\/\/ollama.ai\uff09\u6216 LMStudio\u3002<\/li>\n<li>\u5728 Ollama \u4e2d\u62c9\u53d6\u6a21\u578b\uff0c\u4f8b\u5982\uff1a\n<pre><code>ollama pull llama3\r\n<\/code><\/pre>\n<\/li>\n<li>\u786e\u4fdd\u6a21\u578b\u5728 Ollama \u6216 LMStudio \u4e2d\u52a0\u8f7d\u5e76\u8fd0\u884c\u3002<\/li>\n<\/ul>\n<\/li>\n<li><strong>\u914d\u7f6e\u73af\u5883<\/strong><br \/>\n\u521b\u5efa\u9879\u76ee\u6587\u4ef6\u5939\uff0c\u51c6\u5907\u77e5\u8bc6\u5e93\u6570\u636e\uff08\u5982 PDF \u6587\u4ef6\u5939\u6216 GitHub \u4ed3\u5e93 URL\uff09\u3002\u786e\u4fdd\u7f51\u7edc\u8fde\u63a5\u6b63\u5e38\u4ee5\u8bbf\u95ee GitHub \u6216 HuggingFace\u3002<\/li>\n<\/ol>\n<h3>\u4f7f\u7528 RAGLight \u521b\u5efa\u7b80\u5355 RAG \u7ba1\u9053<\/h3>\n<p>RAGLight \u63d0\u4f9b\u7b80\u6d01\u7684 API \u6765\u6784\u5efa RAG \u7ba1\u9053\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u57fa\u7840\u793a\u4f8b\uff0c\u7528\u4e8e\u4ece\u672c\u5730\u6587\u4ef6\u5939\u548c GitHub \u4ed3\u5e93\u6784\u5efa\u77e5\u8bc6\u5e93\u5e76\u751f\u6210\u56de\u7b54\uff1a<\/p>\n<pre><code>from raglight.rag.simple_rag_api import RAGPipeline\r\nfrom raglight.models.data_source_model import FolderSource, GitHubSource\r\nfrom raglight.config.settings import Settings\r\nSettings.setup_logging()\r\n# \u5b9a\u4e49\u77e5\u8bc6\u5e93\u6765\u6e90\r\nknowledge_base = [\r\nFolderSource(path=\"\/path\/to\/your\/folder\/knowledge_base\"),\r\nGitHubSource(url=\"https:\/\/github.com\/Bessouat40\/RAGLight\")\r\n]\r\n# \u521d\u59cb\u5316 RAG \u7ba1\u9053\r\npipeline = RAGPipeline(\r\nknowledge_base=knowledge_base,\r\nmodel_name=\"llama3\",\r\nprovider=Settings.OLLAMA,\r\nk=5\r\n)\r\n# \u6784\u5efa\u7ba1\u9053\uff08\u5904\u7406\u6587\u6863\u5e76\u521b\u5efa\u5411\u91cf\u5b58\u50a8\uff09\r\npipeline.build()\r\n# \u751f\u6210\u56de\u7b54\r\nresponse = pipeline.generate(\"\u5982\u4f55\u4f7f\u7528 RAGLight \u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684 RAG \u7ba1\u9053\uff1f\")\r\nprint(response)\r\n<\/code><\/pre>\n<h3>\u7279\u8272\u529f\u80fd\u64cd\u4f5c<\/h3>\n<ol>\n<li><strong>\u652f\u6301\u591a\u79cd\u6570\u636e\u6e90<\/strong><br \/>\nRAGLight \u5141\u8bb8\u7528\u6237\u4ece\u672c\u5730\u6587\u4ef6\u5939\u6216 GitHub \u4ed3\u5e93\u5bfc\u5165\u6570\u636e\u3002<\/p>\n<ul>\n<li>\u672c\u5730\u6587\u4ef6\u5939\uff1a\u5c06 PDF \u6216\u6587\u672c\u6587\u4ef6\u653e\u5165\u6307\u5b9a\u6587\u4ef6\u5939\uff0c\u4f8b\u5982\u00a0<code>\/path\/to\/knowledge_base<\/code>\u3002<\/li>\n<li>GitHub \u4ed3\u5e93\uff1a\u63d0\u4f9b\u4ed3\u5e93 URL\uff08\u5982\u00a0<code>https:\/\/github.com\/Bessouat40\/RAGLight<\/code>\uff09\uff0cRAGLight \u4f1a\u81ea\u52a8\u63d0\u53d6\u4ed3\u5e93\u4e2d\u7684\u6587\u6863\u3002<br \/>\n\u793a\u4f8b\u914d\u7f6e\uff1a<\/li>\n<\/ul>\n<pre><code>knowledge_base = [\r\nFolderSource(path=\"\/data\/knowledge_base\"),\r\nGitHubSource(url=\"https:\/\/github.com\/Bessouat40\/RAGLight\")\r\n]\r\n<\/code><\/pre>\n<\/li>\n<li><strong>\u6807\u51c6 RAG \u7ba1\u9053<\/strong><br \/>\n\u6807\u51c6 RAG \u7ba1\u9053\u7ed3\u5408\u6587\u6863\u68c0\u7d22\u548c\u751f\u6210\u3002\u7528\u6237\u8f93\u5165\u67e5\u8be2\u540e\uff0cRAGLight \u5c06\u67e5\u8be2\u8f6c\u6362\u4e3a\u5411\u91cf\uff0c\u901a\u8fc7\u76f8\u4f3c\u6027\u641c\u7d22\u68c0\u7d22\u76f8\u5173\u6587\u6863\u7247\u6bb5\uff0c\u5e76\u5c06\u8fd9\u4e9b\u7247\u6bb5\u4f5c\u4e3a\u4e0a\u4e0b\u6587\u8f93\u5165 LLM \u751f\u6210\u56de\u7b54\u3002<br \/>\n\u64cd\u4f5c\u6b65\u9aa4\uff1a<\/p>\n<ul>\n<li>\u521d\u59cb\u5316\u00a0<code>RAGPipeline<\/code>\u00a0\u5e76\u6307\u5b9a\u77e5\u8bc6\u5e93\u3001\u6a21\u578b\u548c\u00a0<code>k<\/code>\u00a0\u503c\uff08\u68c0\u7d22\u6587\u6863\u6570\u91cf\uff09\u3002<\/li>\n<li>\u8c03\u7528\u00a0<code>pipeline.build()<\/code>\u00a0\u5904\u7406\u6587\u6863\u5e76\u751f\u6210\u5411\u91cf\u5b58\u50a8\u3002<\/li>\n<li>\u4f7f\u7528\u00a0<code>pipeline.generate(\"\u67e5\u8be2\")<\/code>\u00a0\u83b7\u53d6\u56de\u7b54\u3002<\/li>\n<\/ul>\n<\/li>\n<li><strong>Agentic RAG \u548c RAT \u6a21\u5f0f<\/strong>\n<ul>\n<li><strong>Agentic RAG<\/strong>\uff1a\u901a\u8fc7\u00a0<code>AgenticRAGPipeline<\/code>\u00a0\u5b9e\u73b0\uff0c\u589e\u52a0\u667a\u80fd\u4f53\u529f\u80fd\uff0c\u652f\u6301\u591a\u6b65\u63a8\u7406\u548c\u52a8\u6001\u8c03\u6574\u68c0\u7d22\u7b56\u7565\u3002<br \/>\n\u793a\u4f8b\uff1a<\/p>\n<pre><code>from raglight.rag.simple_agentic_rag_api import AgenticRAGPipeline\r\nfrom raglight.config.agentic_rag_config import SimpleAgenticRAGConfig\r\nconfig = SimpleAgenticRAGConfig(k=5, max_steps=4)\r\npipeline = AgenticRAGPipeline(knowledge_base=knowledge_base, config=config)\r\npipeline.build()\r\nresponse = pipeline.generate(\"\u5982\u4f55\u4f18\u5316 RAGLight \u7684\u68c0\u7d22\u6548\u7387\uff1f\")\r\nprint(response)\r\n<\/code><\/pre>\n<\/li>\n<li><strong>RAT\uff08\u68c0\u7d22\u589e\u5f3a\u601d\u8003\uff09<\/strong>\uff1a\u901a\u8fc7\u00a0<code>RATPipeline<\/code>\u00a0\u5b9e\u73b0\uff0c\u589e\u52a0\u53cd\u601d\u6b65\u9aa4\uff08<code><a href=\"https:\/\/www.kdjingpai.com\/reflection-2\/\">reflection<\/a><\/code>\u00a0\u53c2\u6570\uff09\uff0c\u63d0\u5347\u56de\u7b54\u7684\u903b\u8f91\u6027\u548c\u51c6\u786e\u6027\u3002<br \/>\n\u793a\u4f8b\uff1a<\/p>\n<pre><code>from raglight.rat.simple_rat_api import RATPipeline\r\npipeline = RATPipeline(\r\nknowledge_base=knowledge_base,\r\nmodel_name=\"llama3\",\r\nreasoning_model_name=\"<a href=\"https:\/\/www.kdjingpai.com\/deepseek-r1nenglixiang\/\">deepseek-r1<\/a>:1.5b\",\r\nreflection=2,\r\nprovider=Settings.OLLAMA\r\n)\r\npipeline.build()\r\nresponse = pipeline.generate(\"\u5982\u4f55\u7b80\u5316 RAGLight \u7684\u914d\u7f6e\uff1f\")\r\nprint(response)\r\n<\/code><\/pre>\n<\/li>\n<\/ul>\n<\/li>\n<li><strong>\u81ea\u5b9a\u4e49\u5411\u91cf\u5b58\u50a8<\/strong><br \/>\nRAGLight \u4f7f\u7528 Chroma \u4f5c\u4e3a\u9ed8\u8ba4\u5411\u91cf\u5b58\u50a8\uff0c\u652f\u6301 HuggingFace \u5d4c\u5165\u6a21\u578b\uff08\u5982\u00a0<code>all-MiniLM-L6-v2<\/code>\uff09\u3002\u7528\u6237\u53ef\u81ea\u5b9a\u4e49\u5b58\u50a8\u8def\u5f84\u548c\u96c6\u5408\u540d\u79f0\uff1a<\/p>\n<pre><code>from raglight.config.vector_store_config import VectorStoreConfig\r\nvector_store_config = VectorStoreConfig(\r\nembedding_model=\"all-MiniLM-L6-v2\",\r\nprovider=Settings.HUGGINGFACE,\r\ndatabase=Settings.CHROMA,\r\npersist_directory=\".\/defaultDb\",\r\ncollection_name=\"my_collection\"\r\n)\r\n<\/code><\/pre>\n<\/li>\n<\/ol>\n<h3>\u64cd\u4f5c\u6ce8\u610f\u4e8b\u9879<\/h3>\n<ul>\n<li>\u786e\u4fdd Ollama \u6216 LMStudio \u8fd0\u884c\u65f6\u6a21\u578b\u5df2\u52a0\u8f7d\uff0c\u5426\u5219\u4f1a\u62a5\u9519\u3002<\/li>\n<li>\u672c\u5730\u6587\u4ef6\u5939\u8def\u5f84\u9700\u5305\u542b\u6709\u6548\u6587\u6863\uff08\u5982 PDF\u3001TXT\uff09\uff0cGitHub \u4ed3\u5e93\u9700\u516c\u5f00\u8bbf\u95ee\u3002<\/li>\n<li>\u8c03\u6574\u00a0<code>k<\/code>\u00a0\u503c\u4ee5\u63a7\u5236\u68c0\u7d22\u6587\u6863\u6570\u91cf\uff0c<code>k=5<\/code>\u00a0\u901a\u5e38\u662f\u5e73\u8861\u6548\u7387\u548c\u51c6\u786e\u6027\u7684\u9009\u62e9\u3002<\/li>\n<li>\u5982\u679c\u4f7f\u7528 HuggingFace \u5d4c\u5165\u6a21\u578b\uff0c\u9700\u786e\u4fdd\u7f51\u7edc\u53ef\u8bbf\u95ee HuggingFace API\u3002<\/li>\n<\/ul>\n<h2>\u5e94\u7528\u573a\u666f<\/h2>\n<ol>\n<li><strong>\u5b66\u672f\u7814\u7a76<\/strong><br \/>\n\u7814\u7a76\u4eba\u5458\u53ef\u5c06\u8bba\u6587 PDF \u5bfc\u5165\u672c\u5730\u6587\u4ef6\u5939\uff0c\u4f7f\u7528 RAGLight \u5feb\u901f\u68c0\u7d22\u6587\u732e\u5185\u5bb9\u5e76\u751f\u6210\u603b\u7ed3\u6216\u56de\u7b54\u95ee\u9898\u3002\u4f8b\u5982\uff0c\u8f93\u5165\u201c\u67d0\u9886\u57df\u6700\u65b0\u8fdb\u5c55\u201d\u53ef\u83b7\u53d6\u76f8\u5173\u8bba\u6587\u7684\u4e0a\u4e0b\u6587\u56de\u7b54\u3002<\/li>\n<li><strong>\u4f01\u4e1a\u77e5\u8bc6\u5e93<\/strong><br \/>\n\u4f01\u4e1a\u53ef\u5c06\u5185\u90e8\u6587\u6863\uff08\u5982\u6280\u672f\u624b\u518c\u3001FAQ\uff09\u5bfc\u5165 RAGLight\uff0c\u6784\u5efa\u667a\u80fd\u95ee\u7b54\u7cfb\u7edf\u3002\u5458\u5de5\u8f93\u5165\u95ee\u9898\u540e\uff0c\u7cfb\u7edf\u4ece\u6587\u6863\u4e2d\u68c0\u7d22\u5e76\u751f\u6210\u51c6\u786e\u56de\u7b54\u3002<\/li>\n<li><strong>\u5f00\u53d1\u8005\u5de5\u5177<\/strong><br \/>\n\u5f00\u53d1\u8005\u53ef\u5c06 GitHub \u4ed3\u5e93\u7684\u4ee3\u7801\u6587\u6863\u4f5c\u4e3a\u77e5\u8bc6\u5e93\uff0c\u5feb\u901f\u67e5\u8be2 API \u4f7f\u7528\u65b9\u6cd5\u6216\u4ee3\u7801\u7247\u6bb5\u3002\u4f8b\u5982\uff0c\u8f93\u5165\u201c\u5982\u4f55\u8c03\u7528\u67d0\u51fd\u6570\u201d\u53ef\u83b7\u53d6\u76f8\u5173\u6587\u6863\u8bf4\u660e\u3002<\/li>\n<li><strong>\u6559\u80b2\u8f85\u52a9<\/strong><br \/>\n\u6559\u5e08\u6216\u5b66\u751f\u53ef\u5c06\u6559\u6750\u6216\u8bfe\u7a0b\u7b14\u8bb0\u5bfc\u5165 RAGLight\uff0c\u751f\u6210\u9488\u5bf9\u6027\u89e3\u7b54\u6216\u5b66\u4e60\u603b\u7ed3\u3002\u4f8b\u5982\uff0c\u8f93\u5165\u201c\u89e3\u91ca\u67d0\u6982\u5ff5\u201d\u53ef\u83b7\u53d6\u6559\u6750\u4e2d\u7684\u76f8\u5173\u5185\u5bb9\u3002<\/li>\n<\/ol>\n<h2>QA<\/h2>\n<ol>\n<li><strong>RAGLight \u652f\u6301\u54ea\u4e9b\u8bed\u8a00\u6a21\u578b\uff1f<\/strong><br \/>\nRAGLight \u652f\u6301 Ollama \u548c LMStudio \u63d0\u4f9b\u7684\u6a21\u578b\uff0c\u5982\u00a0<code>llama3<\/code>\u3001<code>deepseek-r1:1.5b<\/code>\u00a0\u7b49\u3002\u7528\u6237\u9700\u5728 Ollama \u6216 LMStudio \u4e2d\u9884\u5148\u52a0\u8f7d\u6a21\u578b\u3002<\/li>\n<li><strong>\u5982\u4f55\u6dfb\u52a0\u81ea\u5b9a\u4e49\u6570\u636e\u6e90\uff1f<\/strong><br \/>\n\u4f7f\u7528\u00a0<code>FolderSource<\/code>\u00a0\u6307\u5b9a\u672c\u5730\u6587\u4ef6\u5939\u8def\u5f84\uff0c\u6216\u00a0<code>GitHubSource<\/code>\u00a0\u6307\u5b9a\u516c\u5f00 GitHub \u4ed3\u5e93 URL\u3002\u786e\u4fdd\u8def\u5f84\u6709\u6548\u4e14\u6587\u4ef6\u683c\u5f0f\u652f\u6301\uff08\u5982 PDF\u3001TXT\uff09\u3002<\/li>\n<li><strong>\u5982\u4f55\u4f18\u5316\u68c0\u7d22\u51c6\u786e\u6027\uff1f<\/strong><br \/>\n\u589e\u52a0\u00a0<code>k<\/code>\u00a0\u503c\u4ee5\u68c0\u7d22\u66f4\u591a\u6587\u6863\uff0c\u6216\u4f7f\u7528 RAT 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