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是目前比较新的大模型RAG框架,RAG是指 检索增强生成 (Retrieval-Augmented Generation, RAG)是指在利用大语言模型回答问题之前,先从外部知识库检索相关信息。" itemprop=description></span><header class=post-header><h1 itemprop="name headline" class=post-title>学习下LlamaIndex-初始篇</h1><div class=post-meta-container><div class=post-meta><span class=post-meta-item><span class=post-meta-item-icon><i class="far fa-calendar"></i> </span><span class=post-meta-item-text>发表于</span> <time itemprop="dateCreated datePublished" title="创建时间:2024-04-21 22:00:56" datetime=2024-04-21T22:00:56+08:00>2024-04-21</time> </span><span class=post-meta-item><span class=post-meta-item-icon><i class="far fa-folder"></i> </span><span class=post-meta-item-text>分类于</span> <span itemprop=about itemscope itemtype=http://schema.org/Thing><a href=/categories/LLM/ itemprop=url rel=index><span itemprop=name>LLM</span></a> </span></span><span class="post-meta-item cloudflare-page-counter" data-increment=true data-path=/2024/04/21/%E5%AD%A6%E4%B9%A0%E4%B8%8BLlamaIndex-%E5%88%9D%E5%A7%8B%E7%AF%87/ data-title=学习下LlamaIndex-初始篇 title=阅读次数><span class=post-meta-item-icon><i class="far fa-eye"></i> </span><span class=post-meta-item-text>阅读次数:</span> <span class=cloudflare-page-counter-count></span></span></div><div class=post-description>LlamaIndex 是目前比较新的大模型RAG框架,RAG是指 检索增强生成 (Retrieval-Augmented Generation, RAG)是指在利用大语言模型回答问题之前,先从外部知识库检索相关信息。</div></div></header><div class=post-body itemprop=articleBody><p>LlamaIndex 是目前比较新的大模型RAG框架,RAG是指 检索增强生成 (Retrieval-Augmented Generation, RAG)是指在利用大语言模型回答问题之前,先从外部知识库检索相关信息。RAG 被证明能显著提升答案的准确性,并特别是在知识密集型任务上减少模型的错误输出。通过引用信息来源,用户可以核实答案的准确性,从而增强对模型输出的信任。<br>此外,RAG 有助于快速更新知识并引入特定领域的专业知识。<br>RAG 有效结合了大语言模型的参数化知识和非参数化的外部知识库,成为实施大语言模型的关键方法之一。本文概述了 RAG 在大语言模型时代的发展模式,总结了三种模式:初级 RAG、高级 RAG 和模块化 RAG。<br>首先我们可以了解下LlamaIndex的几大模块,<br>使用大型语言模型(LLMs):无论是OpenAI还是任何托管的LLM,亦或是您自己本地运行的模型,LLM都被用于从索引和存储到查询和解析数据的每一个步骤。LlamaIndex提供了大量可靠经过测试的提示,我们也将向您展示如何自定义您自己的提示。<br>LlamaIndex 主要有这五大块能力<ul><li>Data connectors<li>Data indexes<li>Engines<li>Data agents<li>Application integrations<br><img loading=lazy src=https://img.nicksxs.me/blog/kFxOqW.png></ul><p>其中加载部分可以认为是初始的部分,我们可以从各个数据源去获取信息,比如各种文档,或者confluence,甚至api接口,只需要合适的数据加载器,我们就能将其转化为我们要的数据<br>比如我想要将我博客里的Markdown文件作为知识库加载<figure class="highlight python"><table><tr><td class=gutter><pre><span class=line>1</span><br><span class=line>2</span><br><span class=line>3</span><br><span class=line>4</span><br><span class=line>5</span><br><span class=line>6</span><br></pre><td class=code><pre><span class=line>reader = SimpleDirectoryReader(</span><br><span class=line> input_dir=<span class=string>"./somedir/source/_posts/"</span>,</span><br><span class=line> required_exts=[<span class=string>".md"</span>],</span><br><span class=line> recursive=<span class=literal>True</span></span><br><span class=line>)</span><br><span class=line>docs = reader.load_data()</span><br></pre></table></figure><p>然后由文档来就构建我们的索引<figure class="highlight python"><table><tr><td class=gutter><pre><span class=line>1</span><br></pre><td class=code><pre><span class=line>index = VectorStoreIndex.from_documents(docs)</span><br></pre></table></figure><p>但是这里我们需要提前设置好对应的 embedding 模型,这个很重要,如果有条件推荐使用OpenAI的,如果想用本地的可以考虑使用BAAI智源出品的,只是要注意是否支持多语言,如果要使用本地embedding的话需要安装<figure class="highlight shell"><table><tr><td class=gutter><pre><span class=line>1</span><br></pre><td class=code><pre><span class=line>pip install llama-index-embeddings-huggingface</span><br></pre></table></figure><p>这里我选了BAAI也就是智源研究所的bge-m3模型,因为这个是支持多语言的,如果是纯英文的可以用他们 <code>BAAI/bge-small-en-v1.5</code><figure class="highlight python"><table><tr><td class=gutter><pre><span class=line>1</span><br><span class=line>2</span><br><span class=line>3</span><br><span class=line>4</span><br><span class=line>5</span><br><span class=line>6</span><br><span class=line>7</span><br><span class=line>8</span><br><span class=line>9</span><br><span class=line>10</span><br></pre><td class=code><pre><span class=line><span class=keyword>from</span> llama_index.core <span class=keyword>import</span> Settings</span><br><span class=line><span class=keyword>from</span> llama_index.embeddings.huggingface <span class=keyword>import</span> HuggingFaceEmbedding</span><br><span class=line>Settings.llm = Ollama(model=<span class=string>"gemma:7b"</span>, request_timeout=<span class=number>60.0</span>)</span><br><span class=line>Settings.embed_model = HuggingFaceEmbedding(</span><br><span class=line> model_name=<span class=string>"BAAI/bge-m3"</span></span><br><span class=line>)</span><br><span class=line>index = VectorStoreIndex.from_documents(docs)</span><br><span class=line>query_engine = index.as_query_engine(llm=gemma_7b)</span><br><span class=line>response = query_engine.query(<span class=string>"数据库主从延迟"</span>)</span><br><span class=line><span class=built_in>print</span>(response)</span><br></pre></table></figure><p>另外LLM 我使用了本地部署的 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