案例目标本案例演示如何使用AwaDB作为向量存储后端构建RAG系统。AwaDB是一个高性能的向量数据库专门用于存储和检索高维向量数据适用于语义搜索、推荐系统和AI应用等场景。通过本示例用户可以学习如何集成AwaDB与LlamaIndex实现高效的文档检索和问答功能。技术栈与核心依赖llama-index: 构建RAG系统的核心框架llama-index-vector-stores-awadb: AwaDB向量存储的LlamaIndex集成llama-index-embeddings-huggingface: HuggingFace嵌入模型集成awadb: AwaDB向量数据库客户端transformers: HuggingFace transformers库用于加载嵌入模型torch: PyTorch深度学习框架BAAI/bge-small-en-v1.5: 高效的英文文本嵌入模型环境配置安装依赖%pip install llama-index-embeddings-huggingface %pip install llama-index-vector-stores-awadb !pip install llama-index配置日志import logging import sys logging.basicConfig(streamsys.stdout, levellogging.INFO) logging.getLogger().addHandler(logging.StreamHandler(streamsys.stdout))配置OpenAI API可选import openai openai.api_key 案例实现1. 导入必要的库from llama_index.core import ( SimpleDirectoryReader, VectorStoreIndex, StorageContext, ) from IPython.display import Markdown, display import openai2. 准备数据创建数据目录并下载Paul Graham的文章!mkdir -p data/paul_graham/ !wget https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt -O data/paul_graham/paul_graham_essay.txt加载文档# 加载文档 documents SimpleDirectoryReader(./data/paul_graham/).load_data()3. 配置AwaDB向量存储from llama_index.embeddings.huggingface import HuggingFaceEmbedding from llama_index.vector_stores.awadb import AwaDBVectorStore # 初始化嵌入模型 embed_model HuggingFaceEmbedding(model_nameBAAI/bge-small-en-v1.5) # 创建AwaDB向量存储 vector_store AwaDBVectorStore() storage_context StorageContext.from_defaults(vector_storevector_store)4. 构建向量索引# 使用文档、存储上下文和嵌入模型创建索引 index VectorStoreIndex.from_documents( documents, storage_contextstorage_context, embed_modelembed_model )5. 查询索引基础查询# 创建查询引擎 query_engine index.as_query_engine() # 执行查询 response query_engine.query(What did the author do growing up?) # 显示结果 display(Markdown(f{response}))结果示例Growing up, the author wrote short stories, experimented with programming on an IBM 1401, nagged his father to buy a TRS-80 computer, wrote simple games, a program to predict how high his model rockets would fly, and a word processor. He also studied philosophy in college, switched to AI, and worked on building the infrastructure of the web. He wrote essays and published them online, had dinners for a group of friends every Thursday night, painted, and bought a building in Cambridge.更多查询# 查询作者在Y Combinator之后做了什么 response query_engine.query( What did the author do after his time at Y Combinator? ) # 显示结果 display(Markdown(f{response}))结果示例After his time at Y Combinator, the author wrote essays, worked on Lisp, and painted. He also visited his mother in Oregon and helped her get out of a nursing home.案例效果成功集成了AwaDB向量存储与LlamaIndex框架使用BAAI/bge-small-en-v1.5嵌入模型将文档转换为向量能够准确回答关于Paul Graham文章内容的问题查询结果包含了相关的上下文信息回答准确且详细展示了AwaDB作为向量存储的高效性和易用性案例实现思路环境准备安装必要的依赖库包括AwaDB向量存储和HuggingFace嵌入模型的LlamaIndex集成数据准备创建数据目录下载Paul Graham的文章并使用SimpleDirectoryReader加载文档模型配置初始化BAAI/bge-small-en-v1.5嵌入模型该模型在英文文本嵌入任务上表现优秀向量存储配置创建AwaDBVectorStore实例并将其与StorageContext关联索引构建使用VectorStoreIndex.from_documents方法结合文档、存储上下文和嵌入模型构建向量索引查询实现创建查询引擎执行不同的问题查询并展示结果扩展建议多语言支持尝试使用中文嵌入模型如BAAI/bge-small-zh-v1.5以支持中文文档处理元数据过滤为文档添加元数据实现基于元数据的过滤查询批量处理实现批量文档加载和处理提高大规模文档处理效率自定义查询探索不同的查询模式和参数优化查询结果的相关性和准确性持久化配置配置AwaDB的持久化选项确保向量数据的长期存储性能优化调整嵌入模型和向量存储参数优化系统性能集成其他组件将AwaDB与LlamaIndex的其他组件结合如查询重写、文档后处理等分布式部署探索AwaDB的分布式部署选项支持大规模向量检索总结本案例展示了如何使用AwaDB作为向量存储后端构建RAG系统。AwaDB作为一个高性能的向量数据库与LlamaIndex框架无缝集成提供了高效的文档检索和问答功能。通过使用BAAI/bge-small-en-v1.5嵌入模型系统能够准确理解文档内容并回答相关问题。AwaDB的易用性和高性能使其成为构建RAG应用的理想选择特别是在需要处理大规模向量数据的场景中。这个示例为开发者提供了一个完整的解决方案展示了如何快速搭建基于AwaDB的RAG系统。