To get you started with this flavor of RAG, we've created a newRAG-on-PostgreSQL solutionthat includes a FastAPI backend, React frontend, and infrastructure-as-code for deploying it all to Azure Container Apps
论文题目: 《A Method for Parsing and Vectorization of Semi-structured Data used in Retrieval Augmented Generation》 论文链接: https://arxiv.org/abs/2405.03989 代码: https://github.com/linancn/TianGong-AI-Unstructure/tree/main 这篇论文提出了一种新方法,用于解析和向量化半结构化数据,以增强大型语言...
# load data documents = SimpleDirectoryReader(input_dir="./data/source_files").load_data() # create the pipeline with transformations pipeline = IngestionPipeline( transformations=[ SentenceSplitter(chunk_size=1024, chunk_overlap=20), TitleExtractor(), OpenAIEmbedding(), ] ) # setting num_workers...
Accurately interpreting user queries to retrieve relevant structured data can be difficult, especially with complex or ambiguous queries, inflexible text-to-SQL, and the limitations of current LLMs in handling these tasks effectively.准确解释用户查询以检索相关结构化数据可能很困难,尤其是在查询复杂或不明...
Structured data, such as knowledge graphs (KGs), provide high-quality context and mitigate model hallucinations. 类似地,Graph RAG的核心链路分如下三个阶段: 索引(三元组抽取):通过LLM服务实现文档的三元组提取,写入图数据库。 检索(子图召回):通过LLM服务实现查询的关键词提取和泛化(大小写、别称、同义词等...
有关高级检索的更多详情可访问:https://towardsdatascience.com/jump-start-your-rag-pipelines-with-advanced-retrieval-llamapacks-and-benchmark-with-lighthouz-ai-80a09b7c7d9d 痛点7:不完备输出不完备。给出的响应没有错,但只是一部分,未能提供全部细节,即便这些信息存在于可访问的上下文中。举个例子,如果...
相比于传统的基于Vector格式的知识库存储,Graph RAG引入了知识图谱技术,使用Graph格式存储知识。正如论文[2]所阐述的:基于知识图谱,可以为RAG提供高质量的上下文,以减轻模型幻觉。Structured data, such as knowledge graphs (KGs), provide high-quality context and mitigate model hallucinations....
fromlangchain_openai import OpenAIEmbeddingsOPENAI_EMBEDDING_MODEL_NAME=os.getenv(‘OPENAI_EMBEDDING_MODEL_NAME’, ‘text-embedding-3-small’)# Initialize OpenAI embeddings with the specified modelg_embeddings=OpenAIEmbeddings(model=OPENAI_EMBEDDING_MODEL_NAME,openai_api_key=OPENAI_API_KEY) ...
structured_llm=nvidia_llm.with_structured_output(Choices) structured_llm.invoke("if a user put this query into a search engine, is this result relevant enough that it could be in the first page of results? Answer 'N' if the provided summary does not contain enough information to answer the...
fromlangchain_openai import OpenAIEmbeddingsOPENAI_EMBEDDING_MODEL_NAME=os.getenv(‘OPENAI_EMBEDDING_MODEL_NAME’, ‘text-embedding-3-small’)# Initialize OpenAI embeddings with the specified modelg_embeddings=OpenAIEmbeddings(model=OPENAI_EMBEDDING_MODEL_NAME,openai_api_key=OPENAI_API_KEY) 3.1.3 搜索...