开发人员可以在GitHub仓库上访问mmRAG源代码GitHub - aws-samples/Multi-modal-Assistant-With-Advanced-RAG-And-Amazon-Bedrock。 二、使用LangChain配置Amazon Bedrock 首先配置Amazon Bedrock,使其与来自LangChain Community库的各种组件集成。这允许你与核心FMs一起工作。使用BedrockEmbeddings类创建两个不同的嵌入模型:...
create_index( name=index_name, dimension=2, metric="cosine", spec=ServerlessSpec( cloud='aws', region='us-east-1' ) ) # Retrieving top 3 closest vectors query_results = index.query( namespace="example-namespace1", vector=[1.0, 1.5], top_k=3, include_values=True ) # qu...
One technique for retrieving documents in a RAG orchestration is dense retrieval, which is an approach to information retrieval that aims to understand the semantic meaning and intent behind user queries. Dense retrieval finds the closest documents to a user query in the embedding, as shown in the...
Minimum capacity charges– Each OpenSearch Serverless collection encrypted with a separate KMS key has a minimum of 2 OpenSearch Compute Units (OCUs)charged hourly. These OCUs are charged independently from usage, meaning that you will incur charges for dormant tenants if you choose to have...
This repository enables the user to setup, run, and cleanup a chatbot in an AWS GovCloud account. The chatbot provides a web-based interface for a user to ask questions and receive answers about information contained in a body of documents that can be in.docx, .pdf, or markdown (.md)...
Many complex operations need to be performed - such as generating embeddings, comparing the meaning between different pieces of text, and retrieving data in real-time. These tasks are computationally intensive and can slow down the system as the size of the source data increases. To address this...
As artificial intelligence continues to evolve, tools like RAGChecker will play an essential role in maintaining the balance between innovation and reliability. The AWS AI team concludes that “the metrics of RAGChecker can guide researchers and practitioners in developing more effective RAG systems,”...
This setup makes it easier to include meaning-based memory in RAG apps. It allows for the easy handling of past conversation records through MongoDB's strong vector search tools, leading to a better running app and a nicer experience for the user. ...
Green” – the international color codes for traffic lights – and both Google and ChatGPT are correct. The problem is that I didn’t give either search platform enough context to the question to get the answer I am seeking. I’m asking about RAG in the context of AI – meaning if I...
They tend to perform better when the meaning of the text is more important than the exact wording since the embeddings capture semantic similarities. Sparse Retrievers: These rely on term-matching techniques like TF-IDF or BM25. They excel at finding documents with exact keyword matches which can...