تطبيق Knowledge
Linda Berens
أوضح المطور AMBOSS أن ممارسات خصوصية التطبيق قد تتضمن معالجة البيانات على النحو الموضح أدناه. لمزيد من المعلومات انظر %سياسة خصوصية المطور(ة) سياسة خصوصية المطور.
Modern chatbots can serve as digital agents, providing a new avenue for delivering 24/7 customer service and support across many industries. Their popularity stems from the ability to respond to customer inquiries in real time and handle multiple queries simultaneously in different languages. Chatbots also offer valuable data-driven insights into customer behavior while scaling effortlessly as the user base grows; therefore, they present a cost-effective solution for engaging customers. Chatbots are able to use the advanced natural language capabilities of large language models (LLMs) to respond to customer questions. They can understand conversational language and respond naturally. However, chatbots that merely answer basic questions have limited utility. To become trusted advisors, chatbots need to provide thoughtful, tailored responses that can help the end-user fulfill a task.
The popular architecture pattern of Retrieval Augmented Generation (RAG) is often used to augment user query context and responses. RAG combines the capabilities of LLMs with the grounding in facts and real-world knowledge that comes from retrieving relevant texts and passages from a corpus of data. These retrieved texts are then used to inform and ground the output, reducing hallucination and improving relevance.
In this post, we illustrate contextually enhancing a chatbot by using Knowledge Bases for Amazon Bedrock, a fully managed serverless service. Knowledge Bases for Amazon Bedrock allows your chatbot to provide more relevant, personalized responses by linking user queries to related information data points. Knowledge Bases for Amazon Bedrock securely connects foundation models (FMs) to internal company data sources for RAG, to deliver more relevant and accurate responses. All the information retrieved from Knowledge Bases for Amazon Bedrock is provided with citations to improve transparency and minimize hallucinations. For this post, we use the Amazon letters to shareholders dataset to develop this solution.
RAG is an approach to natural language generation that incorporates information retrieval into the generation process. RAG architecture involves two key workflows: data preprocessing through ingestion, and text generation using enhanced context.
Although the RAG architecture has many advantages, it involves multiple components, including a database, retrieval mechanism, prompt, and generative model. Managing these interdependent parts can introduce complexities in system development and deployment. The integration of retrieval and generation also requires additional engineering effort and computational resources. Some open source libraries provide wrappers to reduce this overhead; however, changes to libraries can introduce errors and add additional overhead of versioning. Even with open source libraries, significant effort is required to write code, determine optimal chunk size, generate embeddings, and more. This setup work alone can take weeks depending on data volume.
Knowledge Bases for Amazon Bedrock is a serverless option to build powerful conversational artificial intelligence (AI) systems using RAG. It offers fully managed data ingestion and text generation workflows.
For data ingestion, Amazon Bedrock provides the StartIngestionJob API to start an ingestion job. It handles creating, storing, managing, and updating text embeddings of document data in the vector database automatically. It splits the documents into manageable chunks for efficient retrieval. The chunks are then converted to embeddings and written to a vector index, while allowing you to see the source documents when answering a question.
For text generation, Amazon Bedrock provides the RetrieveAndGenerate API to create embeddings of user queries, and retrieves relevant chunks from the vector database to generate accurate responses. It also supports source attribution and short-term memory needed for RAG applications.
The solution presented in this post is available in the following GitHub repo. You need to clone the GitHub repository to your local machine. Open a terminal window and run the following command (this is a single git clone command):
The chatbot application backend deployed a knowledge base and S3 data source using resources from the AWS Generative AI Constructs Library for Amazon Bedrock. The AWS Generative AI Constructs Library is an open source extension of the AWS CDK that provides multi-service, well-architected patterns for quickly defining solutions in code to create predictable and repeatable infrastructure, called constructs. The goal of the AWS Generative AI CDK Constructs Library is to help developers build generative AI solutions using pattern-based definitions for their architecture.
During the first call to the Lambda function, the RetrieveAndGenerate API returns a sessionId, which is then passed by the React app along with the subsequent user prompt as an input to the RetrieveAndGenerate API to continue the conversation in the same session. The RetrieveAndGenerate API manages the short-term memory and uses the chat history as long as the same sessionId is passed as an input in the successive calls.
By explaining the value of contextual chatbots, the challenges of RAG systems, and how Knowledge Bases for Amazon Bedrock addresses those challenges, this post aimed to showcase how Amazon Bedrock enables you to build sophisticated conversational AI applications with minimal effort.
In Microsoft Entra ID, if another administrator or non-administrator needs to manage Microsoft Entra resources, you assign them a Microsoft Entra role that provides the permissions they need. For example, you can assign roles to allow adding or changing users, resetting user passwords, managing user licenses, or managing domain names.
This article lists the Microsoft Entra built-in roles you can assign to allow management of Microsoft Entra resources. For information about how to assign roles, see Assign Microsoft Entra roles to users. If you are looking for roles to manage Azure resources, see Azure built-in roles.
This is a privileged role. Users in this role can create and manage all aspects of enterprise applications, application registrations, and application proxy settings. Note that users assigned to this role are not added as owners when creating new application registrations or enterprise applications.
This exception means that you can still consent to application permissions for other apps (for example, other Microsoft apps, 3rd-party apps, or apps that you have registered). You can still request these permissions as part of the app registration, but granting (that is, consenting to) these permissions requires a more privileged administrator, such as Global Administrator.
This is a privileged role. Users in this role can create application registrations when the "Users can register applications" setting is set to No. This role also grants permission to consent on one's own behalf when the "Users can consent to apps accessing company data on their behalf" setting is set to No. Users assigned to this role are added as owners when creating new application registrations.
Users in this role can create and manage all aspects of attack simulation creation, launch/scheduling of a simulation, and the review of simulation results. Members of this role have this access for all simulations in the tenant.
By default, Global Administrator and other administrator roles do not have permissions to read, define, or assign custom security attributes. To work with custom security attributes, you must be assigned one of the custom security attribute roles.
Users with this role can define a valid set of custom security attributes that can be assigned to supported Microsoft Entra objects. This role can also activate and deactivate custom security attributes.
By default, Global Administrator and other administrator roles do not have permissions to read audit logs for custom security attributes. To read audit logs for custom security attributes, you must be assigned this role or the Attribute Log Reader role.
By default, Global Administrator and other administrator roles do not have permissions to read audit logs for custom security attributes. To read audit logs for custom security attributes, you must be assigned this role or the Attribute Log Administrator role.
Users with this role can change credentials for people who may have access to sensitive or private information or critical configuration inside and outside of Microsoft Entra ID. Changing the credentials of a user may mean the ability to assume that user's identity and permissions. For example:
A custom authentication extension is an API endpoint created by a developer for authentication events and is registered in Microsoft Entra ID. Application administrators and application owners can use custom authentication extensions to customize their application's authentication experiences, such as sign in and sign up, or password reset.
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