关于上下文基础

Context Grounding is a component of the UiPath AI Trust Layer which allows you to bring in your data to generate more accurate, reliable GenAI predictions. Context Grounding is designed to make your business data LLM-ready without the need for any additional subscription to embedding models, vector databases, or large language models (LLMs). You can create representative indexes and embeddings of business data that UiPath GenAI features can reference for contextual evidence at runtime.

Context Grounding is a tenant-scoped platform service designed to support UiPath GenAI experiences (such as GenAI Activities and Autopilot for everyone) by grounding your prompts with relevant information before they are executed by the LLM via retrieval augmented generation (RAG).

将 RAG 作为服务提供给 UiPath GenAI 体验有助于：

• 克服 LLM 上下文窗口限制：无论是小型模型还是大型模型，RAG 都有助于提高模型在与知识库交互时的准确性、可靠性、可扩展性和效率。
• 通过引用地面实况数据存储来降低产生错觉的风险。
• 为生成式应用程序提供对专业和专有知识来源的访问权限。
• 使生成式应用程序访问最新的信息来源。
• 在数据存储和用户查询之间启用正反馈循环。

Core components

The terminology and core components of Context Grounding include:

Figure 1. Context Grounding component architecture

提取和索引：使您的业务数据为 LLM 就绪

• Ingestion: Convert business data into representative embeddings using UiPath-managed embedding models.
• Embedding: A representation of business data than an LLM can understand and search through.
• Index: A folder in a vector database that organizes the embeddings.
• Vector DBs: UiPath-managed vector database that stores embeddings organized in indexes.

Retrieval

• Search through LLM-ready business data to find the most relevant information. Context Grounding uses a variety of extraction, chunking, retrieval, and re-ranking techniques that are optimized based on different data formats and queries.
• 将提示解释为查询以搜索嵌入，并根据余弦相似度搜索生成最相关的结果。 这些搜索结果是 RAG 的中间前导步骤，用于使用业务数据中的相关上下文来增强提示。

Retrieval Augmented Generation

• 使用语义相似度搜索结果中最相关的信息修饰和更新提示，然后通过 AI Trust Layer 的 LLM 网关托管的 LLM 执行生成。

关键功能

以下是上下文基础的一些主要功能：

• Multi-document support: PDF, JSON, CSV, XLS, DOCX, TXT files.
• Managed ingestion and indexing pipelines: UiPath optimizes the ingestion and indexing of data in UiPath-managed vector databases.
• Multiple surfaces: Context Grounding is currently available as part of the UiPath GenAI Activities, AI Trust Layer (with a dedicated UI), and Autopilot for everyone.
• Data retrieval: Query within documents or across datasets using a variety of techniques (e.g. query transformation, embedding, fine tuning, etc.) to ensure search results are highly relevant.
• Retrieval Augmented Generation: Ground prompts via just-in-time (JIT) in-memory or over a knowledge base.
• Proof of knowledge: Provides a citation of the reference source and text from the semantic similarity search.
• Streaming support: Streaming API support to show generation as it is being produced.
• Multilingual support: Ability to ingest and query from documents in all UTF-8 encoded languages.
• Support for multiple data sources:
  • UiPath Orchestrator bucket entities: You can ingest, index, and query data stored within shared folders in Orchestrator bucket entities.
  • Document storage systems: Through Integration Service connectors, such as Microsoft OneDrive & SharePoint and Google Drive: Context Grounding can access data directly stored in third-party applications.

Limitations and considerations

• Context Grounding currently supports specific file types: PDF, JSON, CSV, XLS, DOCX, TXT.
• There is a limit of ten indexes per tenant. We recommend you keep a 1-1 relationship with these and the folder path in the data source you want to use.
• Context Grounding respects folder permissions and authorization for shared folder entities. Users who do not have the appropriate permissions may not be able to see, update, delete, or use indexes that are affiliated with folders they do not have permissions to.
• To use Context Grounding through UiPath GenAI Activities, you must use Studio Web or Studio Desktop version 2024.4 or newer. For more information, see the Getting started section.