RAG

Retrieval-Augmented Generation (RAG) is a framework that combines traditional information retrieval with generative AI. It enables AI agents to generate contextually accurate and factually grounded responses by retrieving relevant information from a knowledge base or data source and using it to augment the reasoning process. Learn More

In UPTIQ AI Workbench, RAG is implemented using a sequence of components designed to manage and use data effectively in AI workflows. Below is a detailed explanation of each related concept:

How to create a RAG?

Create a RAG Container in UPTIQ AI Workbench.

What is a Data Store?

• Definition: A data source is the origin of the data, which can be structured or unstructured.

• Examples:

  • Unstructured: Files such as PDFs, CSVs, images, or documents.

  • Structured: Databases such as PostgreSQL, MongoDB, or MySQL.

• Purpose: Data sources feed raw information into the system, which can later be processed and used for workflows.

How to create a data store?

For details on how to create a data store, Watch the setup guide

How to create a data store.

What is a Data Source?

• Definition: A data store in UPTIQ is an entity that groups multiple data sources together. It acts as an organizational layer, allowing developers to manage and access related data sources as a single logical unit.

• Key Features:

  • Supports the integration of multiple data sources.

  • Provides a unified interface for interacting with grouped data.

• Example: A single data store could include a combination of:

  • Product catalogs (from a MySQL database),

  • Policy documents (PDFs), and

  • Logs (CSV files).

  • Postgres Database cluster.

What is a Vector Store?

• Definition: A vector store converts the content of a data store into vector embeddings and stores these embeddings using a vector database.

• How It Works:

  • Takes raw data from the data store (structured or unstructured).

  • Processes the data to generate vector embeddings using AI models.

  • Stores these embeddings in a vector database for efficient retrieval.

• Purpose: Vector embeddings represent the semantic meaning of the data, enabling similarity-based searches. This is crucial for identifying and retrieving contextually relevant information.

• Underlying Technologies: Vector databases by renowned providers like MongoDb, Pinecone, Postgres.

What is a RAG Container?

• Definition: A RAG container is the entity that links to the vector store and exposes its capabilities for use in workflows via a special RAG node.

• How It Works:

  1. Association with Vector Store: The RAG container uses the embeddings stored in the vector store to perform retrieval-augmented reasoning.

  2. Exposed to Workflows: Developers can add the RAG node in a low-code/no-code manner to integrate retrieval functionality directly into workflows.

  3. Dynamic Interaction: The reasoning engine retrieves relevant information via the RAG container and uses it to augment intent classification and response generation.

• Developer's Role:

  • Create or configure the RAG container associated with a vector store.

  • Use the RAG node in workflows to retrieve and incorporate relevant knowledge dynamically.

• Example in Workflow: When a user asks, "Show me the loan terms for Plan B," the RAG container retrieves the most relevant embeddings from the vector store (e.g., a section of a document or database record) and feeds it into the reasoning engine (if used in knowledge) or LLMs (if used in RAG node) for a precise response.

How These Components Work Together

1. Data Ingestion:

   • Developers add multiple data sources (PDFs, databases, etc.) to a data store.

2. Vectorization:

   • The vector store processes the data in the data store, generating vector embeddings and storing them in a vector database.

3. RAG Integration:

   • The RAG container is linked to the vector store.

   • Developers integrate the RAG node into workflows, enabling retrieval and augmentation in their AI agent's logic.

4. Dynamic Query Handling:

   • A user query is processed by the reasoning engine.

   • The RAG container retrieves relevant embeddings from the vector store.

   • Retrieved data is used to generate an accurate, context-rich response.

Key takeaway for developers

✅ Understanding these concepts is essential for leveraging the full potential of UPTIQ’s low-code/no-code workflows.

✅ Think of these components as tools in a developer's toolkit:

• Data Sources: Raw materials.

• Data Store: Organizational structure.

• Vector Store: Advanced search engine.

• RAG Container: Intelligent retrieval and integration.

✅ Mastering the RAG allows developers to build sophisticated AI agents capable of delivering accurate and contextually relevant responses to users