RAG Knowledge Pipeline
QORE’s RAG (Retrieval-Augmented Generation) framework enables apps and agents to retrieve relevant information from connected knowledge sources before generating responses.
The platform allows teams to:
Connect enterprise knowledge sources
Organize and process documents
Enable AI-powered semantic search
Reuse knowledge across apps and agents
Build grounded AI experiences using enterprise data
QORE structures the RAG workflow into reusable components:
Knowledge Pipelines
Data Stores
Vector Stores
This documentation explains how each component works and how to configure them within the platform.
A Knowledge Pipeline combines:
Data Sources
Data Storage
AI Search Indexing
Retrieval & Generation
This allows agents and applications to answer questions using enterprise knowledge instead of relying only on the base LLM.
RAG Architecture
QORE follows a modular RAG architecture that separates:
Knowledge ingestion
Knowledge processing
AI indexing
Retrieval orchestration
How the RAG Flow Works
QORE follows a simple 4-step knowledge flow:
1. Add Knowledge
Connect files, cloud drives, or enterprise systems.
Supported sources include:
Upload Files
Google Drive
OneDrive
SharePoint
2. Organize & Secure
Documents are stored, processed, chunked, and optionally protected using PII masking.
During this stage:
Files are extracted
Text is normalized
Metadata is processed
Sensitive information can be masked
Knowledge is prepared for indexing
3. Enable AI Search
The processed knowledge is converted into embeddings using an embedding model.
This creates an AI-searchable index that helps retrieve the most relevant information for a user query.
QORE supports:
Configurable embedding models
Vector indexing
Similarity search
Retrieval tuning
4. Use in Apps & Agents
Once the pipeline is active:
Agents can retrieve contextual knowledge
Apps can power AI search experiences
Responses become grounded in enterprise data
Core Components
Knowledge Pipeline
A Knowledge Pipeline is the primary RAG orchestration layer.
It connects:
Data Stores
Vector Stores
Retrieval settings
Generation models
A pipeline controls how knowledge is retrieved and used during AI conversations.
Data Store
A Data Store is responsible for storing and processing connected knowledge.
Capabilities:
Document ingestion
Chunking
Metadata extraction
PII masking
Source synchronization
Data Stores can connect to one or more external sources.
Vector Store
A Vector Store creates AI-searchable embeddings from processed documents.
Capabilities:
Embedding generation
Similarity search
Semantic retrieval
Search indexing
Vector Stores are connected to a Data Store.
Navigation
Access the RAG module from:
Sidebar → RAG
The RAG module contains:
Knowledge Pipelines
Vector Stores
Data Stores
Using Knowledge Pipelines with Agents
Knowledge Pipelines can be attached to agents to provide contextual retrieval during conversations.
Agents can:
Retrieve enterprise knowledge
Answer questions using indexed documents
Ground responses in connected data sources
Access organization-specific information
Knowledge Pipelines are reusable across multiple apps and agents.
Common Use Cases
Customer Support Agents
Enable AI agents to answer questions using:
SOPs
FAQs
Product documentation
Help center content
Internal Enterprise Search
Search across:
policies
contracts
knowledge bases
operational documentation
Compliance & Audit Assistance
Ground AI responses using:
compliance frameworks
policy documents
internal procedures
regulatory references
Financial Services Workflows
Power retrieval for:
underwriting
onboarding
collections
risk assessment
loan operations
Key Benefits
QORE’s RAG framework enables:
Context-aware AI responses
Enterprise knowledge grounding
Improved answer accuracy
Retrieval transparency
Secure enterprise AI workflows
Reusable knowledge infrastructure across apps and agents
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