FlowRag

Supported Operations

Key Capabilities

• Automatic embedding generation — text is converted to high-dimensional vectors using a configured embedding model
• Semantic similarity search — find contextually related content even when exact keywords differ
• Top-K retrieval with configurable similarity threshold (min_score)
• Metadata filtering — filter results by tags, categories, and custom key-value pairs
• Structured output — each result includes text, similarity score, and all stored metadata
• Supports PostgreSQL with pgvector extension and Qdrant vector database
• Collection-based organisation — maintain separate knowledge bases per domain or application

Business Benefits

Domain-Specific AI Responses

Pre-load your business's policies, procedures, and product knowledge into FlowRag. When a customer asks a question, the query operation retrieves the relevant context and passes it to an LLM node — delivering accurate, company-specific answers rather than generic AI responses.

Knowledge Base Maintenance

Keep your AI knowledge current. When policies change or new products are added, use the update operation to refresh the corresponding knowledge items. Your AI-powered workflows will immediately reflect the latest information without re-training a model.

Scalable Document Retrieval

Replace manual document search with semantic similarity queries. A support agent workflow can retrieve the most relevant contract clauses, troubleshooting articles, or compliance requirements in milliseconds — dramatically reducing resolution time.

Multi-Collection Organisation

Organise knowledge into separate collections: one for HR policies, one for product documentation, one for legal contracts. Query only the relevant collection in each workflow context, improving retrieval precision and response quality.

Use Cases

Company Policy Q&A Bot

Build an employee self-service bot. Insert all HR, IT, and operations policies into FlowRag. When an employee asks a question, query the relevant collection and pass the results to a Claude/GPT node to generate a clear, policy-accurate answer.

Customer Support FAQ

Pre-load product FAQs and troubleshooting guides. When a support ticket arrives, use FlowRag to retrieve the three most relevant articles, then generate a personalised response using an AI node — with citations to the source articles.

Legal Document Retrieval

Index contract library contents. Lawyers and contract managers query FlowRag to find clauses, precedents, and similar contract language across thousands of documents in seconds.

Technical Documentation Search

Index API documentation, runbooks, and architecture decisions. Development teams query FlowRag to get contextually relevant technical guidance as part of automated ticket resolution workflows.

Product Recommendation

Store product descriptions, features, and customer use cases. When a customer describes their needs, semantically match to the most relevant products and surface personalised recommendations.

In This Guide

Choosing a Vector Database Backend

Embedding Model Selection

The embedding model is configured per collection at the BizFirst workspace level. All items in a collection must use the same model — mixing models produces invalid similarity scores. To change the model, delete all items and re-insert with the new model.

Knowledge Base Design Tips

• Split long documents into focused, single-topic paragraphs of 200–500 tokens before inserting
• Use descriptive item_id values (e.g. policy-pto-2025-v3) to enable targeted updates without a full re-index
• Store rich metadata tags (category, source, date, version, status) to enable precise metadata filtering in queries
• Use a status metadata field (active / deprecated) to hide outdated content without deleting it — preserving history
• Test query quality with real user questions before going live — adjust min_score and top_k based on observed results
• Maintain separate collections per domain (HR, products, legal, support) for better precision and cost control

Common RAG Pipeline Pattern

The most common FlowRag workflow pattern follows these steps:

1. Trigger — user submits a question (WebhookTrigger, FormTrigger, or ChatTrigger)
2. FlowRag query — embed the question and retrieve the top-K most relevant knowledge items from the appropriate collection
3. IfCondition — check result_count > 0 to decide whether to use retrieved context or fall back to a generic answer
4. DataMapping — format the retrieved context into a prompt template combining question + context + instructions
5. AI Chat or HTTP Request — send the formatted prompt to Claude or GPT and generate a grounded response
6. Response — return the AI-generated answer to the user, optionally with citations from sources

This pattern ensures AI responses are grounded in your actual business knowledge rather than the model's general training data, dramatically reducing hallucination on domain-specific questions.

Combine with FlowAiAgent for Autonomous RAG

Register a FlowRag query as a tool in the BizFirst Tool Registry, then give it to a FlowAiAgent node. The agent will autonomously decide when to query the knowledge base — calling it multiple times with different queries if the first retrieval doesn't fully answer the goal. This pattern enables knowledge-augmented agents that can both reason and retrieve, without you needing to hard-code which knowledge base to query at each step.

Related Nodes