Easily search and retrieve information from private knowledgebase documents using semantic search powered by AI. The flow expands user queries, searches across multiple knowledge sources, and presents relevant results in a user-friendly chat interface.
Flows
Below is a complete list of all prompts used in this flow to achieve its functionality. Prompts are the instructions given to the AI model to generate responses or perform actions. They guide the AI in understanding user intent and generating relevant outputs.
Paraphrases the user's input into multiple alternative queries to improve semantic search in the knowledge base using an LLM prompt.
Paraphrase input text into multiple alternative queries to improve semantic search in knowledge base.
Below is a complete list of all components used in this flow to achieve its functionality. Components are the building blocks of every AI Flow. They allow you to create complex interactions and automate tasks by connecting various functionalities. Each component serves a specific purpose, such as handling user input, processing data, or integrating with external services.
The Chat Input component in FlowHunt initiates user interactions by capturing messages from the Playground. It serves as the starting point for flows, enabling the workflow to process both text and file-based inputs.
Discover the Chat Output component in FlowHunt—finalize chatbot responses with flexible, multi-part outputs. Essential for seamless flow completion and creating advanced, interactive AI chatbots.
The Message Widget component displays custom messages within your workflow. Ideal for welcoming users, providing instructions, or showing any important information, it supports Markdown formatting and can be set to appear only once per session.
The Chat Opened Trigger component detects when a chat session starts, enabling workflows to respond instantly as soon as a user opens the chat. It initiates flows with the initial chat message, making it essential for building responsive, interactive chatbots.
Query Expansion in FlowHunt enhances chatbot understanding by finding synonyms, fixing spelling errors, and ensuring consistent, accurate responses for user queries.
FlowHunt's Document Retriever enhances AI accuracy by connecting generative models to your own up-to-date documents and URLs, ensuring reliable and relevant answers using Retrieval-Augmented Generation (RAG).
Showcase relevant documents directly within your chatbot responses using the Knowledge Source Widget. This component displays selected knowledge documents as visually distinct widgets, making it easy for users to access and review supporting information during a conversation.
FlowHunt supports dozens of text generation models, including models by OpenAI. Here's how to use ChatGPT in your AI tools and chatbots.
Flow description
This workflow, titled “Semantic Search”, enables users to search for information within their private knowledgebase by leveraging advanced language models and semantic search techniques. It is designed to scan across all scheduled domains, documents, and Q&A sections, automating the retrieval of the most relevant information in response to user queries.
When a user opens the chat interface, the workflow triggers a welcome message:
👋 Welcome to the Private Knowledgebase Search Tool!
I’m here to help you search through documents in your private knowledgebase 📚. I’ll scan all scheduled domains, private documents, and Q&A sections to find the information you need.
Simply enter your query, and let’s get started on finding the answers! ✨🔍
This friendly message helps orient users and guides them to enter their search query.
User Input:
The user submits a query via the chat input field.
Query Expansion:
gpt-4o-mini), this component generates up to three paraphrased or semantically similar queries.| Component | Purpose |
|---|---|
| Chat Input | Collects the user’s search question |
| OpenAI LLM (gpt-4o-mini) | Generates alternative phrasings of the query |
| Query Expansion | Produces up to 3 query variants for search |
<H1> headers to maximize context relevance.| Step | Component | Output Type |
|---|---|---|
| Retrieve Documents | Document Retriever | Raw Documents |
| Format Results | Document Widget | Message |
| Display to User | Chat Output | Chat Message |
flowchart LR
A[Chat Opened] --> B[Welcome Message]
B --> C[User Query Input]
C --> D[Query Expansion\n(OpenAI LLM)]
D --> E[Document Retriever]
E --> F[Document Widget]
F --> G[Chat Output]
Typical use cases:
By integrating semantic search with LLM-powered query expansion, this workflow ensures users can efficiently access relevant knowledge, boosting productivity and information discovery.
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