"What does the Search Memory component do?"

"Search Memory enables your workflow to fetch relevant information from stored memory or documents using input queries, making your AI solutions more context-aware."

"How does it select which documents to return?"

"It retrieves documents that best match the input query, with options to limit the number of results and control the output format or strategy."

"Can I control the number of results or content type?"

"Yes, you can set a results limit, choose which document content types to include, and adjust strategies for combining document excerpts."

"How does Search Memory help my chatbot or workflow?"

"By allowing access to previous knowledge or long-term memory, your bot can provide more informed, accurate, and contextually relevant responses."

"Is Search Memory suitable for advanced AI applications?"

"Absolutely. It’s designed to plug into complex flows where retrieving context or knowledge from prior data is crucial for intelligent automation."

Search Memory

The Search Memory component lets your flow retrieve information from stored memory based on user queries, supporting context-aware and knowledge-driven workflows.

AI Memory Components Automation

Component description

How the Search Memory component works

The Search Memory component is designed to retrieve relevant information from your workflow’s memory storage, often referred to as “Long Term Memory”. It takes a user query and searches stored documents or knowledge resources, returning the most related content. This is particularly useful for AI workflows that need to reference previous information, retrieve supporting documents, or provide context-aware responses.

What Does the Component Do?

Purpose: The component searches through stored information in the workflow’s memory using a user-defined query and returns the most relevant pieces of information.
Use Case: Useful for chatbots, virtual assistants, or any AI process that requires access to previously stored knowledge or documents to provide informed, contextual answers.

Key Features

Flexible Retrieval: Allows you to specify the number of results, set a similarity threshold, and choose how information is aggregated from documents.
Customizable Output: You can control what sections/types of content (such as headings or paragraphs) are included in the results.
Integration with Tools: The retrieved documents can be formatted as messages, raw documents, or as tools for further use in the workflow.

Settings

Input Name	Type	Required	Description	Default Value
Title	str	No	Title of the block in the output.	Related resources
Result limit	int	Yes	Number of results to return.	3
From pointer	bool	Yes	If true, loads from the best matching point in the document; otherwise, loads all.	true
Hide resources	bool	No	If true, hides the retrieved resources from output.	false
max_tokens	int	No	Maximum number of tokens in the output text.	3000
strategy	str	Yes	Strategy for aggregating content: “Concat documents, fill from first up to tokens limit” or “Include equal size from each document”.	Include equal size from each documents
threshold	float	No	Similarity threshold for retrieved results (0 to 1).	0.8
tool_description	str	No	Description for the tool, used by agents to understand its function.	(empty)
tool_name	str	No	Name for the tool in the agent.	(empty)
use_content	multi-select	No	Which content types to export (e.g., H1-H6, Paragraph).	All (H1-H6, Paragraph)
verbose	bool	No	Whether to print verbose output for debugging or insights.	false

Inputs

Input Name	Type	Required	Description	Default Value
Lookup key	str	No	Key used to locate specific information in Long Term Memory.	(empty)
Input query	str	Yes	The search query to use in memory lookup.	(empty)

Outputs

The component provides multiple output formats to suit different needs:

Documents (Message): The retrieved information as a message, suitable for direct integration into conversational flows.
Raw Documents (Document): The unprocessed, raw content of the matched documents for further parsing or analysis.
Documents As Tool (Tool): The found documents formatted as a tool, enabling chaining or complex agent workflows.

Output Name	Type	Description
documents	Message	Retrieved content as message(s)
documents_raw	Document	Raw, unprocessed document content
documents_as_tool	Tool	Documents formatted for use as a tool in agent workflows

Why Use Search Memory?

Contextual AI: Enhance your AI’s responses by providing access to previously stored data, making interactions more informed and coherent.
Knowledge Management: Efficiently leverage existing documentation or user-provided information without manual searching.
Advanced Customization: Fine-tune retrieval strategies and output formats to fit your specific workflow requirements.

Example Scenarios

Conversational Agents: Retrieve past interactions or knowledge snippets to maintain context across conversations.
Research Assistants: Quickly surface relevant documents or passages from a large knowledge base in response to a query.
Automated Decision Making: Provide supporting evidence from stored memory to justify recommendations or actions.

Summary Table

Feature	Benefit
Query-based search	Finds the most relevant stored information for any user query
Output options	Choose between message, raw document, or tool formats
Custom retrieval	Control over number of results, similarity threshold, and content
Integrates with AI	Ideal for AI agents needing dynamic access to stored knowledge

This component is a versatile building block for any AI workflow that requires memory search, document retrieval, or contextual augmentation.

Frequently asked questions

What does the Search Memory component do?: Search Memory enables your workflow to fetch relevant information from stored memory or documents using input queries, making your AI solutions more context-aware.
How does it select which documents to return?: It retrieves documents that best match the input query, with options to limit the number of results and control the output format or strategy.
Can I control the number of results or content type?: Yes, you can set a results limit, choose which document content types to include, and adjust strategies for combining document excerpts.
How does Search Memory help my chatbot or workflow?: By allowing access to previous knowledge or long-term memory, your bot can provide more informed, accurate, and contextually relevant responses.
Is Search Memory suitable for advanced AI applications?: Absolutely. It’s designed to plug into complex flows where retrieving context or knowledge from prior data is crucial for intelligent automation.

Try FlowHunt Search Memory

Supercharge your AI solutions by integrating memory search and retrieval. Connect to long-term knowledge and deliver smarter responses.

Try it Now Book a demo

Learn more

Add to Memory

Easily store important information in your workflow with the Add to Memory component. Seamlessly save data or documents to long-term memory, categorized by cust...

Jun 9, 2025 2 min read

Memory Automation +3

JSON Document Search

The JSON Document Search component enables powerful search capabilities within JSON files, using either text search or advanced query expressions. Connect exter...

Jun 9, 2025 3 min read

Data Automation +4

Search Gmail Emails

The Search Emails component lets you automatically find and retrieve emails from Gmail based on specific criteria. It supports filtered searches and returns a l...