Overview
The Large Memory node enables you to create searchable document databases called "memory pods" and query them using natural language. This node uses vector database technology to store and retrieve information from your documents, making it ideal for document analysis, knowledge base creation, and content research.
Memory Pod Management
Memory Pod Selection
The Memory Pod section controls which document collection you're working with:
Dropdown Menu: Shows "None" when no pod is selected, or displays available memory pods
Pod Selection: Click the dropdown to choose from previously created memory pods
Connection Point: Can receive pod selection from other workflow nodes
Creating New Memory Pods
Create new document collections using the add button next to the Memory Pod section:
Memory Pod Creation Interface
When creating a new memory pod, a modal opens with the following options:
Memory Pod Naming
Name Field: Enter a descriptive name for your memory pod
Purpose: Helps identify and organize different document collections
Document Upload Options Choose from three document sources:
Upload from computer: Select files directly from your local device
FluxPrompt Uploads: Access previously uploaded documents in FluxPrompt
FluxPrompt Objects: Use existing FluxPrompt workflow objects
File Selection Interface
Computer Upload: "Choose Files" button to browse and select local documents
FluxPrompt Uploads: Dropdown showing available uploaded documents (e.g., extracted PDFs, text files)
FluxPrompt Objects: Dropdown showing workflow objects and processed data
Memory Pod Creation
Create Memory Pod Button: Finalizes the creation process with selected documents
Close Option: Cancel creation and return to the main interface
Editing Memory Pods
Modify existing memory pods using the edit button (pencil icon):
Add Documents: Include additional files in the existing pod
Remove Documents: Delete specific documents from the pod
Pod Management: Reorganize or update pod contents
Delete Pod: Remove the entire memory pod and its contents
Search and Query Interface
Prompt Section
Configure your search queries in the Prompt area:
Query Input: Enter natural language questions about your documents
Placeholder Text: "Type something to search in vector database"
Connection Point: Can receive queries from other workflow nodes
Search Context: Queries are processed against the selected memory pod only
AI Model Configuration
Select the AI model for processing queries:
Model Dropdown: Shows current model (e.g., "OpenAI: gpt-4")
Model Options: Choose from available AI models for query processing
Performance: Different models offer varying capabilities for document analysis
Advanced Settings
Access detailed configuration through the Settings panel:
Search Configuration
Return Raw Toggle
Purpose: Control output format of search results
Options: Enable to receive raw data, disable for formatted responses
Search Type Selection
Similarity Search: Find documents most similar to your query
Similarity Search (NER): Enhanced search with Named Entity Recognition
OpenAI Models: Complete range including GPT-5, GPT-4.1, GPT-4o series, GPT-3.5 variants, and reasoning models (O3-Mini, O1)
Search Parameters
Top K Setting
Purpose: Control number of search results returned
Default: 10 results
Range: Adjustable based on query requirements
Search Execution
Search in Memory Pod Button
Execute queries against your selected memory pod:
Function: Processes natural language queries against stored documents
Processing: Uses vector similarity to find relevant document sections
Results: Returns contextually relevant information from your documents
Output Display
Search Results
View query results in the Output section:
Document Excerpts: Relevant sections from your stored documents
Contextual Answers: AI-generated responses based on document content
Source Attribution: Information about which documents provided the answers
Connection Point: Results can be passed to other workflow nodes
Use Cases and Applications
Document Analysis
Research Assistance: Query large document collections for specific information
Content Discovery: Find relevant sections across multiple documents
Knowledge Extraction: Extract insights from extensive document libraries
Knowledge Base Creation
Internal Documentation: Create searchable company knowledge bases
Reference Materials: Store and query technical documentation, manuals, and guides
Educational Resources: Build searchable collections of learning materials
Workflow Integration
Dynamic Queries: Receive search terms from other workflow nodes
Automated Research: Integrate document search into larger automation workflows
Content Processing: Combine with other nodes for comprehensive document workflows
Best Practices
Memory Pod Organization
Descriptive Names: Use clear, descriptive names for easy identification
Thematic Grouping: Group related documents in the same memory pod
Size Management: Balance pod size with search performance and relevance
Query Optimization
Specific Questions: Use clear, specific queries for better results
Natural Language: Write queries as natural questions rather than keywords
Context Consideration: Frame queries with appropriate context for your documents
Model Selection
Task Matching: Choose AI models based on your specific document analysis needs
Performance Balance: Consider speed vs. accuracy requirements
Cost Management: Select models appropriate for your usage patterns
The Large Memory node transforms static document collections into dynamic, quarriable knowledge bases, enabling sophisticated document analysis and information retrieval within FluxPrompt workflows.