Goodmem Piece

This piece provides a complete integration with Goodmem, a powerful vector-based memory storage and semantic retrieval system for AI applications. Store documents as memories with vector embeddings and perform similarity-based semantic search across your data.

Prerequisites

1. Install Goodmem

You need a running Goodmem instance. Install it on your VM or local machine:

Visit: https://goodmem.ai/

Follow the installation instructions for your platform (Docker, local installation, or cloud deployment).

2. Create an Embedder

Before you can create spaces and memories, you need to set up an embedder model:

3. Get Your API Key

Authentication

This piece uses Custom Authentication:

• Base URL: The base URL of your Goodmem instance (e.g., http://localhost:8080, https://api.goodmem.ai)
• API Key: Your Goodmem API key (starts with gm_)

Available Actions

Create Space

Create a new space (container for memories) with configurable settings. If a space with the same name already exists, it will be reused instead of creating a duplicate.

Options:

• Space Name (required) — Unique name for the space
• Embedder (required, dropdown) — Select from available embedder models that convert text to vector embeddings
• Advanced Chunking Options (optional, collapsible) — Fine-tune how documents are split into chunks:
  • Chunk Size (default: 256) — Number of characters/tokens per chunk
  • Chunk Overlap (default: 25) — Overlapping characters between consecutive chunks
  • Keep Separator Strategy (default: Keep at End) — Where to attach separators when splitting (Keep at End, Keep at Start, or Discard)
  • Length Measurement (default: Character Count) — How chunk size is measured (Character Count or Token Count)

Create Memory

Store a document or plain text as a memory in a space. The content is automatically chunked and embedded for semantic search.

Options:

• Space (required, dropdown) — Select the space to store the memory in
• File (optional) — Upload a file (PDF, DOCX, TXT, images, etc.). Content type is auto-detected.
• Text Content (optional) — Plain text content. If both file and text are provided, file takes priority.
• Source (optional) — Where this memory came from (e.g., "google-drive", "gmail")
• Author (optional) — The author or creator of the content
• Tags (optional) — Comma-separated tags for categorization (e.g., "legal,research,important")
• Additional Metadata (optional) — Extra key-value metadata as JSON

Retrieve Memories

Perform semantic search across one or more spaces to find relevant memory chunks. Supports advanced post-processing with reranking and LLM-generated contextual responses.

Options:

• Query (required) — Natural language search query
• Spaces (required, multi-select dropdown) — Select one or more spaces to search across
• Maximum Results (default: 5) — Limit the number of returned memories
• Include Memory Definition (default: true) — Fetch full memory metadata alongside matched chunks
• Wait for Indexing (default: true) — Retry for up to 60 seconds when no results found (useful when memories were just added)

Advanced Post-Processing:

• Reranker (optional, dropdown) — Select a reranker model to improve result ordering
• LLM (optional, dropdown) — Select an LLM to generate contextual responses alongside retrieved chunks
• Relevance Threshold (optional) — Minimum score (0-1) for including results. Used with Reranker or LLM.
• LLM Temperature (optional) — Creativity setting for LLM generation (0-2). Used when LLM is selected.
• Chronological Resort (default: false) — Reorder results by creation time instead of relevance score

Get Memory

Retrieve a specific memory by its ID, including metadata, processing status, and optionally the original content.

Options:

• Memory ID (required) — The UUID of the memory to fetch
• Include Content (default: true) — Fetch the original document content in addition to metadata

Delete Memory

Permanently delete a memory and all its associated chunks and vector embeddings.

Options:

• Memory ID (required) — The UUID of the memory to delete