Built-in Memory Provider

The built-in memory provider is the standard memory backend shipped with Memoh. It works with Memoh's memory pipeline and supports:

• Automatic memory extraction from conversations
• Semantic memory retrieval during chat
• Manual memory creation and editing
• Memory compaction and rebuild workflows

The built-in provider operates in one of three memory modes, each with different infrastructure requirements and retrieval capabilities.

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Memory Modes

Mode	Index	Requirements	Use Case
Off	File-based only	None	Lightweight setup, no vector search
Sparse	Neural sparse vectors	Sparse service + Qdrant (--profile sparse)	Good retrieval quality without embedding API costs
Dense	Dense embeddings	Embedding model + Qdrant (--profile qdrant)	Highest-quality semantic search

How Sparse Mode Works

Sparse mode uses the opensearch-neural-sparse-encoding-multilingual-v1 model (from the OpenSearch project) to convert text into sparse vectors — compact lists of token indices with importance weights. Unlike dense mode, which requires an external embedding API, the sparse model runs locally in the sparse container with no API key or cost. It supports multiple languages and provides significantly better retrieval quality than keyword-only search.

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Creating a Built-in Provider

1. Navigate to the Memory Providers page.
2. Click Add Memory Provider.
3. Fill in the following fields:
   • Name: A display name for this provider.
   • Provider Type: Select builtin.
4. Click Create.

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Configuring a Built-in Provider

After creating a provider, select it from the list and configure its settings.

Field	Description
Memory Mode	off (default), sparse, or dense. Controls how memories are indexed and retrieved.
Embedding Model	Embedding model for dense vector search. Only used in dense mode.
Qdrant Collection	Qdrant collection name. Defaults to memory_sparse.

Managing Providers

• Edit: Select a provider and update its settings.
• Delete: Remove a provider you no longer use.

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Infrastructure Requirements

Off Mode

No additional infrastructure required. Memories are stored and retrieved using file-based indexing only.

Sparse Mode

Requires the sparse service (runs the opensearch-neural-sparse-encoding-multilingual-v1 model locally) and Qdrant vector database. Enable both with Docker Compose profiles:

docker compose --profile qdrant --profile sparse up -d

The following sections must be present in config.toml:

[qdrant]
base_url = "http://qdrant:6334"

[sparse]
base_url = "http://sparse:8085"

Dense Mode

Requires an embedding model (configured in the provider settings) and Qdrant:

docker compose --profile qdrant up -d

The Qdrant section must be present in config.toml:

[qdrant]
base_url = "http://qdrant:6334"

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Assigning a Memory Provider to a Bot

1. Navigate to the Bots page and open your bot.
2. Go to the General tab.
3. Find the Memory Provider dropdown.
4. Select the provider you created.
5. Click Save.

If no memory provider is selected, the bot will not use that provider configuration in its runtime settings.

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Using Memory After Setup

Once a memory provider is assigned to the bot, you can manage actual memories from the bot's Memory tab:

• Create memories manually
• Extract memories from conversations
• Search, edit, and delete memories
• Compact or rebuild the memory store

For day-to-day memory operations, continue with Bot Memory Management.