Memory Types

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Storage Types

Storage types handle the persistence of messages and metadata.

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In-Memory

Default storage type when no storage is specified.

# No configuration needed - this is the default
memory:
  id: my_memory
  vector:
    use: compozy/vector:pg-vector
    dimensions: 1536

Best Used For:

• Development and testing
• Quick prototyping
• Temporary storage needs
• Single-instance applications

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PostgreSQL

Production-grade storage built on PostgreSQL.

storage:
  use: compozy/storage:postgres
  config:
    url: "{{ env.POSTGRES_URL }}"

Best Used For:

• Production deployments
• High-concurrency workloads
• Complex querying needs
• Data persistence requirements

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Redis

In-memory data store with optional persistence.

storage:
  use: compozy/storage:redis
  config:
    url: "{{ env.REDIS_URL }}"

Best Used For:

• High-performance needs
• Caching requirements
• Real-time applications
• Temporary storage with persistence

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Upstash

Serverless Redis-compatible storage.

storage:
  use: compozy/storage:upstash
  config:
    url: "{{ env.UPSTASH_URL }}"
    token: "{{ env.UPSTASH_TOKEN }}"

Best Used For:

• Serverless deployments
• Edge computing
• Pay-per-use scenarios
• Low-latency requirements

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SQLite

Lightweight file-based storage.

storage:
  use: compozy/storage:sqlite
  config:
    path: ./data/memory.db

Best Used For:

• Local development
• Single-file deployments
• Simple applications
• Embedded systems

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LibSQL

Distributed SQLite-compatible storage.

storage:
  use: compozy/storage:libsql
  config:
    url: "{{ env.LIBSQL_URL }}"

Best Used For:

• Edge deployments
• Distributed systems
• SQLite compatibility
• Serverless applications

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Vector Types

Vector types handle the storage and retrieval of embeddings.

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PgVector

PostgreSQL-based vector storage.

vector:
  use: compozy/vector:pg-vector
  dimensions: 1536
  config:
    url: "{{ env.POSTGRES_URL }}"

Best Used For:

• Production deployments
• Integrated PostgreSQL setups
• Cost-effective vector storage
• Simple vector search needs

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Pinecone

Managed vector database service.

vector:
  use: compozy/vector:pinecone
  dimensions: 1536
  config:
    apiKey: "{{ env.PINECONE_API_KEY }}"

Best Used For:

• Large-scale deployments
• High-performance vector search
• Managed infrastructure
• Production ML applications

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Qdrant

Open-source vector database.

vector:
  use: compozy/vector:qdrant
  dimensions: 1536
  config:
    url: "{{ env.QDRANT_URL }}"

Best Used For:

• Complex filtering needs
• Self-hosted deployments
• High-performance search
• Flexible deployment options

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Chroma

Open-source embedding database.

vector:
  use: compozy/vector:chroma
  dimensions: 1536
  config:
    path: ./data/embeddings

Best Used For:

• Local development
• Simple vector search
• Quick prototyping
• Small to medium datasets

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Milvus

Distributed vector database.

vector:
  use: compozy/vector:milvus
  dimensions: 1536
  config:
    url: "{{ env.MILVUS_URL }}"

Best Used For:

• Large-scale deployments
• Distributed search
• High availability needs
• Complex vector operations

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Embedder Types

Embedder types handle the conversion of text to vector embeddings.

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OpenAI

OpenAI’s text embedding models.

embedder:
  use: compozy/embedder:openai
  config:
    model: text-embedding-3-small

Best Used For:

• High-quality embeddings
• Production use cases
• Multi-lingual support
• Latest embedding models

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FastEmbed

Lightweight local embedding models.

embedder:
  use: compozy/embedder:fastembed
  config:
    model: bge-small-en

Best Used For:

• Local deployment
• Privacy requirements
• Cost-effective solutions
• Quick prototyping

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Cohere

Cohere’s embedding service.

embedder:
  use: compozy/embedder:cohere
  config:
    model: embed-english-v3.0

Best Used For:

• Enterprise use cases
• Specialized embeddings
• Multi-lingual needs
• Alternative to OpenAI

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HuggingFace

Open-source models from HuggingFace.

embedder:
  use: compozy/embedder:huggingface
  config:
    model: sentence-transformers/all-mpnet-base-v2

Best Used For:

• Custom models
• Self-hosted solutions
• Research applications
• Specialized domains

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Vertex AI

Google Cloud’s embedding service.

embedder:
  use: compozy/embedder:vertex
  config:
    model: textembedding-gecko
    project: "{{ env.GOOGLE_CLOUD_PROJECT }}"

Best Used For:

• Google Cloud integration
• Enterprise deployments
• Scalable solutions
• Managed infrastructure

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Best Practices