> ## Documentation Index > Fetch the complete documentation index at: https://docs.compozy.ai/llms.txt > Use this file to discover all available pages before exploring further. # Memory Types > Memory in Compozy comes in various types, each designed for specific use cases and deployment scenarios. Understanding these types helps in choosing the right solution for your needs. ## Storage Types Storage types handle the persistence of messages and metadata. ### In-Memory Default storage type when no storage is specified. ```yaml theme={"dark"} # 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 ### PostgreSQL Production-grade storage built on PostgreSQL. ```yaml theme={"dark"} storage: use: compozy/storage:postgres config: url: "{{ env.POSTGRES_URL }}" ``` **Best Used For:** * Production deployments * High-concurrency workloads * Complex querying needs * Data persistence requirements ### Redis In-memory data store with optional persistence. ```yaml theme={"dark"} 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 ### Upstash Serverless Redis-compatible storage. ```yaml theme={"dark"} 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 ### SQLite Lightweight file-based storage. ```yaml theme={"dark"} storage: use: compozy/storage:sqlite config: path: ./data/memory.db ``` **Best Used For:** * Local development * Single-file deployments * Simple applications * Embedded systems ### LibSQL Distributed SQLite-compatible storage. ```yaml theme={"dark"} storage: use: compozy/storage:libsql config: url: "{{ env.LIBSQL_URL }}" ``` **Best Used For:** * Edge deployments * Distributed systems * SQLite compatibility * Serverless applications ## Vector Types Vector types handle the storage and retrieval of embeddings. ### PgVector PostgreSQL-based vector storage. ```yaml theme={"dark"} 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 ### Pinecone Managed vector database service. ```yaml theme={"dark"} 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 ### Qdrant Open-source vector database. ```yaml theme={"dark"} 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 ### Chroma Open-source embedding database. ```yaml theme={"dark"} 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 ### Milvus Distributed vector database. ```yaml theme={"dark"} 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 ## Embedder Types Embedder types handle the conversion of text to vector embeddings. ### OpenAI OpenAI's text embedding models. ```yaml theme={"dark"} 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 ### FastEmbed Lightweight local embedding models. ```yaml theme={"dark"} embedder: use: compozy/embedder:fastembed config: model: bge-small-en ``` **Best Used For:** * Local deployment * Privacy requirements * Cost-effective solutions * Quick prototyping ### Cohere Cohere's embedding service. ```yaml theme={"dark"} 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 ### HuggingFace Open-source models from HuggingFace. ```yaml theme={"dark"} 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 ### Vertex AI Google Cloud's embedding service. ```yaml theme={"dark"} 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 ## Best Practices Select storage based on your persistence and performance needs: * Use in-memory for development * Use PostgreSQL for traditional deployments * Use Redis for high-performance needs * Use Upstash for serverless deployments Choose vector storage based on scale and requirements: * Use PgVector for integrated PostgreSQL setups * Use Pinecone for managed vector search * Use Qdrant for self-hosted deployments * Use Chroma for local development Select embedder based on your use case: * Use OpenAI for production quality * Use FastEmbed for local deployment * Use Cohere for enterprise needs * Use HuggingFace for custom models Implement security best practices: * Use environment variables for credentials * Enable SSL/TLS in production * Implement proper access controls