Database - PraisonAI

Database integration provides persistent and in-memory storage for agent knowledge and sessions.

Quick Start

In-Memory Vector Store

use praisonai::{Agent, InMemoryVectorStore, VectorRecord};

// Create vector store for semantic search
let mut store = InMemoryVectorStore::new();

// Add knowledge
let record = VectorRecord::new("doc-1", "AI agents are autonomous systems")
    .with_embedding(vec![0.1, 0.2, 0.3]);
store.add(record).await?;

// Create agent with knowledge
let agent = Agent::new()
    .name("Researcher")
    .knowledge(store)
    .build()?;

agent.start("What are AI agents?").await?;

File-Based Sessions

use praisonai::{Agent, FileSessionStore};

// Persist conversations to disk
let store = FileSessionStore::new();

let agent = Agent::new()
    .name("Assistant")
    .session_store(store)
    .build()?;

// Conversations persist across restarts
agent.start("Remember this context").await?;

User Interaction Flow

InMemoryVectorStore

Fast, in-memory vector storage for development and small knowledge bases.

pub struct InMemoryVectorStore {
    records: Vec<VectorRecord>,
}

Methods

Method	Signature	Description
`new()`	`fn new() -> Self`	Create empty store
`add(record)`	`async fn add(&mut self, VectorRecord) -> Result<String>`	Add a record
`search(embedding, limit)`	`async fn search(&self, &[f32], usize) -> Result<Vec<SearchResultItem>>`	Search by embedding
`get(id)`	`async fn get(&self, &str) -> Result<Option<VectorRecord>>`	Get by ID
`delete(id)`	`async fn delete(&mut self, &str) -> Result<bool>`	Delete by ID

Example

use praisonai::{InMemoryVectorStore, VectorRecord, VectorStoreProtocol};

let mut store = InMemoryVectorStore::new();

// Add records with embeddings
let record = VectorRecord::new("id-1", "Document content")
    .with_embedding(vec![0.1, 0.2, 0.3, 0.4]);

store.add(record).await?;

// Search for similar content
let results = store.search(&[0.1, 0.2, 0.3, 0.4], 5).await?;
for item in results {
    println!("Found: {} (score: {})", item.text, item.score);
}

FileSessionStore

Persistent file-based storage for session data.

pub struct FileSessionStore {
    session_dir: PathBuf,
    max_messages: usize,
}

Configuration Options

Option	Type	Default	Description
`session_dir`	`PathBuf`	`~/.praisonai/sessions/`	Storage directory
`max_messages`	`usize`	`100`	Max messages per session

Methods

Method	Signature	Description
`new()`	`fn new() -> Self`	Default directory
`with_dir(dir)`	`fn with_dir(impl Into<PathBuf>) -> Self`	Custom directory
`max_messages(n)`	`fn max_messages(self, usize) -> Self`	Set limit

Storage Comparison

Store	Persistence	Use Case	Performance
`InMemoryVectorStore`	Memory only	Development, testing	⚡ Fastest
`FileSessionStore`	Disk	Production sessions	💾 Durable

Best Practices

Use InMemoryVectorStore for development

Fast iteration without database setup. Switch to persistent store for production.

Set message limits for long-running agents

Use FileSessionStore::new().max_messages(100) to prevent unbounded growth.

Index by semantic meaning, not keywords

Vector stores search by embedding similarity - structure your documents for semantic retrieval.

Vector Store

Vector store protocol

Knowledge

Knowledge management

​Quick Start

​User Interaction Flow

​InMemoryVectorStore

​Methods

​Example

​FileSessionStore

​Configuration Options

​Methods

​Storage Comparison

​Best Practices

​Related

Vector Store

Knowledge

Quick Start

User Interaction Flow

InMemoryVectorStore

Methods

Example

FileSessionStore

Configuration Options

Methods

Storage Comparison

Best Practices

Related