Database

Storage backends for agent data in PraisonAI Rust SDK

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

Quick Start

1. 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?;

2. 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

| Add a record | | search(embedding, limit) | async fn search(&self, &[f32], usize) -> Result<Vec

` | Search by embedding |

| get(id) | `async fn get(&self, &str) -> Result<Option

` | 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

Methods

) -> Self | Custom directory | | max_messages(n) | fn max_messages(self, usize) -> Self` | Set limit |

Storage Comparison

Best Practices

Related