> ## Documentation Index
> Fetch the complete documentation index at: https://praison.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Database

> Storage backends for agent data in PraisonAI Rust SDK

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

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph LR
    subgraph "Storage Options"
        A[🤖 Agent] --> M[🧠 InMemoryVectorStore]
        A --> F[📁 FileSessionStore]
        M --> Q[🔍 Search]
        F --> P[💾 Persist]
    end
    
    classDef agent fill:#6366F1,stroke:#7C90A0,color:#fff
    classDef memory fill:#F59E0B,stroke:#7C90A0,color:#fff
    classDef file fill:#10B981,stroke:#7C90A0,color:#fff
    
    class A agent
    class M memory
    class F,Q,P file
```

## Quick Start

<Steps>
  <Step title="In-Memory Vector Store">
    ```rust theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    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?;
    ```
  </Step>

  <Step title="File-Based Sessions">
    ```rust theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    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?;
    ```
  </Step>
</Steps>

***

## User Interaction Flow

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
sequenceDiagram
    participant User
    participant Agent
    participant VectorStore
    participant SessionStore
    
    User->>Agent: "What do you know about X?"
    Agent->>VectorStore: search(embedding, limit)
    VectorStore-->>Agent: Relevant documents
    Agent->>Agent: Generate response
    Agent->>SessionStore: save(session_id, messages)
    Agent-->>User: "Based on my knowledge..."
```

***

## InMemoryVectorStore

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

```rust theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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

```rust theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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.

```rust theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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

<AccordionGroup>
  <Accordion title="Use InMemoryVectorStore for development">
    Fast iteration without database setup. Switch to persistent store for production.
  </Accordion>

  <Accordion title="Set message limits for long-running agents">
    Use `FileSessionStore::new().max_messages(100)` to prevent unbounded growth.
  </Accordion>

  <Accordion title="Index by semantic meaning, not keywords">
    Vector stores search by embedding similarity - structure your documents for semantic retrieval.
  </Accordion>
</AccordionGroup>

***

## Related

<CardGroup cols={2}>
  <Card title="Vector Store" icon="database" href="/docs/rust/vector-store">
    Vector store protocol
  </Card>

  <Card title="Knowledge" icon="book" href="/docs/rust/knowledge">
    Knowledge management
  </Card>
</CardGroup>