Vector Store - PraisonAI

Give your Rust agents semantic search — store text as vector embeddings and retrieve the most relevant chunks by meaning, not just keywords.

Quick Start

Agent with Knowledge (Simplest)

The easiest way to use vector storage — attach files to an agent and it handles indexing automatically.

use praisonai::{Agent, KnowledgeConfig, VectorStore};

let knowledge = KnowledgeConfig::new()
    .source("docs/")
    .vector_store(VectorStore::InMemory);

let agent = Agent::new()
    .name("Researcher")
    .instructions("Answer questions using the provided documents")
    .knowledge(knowledge)
    .build()?;

let answer = agent.chat("How do I configure authentication?").await?;
println!("{}", answer);

Persistent Storage with SQLite

Switch to SQLite for data that survives process restarts.

use praisonai::{Agent, KnowledgeConfig, VectorStore};

let knowledge = KnowledgeConfig::new()
    .source("docs/")
    .vector_store(VectorStore::SQLite {
        path: "knowledge.db".into()
    });

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

Production with Qdrant

Use a dedicated vector database for production-scale deployments.

use praisonai::{Agent, KnowledgeConfig, VectorStore};

let knowledge = KnowledgeConfig::new()
    .source("docs/")
    .vector_store(VectorStore::Qdrant {
        url: "http://localhost:6333".into(),
        collection: "my_docs".into(),
    });

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

How It Works

Vector Store Options

Choose the right backend for your needs:

Store	Type	Use Case
`VectorStore::InMemory`	In-process	Development, testing, short-lived agents
`VectorStore::SQLite { path }`	Local file	Persistent, single-machine deployments
`VectorStore::Postgres { url }`	Remote DB	Production, multi-instance
`VectorStore::Qdrant { url, collection }`	Dedicated vector DB	High-scale production

Common Patterns

Multiple Document Sources

use praisonai::{Agent, KnowledgeConfig, VectorStore};

let knowledge = KnowledgeConfig::new()
    .source("docs/api.pdf")
    .source("docs/guide.md")
    .source("https://docs.example.com/faq")
    .vector_store(VectorStore::InMemory);

let agent = Agent::new()
    .name("Support Agent")
    .instructions("Answer customer questions from the documentation")
    .knowledge(knowledge)
    .build()?;

Namespace Isolation

Keep different datasets separate within the same store.

use praisonai::{Agent, KnowledgeConfig, VectorStore};

// Agent for English docs
let en_knowledge = KnowledgeConfig::new()
    .source("docs/en/")
    .namespace("en")
    .vector_store(VectorStore::SQLite { path: "kb.db".into() });

// Agent for Spanish docs (same DB, different namespace)
let es_knowledge = KnowledgeConfig::new()
    .source("docs/es/")
    .namespace("es")
    .vector_store(VectorStore::SQLite { path: "kb.db".into() });

Best Practices

Start with InMemory, migrate when ready

Use VectorStore::InMemory during development — no setup required. When you need persistence or scale, switch to SQLite or Qdrant by changing one line. The agent code stays identical.

Use namespaces for multi-tenant data

Namespaces let multiple agents share one database without data bleeding between them. Use a per-user or per-project namespace: "user:alice", "project:v2".

Chunk documents for better retrieval

Very long documents score poorly in semantic search. Split them into 500–1000 character chunks so the embedder captures focused meaning per chunk. Use the chunking options in KnowledgeConfig to control this automatically.

Prefer Qdrant for production

For production workloads with thousands of documents, Qdrant delivers faster approximate nearest-neighbour search than a full table scan in SQLite or Postgres.

Knowledge

Full knowledge base configuration

Embeddings

Generate and manage embeddings

​Quick Start

​How It Works

​Vector Store Options

​Common Patterns

​Multiple Document Sources

​Namespace Isolation

​Best Practices

​Related