Vector Store

Store and search embeddings for semantic knowledge retrieval in Rust agents

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

Quick Start

1. 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);

2. 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()?;

3. 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:

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

Related