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LlamaIndex.TS provides storage abstractions for persisting different types of data. Storage systems enable caching, state management, and efficient data retrieval across sessions.

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Storage Types

LlamaIndex uses specialized stores for different data types:
  • Document Stores: Store and manage source documents and nodes
  • Index Stores: Persist index structures and metadata
  • Vector Stores: Store embeddings for semantic search
  • Chat Stores: Manage conversation history
  • KV Stores: General key-value storage

​
Document Stores

Document stores manage your source documents and nodes with deduplication and versioning.

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SimpleDocumentStore

import { SimpleDocumentStore } from "llamaindex/storage";
import { Document } from "llamaindex";

// Create a document store
const docStore = new SimpleDocumentStore();

// Add documents
const doc = new Document({ 
  text: "Content here", 
  id_: "doc_1" 
});

await docStore.addDocuments([doc], false);

// Retrieve document
const retrieved = await docStore.getDocument("doc_1", false);

// Check existence
const exists = await docStore.documentExists("doc_1");

// Get all documents
const allDocs = await docStore.docs();

// Delete document
await docStore.deleteDocument("doc_1", false);

​
Persistence

import { SimpleDocumentStore } from "llamaindex/storage";

const docStore = new SimpleDocumentStore();

// Add documents
await docStore.addDocuments([doc1, doc2]);

// Persist to disk
await docStore.persist("./storage/docstore.json");

// Load from disk
const loadedStore = await SimpleDocumentStore.fromPersistPath(
  "./storage/docstore.json"
);

​
Document Hashing

Track document changes with hashes: 
// Set document hash
await docStore.setDocumentHash("doc_1", doc.hash);

// Get document hash
const hash = await docStore.getDocumentHash("doc_1");

// Get all hashes
const allHashes = await docStore.getAllDocumentHashes();

// Check if document changed
if (hash !== doc.hash) {
  console.log("Document has been modified");
}

​
Reference Document Info

// Get ref doc info (tracks which nodes belong to a document)
const refInfo = await docStore.getRefDocInfo("doc_1");

// Contains:
// - nodeIds: string[] - IDs of nodes derived from this doc
// - extraInfo: Record<string, any> - Additional metadata

// Get all ref doc info
const allRefInfo = await docStore.getAllRefDocInfo();

​
Index Stores

Index stores persist index structures for quick loading.

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SimpleIndexStore

import { SimpleIndexStore } from "llamaindex/storage";

const indexStore = new SimpleIndexStore();

// Add index structure
await indexStore.addIndexStruct(indexStruct);

// Get index structure
const struct = await indexStore.getIndexStruct("struct_id");

// Get all structures
const allStructs = await indexStore.getIndexStructs();

// Delete structure
await indexStore.deleteIndexStruct("struct_id");

​
Persistence

// Persist to disk
await indexStore.persist("./storage/index_store.json");

// Load from disk
const loadedStore = await SimpleIndexStore.fromPersistPath(
  "./storage/index_store.json"
);

// Or from directory
const dirStore = await SimpleIndexStore.fromPersistDir(
  "./storage"
);

​
Vector Stores

Vector stores persist embeddings for semantic search. See Vector Stores for detailed documentation. 
import { VectorStoreIndex } from "llamaindex";
import { PineconeVectorStore } from "@llamaindex/pinecone";

const vectorStore = new PineconeVectorStore({
  indexName: "my-index"
});

const index = await VectorStoreIndex.fromVectorStore(vectorStore);

​
Chat Stores

Chat stores manage conversation history for chat engines.

​
SimpleChatStore

import { SimpleChatStore } from "llamaindex/storage";
import { ChatMessage } from "llamaindex";

const chatStore = new SimpleChatStore();

// Store a message
const message = ChatMessage.user("Hello!");
await chatStore.setMessages("conversation_1", [message]);

// Get messages
const messages = await chatStore.getMessages("conversation_1");

// Add message to existing conversation
await chatStore.addMessage("conversation_1", 
  ChatMessage.assistant("Hi there!")
);

// Delete conversation
await chatStore.deleteMessages("conversation_1");

// Get all keys
const keys = await chatStore.getAllKeys();

​
Persistence

// Persist chat history
await chatStore.persist("./storage/chat_store.json");

// Load chat history
const loadedChatStore = await SimpleChatStore.fromPersistPath(
  "./storage/chat_store.json"
);

​
With Chat Engine

import { ContextChatEngine } from "llamaindex";
import { SimpleChatStore } from "llamaindex/storage";

const chatStore = new SimpleChatStore();

const chatEngine = new ContextChatEngine({
  retriever,
  chatHistory: await chatStore.getMessages("session_123")
});

// Chat will use persisted history
const response = await chatEngine.chat({ message: "Continue our discussion" });

// Update store
await chatStore.addMessage("session_123", response.message);
await chatStore.persist();

​
KV Stores

Key-value stores provide general-purpose persistence.

​
SimpleKVStore

import { SimpleKVStore } from "llamaindex/storage";

const kvStore = new SimpleKVStore();

// Put value
await kvStore.put("key1", { data: "value" }, "collection1");

// Get value
const value = await kvStore.get("key1", "collection1");

// Get all values in collection
const allValues = await kvStore.getAll("collection1");

// Delete value
await kvStore.delete("key1", "collection1");

​
Persistence

const kvStore = new SimpleKVStore();

// Add data
await kvStore.put("config", { theme: "dark" });

// Persist
await kvStore.persist("./storage/kv_store.json");

// Load from file
const loadedKV = await SimpleKVStore.fromPersistPath(
  "./storage/kv_store.json"
);

​
Collections

Organize data with collections: 
// Different collections for different data types
await kvStore.put("user_1", userData, "users");
await kvStore.put("session_1", sessionData, "sessions");
await kvStore.put("cache_1", cacheData, "cache");

// Retrieve from specific collection
const user = await kvStore.get("user_1", "users");
const allUsers = await kvStore.getAll("users");

​
Storage Context

Combine multiple stores for complete state management: 
import { 
  StorageContext,
  VectorStoreIndex
} from "llamaindex";
import { 
  SimpleDocumentStore,
  SimpleIndexStore,
  SimpleChatStore
} from "llamaindex/storage";

const storageContext = await StorageContext.fromDefaults({
  docStore: new SimpleDocumentStore(),
  indexStore: new SimpleIndexStore(),
  vectorStore: vectorStore,
  persistDir: "./storage"
});

const index = await VectorStoreIndex.fromDocuments(
  documents,
  { storageContext }
);

// All stores are automatically persisted

​
Complete Example

import { 
  Document,
  VectorStoreIndex,
  IngestionPipeline,
  SentenceSplitter,
  StorageContext,
  DocStoreStrategy
} from "llamaindex";
import { OpenAIEmbedding } from "@llamaindex/openai";
import { 
  SimpleDocumentStore,
  SimpleIndexStore,
  SimpleChatStore
} from "llamaindex/storage";
import { PineconeVectorStore } from "@llamaindex/pinecone";
import fs from "fs/promises";

async function main() {
  const persistDir = "./storage";
  
  // Set up storage
  const docStore = await SimpleDocumentStore.fromPersistPath(
    `${persistDir}/docstore.json`
  );
  
  const indexStore = await SimpleIndexStore.fromPersistPath(
    `${persistDir}/index_store.json`
  );
  
  const vectorStore = new PineconeVectorStore({
    indexName: "my-index"
  });
  
  const chatStore = await SimpleChatStore.fromPersistPath(
    `${persistDir}/chat_store.json`
  );
  
  // Create documents
  const text = await fs.readFile("document.txt", "utf-8");
  const document = new Document({ 
    text, 
    id_: "doc_1" 
  });
  
  // Check if already processed
  const docHash = document.hash;
  const storedHash = await docStore.getDocumentHash("doc_1");
  
  if (docHash === storedHash) {
    console.log("Document unchanged, using cached version");
  } else {
    console.log("Processing document...");
    
    // Process with pipeline
    const pipeline = new IngestionPipeline({
      transformations: [
        new SentenceSplitter({ chunkSize: 1024 }),
        new OpenAIEmbedding()
      ],
      vectorStore,
      docStore,
      docStoreStrategy: DocStoreStrategy.UPSERTS
    });
    
    await pipeline.run({ documents: [document] });
    
    // Update hash
    await docStore.setDocumentHash("doc_1", docHash);
    await docStore.persist(`${persistDir}/docstore.json`);
  }
  
  // Create index from vector store
  const storageContext = await StorageContext.fromDefaults({
    docStore,
    indexStore,
    vectorStore,
    persistDir
  });
  
  const index = await VectorStoreIndex.fromVectorStore(
    vectorStore,
    { storageContext }
  );
  
  // Create chat engine with history
  const sessionId = "user_session_123";
  const chatHistory = await chatStore.getMessages(sessionId) || [];
  
  const chatEngine = index.asChatEngine({
    chatHistory
  });
  
  // Chat
  const response = await chatEngine.chat({
    message: "What does the document say?"
  });
  
  console.log(response.toString());
  
  // Save chat history
  await chatStore.addMessage(sessionId, response.message);
  await chatStore.persist(`${persistDir}/chat_store.json`);
  
  // Persist index
  await indexStore.persist(`${persistDir}/index_store.json`);
}

main().catch(console.error);

​
Best Practices

  1. Use document hashing
    • Track document changes efficiently
    • Avoid reprocessing unchanged content
    • Enable incremental updates
  2. Organize storage
    • Use consistent directory structure
    • Separate different store types
    • Version your storage format
  3. Handle persistence errors
    • Validate file paths before writing
    • Use atomic writes when possible
    • Backup before major updates
  4. Manage chat history
    • Set retention policies
    • Limit history size for context windows
    • Archive old conversations
  5. Choose appropriate stores
    • SimpleKVStore for development
    • Database-backed stores for production
    • Vector stores for semantic search
    • Chat stores for conversations

​
Advanced Storage

For production applications, consider:
  • Database-backed stores: PostgreSQL, MongoDB, Redis
  • Cloud vector stores: Pinecone, Weaviate, Qdrant
  • Distributed storage: For large-scale applications
  • Custom stores: Implement BaseDocumentStore or BaseKVStore

​
Next Steps

Vector Stores

Explore vector storage options

Ingestion

Build data processing pipelines

Chat Engines

Create conversational interfaces

Documents

Work with Document objects

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