LlamaIndex

Integrate Memvid with LlamaIndex to build powerful RAG applications. The llamaindex adapter provides native LlamaIndex components for seamless integration.

Node.js
Python

Installation

npm install @memvid/sdk llamaindex @llamaindex/openai

Quick Start

import { use } from '@memvid/sdk';

// Open with LlamaIndex adapter
const mem = await use('llamaindex', 'knowledge.mv2');

// Access LlamaIndex tools
const tools = mem.tools;       // FunctionTool array
const functions = mem.functions; // Raw function schemas

// Use query engine
const queryEngine = mem.asQueryEngine();
const response = await queryEngine.query({ query: 'What is Memvid?' });
console.log(response.response);

Installation

pip install memvid-sdk llama-index llama-index-llms-openai

Quick Start

from memvid_sdk import use

# Open with LlamaIndex adapter
mem = use('llamaindex', 'knowledge.mv2')

# Access LlamaIndex tools and query engine
tools = mem.tools
query_engine = mem.as_query_engine()

Available Tools

The LlamaIndex adapter provides three tools:

Tool	Description
`memvid_put`	Store documents in memory with title, label, and text
`memvid_find`	Search for relevant documents by query
`memvid_ask`	Ask questions with RAG-style answer synthesis

Using with Agents

Node.js
Python

import { use } from '@memvid/sdk';

// Get Memvid tools
const mem = await use('llamaindex', 'knowledge.mv2');
const tools = mem.tools;

// Tools can be used directly
for (const tool of tools) {
  console.log(`Tool: ${tool.metadata.name}`);
  console.log(`Description: ${tool.metadata.description}`);
}

// Or use with LlamaIndex agents (when available)
// Note: LlamaIndex.TS agent API is evolving

from memvid_sdk import use
from llama_index.llms.openai import OpenAI
from llama_index.core.agent import ReActAgent
import asyncio

# Get Memvid tools
mem = use('llamaindex', 'knowledge.mv2')
tools = mem.tools

# Create ReAct agent
llm = OpenAI(model="gpt-4o")
agent = ReActAgent(
    name="MemvidResearcher",
    tools=tools,
    llm=llm,
    verbose=True
)

# Run agent
async def run():
    response = await agent.run("Search for information about vector stores")
    print(response)

asyncio.run(run())

Using as a Query Engine

Node.js
Python

import { use } from '@memvid/sdk';

// Initialize
const mem = await use('llamaindex', 'knowledge.mv2');

// Get query engine factory
const queryEngine = mem.asQueryEngine();

// Query
const response = await queryEngine.query({ query: 'What is Memvid?' });
console.log(`Answer: ${response.response}`);

// Access sources
if (response.sourceNodes) {
  for (const node of response.sourceNodes) {
    console.log(`Source: ${node.node.metadata?.title}`);
  }
}

from memvid_sdk import use

# Initialize
mem = use('llamaindex', 'knowledge.mv2', read_only=True)

# Get query engine
query_engine = mem.as_query_engine()

# Query
response = query_engine.query("What are the best practices?")
print(response.response)

# Access sources
for source in response.source_nodes:
    print(f"Source: {source.node.metadata.get('title')}")

Using as a Vector Store (Python)

from memvid_sdk import use
from llama_index.core import VectorStoreIndex
from llama_index.llms.openai import OpenAI

# Initialize with llamaindex adapter
mem = use('llamaindex', 'knowledge.mv2', read_only=True)

# Get the vector store
vector_store = mem.as_vector_store()

# Build index from vector store
index = VectorStoreIndex.from_vector_store(vector_store)

# Create query engine
query_engine = index.as_query_engine(
    llm=OpenAI(model="gpt-4o")
)

# Query
response = query_engine.query("Explain the architecture")
print(response)

Chat Engine (Python)

from memvid_sdk import use
from llama_index.core import VectorStoreIndex
from llama_index.core.memory import ChatMemoryBuffer
from llama_index.llms.openai import OpenAI

# Initialize
mem = use('llamaindex', 'knowledge.mv2', read_only=True)
vector_store = mem.as_vector_store()

# Build index
index = VectorStoreIndex.from_vector_store(vector_store)

# Create chat engine with memory
chat_engine = index.as_chat_engine(
    chat_mode="context",
    llm=OpenAI(model="gpt-4o"),
    memory=ChatMemoryBuffer.from_defaults(token_limit=3000)
)

# Chat
response = chat_engine.chat("What is Memvid?")
print(response)

# Follow-up (maintains context)
response = chat_engine.chat("How does search work?")
print(response)

Custom Search Options

from memvid_sdk import use

mem = use('llamaindex', 'knowledge.mv2')

# Search with specific mode
results = mem.find('authentication', mode='lex', k=10)  # Lexical only
results = mem.find('user login flow', mode='sem', k=10)  # Semantic only
results = mem.find('auth best practices', mode='auto', k=10)  # Hybrid

# With scope filtering
results = mem.find('API', scope='mv2://docs/', k=5)

Best Practices

Use read-only mode for retrieval-only applications
Set appropriate k values based on your context window
Use hybrid mode for best recall
Close the memory when done

mem = use('llamaindex', 'knowledge.mv2', read_only=True)
try:
    # Do work
    retriever = mem.as_retriever(k=10)
    # ... use retriever
finally:
    mem.seal()

Installation

Quick Start

Installation

Quick Start

Available Tools

Using with Agents

Using as a Query Engine

Using as a Vector Store (Python)

Chat Engine (Python)

Custom Search Options

Best Practices

Next Steps

LangChain

Vercel AI SDK

Documentation Index

​Installation

​Quick Start

​Installation

​Quick Start

​Available Tools

​Using with Agents

​Using as a Query Engine

​Using as a Vector Store (Python)

​Chat Engine (Python)

​Custom Search Options

​Best Practices

​Next Steps

LangChain

Vercel AI SDK

Installation

Quick Start

Installation

Quick Start

Available Tools

Using with Agents

Using as a Query Engine

Using as a Vector Store (Python)

Chat Engine (Python)

Custom Search Options

Best Practices

Next Steps