npm install @memvid/sdk

import { create, use } from '@memvid/sdk';
import { existsSync } from 'fs';

const path = 'knowledge.mv2';

// IMPORTANT: create() for NEW files, use() for EXISTING files
const mem = existsSync(path)
  ? await use('basic', path)     // Open existing
  : await create(path, 'basic'); // Create new

// Add documents
await mem.put({
  title: 'Team Info',
  label: 'team',
  text: 'Alice works at Anthropic as a Senior Engineer in San Francisco.'
});

await mem.put({
  title: 'New Hires',
  label: 'team',
  text: 'Bob joined OpenAI last month as a Research Scientist.'
});

await mem.put({
  title: 'Projects',
  label: 'project',
  text: 'Project Alpha has a budget of $500k and is led by Alice.'
});

// Search works immediately (BM25 lexical search)
const results = await mem.find('who works at AI companies', { k: 5 });
console.log(results.hits.map(h => h.title));
// ['Team Info', 'New Hires']

// Ask with LLM synthesis
const answer = await mem.ask("What is Alice's role?", {
  model: 'gpt-4o-mini',
  modelApiKey: process.env.OPENAI_API_KEY
});
console.log(answer.answer);
// "Alice is a Senior Engineer at Anthropic in San Francisco."

// Extract structured facts
await mem.enrich('rules');

// Query entity state (O(1) lookup)
const alice = await mem.state('Alice');
console.log(alice.slots);
// { employer: 'Anthropic', role: 'Senior Engineer', location: 'San Francisco' }

pip install memvid-sdk

from memvid_sdk import create, use
import os

path = 'knowledge.mv2'

# IMPORTANT: create() for NEW files, use() for EXISTING files
if os.path.exists(path):
    mem = use('basic', path)      # Open existing
else:
    mem = create(path)            # Create new (kind='basic' is default)

# enable_lex() not needed - lexical search enabled by default

# Add documents
mem.put(
    title='Team Info',
    label='team',
    metadata={},
    text='Alice works at Anthropic as a Senior Engineer in San Francisco.'
)

mem.put(
    title='New Hires',
    label='team',
    metadata={},
    text='Bob joined OpenAI last month as a Research Scientist.'
)

mem.put(
    title='Projects',
    label='project',
    metadata={},
    text='Project Alpha has a budget of $500k and is led by Alice.'
)

# Search works immediately (BM25 lexical search)
results = mem.find('who works at AI companies', k=5)
print([h['title'] for h in results['hits']])
# ['Team Info', 'New Hires']

# Ask with LLM synthesis
answer = mem.ask(
    "What is Alice's role?",
    model='gpt-4o-mini',
    api_key=os.environ['OPENAI_API_KEY']
)
print(answer['answer'])
# "Alice is a Senior Engineer at Anthropic in San Francisco."

# Extract structured facts
mem.enrich(engine='rules')

# Query entity state (O(1) lookup)
alice = mem.state('Alice')
print(alice['slots'])
# {'employer': 'Anthropic', 'role': 'Senior Engineer', 'location': 'San Francisco'}

from memvid_sdk import create, use
from memvid_sdk.embeddings import OpenAIEmbeddings
import os

embedder = OpenAIEmbeddings(
    api_key=os.environ['OPENAI_API_KEY'],
    model='text-embedding-3-small'
)

path = 'project.mv2'
if os.path.exists(path):
    mem = use('basic', path)
else:
    mem = create(path)

mem.put_many(docs, embedder=embedder)
mem.find('query', embedder=embedder)

docs = [
    {'title': 'Doc 1', 'label': 'kb', 'text': 'Content 1'},
    {'title': 'Doc 2', 'label': 'kb', 'text': 'Content 2'},
    {'title': 'Doc 3', 'label': 'kb', 'text': 'Content 3'}
]

mem.put_many(docs)

await mem.put({ file: 'report.pdf', title: 'Q4 Report', label: 'report' });
await mem.putPdfTables('report.pdf', true);
const tables = await mem.listTables();

mem.put(title='Q4 Report', label='report', metadata={}, file='report.pdf')
mem.put_pdf_tables('report.pdf', embed_rows=True)
tables = mem.list_tables()