> ## Documentation Index
> Fetch the complete documentation index at: https://docs.memvid.com/llms.txt
> Use this file to discover all available pages before exploring further.
# 5-Minute Quickstart
> Create your first AI memory with search and Q&A in under 5 minutes
Build an AI memory system with hybrid search and LLM-powered Q\&A. Choose your platform:
**`create()` will OVERWRITE existing files without warning!**
| Function | Purpose | If File Exists | Parameter Order |
| -------------------- | --------------------------- | -------------------- | --------------------- |
| `create(path, kind)` | Create **new** .mv2 file | **DELETES all data** | path first, then kind |
| `use(kind, path)` | Open **existing** .mv2 file | Preserves data | kind first, then path |
**Always check if the file exists before choosing:**
```typescript theme={null}
const mem = existsSync(path) ? await use('basic', path) : await create(path, 'basic');
```
### Install
```bash theme={null}
npm install -g memvid-cli
```
Works on macOS, Linux, and Windows. Requires Node.js 14+.
### Create & Ingest
```bash theme={null}
# Create a new memory
memvid create knowledge.mv2
# Add documents
echo "Alice works at Anthropic as a Senior Engineer in San Francisco." | \
memvid put knowledge.mv2 --title "Team Info"
echo "Bob joined OpenAI last month as a Research Scientist." | \
memvid put knowledge.mv2 --title "New Hires"
echo "Project Alpha has a budget of $500k and is led by Alice." | \
memvid put knowledge.mv2 --title "Projects"
```
### Search
```bash theme={null}
# Search works immediately (BM25 lexical search)
memvid find knowledge.mv2 --query "who works at AI companies"
```
### Ask Questions
```bash theme={null}
# Ask with LLM synthesis (requires OPENAI_API_KEY)
export OPENAI_API_KEY=sk-...
memvid ask knowledge.mv2 --question "What is Alice's role?" --use-model openai
```
### Extract Facts
```bash theme={null}
# Extract structured facts
memvid enrich knowledge.mv2 --engine rules
# Query entity state (O(1) lookup)
memvid state knowledge.mv2 "Alice"
```
**Output:**
```
Entity: Alice
employer: Anthropic
role: Senior Engineer
location: San Francisco
```
### Install
```bash theme={null}
npm install @memvid/sdk
```
### Create & Ingest
```typescript theme={null}
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
```typescript theme={null}
// 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 Questions
```typescript theme={null}
// 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 Facts
```typescript theme={null}
// 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' }
```
### Install
```bash theme={null}
pip install memvid-sdk
```
### Create & Ingest
```python theme={null}
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
```python theme={null}
# 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 Questions
```python theme={null}
# 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 Facts
```python theme={null}
# 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'}
```
***
## What You Built
In 5 minutes, you created a complete AI memory system with:
| Feature | Description |
| --------------------- | ---------------------------------------------------- |
| **Hybrid Search** | Combines lexical (BM25) and semantic (vector) search |
| **LLM Q\&A** | Natural language questions with sourced answers |
| **Entity Extraction** | Structured facts with O(1) lookups |
| **Single File** | Everything stored in one portable `.mv2` file |
***
## Next Steps
Complete command reference for all 38+ commands
Full API reference with TypeScript types
Complete Python API with examples
OpenAI, Gemini, Mistral, and local models
Deep dive into O(1) entity lookups
LangChain, LlamaIndex, Vercel AI, and more
***
## Common Patterns
### Using External Embeddings
```typescript theme={null}
import { create, use, OpenAIEmbeddings } from '@memvid/sdk';
import { existsSync } from 'fs';
const embedder = new OpenAIEmbeddings({
apiKey: process.env.OPENAI_API_KEY,
model: 'text-embedding-3-small'
});
const path = 'project.mv2';
const mem = existsSync(path)
? await use('basic', path)
: await create(path, 'basic');
await mem.putMany(docs, { embedder });
await mem.find('query', { embedder });
```
```python theme={null}
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)
```
### Batch Ingestion
```typescript theme={null}
const 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' }
];
await mem.putMany(docs);
```
```python theme={null}
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)
```
### PDF Ingestion with Tables
```bash theme={null}
memvid put project.mv2 --input report.pdf --title "Q4 Report" --tables
memvid tables list project.mv2
memvid tables export project.mv2 --table-id tbl_001 -o table.csv
```
```typescript theme={null}
await mem.put({ file: 'report.pdf', title: 'Q4 Report', label: 'report' });
await mem.putPdfTables('report.pdf', true);
const tables = await mem.listTables();
```
```python theme={null}
mem.put(title='Q4 Report', label='report', metadata={}, file='report.pdf')
mem.put_pdf_tables('report.pdf', embed_rows=True)
tables = mem.list_tables()
```