Getting Started

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.

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What Makes Memvid Different

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Single-File Architecture

• Your data: Documents, text, images, audio, videos
• Embeddings: Vector representations for semantic search
• Indices: BM25 lexical index and vector index
• Time index: Temporal ordering for timeline queries
• Write-ahead log: Crash-safe transaction logging

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Hybrid Search Engine

# Hybrid search (recommended)
memvid find knowledge.mv2 --query "user authentication" --mode auto

# Lexical search - exact keyword matching
memvid find knowledge.mv2 --query "authentication" --mode lex

# Semantic search - conceptual understanding
memvid find knowledge.mv2 --query "how do users log in" --mode sem

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Lightning Performance

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Quick Start

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1. Install the CLI

npm install -g memvid-cli

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2. Create Your First Memory

# Create a new memory file (1 GB capacity by default)
memvid create my-knowledge.mv2

# Ingest documents with vector compression
memvid put my-knowledge.mv2 --input ./documents/ --vector-compression

# Search your knowledge
memvid find my-knowledge.mv2 --query "your search query"

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3. Build Your Application

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

    // Open your memory
    const mem = await use('basic', 'my-knowledge.mv2', { readOnly: true });

    // Search
    const results = await mem.find('machine learning', { k: 10 });
    results.hits.forEach(hit => {
      console.log(`${hit.score.toFixed(2)}: ${hit.title}`);
    });

    // Ask questions
    const answer = await mem.ask('What are the key concepts?');
    console.log(answer.answer);

    from memvid_sdk import use

    # Open your memory
    mem = use('basic', 'my-knowledge.mv2', read_only=True)

    # Search
    results = mem.find('machine learning', k=10)
    for hit in results.get('hits', []):
        print(f"{hit['score']:.2f}: {hit['title']}")

    # Ask questions with AI synthesis
    answer = mem.ask('What are the key concepts?')
    print(answer.get('answer'))

    # Always close when done
    mem.close()

from memvid_sdk import use
from langchain_openai import ChatOpenAI
from langchain.chains import RetrievalQA

# Open with LangChain adapter
mem = use('langchain', 'my-knowledge.mv2', read_only=True)
retriever = mem.as_retriever(k=5)

# Create QA chain
qa = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(model="gpt-4o"),
    retriever=retriever
)

result = qa.run("What are the main concepts?")
print(result)

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Key Features in v2

• Frame Architecture: Video-inspired append-only storage for crash safety and time-travel queries
• Time Index Track: Query documents by temporal order
• Embedded WAL: Crash-safe transactions with automatic recovery
• Parallel Ingestion: Multi-threaded document processing
• Framework Adapters: Native integrations for LangChain, LlamaIndex, AutoGen, and more

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Next Steps

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Getting Help

• FAQ: Answers to common questions
• Troubleshooting: Solutions to common issues
• GitHub Issues: Report bugs and request features