> ## 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.

# FAQ – General

> Frequently asked questions about Memvid

## General Questions

### What is Memvid?

Memvid is a portable AI memory system that packages your data, embeddings, and search indices into a single `.mv2` file. It's designed for building RAG applications, AI agents, and knowledge bases without the complexity of traditional vector databases.

### Is Memvid open source?

Yes, the core library (`memvid-core`) is open source. The Python SDK and Node.js SDK are available as packages with comprehensive documentation.

### What makes Memvid different from other vector databases?

Memvid's key differentiator is **single-file portability**. Unlike traditional vector databases that require servers and complex configurations, a `.mv2` file contains everything, your data, embeddings, indices, and metadata, in one portable file.

### What platforms does Memvid support?

Memvid supports:

* **macOS** (Intel and Apple Silicon)
* **Linux** (x86\_64 and ARM64)
* **Windows** (x86\_64)

***

## File Format

### Can I rely on a single `.mv2` file in production?

Yes. Memvid is designed for production use. The `.mv2` file is completely self-contained with no sidecar files, no external dependencies, and no hidden state. Copying the file transfers the entire memory, including the write-ahead log and all indices.

### How large can a `.mv2` file be?

File size depends on your capacity tier:

| Tier       | Capacity  | WAL Size |
| ---------- | --------- | -------- |
| Free       | 1 GB      | 4 MB     |
| Developer  | 25 GB     | 16 MB    |
| Enterprise | Unlimited | 64 MB    |

The embedded WAL automatically scales with file size for optimal performance.

### Can multiple processes access the same file?

Yes, with some rules:

* **Multiple readers**: Allowed simultaneously
* **Single writer**: Only one writer at a time
* **Read-only mode**: Use `read_only=True` for concurrent read access

Writers use OS-level exclusive locks to prevent conflicts.

***

## Performance

### How fast is Memvid?

Memvid is built in Rust for maximum performance:

| Operation                    | Performance       |
| ---------------------------- | ----------------- |
| Search (1K docs)             | \< 1ms            |
| Search (100K docs)           | \< 10ms           |
| Single doc ingestion         | 1-10 docs/sec     |
| Batch ingestion (`put_many`) | 500-1000 docs/sec |
| WAL append                   | \< 0.1ms          |

### What search modes are available?

* **`Lexical (lex)`**: BM25 keyword search for exact matches
* **`Semantic (sem)`**: Vector search for conceptual similarity
* **`Hybrid (auto)`**: Combines both for best results (recommended)

### How do I optimize search performance?

1. **Build indices**: Ensure lexical and vector indices are enabled
2. **Use batch ingestion**: Use `put_many()` for 100-200x faster ingestion
3. **Enable parallel segments**: Use `--parallel-segments` for large datasets
4. **Choose the right mode**: Use `lex` for keywords, `sem` for concepts, `auto` for general queries

***

## SDKs and Integration

### Which programming languages are supported?

* **Python**: `pip install memvid-sdk`
* **Node.js**: `npm install @memvid/sdk`
* **Rust**: Use `memvid-core` crate directly
* **CLI**: `cargo install memvid-cli`

### Can I use Memvid with LangChain?

Yes! Both Python and Node.js SDKs support framework adapters:

**Python:**

```python theme={null}
from memvid_sdk import use
mem = use('langchain', 'knowledge.mv2')
tools = mem.tools  # LangChain StructuredTool objects
```

**Node.js:**

```typescript theme={null}
import { use } from '@memvid/sdk';
const mv = await use('langchain', 'knowledge.mv2');
```

### What AI frameworks are supported?

**Python SDK:**

* LangChain
* LlamaIndex
* CrewAI
* AutoGen
* Haystack

**Node.js SDK:**

* Vercel AI SDK
* OpenAI Functions
* LangChain.js
* Semantic Kernel

***

## Capacity and Storage

### What happens when I exceed capacity?

You'll receive a `CapacityExceeded` error (MV001). Solutions:

1. Delete unused frames: `memvid delete knowledge.mv2 --frame-id <id>`
2. Vacuum to reclaim space: `memvid doctor knowledge.mv2 --vacuum`
3. Create a larger memory file with a higher tier

### How do I check my storage usage?

```bash theme={null}
memvid stats knowledge.mv2
```

This shows document count, size, capacity, and utilization percentage.

### Can I reduce storage size?

Yes, use vector compression:

```bash theme={null}
memvid put knowledge.mv2 --input docs/ --vector-compression
```

Vector compression provides 16x smaller vectors with minimal quality loss.

***

## Troubleshooting

### Why is my file locked?

Another process is using the file. Check for:

* Other terminals running `memvid` commands
* Running applications with open handles
* Stale processes (use `lsof your-file.mv2` to find them)

Use `memvid who your-file.mv2` to see who holds the lock.

### Why are my searches returning no results?

1. **Check indices**: Run `memvid stats your-file.mv2` to verify indices exist
2. **Try different modes**: Use `--mode lex` for keywords or `--mode sem` for concepts
3. **Rebuild indices**: Run `memvid doctor your-file.mv2 --rebuild-lex-index`

### How do I recover from corruption?

Use the `doctor` command:

```bash theme={null}
# Preview repairs
memvid doctor your-file.mv2 --plan-only

# Apply repairs
memvid doctor your-file.mv2 --rebuild-time-index --rebuild-lex-index

# Verify
memvid verify your-file.mv2 --deep
```

The embedded WAL ensures your data survives unexpected shutdowns.

### Why is ingestion slow?

Use batch ingestion for better performance:

```python theme={null}
# Instead of individual puts (1-10 docs/sec)
for doc in docs:
    mem.put(text=doc['text'], title=doc['title'])

# Use put_many (500-1000 docs/sec)
mem.put_many(docs)
```

***

## Getting Help

### Where can I report bugs?

Report issues on GitHub: [github.com/memvid/memvid/issues](https://github.com/memvid/memvid/issues)

### Is there a community?

Yes! Join us on:

* **Discord**: [discord.gg/2mynS7fcK7](https://discord.gg/2mynS7fcK7)
* **Twitter**: [@memvid](https://x.com/memvidai)
* **GitHub Discussions**: [github.com/memvid/memvid/discussions](https://github.com/memvid/memvid/discussions)