> ## 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.

# Use Cases

> Real-world use cases and examples for Memvid

Learn from complete, working examples that demonstrate common Memvid use cases.

## Use Cases

<CardGroup cols={2}>
  <Card title="Chatbot Memory" icon="robot" href="/examples/chatbot-memory">
    Persistent memory for conversational AI
  </Card>

  <Card title="Document Q&A" icon="file-lines" href="/examples/document-qa">
    Build a RAG system for document querying
  </Card>

  <Card title="Knowledge Base" icon="book-open" href="/examples/knowledge-base">
    Create a searchable knowledge repository
  </Card>

  <Card title="Research Assistant" icon="microscope" href="/examples/research-assistant">
    Organize and query research papers
  </Card>
</CardGroup>

## Quick Examples

### Chatbot with Memory

```python theme={null}
from memvid_sdk import use

mem = use('basic', 'chat_memory.mv2')

# Store conversation turns
mem.put({
    'title': f'User message',
    'text': user_message,
    'label': 'user',
    'metadata': {'session_id': session_id}
})

# Retrieve relevant context for responses
context = mem.find(user_message, k=5, scope=f'session:{session_id}')
```

### Document RAG

```python theme={null}
from memvid_sdk import create

# Ingest documents
mem = create('docs.mv2')
for pdf in Path('documents/').glob('*.pdf'):
    mem.put({
        'title': pdf.stem,
        'file': str(pdf),
        'label': 'document'
    })
mem.seal()

# Query with RAG
answer = mem.ask(
    'What are the key findings?',
    model='openai:gpt-4o',
    k=10
)
print(answer['answer'])
```

### Knowledge Graph

```python theme={null}
from memvid_sdk import create
from memvid_sdk.entities import get_entity_extractor

mem = create('knowledge.mv2')
ner = get_entity_extractor('openai', entity_types=['PERSON', 'ORG', 'TOPIC'])

# Ingest with entity extraction
for doc in documents:
    entities = ner.extract(doc.text)
    mem.put({
        'title': doc.title,
        'text': doc.text,
        'metadata': {'entities': entities}
    })

# Search by entity
results = mem.find('Microsoft', k=10)
```

## Framework Examples

### LangChain

```python theme={null}
from memvid_sdk import use
from langchain_openai import ChatOpenAI

mem = use('langchain', 'knowledge.mv2')
retriever = mem.as_retriever(k=5)

# Use in a chain
from langchain.chains import RetrievalQA
qa = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(),
    retriever=retriever
)
```

### Vercel AI SDK

```typescript theme={null}
import { use } from '@memvid/sdk';
import { generateText } from 'ai';

const mem = await use('vercel-ai', 'knowledge.mv2');

const result = await generateText({
  model: openai('gpt-4o'),
  tools: mem.tools,
  prompt: 'Search for information about authentication'
});
```

## Browse All Examples

<Card title="GitHub Examples" icon="github" href="https://github.com/memvid/memvid/tree/main/examples">
  Full source code for all examples on GitHub
</Card>