Use Cases

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}')

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'])

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)

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
)

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'
});