Examples

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

# Initialize with LangChain adapter
mem = use('langchain', 'knowledge.mv2')

# Create RAG chain
qa = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(model="gpt-4o"),
    retriever=mem.as_retriever(k=5),
    return_source_documents=True
)

# Ask questions
result = qa.invoke("What are the main features?")
print(result['result'])

// app/api/chat/route.ts
import { use } from '@memvid/sdk';
import { openai } from '@ai-sdk/openai';
import { streamText } from 'ai';

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

export async function POST(req: Request) {
  const { messages } = await req.json();

  const result = await streamText({
    model: openai('gpt-4o'),
    system: 'You are a helpful assistant. Use the tools to search the knowledge base.',
    tools: mem.tools,
    messages,
    maxSteps: 5,
  });

  return result.toDataStreamResponse();
}

from memvid_sdk import use
from autogen import AssistantAgent, UserProxyAgent

mem = use('autogen', 'research-papers.mv2')

# Create researcher agent with Memvid tools
researcher = AssistantAgent(
    name="researcher",
    llm_config={"tools": mem.tools},
    system_message="You research topics using the knowledge base."
)

# Create writer agent
writer = AssistantAgent(
    name="writer",
    system_message="You write summaries based on research findings."
)

# Create user proxy
user = UserProxyAgent(name="user", human_input_mode="NEVER")

# Start research task
user.initiate_chat(
    researcher,
    message="Research the latest advances in transformer architectures"
)