Documentation Index Fetch the complete documentation index at: https://docs.memv.ai/llms.txt
Use this file to discover all available pages before exploring further.
Explore advanced features of the Mem[v] SDK including custom HTTP clients, raw responses, proxies, and more.
Raw response access from memvai import Memv client = Memv() # Get raw response with headers response = client.spaces.with_raw_response().list() # Access headers print (response.headers.get( 'X-Request-ID' )) print (response.headers.get( 'X-RateLimit-Remaining' )) # Parse the response spaces = response.parse() print (spaces.spaces)
Custom HTTP client import httpx from memvai import Memv, DefaultHttpxClient # Custom HTTP client with proxy client = Memv( http_client = DefaultHttpxClient( proxy = "http://proxy.example.com:8080" ) )
import Memv from 'memvai' ; import fetch from 'node-fetch' ; const client = new Memv ({ fetch: fetch as any , });
import httpx from memvai import Memv, DefaultHttpxClient client = Memv( http_client = DefaultHttpxClient( proxy = "http://localhost:8888" ) )
import Memv from 'memvai' ; import * as undici from 'undici' ; const proxyAgent = new undici . ProxyAgent ( 'http://localhost:8888' ); const client = new Memv ({ fetchOptions: { dispatcher: proxyAgent , }, });
import Memv from 'memvai' ; const client = new Memv ({ fetchOptions: { proxy: 'http://localhost:8888' , }, });
import Memv from 'npm:memvai' ; const httpClient = Deno . createHttpClient ({ proxy: { url: 'http://localhost:8888' }, }); const client = new Memv ({ fetchOptions: { client: httpClient , }, });
Concurrent operations Parallel requests import asyncio from memvai import AsyncMemv async def main (): client = AsyncMemv() # Execute multiple requests in parallel spaces_task = client.spaces.list() stats_task = client.spaces.get_stats() spaces, stats = await asyncio.gather(spaces_task, stats_task) print ( f "Spaces: { len (spaces.spaces) } " ) print ( f "Total memories: { stats.total_memories } " ) asyncio.run(main())
Batch uploads async def upload_files_parallel ( space_id : str , file_paths : list[ str ]): """Upload multiple files concurrently.""" client = AsyncMemv() async def upload_file ( file_path : str ): with open (file_path, 'rb' ) as f: return await client.upload.batch.create( space_id = space_id, files = [f] ) # Upload all files concurrently tasks = [upload_file(path) for path in file_paths] results = await asyncio.gather( * tasks) return [r.batch_id for r in results] # Usage files = [ "doc1.pdf" , "doc2.pdf" , "doc3.pdf" ] batch_ids = asyncio.run(upload_files_parallel( "space_abc123" , files))
Custom logging import logging from memvai import Memv # Configure logging logging.basicConfig( level = logging. INFO ) logger = logging.getLogger( __name__ ) client = Memv() # Logging is automatic with MEMV_LOG environment variable
Best practices
Create one client instance and reuse it throughout your application.
Use async for I/O-bound operations
Async is more efficient for multiple concurrent requests.
Track rate limit headers to avoid throttling.
Implement proper error handling
Always handle potential errors gracefully.
Next steps
Error handling Comprehensive error handling
API Reference Complete API documentation
