Looking for best practices for using vector database + storing metadata + caching.
I need to embed continuously new documents into my vector database and want to make them searchable (the pages) and thus want to store somewhere the metadata but still be able to scale the application and not be limited by storing metadata in the vector database (like in pinecone)
我在港口都快接不上这里的信号了，有可以用的代码帮助对接一下端口信号么！？矢量数据代码应该可以解决端口链接单元上系统出故障的日志问题！。
I recommend starting with a simple JSON file. This will give you an easy, flexible, and forgiving environment for experimenting and figuring out what works for you.
500MB JSON file ~= 25,000 ada embeddings
JavaScript’s JSON.stringify scales to ~500MB file with no problem. And nearly all modern computers have 500MB of RAM to spare, so you can parse the file and keep the vectors in memory for fast access.
I haven’t had reason to migrate away from this for my personal notes since the 500MB/25K embeddings limit handles my requirements and then some.
how to convert json data to documents in langchain? Later I would be able to convert those documents into embeddings