Hello the simple description about what i want to do.
at my program i was creating a Collection for any data or archives, with this properties
“file_name”: wc.DataType.TEXT,
“file_type”: wc.DataType.TEXT,
“file_version”: wc.DataType.TEXT,
“splitter_method”: wc.DataType.TEXT,
“splitter_args”: wc.DataType.TEXT,
“type”: wc.DataType.TEXT,
“url”: wc.DataType.TEXT,
“uuid”: wc.DataType.UUID,
“version”: wc.DataType.TEXT,
“page_content”: wc.DataType.TEXT_ARR
“metadata”: wc.DataType.TEXT_ARRAY,
also use the t2v_transformers from cr.weaviate.io
i want to create a retriever from the “page_content”(vectorized) and “metadata”(non vectorized) without use another embedding sistem and also without create a new collection from weaviate Abstractions,
can i do it ?
Server Setup Information
Weaviate Server Version: 1.25.1
Deployment Method: docker - embedded
Client Language and Version: python
Multitenancy?: idk
Any additional Information
i already can insert the data into a weaviate DB, i just want make a retriever from this DB TEXT_ARRAY in a object with especifica UUID5
i also want to ask if have any tutorial from weaviate about how to do a VectorStore and retriever without langchain abstraction.
any other questions quem ask me
isen’t this what i want, but this one work well, i just changed the class
let me explain in a better way,
at my idea i want have the “master” Collection called Collection_ingestor.
why?
reason: i want create more them one Vector Store with many types of documents and different types of text_Split.
at the properties i sendo before i just insert all of documentos Splits inside the Page_Content type of Text_ARRAY.
this didn’t work because at vetorizarion they vectorize the Object, didin’t Vectorize each part for each part.
now can you send me the recipe of Weaviate HybridSearch, this recipe its 100% better to understand.
was any way to use 2 vector store as a same retriever, like MultiRetrievalQAChain but from Weaviate side ?
reason why i ask from a Weaviate side: Weaviate repository from langchain its easier to understand, more efficient and make the things better and trustworthy