Langchain LLM model that query and provide response based on json

Question

Im planning to develop an langchain that will take user input and provide them with url related to their request.

My data format is in json (its around 35 pages)

{ page_name:{data:"",url:""}, .. }

• data is the content in that page
• url is the path

I tried using RAQ but it didn't work

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain_google_genai import GoogleGenerativeAIEmbeddings

text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20)
embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key = GOOGLE_API_KEY)

all_splits = text_splitter.split_documents(documents)
vectordb = Chroma.from_documents(documents=all_splits, embedding=embeddings, persist_directory="chroma_db")
retriever = vectordb.as_retriever()

qa = RetrievalQA.from_chain_type(
    llm=llm, 
    chain_type="stuff", 
    retriever=retriever, 
    verbose=True
)

qa.run('about this website')

I tried also combining data and url but I didn't work correct

data_dict={}
for name, info in data.items():
    print(f'Name: {name}, URL: {info["url"]}, Data: {info["data"]}')
    data_dict[name] = f'URL: {info["url"]}, Data: {info["data"]}'

would appreciate if someone can guide me to the right path to develop this model/functionality