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How to create query engine with webhook response? #353

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opened 2026-02-16 00:17:36 -05:00 by yindo · 1 comment
yindo commented 2026-02-16 00:17:36 -05:00
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Originally created by @AyushParikh on GitHub (Nov 25, 2024).

Hi,

I want to create a query engine using the json from webhook response from llama parse. is this possible? it's missing some attributes like embedding, etc.

    index = VectorStoreIndex.from_documents(document_objects)

    # create a query engine for the index
    query_engine = index.as_query_engine()
Originally created by @AyushParikh on GitHub (Nov 25, 2024). Hi, I want to create a query engine using the json from webhook response from llama parse. is this possible? it's missing some attributes like embedding, etc. ``` index = VectorStoreIndex.from_documents(document_objects) # create a query engine for the index query_engine = index.as_query_engine() ```
yindo added the enhancement label 2026-02-16 00:17:36 -05:00
yindo closed this issue 2026-02-16 00:17:36 -05:00
yindo commented 2026-02-16 00:17:36 -05:00
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@logan-markewich commented on GitHub (Nov 25, 2024):

@AyushParikh you would have to take the resulting text, and embed them. Thats nearly what your code sample is doing

from llama_index.core import Document

# take the text from the webhook
documents = [Document(text=text, ...), ...]

index = VectorStoreIndex.from_documents(documents)

# create a query engine for the index
query_engine = index.as_query_engine()
@logan-markewich commented on GitHub (Nov 25, 2024): @AyushParikh you would have to take the resulting text, and embed them. Thats nearly what your code sample is doing ```python from llama_index.core import Document # take the text from the webhook documents = [Document(text=text, ...), ...] index = VectorStoreIndex.from_documents(documents) # create a query engine for the index query_engine = index.as_query_engine() ```
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Reference: run-llama/llama_cloud_services#353
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