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Question Answering
Question Answering turns your store into an AI-powered knowledge base.
Instead of returning raw content like search, it generates complete answers based on the retrieved context.
Basic Question AnsweringLink to section
Ask questions and get augmented answers based on your Store content:
Question Answering
from mixedbread import Mixedbread
mxbai = Mixedbread(api_key="YOUR_API_KEY")
response = mxbai.stores.question_answering(
store_identifiers=["my-knowledge-base"],
query="What are the main features of the product?",
top_k=5,
instructions=(
"Answer for an API evaluator. Use concise bullets and cite the most relevant sources."
),
qa_options={"cite": True, "multimodal": True},
)
print(response)Multimodal support enables:
- Image understanding: Answers based on diagrams, charts, and visuals
- OCR text: Extracted text from images contributes to answers
Pass instructions when you want to control answer style, scope, or formatting.
Typical uses include enforcing concise bullet points, prioritizing specific facts,
or asking the model to cite only the strongest supporting passages.
Defaults and configurationLink to section
By default, Question Answering uses citations and multimodal context.
To change behavior, pass qa_options explicitly.
Disable citations and multimodal
from mixedbread import Mixedbread
mxbai = Mixedbread(api_key="YOUR_API_KEY")
response = mxbai.stores.question_answering(
store_identifiers=["my-knowledge-base"],
query="What were the key decisions made last quarter?",
top_k=5,
qa_options={"cite": False, "multimodal": False},
)
print(response)Example ResponseLink to section
QA Response
JSON
{
"answer": "Based on the provided source, the key decision made last quarter involved finalizing the budget for Project Alpha <cite i=\"0\"/>.",
"sources": [
{
"chunk_index": 2,
"mime_type": "text/plain",
"model": "mxbai-omni",
"score": 0.92,
"file_id": "your_file_id_1",
"filename": "meeting_notes_q1.docx",
"store_id": "my-knowledge-base",
"metadata": {},
"type": "text",
"text": "In the Q1 board meeting, we agreed to finalize the budget for Project Alpha with a total allocation of $2.5M..."
}
]
}- Answer: AI-generated answer. May include citation tags like
<cite i="0"/>. - Sources: Context used for the answer. Each entry includes:
chunk_index,score,file_id,filename- Content fields:
text,image_url, with optionalocr_text - Use indices from
<cite i="n"/>to map tosources[n]
Last updated: April 7, 2026