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Support Long context for DocSum #1255
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Signed-off-by: Xinyao Wang <[email protected]>
for more information, see https://pre-commit.ci
Signed-off-by: Xinyao Wang <[email protected]>
Dependency Review✅ No vulnerabilities or license issues found.Scanned Files |
Signed-off-by: Xinyao Wang <[email protected]>
Signed-off-by: Xinyao Wang <[email protected]>
Signed-off-by: Xinyao Wang <[email protected]>
for more information, see https://pre-commit.ci
@lvliang-intel Dockerfile check fails to a pre-existing "MultimodalQnA" docs issue, this PR does not touch them:
"DocSum, xeon" CI test failed to:
Which is rather surprising when test is supposed to run on Xeon, and there's a separate test that runs same things. "DocSum, gaudi" test fails due to backend error exit:
"DocSum, rocm" test fails to something that looks like CI issue: |
Signed-off-by: Xinyao Wang <[email protected]>
Signed-off-by: Xinyao Wang <[email protected]>
Signed-off-by: Xinyao Wang <[email protected]>
Signed-off-by: Xinyao Wang <[email protected]>
CICD pending for this PR: opea-project/GenAIComps#1046 |
Signed-off-by: Xinyao Wang <[email protected]>
Signed-off-by: Xinyao Wang <[email protected]>
Signed-off-by: Xinyao Wang <[email protected]>
Signed-off-by: Xinyao Wang <[email protected]>
for more information, see https://pre-commit.ci
@XinyaoWa thank you for this PR! I am curious on a couple of issues. 1) what happens with map-reduce mode if the number of summaries totals an input token length greater than max-input-length .. hierarchical map-reduce or refine? 2) Also if possible would you explain the formulas used, 50 etc. 3) Are there speed/accuracy trade-offs to using smaller chunks? |
Description
Support Long context for DocSum with five modes:
• Auto(Default mode): switch to stuff mode if input token < max_input_token, otherwise switch to "refine" mode
• Stuff (Default mode): input actual tokens, need to increase the max_input_token if want to use large context
• Truncate: truncate the tokens exceed the limitation
• Map_reduce: split the inputs into multiple chunks, map each document to an individual summary, then consolidate those summaries into a single global summary
• Refine: split the inputs into multiple chunks, generate summary for the first one, then combine with the second, loops over every remaining chunks to get the final summary
Related PR: opea-project/GenAIComps#981 opea-project/GenAIComps#1046
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