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Improve RAG with Real-World Benchmarks | KDD Cup 2025
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Improve RAG with Real-World Benchmarks
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Testing RAG Systems with Limited Web Pages
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Synthesising answers from image and web sources
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Meta CRAG - MM Challenge 2025

Starter‑Kit Update – **`WebSearchResult` helper for full‑page retrieval**
About 2 months agowhat about the image in the image api output, are such features available for images?

📢 Update of the search API (v0.4.0) and indices (v0.4)
2 months agoCan you clarify whether we can use the image in the image retrieval output or can we only use the text descriptions of the retrieved images? If we can use the image, how can we access it during evaluation. This is crucial because it determines the input information of the RAG system.

📢 Update of the search API (v0.4.0) and indices (v0.4)
2 months agowhich attribute should we access to get the retrieved image? Is there an example how we can acceess the image? (Not the image_url,but the raw image in the retrieval results)

📢 Update of the search API (v0.4.0) and indices (v0.4)
2 months agocan we get access to the retrieved image using the image_url on the evaluation server?

Does "search_pipeline" source change during LB submission
3 months agowhich search pieline should we use while we are developing our agents? For the crag-mm-2025/crag-mm-single-turn-public qas, should we use crag-mm-2025/image-search-index-validation or crag-mm-2025/image-search-index-public during developing?


Question about image retrieval format and retrieval rules?
3 months agoq1:The current image retrieval results only contain url of the retrieved image, will the image be available in the contest?
Meta Comprehensive RAG Benchmark: KDD Cup 2-9d1937

🚨 IMP: Phase 2 Announcement
About 1 year agowhat does api update mean, is the test environment updated to the new api?

Issues about submission LFS file issues
About 1 year agoDuring our last submission, we successfully go throught the docker building process. And there’s an error during the inference stage when we are attempting to load the large model:
File “/src/models/dummy_model.py”, line 86, in init
File “/src/models/dummy_model.py”, line 86, in init
self.m = LlamaForCausalLM.from_pretrained(model, device_map="balanced", self.m = LlamaForCausalLM.from_pretrained(model, device_map="balanced",
File “/home/aicrowd/.conda/lib/python3.8/site-packages/transformers/modeling_utils.py”, line 3531, in from_pretrained
File “/home/aicrowd/.conda/lib/python3.8/site-packages/transformers/modeling_utils.py”, line 3531, in from_pretrained
) = cls._load_pretrained_model( ) = cls._load_pretrained_model(
File “/home/aicrowd/.conda/lib/python3.8/site-packages/transformers/modeling_utils.py”, line 3938, in _load_pretrained_model
File “/home/aicrowd/.conda/lib/python3.8/site-packages/transformers/modeling_utils.py”, line 3938, in _load_pretrained_model
state_dict = load_state_dict(shard_file, is_quantized=is_quantized) state_dict = load_state_dict(shard_file, is_quantized=is_quantized)
File “/home/aicrowd/.conda/lib/python3.8/site-packages/transformers/modeling_utils.py”, line 542, in load_state_dict
File “/home/aicrowd/.conda/lib/python3.8/site-packages/transformers/modeling_utils.py”, line 542, in load_state_dict
raise OSError( raise OSError(
OSError: You seem to have cloned a repository without having git-lfs installed. Please install git-lfs and run
git lfs install
followed bygit lfs pull
in the folder you cloned.OSError: You seem to have cloned a repository without having git-lfs installed. Please install git-lfs and run
git lfs install
followed bygit lfs pull
in the folder you cloned.
In the website page of our repository, the model files are correctly uploaded with an LFS tag on the left hand. The log message suggest installing git-lfs. So we made another try.
We add “git-lfs” in the original “apt.txt”, but this time, it doesn’t go through the docker buiding stage.
How can I fix this issue?
Another question is that: what’s the submission number limitations now. Is it 6 times a week, and does that include a failure submission?
Image and web search API updates and feedback
About 1 month agoThe quality of the new search_pipeline for web is far worse than the previous version.
1.Why the currecnt public-test set and validatation set has similar number of queries, but the corresponding pipeline for web search have different number of entries. (public-test web search~130k,current validation web search~70k). This indicates that many information can’t be found in the current validation web search pipeline
2. I randomly check 10 entities in the ground truth validation dataset, 5 of them can not be found in the search pipeline, (the entites are: millipedes, gatorade, suits TV show, st.patrick’s cathedral, hugo boss)). In the last round validation pipeline and the claimed settings, we can find almost all related information in the pipeline. There must be a problem in the current version of web search pipeline. Besides, the end-2-end performance of our local baseline is worsened a lot using the current search pipeline compared with the pipeline proposed in phase 1. Can you check whether there’s problem with the web search index in the current version?