An Exploration of Serverless Architectures for Information Retrieval
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Serverless architectures represent a new approach to designing applications in the cloud without having to explicitly provision or manage servers. The developer specifies functions with well-defined entry and exit points, and the cloud provider handles all other aspects of execution. In this paper, we explore a novel application of serverless architectures to information retrieval and describe a search engine built in this manner with Amazon Web Services: postings lists are stored in the DynamoDB NoSQL store and the postings traversal algorithm for query evaluation is implemented in the Lambda service. The result is a search engine that scales elastically with a pay-per-request model, in contrast to a server-based model that requires paying for running instances even if there are no requests. We empirically assess the performance and economics of our serverless architecture. While our implementation is currently too slow for interactive searching, analysis shows that the pay-per-request model is economically compelling, and future infrastructure improvements will increase the attractiveness of serverless designs over time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it