spock
LLM Literature
- Free software: MIT License
- Documentation: Spock Documentation
Project Presentation:
An LLM-powered Slack bot for monitoring LLM literature and AC members' publications (e.g., Heisenberg).
Features:
- Notifies whenever an AC member publishes a paper.
- Provides the topics of AC members' papers.
- Retrieves information from scientific papers.
- Allows interaction from Slack chat.
- Monitors and analyzes various types of publications and outputs by Acceleration Consortium Members, including:
- Peer-reviewed publications in relevant disciplines such as AI and materials science.
- High-impact publications by citation count and journal impact factor.
- Non-peer-reviewed scholarly publications, including arXiv submissions.
- Other scholarly outputs like conference proceedings.
- Estimations of new compositions of matter, AI algorithms, and datasets shared by members.
Implemented Features:
- Notification when an AC member publishes a paper.
- Identifies topics of given papers using a set of keywords.
- Retrieval-Augmented Generation (RAG) from given papers to extract data (currently in progress).
- Only working on last publications of the AC members
LLM Literature Scraping
- Define and focus on publications in relevant disciplines by Acceleration Consortium Members.
- Relevant Disciplines Include:
- AI
- Accelerated materials discovery
- Self-Driving Labs (SDLs)
- Autonomous labs
- High-throughput experimentation
- High-throughput Density Functional Theory (DFT), etc.
- Scrape the number of publications in peer-reviewed (non-arXiv) literature.
- Calculate the annual average over the last 5 years.
High Impact Publications
By Citation Count
- Identify publications in relevant disciplines that are in the top 10% by citation count across all journals.
- Calculate the annual average over the last 5 years.
By Journal Impact Factor
- Identify publications in relevant disciplines that are in the top 10% by journal impact factor.
- Calculate the annual average over the last 5 years.
Non-Peer-Reviewed Scholarly Publications
- Count non-peer-reviewed scholarly publications in relevant disciplines, including those on arXiv.
- Aim to filter out papers that are on both arXiv and published in journals.
- Calculate the annual average over the last 5 years.
Other Scholarly Creations or Outputs
- Include scholarly outputs that do not fit typical publication categories, such as conference proceedings.
- Identify any other outputs found by LLM that don't fit into the above categories.
- Calculate the annual average over the last 5 years.
New Compositions of Matter
- Estimate the number of new materials identified in patent applications or published literature by Acceleration Consortium Members.
- Provide a best estimate or a range for these numbers.
- Calculate either the annual average over the last 5 years or a total over the previous 5 years.
New AI or Screening Algorithms, Methods, Workflows, Models
- Estimate the number of new AI/screening algorithms, methods, workflows, or models identified in patent applications or published literature by Acceleration Consortium Members.
- Provide a best estimate or a range for these numbers.
- Calculate either the annual average over the last 5 years or a total over the previous 5 years.
AI and Material Related Datasets Shared
- Estimate the number of datasets related to AI and materials released to the public by Acceleration Consortium Members.
- Provide a best estimate or a range for these numbers.
- Calculate either the annual average over the last 5 years or a total over the previous 5 years.
Code Overview
Author object:
- Takes as input the name of an AC author
- Has the ability to fetch their last publication
- Uses the scholarly API to get the data (filled)
- Updates a json file with the data it had about the publication
The Publication object:
- Takes as input a publication filled (output of a scholarly)
- Gets data such as the year of publication, abstract, link etc...
- Uses a LLM on the abstract to get the topics of the publication (Updating it)
- Uses webscrapping of the scholarly home page of the author to get the link to the PDF of the publication (if found - Working on it)
RAG use - still working on it:
- Embedding used: fast emnbedding (updating it to Ollama's)
- Getting pertinent information from the PDF
Bot object:
- Runs a Slack bot which is able to send messages and respond to slack slash(/) commands
Metadata
Release files for spock-literature 0.0.7
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| spock_literature-0.0.7.tar.gz | 29.1 kB | Without provenance | Details for spock_literature-0.0.7.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| spock_literature-0.0.7-py3-none-any.whl | Python 3 | none | any | Details for spock_literature-0.0.7-py3-none-any.whl |
Total release size: 40.9 kB
Release files / spock_literature-0.0.7.tar.gz
| Download URL | spock_literature-0.0.7.tar.gz |
|---|---|
| Size | 29.1 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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No |
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twine/5.1.0 CPython/3.11.9
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Release files / spock_literature-0.0.7-py3-none-any.whl
| Download URL | spock_literature-0.0.7-py3-none-any.whl |
|---|---|
| Size | 11.8 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/5.1.0 CPython/3.11.9
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