CurateGPT is a prototype web application and framework for performing general purpose AI-guided curation and curation-related operations over collections of objects.
See also the app on curategpt.io (note: this is sometimes down, and may only have a subset of the functionality of the local app)
CurateGPT is available on Pypi and may be installed with pip:
pip install curategpt
You will first need to install Poetry.
Then clone this repo.
git clone https://github.com/monarch-initiative/curategpt.git
cd curategpt
and install the dependencies:
poetry install
In order to get the best performance from CurateGPT, we recommend getting an OpenAI API key, and setting it:
export OPENAI_API_KEY=<your key>
(for members of Monarch: ask on Slack if you would like to use the group key)
CurateGPT will also work with other large language models - see "Selecting models" below.
You initially start with an empty database. You can load whatever you like into this
database! Any JSON, YAML, or CSV is accepted.
CurateGPT comes with wrappers for some existing local and remote sources, including
ontologies. The Makefile contains some examples of how to load these. You can
load any ontology using the ont-<name> target, e.g.:
make ont-cl
This loads CL (via OAK) into a collection called ont_cl
Note that by default this loads into a collection set stored at stagedb, whereas the app works off
of db. You can copy the collection set to the db with:
cp -r stagedb/* db/
You can then run the streamlit app with:
make app
CurateGPT depends on vector database indexes of the databases/ontologies you want to curate.
The flagship application is ontology curation, so to build an index for an OBO ontology like CL:
make ont-cl
This requires an OpenAI key.
(You can build indexes using an open embedding model, modify the command to leave off
the -m option, but this is not recommended as currently oai embeddings seem to work best).
To load the default ontologies:
make all
(this may take some time)
To load different databases:
make load-db-hpoa
make load-db-reactome
You can load an arbitrary json, yaml, or csv file:
curategpt view index -c my_foo foo.json
(you will need to do this in the poetry shell)
To load a GitHub repo of issues:
curategpt -v view index -c gh_uberon -m openai: --view github --init-with "{repo: obophenotype/uberon}"
The following are also supported:
- Google Drives
- Google Sheets
- Markdown files
- LinkML Schemas
- HPOA files
- GOCAMs
- MAXOA files
- Many more
- See notebooks for examples.
Currently this tool works best with the OpenAI gpt-4 model (for instruction tasks) and OpenAI ada-text-embedding-002 for embedding.
CurateGPT is layered on top of simonw/llm which has a plugin architecture for using alternative models. In theory you can use any of these plugins.
Additionally, you can set up an openai-emulating proxy using litellm.
The litellm proxy may be installed with pip as pip install litellm[proxy].
Let's say you want to run mixtral locally using ollama. You start up ollama (you may have to run ollama serve first):
ollama run mixtral
Then start up litellm:
litellm -m ollama/mixtral
Next edit your extra-openai-models.yaml as detailed in the llm docs:
- model_name: ollama/mixtral
model_id: litellm-mixtral
api_base: "http://0.0.0.0:8000"
You can now use this:
curategpt ask -m litellm-mixtral -c ont_cl "What neurotransmitter is released by the hippocampus?"But be warned that many of the prompts in curategpt were engineered
against openai models, and they may give suboptimal results or fail
entirely on other models. As an example, ask seems to work quite
well with mixtral, but complete works horribly. We haven't yet
investigated if the issue is the model or our prompts or the overall
approach.
Welcome to the world of AI engineering!
curategpt --helpYou will see various commands for working with indexes, searching, extracting, generating, etc.
These functions are generally available through the UI, and the current priority is documenting these.
curategpt ask -c ont_cl "What neurotransmitter is released by the hippocampus?"
may yield something like:
The hippocampus releases gamma-aminobutyric acid (GABA) as a neurotransmitter [1](#ref-1).
...
## 1
id: GammaAminobutyricAcidSecretion_neurotransmission
label: gamma-aminobutyric acid secretion, neurotransmission
definition: The regulated release of gamma-aminobutyric acid by a cell, in which the
gamma-aminobutyric acid acts as a neurotransmitter.
...
curategpt view ask -V pubmed "what neurons express VIP?"
curategpt ask -c gh_obi "what are some new term requests for electrophysiology terms?"
curategpt complete -c ont_cl "mesenchymal stem cell of the apical papilla"
yields
id: MesenchymalStemCellOfTheApicalPapilla
definition: A mesenchymal cell that is part of the apical papilla of a tooth and has
the ability to self-renew and differentiate into various cell types such as odontoblasts,
fibroblasts, and osteoblasts.
relationships:
- predicate: PartOf
target: ApicalPapilla
- predicate: subClassOf
target: MesenchymalCell
- predicate: subClassOf
target: StemCell
original_id: CL:0007045
label: mesenchymal stem cell of the apical papillaYou can compare all objects in one collection
curategpt all-by-all --threshold 0.80 -c ont_hp -X ont_mp --ids-only -t csv > ~/tmp/allxall.mp.hp.csv
This takes 1-2s, as it involves comparison over pre-computed vectors. It reports top hits above a threshold.
Results may vary. You may want to try different texts for embeddings (the default is the entire json object; for ontologies it is concatenation of labels, definition, aliases).
sample:
HP:5200068,Socially innappropriate questioning,MP:0001361,social withdrawal,0.844015132437909
HP:5200069,Spinning,MP:0001411,spinning,0.9077306606290237
HP:5200071,Delayed Echolalia,MP:0013140,excessive vocalization,0.8153252835818089
HP:5200072,Immediate Echolalia,MP:0001410,head bobbing,0.8348177036912526
HP:5200073,Excessive cleaning,MP:0001412,excessive scratching,0.8699103725005582
HP:5200104,Abnormal play,MP:0020437,abnormal social play behavior,0.8984862078522344
HP:5200105,Reduced imaginative play skills,MP:0001402,decreased locomotor activity,0.85571629684631
HP:5200108,Nonfunctional or atypical use of objects in play,MP:0003908,decreased stereotypic behavior,0.8586700411012859
HP:5200129,Abnormal rituals,MP:0010698,abnormal impulsive behavior control,0.8727804272023427
HP:5200134,Jumping,MP:0001401,jumpy,0.9011393233129765
Note that CurateGPT has a separate component for using an LLM to evaluate candidate matches (see also https://arxiv.org/abs/2310.03666); this is not enabled by default, this would be expensive to run for a whole ontology.