Remove hardcoded values and references to gpt-3.5-turbo models

docs/custom.md

@@ -365,7 +365,7 @@ The output of this is then passed through further SPIRES iterations.
 
 #### Text length limit
 
-LLMs have context sizes limiting the combined length of their inputs and outputs. The `gpt-3.5-turbo` model, for example, has a 4,096 token limit (prompt + completion), while the `gpt-3.5-turbo-16k` model has a larger context of 16,384 tokens.
+LLMs have context sizes limiting the combined length of their inputs and outputs. 
 
 To see the token limit for each model, use `ontogpt list-models`. The Max Tokens value will be in the fourth column.
 

docs/functions.md

@@ -596,7 +596,7 @@ Options:
 Examples:
 
 ```bash
-ontogpt pubmed-annotate -t phenotype "Takotsubo Cardiomyopathy: A Brief Review" --get-pmc --model gpt-3.5-turbo-16k --limit 3
+ontogpt pubmed-annotate -t phenotype "Takotsubo Cardiomyopathy: A Brief Review" --get-pmc --model gpt-4o --limit 3
 ```
 
 ```bash
@@ -770,7 +770,7 @@ Even relatively short pages may exceed a model's context size, so larger context
 Example:
 
 ```bash
-ontogpt web-extract -t reaction.Reaction -m gpt-3.5-turbo-16k https://www.scienceofcooking.com/maillard_reaction.htm 
+ontogpt web-extract -t reaction.Reaction -m gpt-4o https://www.scienceofcooking.com/maillard_reaction.htm 
 ```
 
 ### wikipedia-extract
@@ -784,7 +784,7 @@ Even relatively short pages may exceed a model's context size, so larger context
 Example:
 
 ```bash
-ontogpt wikipedia-extract -t mendelian_disease.MendelianDisease -m gpt-3.5-turbo-16k "Cartilage–hair hypoplasia"
+ontogpt wikipedia-extract -t mendelian_disease.MendelianDisease -m gpt-4o  "Cartilage–hair hypoplasia"
 ```
 
 ### wikipedia-search
@@ -798,5 +798,5 @@ Even relatively short pages may exceed a model's context size, so larger context
 Example:
 
 ```bash
-ontogpt wikipedia-search -t biological_process -m gpt-3.5-turbo-16k "digestion"
+ontogpt wikipedia-search -t biological_process -m gpt-4o "digestion"
 ```

src/ontogpt/cli.py

@@ -714,7 +714,7 @@ def pubmed_annotate(
 
     Example:
     ontogpt pubmed-annotate -t phenotype "Takotsubo Cardiomyopathy: A Brief Review"
-        --get-pmc --model gpt-3.5-turbo-16k --limit 3
+        --get-pmc --model gpt-4o --limit 3
     """
     if not model:
         model = DEFAULT_MODEL
@@ -1174,7 +1174,6 @@ def convert(
 @model_option
 @output_option_txt
 @temperature_option
-@cut_input_text_option
 @api_base_option
 @api_version_option
 @model_provider_option

src/ontogpt/engines/halo_engine.py

@@ -57,7 +57,6 @@ def text(self) -> str:
 class HALOEngine(KnowledgeEngine):
     """Engine for Hallucinating Latent Ontologies."""
 
-    engine: str = "gpt-3.5-turbo"
     ontology: Ontology = None
     traverse_slots: List[FIELD] = field(
         default_factory=lambda: ["subtypes", "parts", "subclass_of", "part_of"]

src/ontogpt/engines/spires_engine.py

@@ -286,10 +286,6 @@ def map_terms(
         """
         Map the given terms to the given ontology.
 
-        EXPERIMENTAL
-
-        currently GPT-3 does not do so well with this task.
-
         :param terms:
         :param ontology:
         :return:

src/ontogpt/engines/synonym_engine.py

@@ -9,8 +9,6 @@
 class SynonymEngine(KnowledgeEngine):
     """Engine for generating synonyms."""
 
-    engine: str = "gpt-3.5-turbo-instruct"
-
     def synonyms(self, named_entity: str, domain: str) -> List[str]:
         """Get synonyms for a given text."""
         prompt = f"List the example formal scientific\

src/ontogpt/webapp/main.py

@@ -31,7 +31,6 @@
     "gpt-4o",
     "gpt-4",
     "gpt-4-turbo",
-    "gpt-3.5-turbo",
     "ollama/llama2",
     "ollama/llama3",
     "ollama/orca-mini",

tests/input/prompts/prompts.yaml

@@ -1,6 +1,6 @@
-default_engine: gpt-3.5-turbo
+default_engine: gpt-4o
 prompts:
-- engine: gpt-3.5-turbo
+- engine: gpt-4o
   prompt: |
     From the text below, extract the following entities in the following format:
 
@@ -15,7 +15,7 @@ prompts:
   expected:
     - "predicate: INDUCES"
     - "object: .*nystagmus"
-- engine: gpt-3.5-turbo
+- engine: gpt-4o
   pre_prompt: |
     ## Instructions:
     ## Add an additional element to the YAML below, which is for elements
@@ -99,31 +99,31 @@ prompts:
       equivalent_to: Transport and utilizes some Train
     - name: CarEngine
       context: IndustrialOntology
-- engine: gpt-3.5-turbo
+- engine: gpt-4o
   prompt: |
     Answer the following question where the answer is one of: subtype of; part of; equivalent to.
     Question: The relationship between the concepts Car and Vehicle is:
-- engine: gpt-3.5-turbo
+- engine: gpt-4o
   prompt: |
     Answer the following question where the answer is one of: subtype of; supertype of; part of; has part; equivalent to.
     Question: The relationship between the concepts Car and Vehicle is:
-- engine: gpt-3.5-turbo
+- engine: gpt-4o
   prompt: |
     Answer the following question where the answer is one of: subtype of; supertype of; part of; has part; equivalent to.
     Question: The relationship between the concepts Tire and Bicycle is:
-- engine: gpt-3.5-turbo
+- engine: gpt-4o
   prompt: |
     In the context of cell biology, list all the parts of a nucleus as a semi-colon separated list.
-- engine: gpt-3.5-turbo
+- engine: gpt-4o
   prompt: |
     In the context of cell biology, list all the things a nucleus is a part of, as a semi-colon separated list.
-- engine: gpt-3.5-turbo
+- engine: gpt-4o
   prompt: |
     In the context of cell biology, list all parent concepts for the concept of nucleus, as a semi-colon separated list.
-- engine: gpt-3.5-turbo
+- engine: gpt-4o
   prompt: |
     In the context of cell biology, list all child concepts for the concept of nucleus, as a semi-colon separated list.
-- engine: gpt-3.5-turbo
+- engine: gpt-4o
   prompt: |
     From the text below, extract the following entities in the following format:
     triples: <A semi-colon separated list of chemical to disease relationships, where the relationship is either INDUCES or TREATS. 
@@ -134,7 +134,7 @@ prompts:
     In vivo evidences suggesting the role of oxidative stress in pathogenesis of vancomycin-induced nephrotoxicity:
     protection by erdosteine
     In the context of cell biology, list all child concepts for the concept of nucleus, as a semi-colon separated list.
-- engine: gpt-3.5-turbo
+- engine: gpt-4o
   prompt: |
     From the text below, extract the following entities in the following format:
 
@@ -145,7 +145,7 @@ prompts:
     ===
   #expected:
   #  - "Veralipride INDUCES Parkinsonism"
-- engine: gpt-3.5-turbo
+- engine: gpt-4o
   prompt: |
     From the text below, extract the following entities in the following format:
 
@@ -154,7 +154,7 @@ prompts:
     Text:
     Kaliuretic effect of L-dopa treatment in parkinsonian patients.
     ===
-- engine: gpt-3.5-turbo
+- engine: gpt-4o
   prompt: |
     From the text below, extract the following entities in the following format:
 
@@ -163,7 +163,7 @@ prompts:
     Text:
     Electrocardiographic evidence of myocardial injury in psychiatrically hospitalized cocaine abusers.
     ===
-- engine: gpt-3.5-turbo
+- engine: gpt-4o
   prompt: |
     Generate a list of exact synonyms for the chemical concept "heavy metal", as a semi-colon separated list.
     Only include concepts with the identical meaning. Do not include more specific concepts or broader concepts.

tests/integration/test_clients/test_llmclient.py

@@ -153,7 +153,7 @@ def setUp(self) -> None:
     def test_all_prompts(self):
         """Test all prompts."""
         prompt_doc = yaml.safe_load(open(PROMPTS_FILE))
-        default_engine = prompt_doc.get("default_engine", "gpt-3.5-turbo-instruct")
+        default_engine = prompt_doc.get("default_engine", "gpt-4o")
         for prompt in prompt_doc["prompts"]:
             prompt_text = prompt["prompt"]
             if not isinstance(prompt_text, str):
@@ -199,15 +199,15 @@ def test_drug_mech_db(self):
 
     def test_code_completion_generalization(self):
         """Tests structured responses."""
-        engine = "gpt-3.5-turbo"
+        engine = "gpt-4o"
         client = LLMClient(model=engine)
         print(len(CODE_PROMPT_GENERALIZATION))
         ann = client.complete(CODE_PROMPT_GENERALIZATION)
         print(ann)
 
     def test_extract_via_code_completion(self):
         """Tests structured responses."""
-        engine = "gpt-3.5-turbo"
+        engine = "gpt-4o"
         client = LLMClient(model=engine)
         ann = client.complete(CODE_PROMPT_EXTRACT)
         print(ann)

tests/integration/test_evaluation/test_eval_drugmechdb.py

@@ -145,7 +145,7 @@ def test_training_set(self):
         evaluator = self.engine
         ke = evaluator.extractor
         training_set = list(evaluator.create_training_set(100))
-        t = dict(base_model="gpt-3.5-turbo-instruct", template=ke.template, examples=training_set)
+        t = dict(base_model="gpt-4o", template=ke.template, examples=training_set)
         with open(TRAINING_OUT, "w") as f:
             yaml.dump(t, f)
         # print(yaml.dump(training_set))