1 files changed, 57 insertions, 0 deletions
diff --git a/kg_rag/rag_based_generation/GPT/run_drug_repurposing.py b/kg_rag/rag_based_generation/GPT/run_drug_repurposing.py
new file mode 100644
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+++ b/kg_rag/rag_based_generation/GPT/run_drug_repurposing.py
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+'''
+This script takes the drug repurposing style questions from the csv file and save the result as another csv file. 
+Before running this script, make sure to configure the filepaths in config.yaml file.
+Command line argument should be either 'gpt-4' or 'gpt-35-turbo'
+'''
+
+from kg_rag.utility import *
+import sys
+
+
+
+CHAT_MODEL_ID = sys.argv[1]
+
+QUESTION_PATH = config_data["DRUG_REPURPOSING_PATH"]
+SYSTEM_PROMPT = system_prompts["DRUG_REPURPOSING"]
+CONTEXT_VOLUME = int(config_data["CONTEXT_VOLUME"])
+QUESTION_VS_CONTEXT_SIMILARITY_PERCENTILE_THRESHOLD = float(config_data["QUESTION_VS_CONTEXT_SIMILARITY_PERCENTILE_THRESHOLD"])
+QUESTION_VS_CONTEXT_MINIMUM_SIMILARITY = float(config_data["QUESTION_VS_CONTEXT_MINIMUM_SIMILARITY"])
+VECTOR_DB_PATH = config_data["VECTOR_DB_PATH"]
+NODE_CONTEXT_PATH = config_data["NODE_CONTEXT_PATH"]
+SENTENCE_EMBEDDING_MODEL_FOR_NODE_RETRIEVAL = config_data["SENTENCE_EMBEDDING_MODEL_FOR_NODE_RETRIEVAL"]
+SENTENCE_EMBEDDING_MODEL_FOR_CONTEXT_RETRIEVAL = config_data["SENTENCE_EMBEDDING_MODEL_FOR_CONTEXT_RETRIEVAL"]
+TEMPERATURE = config_data["LLM_TEMPERATURE"]
+SAVE_PATH = config_data["SAVE_RESULTS_PATH"]
+
+
+CHAT_DEPLOYMENT_ID = CHAT_MODEL_ID
+
+save_name = "_".join(CHAT_MODEL_ID.split("-"))+"_drug_repurposing_questions_response.csv"
+
+
+vectorstore = load_chroma(VECTOR_DB_PATH, SENTENCE_EMBEDDING_MODEL_FOR_NODE_RETRIEVAL)
+embedding_function_for_context_retrieval = load_sentence_transformer(SENTENCE_EMBEDDING_MODEL_FOR_CONTEXT_RETRIEVAL)
+node_context_df = pd.read_csv(NODE_CONTEXT_PATH)
+
+
+def main():
+    start_time = time.time()
+    question_df = pd.read_csv(QUESTION_PATH)
+    answer_list = []
+    for index, row in question_df.iterrows():
+        question = row["text"]
+        context = retrieve_context(question, vectorstore, embedding_function_for_context_retrieval, node_context_df, CONTEXT_VOLUME, QUESTION_VS_CONTEXT_SIMILARITY_PERCENTILE_THRESHOLD, QUESTION_VS_CONTEXT_MINIMUM_SIMILARITY)
+        enriched_prompt = "Context: " + context + "\n" + "Question: " + question
+        output = get_GPT_response(enriched_prompt, SYSTEM_PROMPT, CHAT_MODEL_ID, CHAT_DEPLOYMENT_ID, temperature=TEMPERATURE)
+        answer_list.append((row["disease_in_question"], row["refDisease"], row["compoundGroundTruth"], row["text"], output))
+    answer_df = pd.DataFrame(answer_list, columns=["disease_in_question", "refDisease", "compoundGroundTruth", "text", "llm_answer"])
+    answer_df.to_csv(os.path.join(SAVE_PATH, save_name), index=False, header=True)
+    print("Completed in {} min".format((time.time()-start_time)/60))
+
+
+
+if __name__ == "__main__":
+    main()
+
+
+