1 files changed, 68 insertions, 0 deletions
diff --git a/kg_rag/rag_based_generation/GPT/drug_repurposing_v2.py b/kg_rag/rag_based_generation/GPT/drug_repurposing_v2.py
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+++ b/kg_rag/rag_based_generation/GPT/drug_repurposing_v2.py
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+from kg_rag.utility import *
+import argparse
+
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('-g', type=str, default='gpt-35-turbo', help='GPT model selection')
+parser.add_argument('-i', type=bool, default=False, help='Flag for interactive mode')
+parser.add_argument('-e', type=bool, default=False, help='Flag for showing evidence of association from the graph')
+args = parser.parse_args()
+
+
+CHAT_MODEL_ID = args.g
+INTERACTIVE = args.i
+EDGE_EVIDENCE = bool(args.e)
+
+SYSTEM_PROMPT = system_prompts["DRUG_REPURPOSING_V2"]
+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"]
+
+
+CHAT_DEPLOYMENT_ID = CHAT_MODEL_ID
+
+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)
+
+print('')
+question = input("Question : ")
+
+question_template = f'''
+To the question asked at the end, answer by referring the context. 
+See example below
+Example 1:
+    Question:
+    What drugs can be repurposed for disease X?
+    Context:
+    Compound Alizapride DOWNREGULATES Gene APOE and Provenance of this association is XX. Gene APOE ASSOCIATES Disease X  and Provenance of this association is YY. Gene TTR encodes Protein Transthyretin (ATTR)  and Provenance of this association is ZZ. Compound Acetylcysteine treats Disease X  and Provenance of this association is PP.
+    Answer:
+    Since Alizapride downregulates gene APOE (Provenance XX) and APOE is associated with Disease X (Provenance YY), Alizapride can be repurposed to treat Disease X. p-value for these associations is XXXX and z-score values for these associations is YYYY.
+Question:
+{question} 
+'''
+
+def main():
+    if not INTERACTIVE:
+        print("Retrieving context from SPOKE graph...")
+        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, EDGE_EVIDENCE)
+        print("Here is the KG-RAG based answer:")
+        print("")
+        enriched_prompt = "Context: "+ context + "\n" + "Question: " + question
+        output = get_GPT_response(enriched_prompt, SYSTEM_PROMPT, CHAT_MODEL_ID, CHAT_DEPLOYMENT_ID, temperature=TEMPERATURE)
+        stream_out(output)
+    else:
+        interactive(question, vectorstore, node_context_df, embedding_function_for_context_retrieval, CHAT_MODEL_ID, EDGE_EVIDENCE, SYSTEM_PROMPT)
+
+                
+
+if __name__ == "__main__":
+    main()
+
+