1 files changed, 60 insertions, 0 deletions
diff --git a/kg_rag/rag_based_generation/Llama/text_generation.py b/kg_rag/rag_based_generation/Llama/text_generation.py
new file mode 100644
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--- /dev/null
+++ b/kg_rag/rag_based_generation/Llama/text_generation.py
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+from langchain import PromptTemplate, LLMChain
+from kg_rag.utility import *
+import argparse
+
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('-i', type=bool, default=False, help='Flag for interactive mode')
+parser.add_argument('-m', type=str, default='method-1', help='Method to choose for Llama model')
+parser.add_argument('-e', type=bool, default=False, help='Flag for showing evidence of association from the graph')
+args = parser.parse_args()
+
+INTERACTIVE = args.i
+METHOD = args.m
+EDGE_EVIDENCE = bool(args.e)
+
+
+SYSTEM_PROMPT = system_prompts["KG_RAG_BASED_TEXT_GENERATION"]
+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"]
+MODEL_NAME = config_data["LLAMA_MODEL_NAME"]
+BRANCH_NAME = config_data["LLAMA_MODEL_BRANCH"]
+CACHE_DIR = config_data["LLM_CACHE_DIR"]
+
+
+INSTRUCTION = "Context:\n\n{context} \n\nQuestion: {question}"
+
+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():
+    print(" ")
+    question = input("Enter your question : ")    
+    if not INTERACTIVE:
+        template = get_prompt(INSTRUCTION, SYSTEM_PROMPT)
+        prompt = PromptTemplate(template=template, input_variables=["context", "question"])
+        llm = llama_model(MODEL_NAME, BRANCH_NAME, CACHE_DIR, stream=True, method=METHOD) 
+        llm_chain = LLMChain(prompt=prompt, llm=llm)            
+        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 using Llama:")
+        print("")
+        output = llm_chain.run(context=context, question=question)
+    else:
+        interactive(question, vectorstore, node_context_df, embedding_function_for_context_retrieval, "llama", EDGE_EVIDENCE, SYSTEM_PROMPT, llama_method=METHOD)
+
+
+
+        
+
+
+
+if __name__ == "__main__":
+    main()