Update main.py
Browse files
main.py
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@@ -5,6 +5,7 @@ from wiki import search as search_wikipedia # Import the search function from t
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from concurrent.futures import ThreadPoolExecutor # Import ThreadPoolExecutor for concurrent execution
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from llm_handler import send_to_llm # Import the send_to_llm function from the llm_handler module
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from params import OUTPUT_FILE_PATH, NUM_WORKERS # Import constants from the params module
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# Import system messages from the system_messages module
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from system_messages import (
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@@ -12,6 +13,9 @@ from system_messages import (
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)
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from topics import TOPICS # Import topics from the topics module
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# Set the system messages to those specified in SYSTEM_MESSAGES_VODALUS
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SYSTEM_MESSAGES = SYSTEM_MESSAGES_VODALUS
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@@ -54,7 +58,7 @@ async def generate_data(
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msg_list = [msg_context, {"role": "user", "content": f"Generate a question based on the SUBJECT_AREA: {topic_selected}"}]
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# Send to LLM for question generation
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question, _ = send_to_llm(msg_list)
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# Prepare message list for LLM to generate the answer
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msg_list_answer = [
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@@ -63,7 +67,7 @@ async def generate_data(
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]
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# Send to LLM for answer generation
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answer, _ = send_to_llm(msg_list_answer)
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# Prepare data for output (excluding usage information)
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data = {
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from concurrent.futures import ThreadPoolExecutor # Import ThreadPoolExecutor for concurrent execution
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from llm_handler import send_to_llm # Import the send_to_llm function from the llm_handler module
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from params import OUTPUT_FILE_PATH, NUM_WORKERS # Import constants from the params module
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import llamanet
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# Import system messages from the system_messages module
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from system_messages import (
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)
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from topics import TOPICS # Import topics from the topics module
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# Initialize LlamaNet
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llamanet.run()
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# Set the system messages to those specified in SYSTEM_MESSAGES_VODALUS
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SYSTEM_MESSAGES = SYSTEM_MESSAGES_VODALUS
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msg_list = [msg_context, {"role": "user", "content": f"Generate a question based on the SUBJECT_AREA: {topic_selected}"}]
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# Send to LLM for question generation
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question, _ = send_to_llm("llamanet", msg_list)
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# Prepare message list for LLM to generate the answer
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msg_list_answer = [
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]
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# Send to LLM for answer generation
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answer, _ = send_to_llm("llamanet", msg_list_answer)
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# Prepare data for output (excluding usage information)
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data = {
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