forked from phito/darknet_diaries_llm
52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
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from llama_index import (SimpleDirectoryReader, ServiceContext, StorageContext, PromptTemplate,
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load_index_from_storage, Document, set_global_service_context)
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from llama_index.node_parser import SimpleNodeParser
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from llama_index import VectorStoreIndex
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from llama_index.llms import OpenAI
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import os
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import re
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llm = OpenAI(model="gpt-3.5-turbo", temperature=0, max_tokens=256)
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service_context = ServiceContext.from_defaults(llm=llm)
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set_global_service_context(service_context)
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if not os.path.exists("./index/lock"):
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documents = []
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for filename in os.listdir("./data"):
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episode_number = re.search(r'\d+', filename).group()
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with open("./data/" + filename, 'r') as f:
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content = f.read()
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document = Document(
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text=content,
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metadata={
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"episode_number": episode_number
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}
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)
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documents = SimpleDirectoryReader('./data').load_data()
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parser = SimpleNodeParser.from_defaults()
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nodes = parser.get_nodes_from_documents(documents)
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index = VectorStoreIndex(nodes, show_progress=True)
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index.storage_context.persist(persist_dir="./index")
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open("./index/lock", 'a').close()
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else:
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storage_context = StorageContext.from_defaults(persist_dir="./index")
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index = load_index_from_storage(storage_context)
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template = (
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"You are now an expert on the Darknet Diaries podcast. \n"
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"Please answer this question by referring to the podcast: {query_str}\n"
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)
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qa_template = PromptTemplate(template)
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query_engine = index.as_query_engine(text_qa_template=qa_template)
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while True:
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try:
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user_prompt = input("Prompt: ")
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response = query_engine.query(user_prompt)
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print(response)
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except KeyboardInterrupt:
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break
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