103 lines
3.5 KiB
Python
103 lines
3.5 KiB
Python
from llama_index import (SimpleDirectoryReader, ServiceContext, StorageContext,
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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, ChatMessage, MessageRole
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from llama_index.prompts import ChatPromptTemplate
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import os
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import re
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llm = OpenAI(model="gpt-4", 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("./transcripts"):
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episode_number = re.search(r'\d+', filename).group()
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with open("./transcripts/" + filename, 'r') as f:
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title = f.readline().strip()
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content = f.read()
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document = Document(
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text=content,
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doc_id=filename,
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metadata={
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"episode_number": episode_number,
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"episode_title": title
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}
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)
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documents.append(document)
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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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print("Loading index...")
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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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chat_text_qa_msgs = [
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ChatMessage(
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role=MessageRole.SYSTEM,
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content=(
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"You have been trained on the Darknet Diaries podcast transcripts with data from october 6 2023."
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"You are an expert about it and will answer as such. You know about every episode up to number 138."
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"Always answer the question, even if the context isn't helpful."
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"Mention the number and title of the episodes you are referring to."
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)
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),
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ChatMessage(
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role=MessageRole.USER,
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content=(
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"Context information is below.\n"
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"---------------------\n"
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"{context_str}\n"
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"---------------------\n"
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"Given the context information and not prior knowledge,"
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"answer the question: {query_str}\n"
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)
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)
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]
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text_qa_template = ChatPromptTemplate(chat_text_qa_msgs)
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chat_refine_msgs = [
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ChatMessage(
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role=MessageRole.SYSTEM,
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content="Always answer the question, even if the context isn't helpful.",
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),
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ChatMessage(
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role=MessageRole.USER,
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content=(
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"We have the opportunity to refine the original answer "
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"(only if needed) with some more context below.\n"
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"------------\n"
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"{context_msg}\n"
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"------------\n"
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"Given the new context, refine the original answer to better "
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"answer the question: {query_str}. "
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"If the context isn't useful, output the original answer again.\n"
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"Original Answer: {existing_answer}"
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),
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),
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]
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refine_template = ChatPromptTemplate(chat_refine_msgs)
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chat_engine = index.as_chat_engine(
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text_qa_template=text_qa_template,
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refine_template=refine_template
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)
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while True:
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try:
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user_prompt = input("Prompt: ")
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streaming_response = chat_engine.stream_chat(user_prompt)
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for token in streaming_response.response_gen:
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print(token, end="")
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print("\n")
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except KeyboardInterrupt:
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break
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