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Update app.py
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app.py
CHANGED
@@ -22,11 +22,9 @@ template = """Use the following pieces of context to answer the question at the
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QA_CHAIN_PROMPT = PromptTemplate(input_variables = ["context", "question"], template = template)
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is_initial_load = True
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def invoke(openai_api_key, youtube_url, prompt):
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openai.api_key = openai_api_key
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if
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youtube_dir = "docs/youtube/"
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loader = GenericLoader(YoutubeAudioLoader([youtube_url], youtube_dir), OpenAIWhisperParser())
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docs = loader.load()
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@@ -36,7 +34,6 @@ def invoke(openai_api_key, youtube_url, prompt):
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vectordb = Chroma.from_documents(documents = splits, embedding = OpenAIEmbeddings(), persist_directory = chroma_dir)
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llm = ChatOpenAI(model_name = "gpt-4", temperature = 0)
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qa_chain = RetrievalQA.from_chain_type(llm, retriever = vectordb.as_retriever(), return_source_documents = True, chain_type_kwargs = {"prompt": QA_CHAIN_PROMPT})
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is_initial_load = False
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result = qa_chain({"query": prompt})
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#shutil.rmtree(youtube_dir)
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#shutil.rmtree(chroma_dir)
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QA_CHAIN_PROMPT = PromptTemplate(input_variables = ["context", "question"], template = template)
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def invoke(openai_api_key, youtube_url, prompt):
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openai.api_key = openai_api_key
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if vectordb is not None:
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youtube_dir = "docs/youtube/"
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loader = GenericLoader(YoutubeAudioLoader([youtube_url], youtube_dir), OpenAIWhisperParser())
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docs = loader.load()
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vectordb = Chroma.from_documents(documents = splits, embedding = OpenAIEmbeddings(), persist_directory = chroma_dir)
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llm = ChatOpenAI(model_name = "gpt-4", temperature = 0)
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qa_chain = RetrievalQA.from_chain_type(llm, retriever = vectordb.as_retriever(), return_source_documents = True, chain_type_kwargs = {"prompt": QA_CHAIN_PROMPT})
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result = qa_chain({"query": prompt})
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#shutil.rmtree(youtube_dir)
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#shutil.rmtree(chroma_dir)
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