delete

fine-tune.py

@@ -1,56 +0,0 @@
-import os
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.vectorstores import DeepLake
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.document_loaders import SeleniumURLLoader
-from langchain import PromptTemplate
-
-def embedding():
-    os.environ['OPENAI_API_KEY'] = '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'
-    os.environ["ACTIVELOOP_TOKEN"] = "eyJhbGciOiJIUzUxMiIsImlhdCI6MTY5NTE5MTAyNiwiZXhwIjoxNzU4MzQ5NDA3fQ.eyJpZCI6InV0a2Fyc2h0aXdhcmkifQ.PK_iz7uybeSmgqFvOYrICw-CQDbDY1aOjYhkMu-0Jle6gU33dCwxah7bmy39O0hPN4jYLu_RfLuU-XejyNvXrw"
-
-    #llm = ChatOpenAI(temperature=1.0, model='gpt-3.5-turbo-0613')
-
-    # URLs of articles to scrape
-    urls = ["https://modelwise.ai/product/","https://modelwise.ai/category/blog-articles/","https://modelwise.ai/company/"]
-
-    # Load documents using Selenium
-    loader = SeleniumURLLoader(urls=urls)
-    docs_not_splitted = loader.load()
-
-    # Split documents into smaller chunks
-    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-    docs = text_splitter.split_documents(docs_not_splitted)
-
-    # Create OpenAIEmbeddings instance
-    embeddings = OpenAIEmbeddings(model="text-embedding-ada-002")
-
-    # Specify your ActiveLoop organization ID
-    my_activeloop_org_id = "utkarshtiwari"
-    my_activeloop_dataset_name = "chatbot_modelwise"
-    dataset_path = f"hub://{my_activeloop_org_id}/{my_activeloop_dataset_name}"
-    db = DeepLake(dataset_path=dataset_path, embedding_function=embeddings)
-
-    # Add documents to the Deep Lake dataset
-    db.add_documents(docs)
-
-    template = """You are an exceptional customer support chatbot for the company Modelwise that gently answers questions related to the company.
-
-    You know the following context information.
-
-    {chunks_formatted}
-
-    Answer the following question from a customer. Use only information from the context. If you don't know the answer just ask the customer to contact Arnold and provide  his contact details. Do not make up any answer.
-
-    Question: {query}
-
-    Answer:"""
-
-    # Create a PromptTemplate instance
-    prompt = PromptTemplate(
-        input_variables=["chunks_formatted", "query"],
-        template=template,
-    )
-
-if __name__ == '__main__':
-    embedding()