Created
May 25, 2023 10:04
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| # imports | |
| import getpass | |
| import os | |
| from langchain import LLMChain | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.document_loaders import UnstructuredMarkdownLoader | |
| from langchain.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, | |
| ChatPromptTemplate, | |
| SystemMessagePromptTemplate, | |
| AIMessagePromptTemplate, | |
| HumanMessagePromptTemplate | |
| ) | |
| from langchain.schema import ( | |
| AIMessage, | |
| HumanMessage, | |
| SystemMessage | |
| ) | |
| # open ai | |
| os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:') | |
| openai = ChatOpenAI(model_name='gpt-3.5-turbo', temperature=0.2) | |
| # docs | |
| docs_dir = './books/' | |
| ouput_dir = './output/books/' | |
| # prompt templates | |
| template_sys="You are a helpful assistant that extracts character names and descriptions from book summaries. Specifically, you must extract all the characters unique to this book along with a basic description and return them as a JSON object." | |
| example_human="The book summary is:\n\nThis is a book about a dog. The dogs name is Spot. Spot is a good dog." | |
| example_ai='[\n {\n "name":"Spot",\n "description":"Spot is a good dog."\n }\n]' | |
| template_base = "The book summary is: {input}" | |
| # get all he md files in the docs dir | |
| docs = [f for f in os.listdir(docs_dir) if f.endswith(".md")] | |
| for doc in docs: | |
| # load single document from folder | |
| loader = UnstructuredMarkdownLoader(docs_dir + doc) | |
| data = loader.load() | |
| # prepare chat prompt | |
| chat_prompt = ChatPromptTemplate.from_messages([ | |
| SystemMessagePromptTemplate.from_template(template_sys), | |
| HumanMessagePromptTemplate.from_template(example_human), | |
| AIMessagePromptTemplate.from_template(example_ai, additional_kwargs={"name": "example_assistant"}), | |
| HumanMessagePromptTemplate.from_template(template_base), | |
| ]) | |
| chat_prompt.format_prompt(input=data).to_messages() | |
| # prepare request and run chat | |
| llm_chain = LLMChain( | |
| prompt=chat_prompt, | |
| llm=openai, | |
| verbose=True | |
| ) | |
| response = llm_chain.run(data) | |
| # save respnse to output dir | |
| with open(ouput_dir + doc.replace('md', 'json'), "w") as f: | |
| f.write(response) |
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