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| import os | |
| import pathlib | |
| import re | |
| import gradio as gr | |
| from langchain.docstore.document import Document | |
| from langchain.document_loaders import TextLoader | |
| from langchain.text_splitter import CharacterTextSplitter | |
| from langchain.embeddings.openai import OpenAIEmbeddings | |
| from langchain.vectorstores import FAISS | |
| os.environ["OPENAI_API_KEY"] = "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" | |
| from langchain.prompts.chat import ( | |
| ChatPromptTemplate, | |
| SystemMessagePromptTemplate, | |
| HumanMessagePromptTemplate, | |
| ) | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.chains import RetrievalQAWithSourcesChain | |
| # Set the data store directory | |
| DATA_STORE_DIR = "data_store" | |
| if os.path.exists(DATA_STORE_DIR): | |
| vector_store = FAISS.load_local( | |
| DATA_STORE_DIR, | |
| OpenAIEmbeddings() | |
| ) | |
| else: | |
| print(f"Missing files. Upload index.faiss and index.pkl files to {DATA_STORE_DIR} directory first") | |
| system_template = """Use the following pieces of context to answer the user's question. | |
| Take note of the sources and include them in the answer in the format: "SOURCES: source1", use "SOURCES" in capital letters regardless of the number of sources. | |
| If you don't know the answer, just say "I don't know", don't try to make up an answer. | |
| ---------------- | |
| {summaries}""" | |
| messages = [ | |
| SystemMessagePromptTemplate.from_template(system_template), | |
| HumanMessagePromptTemplate.from_template("{question}") | |
| ] | |
| prompt = ChatPromptTemplate.from_messages(messages) | |
| llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0, | |
| max_tokens=256) # Modify model_name if you have access to GPT-4 | |
| chain_type_kwargs = {"prompt": prompt} | |
| chain = RetrievalQAWithSourcesChain.from_chain_type( | |
| llm=llm, | |
| chain_type="stuff", | |
| retriever=vector_store.as_retriever(), | |
| return_source_documents=True, | |
| chain_type_kwargs=chain_type_kwargs | |
| ) | |
| def chatbot_interface(query): | |
| result = chain(query) | |
| return result['answer'] | |
| # Create a Gradio interface | |
| gr.Interface( | |
| fn=chatbot_interface, | |
| inputs="text", | |
| outputs="text", | |
| title="LLM Chatbot", | |
| description="Chat with the LLM Chatbot on Custom Data" | |
| ).launch() | |