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| import os | |
| from langchain_nvidia_ai_endpoints import ChatNVIDIA | |
| from llama_index.core import VectorStoreIndex, SimpleDirectoryReader | |
| from llama_index.embeddings.huggingface import HuggingFaceEmbedding | |
| from llama_index.core import Settings | |
| from langchain_core.prompts import ChatPromptTemplate | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| # Initialize global variables | |
| index = None | |
| query_engine = None | |
| prompt_template = None | |
| def load_model(): | |
| global index, query_engine, prompt_template | |
| # Retrieve the NVIDIA API key from the environment variables | |
| api_key = os.getenv("NVIDIA_API_KEY") | |
| # Initialize the ChatNVIDIA instance with the retrieved API key | |
| llm = ChatNVIDIA(model="mixtral_8x7b", api_key=api_key) | |
| # Settings.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5") | |
| # Settings.embed_model = HuggingFaceEmbedding(model_name="distilbert-base-uncased") | |
| # Settings.embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/paraphrase-TinyBERT-L6-v2") | |
| Settings.embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/all-MiniLM-L6-v2") | |
| Settings.llm = llm | |
| documents = SimpleDirectoryReader('pdf/').load_data() | |
| index = VectorStoreIndex.from_documents(documents) | |
| query_engine = index.as_query_engine() | |
| # Correctly initializing the prompt template using the correct class name and method | |
| prompt_template = ChatPromptTemplate.from_messages( | |
| [ | |
| ("system", "Act as my personal assistant and handle inquiries from individuals interested in my professional journey. I am actively seeking job opportunities in the field of AI and Machine Learning. Respond to all questions about me smartly, highlighting my relevant experience. Ensure that your answers are precise and engaging."), | |
| ("user", "Question: {question}") | |
| ] | |
| ) | |
| def query_chatbot_with_prompt(question): | |
| # Assuming format_messages should create a simple query string. Check if you need to adjust how you use format_messages. | |
| try: | |
| # Ensure that formatted_prompt is correctly prepared as a string | |
| formatted_prompt = prompt_template.format_messages(question=question) | |
| # Convert formatted_prompt to string if it is not already (just a safety check; adjust as necessary) | |
| if not isinstance(formatted_prompt, str): | |
| formatted_prompt = str(formatted_prompt) | |
| # Now call the query method with the correctly formatted string | |
| response = query_engine.query(formatted_prompt) | |
| return response | |
| except Exception as e: | |
| return f"An error occurred: {str(e)}" | |