| from langchain.embeddings import HuggingFaceEmbeddings |
|
|
|
|
| def setup_embedding_model(model_name: str): |
| """ |
| Setup embedding model for RAG chatbot. |
| Returns: |
| Embedding_model: HuggingFaceEmbeddings object. |
| """ |
| |
| embedding_model = HuggingFaceEmbeddings(model_name=model_name) |
| return embedding_model |
|
|
|
|
| def load_text_data(file_path: str): |
| """ |
| Load text data from a file. |
| Args: |
| file_path (str): Path to the file. |
| Returns: |
| data (list): List of strings representing the text data. |
| """ |
| try: |
| with open(file_path, "r") as file: |
| data = file.read() |
| return data |
| except FileNotFoundError: |
| raise FileNotFoundError(f"The file at {file_path} was not found.") |
| except Exception as e: |
| raise RuntimeError(f"An error occurred while reading the file: {e}") |
|
|
|
|
| def semantic_search(prompt, retriever): |
| """ |
| Perform semantic search using the retriever. |
| |
| :param prompt: str, The user query or prompt. |
| :param retriever: retriever object, Initialized retriever for the database. |
| :return: List of relevant documents. |
| """ |
| |
| results = retriever.get_relevant_documents(prompt) |
| final = "" |
| |
| for idx, doc in enumerate(results, 1): |
| final += doc.page_content + "\n\n" |
|
|
| return final |
|
|
|
|
| def resume_chat_completion(client, model, user_question, relevant_excerpts): |
| """ |
| Generate a response to the user's question using the pre-trained model. |
| Args: |
| client (Groq): Initialized Groq client. |
| model (str): The model to use for the chat completion. |
| user_question (str): The user's question. |
| relevant_excerpts (str): The relevant excerpts from the resume. |
| Returns: |
| response (str): The generated response to the user's question. |
| """ |
|
|
| |
| system_prompt = """ |
| You are an intelligent assistant designed to answer queries about Kacem Mathlouthi's professional background and experiences based on his resume. |
| |
| Guidelines for generating responses: |
| - Only use information directly found in the provided resume excerpts. |
| - If the information is incomplete or ambiguous in the excerpts, inform the user that you lack sufficient data to answer. |
| - If a user asks a general or unrelated question (e.g., about something that isn't part of the resume), you should politely indicate that you can only respond related to Kacem's resume. |
| |
| Please ensure that your answers are factual and reflect only the information available in the resume. Do not provide opinions or speculate beyond what is provided in the document. |
| """ |
|
|
| |
| chat_completion = client.chat.completions.create( |
| messages=[ |
| {"role": "system", "content": system_prompt}, |
| { |
| "role": "user", |
| "content": "User Question: " |
| + user_question |
| + "\n Relevant Kacem's Resume/CV Exerpt(s): \n" |
| + relevant_excerpts, |
| }, |
| ], |
| model=model, |
| ) |
|
|
| |
| response = chat_completion.choices[0].message.content |
|
|
| return response |
|
|
