Use Vector database and GROQ to create AI -driven mentors for retrieval (RAG): Graduation Guide

At present, the three trend themes in AI implementation are LLM, rags and databases. These enable us to create a system that suits us. This AI -driven system combines the response generated by the vector database and AI, which is applied in various industries. In customer support, AI chat robotically retrieves the answer to the knowledge base. The legal and financial sector benefits from AI -driven document abstracts and case studies. Healthcare AI assistant helps doctors to conduct medical research and drug interaction. Electronic learning platform provides personalized company training. The news industry uses AI for news abstract and factual inspection. Software development uses AI for encoding and debugging. Scientific research benefits from AI -driven literature reviews. This method can enhance knowledge retrieval, automated content creation, and personalized user interaction in multiple domains.

In this tutorial, we will use a rag to create an English teacher driven by AI. The system integrates a vector database (Chromadb) to store and retrieve related English learning materials and AI -driven text generating (GROQ API) to create structured and fascinating courses. The workflow includes extract text from PDF, stores knowledge in a vector database, retrieves related content and generates detailed AI driving courses. The purpose is to establish an interactive English mentor, which dynamically generates the theme courses, while using the knowledge previously stored to improve accuracy and context correlation.

Step 1: Install the necessary library

!pip install PyPDF2
!pip install groq
!pip install chromadb
!pip install sentence-transformers
!pip install nltk
!pip install fpdf
!pip install torch

Pypdf2 extracts text from the PDF file to make it useful for processing information -based information. GROQ is a library that can access GROQ’s AI API to achieve high -end text generation function. Chromadb is a carrier database designed to effectively retrieve text. Sentence converters generate text embedding, which helps to store and retrieve information. NLTK (Natural Language Tool Pack) is a famous NLP library for text pre -processing, token, and analysis. FPDF is a lightweight library for creating and manipulating PDF documents, allowing courses to be generated in a structural format. Torch is a deep learning framework that is often used for machine learning tasks, including AI -based text generation.

Step 2: Download NLP token data

import nltk
nltk.download('punkt_tab')

Use the above code to download the PUNKT_TAB data set. NLTK.DOWNLOAD (‘Punkt_tab’) to get the data set required for the sentence token. The token is divided into sentences or words, which is essential for processing and retrieval of large -scale subjects into managable segments.

Step 3: Set the NLTK data directory

working_directory = os.getcwd()
nltk_data_dir = os.path.join(working_directory, 'nltk_data')

nltk.data.path.append(nltk_data_dir)
nltk.download('punkt_tab', download_dir=nltk_data_dir)

We will set a dedicated directory for NLTK data. OS.GETCWD () function retrieves the current working directory and create a new directory NLTK_DATA to store resources related to NLP. NLTK.Data.Path.append (NLTK_DATA_DIR) command ensure that the directory stores the downloaded NLTK dataset. Download and store the PUNKT_TAB dataset required in the specified directory.

Step 4: Import the required library

import os
import torch
from sentence_transformers import SentenceTransformer
import chromadb
from chromadb.utils import embedding_functions
import numpy as np
import PyPDF2
from fpdf import FPDF
from functools import lru_cache
from groq import Groq
import nltk
from nltk.tokenize import sent_tokenize
import uuid
from dotenv import load_dotenv

Here, we import all the necessary libraries used in the entire notebook. OS is used for file system operation. Torch imports to handle tasks related to learning. Sentence converter provides a simple way to generate embedding from text. Chromadb and its embedded Infunctions module helps to store and retrieve related texts. Numpy is a mathematical library for processing array and numerical calculations. Pypdf2 is used to extract text from PDF. FPDF allows PDF documents. LRU_CACHE is used for cache function output for optimization. GROQ is an AI service that generates human response. NLTK provides NLP function, and has been introduced to Send_tokenize to divide the text into sentences. UUID generates the only ID, load_dotenv loads environment variables from the .env file.

Step 5: Load environmental variables and API keys

load_dotenv()
api_key = os.getenv('api_key')
os.environ["GROQ_API_KEY"] = api_key

#or manually retrieve key from  and add it here

Through the above code, we will load the environment variables in the.env file. The load_dotenv () function read the environment variable from the .env file and available in the Python environment. Use OS.Getenv (‘API_KEY’) to retrieve API_KEY to ensure that the safe API key management does not need to be hardcoded in the script. Then store the key in Os.environ[“GROQ_API_KEY”]Make it available for future API calls.

Step 6: Define the vector database class

class VectorDatabase:
    def __init__(self, collection_name="english_teacher_collection"):
        self.client = chromadb.PersistentClient(path="./chroma_db")
        self.encoder = SentenceTransformer('all-MiniLM-L6-v2')
        self.embedding_function = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="all-MiniLM-L6-v2")
        self.collection = self.client.get_or_create_collection(name=collection_name, embedding_function=self.embedding_function)

    def add_text(self, text, chunk_size):
        sentences = sent_tokenize(text, language="english")
        chunks = self._create_chunks(sentences, chunk_size)
        ids = [str(uuid.uuid4()) for _ in chunks]
        self.collection.add(documents=chunks, ids=ids)

    def _create_chunks(self, sentences, chunk_size):
        chunks = []
        for i in range(0, len(sentences), chunk_size):
            chunk = ' '.join(sentences[i:i+chunk_size])
            chunks.append(chunk)
        return chunks

    def retrieve(self, query, k=3):
        results = self.collection.query(query_texts=[query], n_results=k)
        return results['documents'][0]

The course defines a vectordatabase, which interacts with Chromadb to store and retrieve text -based knowledge. The __init __ () function initializes the database and creates a long -lasting Chroma_DB directory for long -term storage. The SENCENCENCETRANSFORMER model (All-Minilm-L6-V2) generates text embedded, which convert text information to numerical representation, which can be effectively stored and searched effectively. The add_text () function decompose the input text into sentences, divides it into a smaller block, and then stores it in a vector database. The _Create_chunks () function ensures correctly dividing text, so that the retrieval is more effective. The search () function is queried, and the most related storage documents are returned according to similarity.

Step 7: Use GROQ to implement AI courses

class GroqGenerator:
    def __init__(self, model_name="mixtral-8x7b-32768"):
        self.model_name = model_name
        self.client = Groq()

    def generate_lesson(self, topic, retrieved_content):
        prompt = f"Create an engaging English lesson about {topic}. Use the following information:n"
        prompt += "nn".join(retrieved_content)
        prompt += "nnLesson:"

        chat_completion = self.client.chat.completions.create(
            model=self.model_name,
            messages=[
                {"role": "system", "content": "You are an AI English teacher designed to create an elaborative and engaging lesson."},
                {"role": "user", "content": prompt}
            ],
            max_tokens=1000,
            temperature=0.7
        )
        return chat_completion.choices[0].message.content

This course is responsible for generating an AI -driven English course. It interacts with the GROQ AI model through API calls. __init __ () Function uses the Mixtral-8X7B-32768 model for the AI ​​designed by the dialogue to initialize the generator. The generate_lesson () function records the theme to input, formatting prompts, and then sends it to the GROQ API for course generation. The AI ​​system returns structured courses with interpretation and examples, and then can be stored or displayed.

Step 8: Combined with vector retrieval and AI generation

class RAGEnglishTeacher:
    def __init__(self, vector_db, generator):
        self.vector_db = vector_db
        self.generator = generator

    @lru_cache(maxsize=32)
    def teach(self, topic):
        relevant_content = self.vector_db.retrieve(topic)
        lesson = self.generator.generate_lesson(topic, relevant_content)
        return lesson

The previous type of RagenGlishTeacher integrated VectorDataBase and Gloqgenrator components to create an enhanced retrieval function (RAG) system. The Teach () function retrieves related content from the vector database and passes it to Groqgnerator to generate structured courses. LRU_CACHE (MaxSize = 32) The decorator cache up to 32 previously generated courses to avoid repeated calculations to improve efficiency.

In short, we successfully established an English mentor driven by AI. The hometown combines the vector database (Chromadb) and the AI ​​model of GROQ to achieve the generation of enhancement of retrieval function (RAG). The system can extract text from the PDF, store relevant knowledge in a structured manner, retrieve the context information, and dynamically generate detailed courses. The instructor uses a sentence to embed it to provide a structural learning to effectively retrieve the context, context, and personalized courses in order to effectively retrieve and response generated by AI. This method ensures that learners get accurate, rich in information, and organize good English courses without creating manual content. By integrating other learning modules to improve database efficiency or fine -tuning AI response to make the counseling process more interactive and intelligent to further expand the system.

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