Build Your Own Generative Search Engine with Llama 3

Introduction

Using Llama 3 to develop a generative search engine is a voyage into the future of artificial intelligence. This book tries to give you the knowledge and resources you need to build a potent AI-driven search engine, starting with the basics of Llama 3 and concluding with the comprehensive instructions to design your own generative search engine.

Understanding Llama 3

Strong language model Llama 3 was developed especially for dialogue and online navigation requirements. It is the ideal foundation for a generative search engine since it can browse the web and process queries, responses, and instructions. Llama 3's capabilities enable it to comprehend intricate inquiries, obtain pertinent data, and produce logical and contextually appropriate answers.

Setting Up Your Environment

To build the required environment, the developer must first make some preparations. The first step in this process is to download the Llama 3 model from the Hugging Face Model Hub and install the necessary hub libraries, transformers, datasets, and structures.

Downloading Llama 3

1. Downloading Llama 3

   1. Install Hugging Face Transformers:

      pip install transformers
      

   2. Download the Llama 3 Model:

      from transformers import AutoModelForCausalLM, AutoTokenizer
      model_name = "huggingface/llama-3"
      tokenizer = AutoTokenizer.from_pretrained(model_name)
      model = AutoModelForCausalLM.from_pretrained(model_name)
      

   Setting Up Libraries

   Ensure that you have all necessary libraries installed. You will need transformers, datasets, and huggingface_hub for handling the Llama 3 model and related tasks.

   pip install transformers datasets huggingface_hub

System Design

A generative search engine's system design consists of three primary parts:

1. Semantic Index: A local file content index with an information retrieval system that may return the most pertinent documents in response to a given query.
2. Large Language Model (LLM): To provide a condensed response by utilizing specific content from local documents.
3. The purpose of the user interface (UI) is to communicate with the user by letting them submit questions and see answers.

Semantic Index

We are developing a semantic index that, given the contents of a given file, will return to us the bodies of documents that are most comparable to a query. Using Qdrant as a vector store, such an index is constructed.

Initializing Qdrant

1. Install Qdrant Client:

   pip install qdrant-client
   

2. Set Up Qdrant Server: Qdrant can be used either as a local server or via a cloud service. For a local setup, you can use Docker to run the Qdrant server.

   docker run -p 6333:6333 qdrant/qdrant
   

Initialize Qdrant Client:

from qdrant_client import QdrantClient

client = QdrantClient("localhost", port=6333)

Embedding Documents

The documents on the corresponding hard disc must be embedded and indexed in order to enable vector machines. Select a suitable method and vector similarity measure for embeddings.

from sentence_transformers import SentenceTransformer 

model = SentenceTransformer('all-MiniLM-L6-v2') 

documents = ["doc1 content", "doc2 content", "doc3 content"] 

embeddings = model.encode(documents)lems

When the lengths of the queries and documents differ greatly, asymmetric search difficulties arise. Make sure your embedding technique can handle varying lengths efficiently in order to address this.

query_embedding = model.encode(["query content"])

Generative Search API

The generative search engine will be exposed as a web service developed using FastAPI.

Setting Up FastAPI

1. Install FastAPI and Uvicorn:

   
   pip install fastapi uvicorn
   

2. Create API Endpoints:

   
   from fastapi import FastAPI
   from pydantic import BaseModel
   
   app = FastAPI()
   
   class Query(BaseModel):
       text: str
   
   @app.post("/search/")
   async def search(query: Query):
       # Process the query and return the result
       return {"result": "This is a mock result"}
   

3. Run the API:

   uvicorn main:app --reload
   

Integrating Qdrant with FastAPI

Use the already created Qdrant client with the indexed data. Search using a vector similarity query, pass the returned chunks of vectors to the Llama 3 model to generate an answer, and feed this back to the user.

@app.post("/search/")
async def search(query: Query):
    query_embedding = model.encode([query.text])
    search_results = client.search(query_embedding)
    # Generate answer using Llama 3
    answer = generate_answer(search_results)
    return {"result": answer}
Simple User Interface
The user interface is the final component of a local generative search engine. This application will be developed using Streamlit for its basic design.
Setting Up Streamlit
Install Streamlit:

pip install streamlit
Create the UI:

import streamlit as st

st.title("Generative Search Engine")

query = st.text_input("Enter your query:")
if st.button("Search"):
    # Call FastAPI endpoint
    result = call_api(query)
    st.write(result)
Calling FastAPI from Streamlit

import requests

def call_api(query):
    response = requests.post("http://localhost:8000/search/", json={"text": query})
    return response.json()["result"]
Training Your Model
Training the Llama 3 model is essential to ensure it can navigate the web effectively and generate accurate responses.
Data Collection
Collect a set of web interactions, including mouse clicks, text entries, and form submissions. This data can be generated or sourced from various repositories.
Training Procedure
Prepare Data:

from datasets import load_dataset

dataset = load_dataset("web_interactions")
Fine-tune Llama 3:

from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(output_dir="./results")
trainer = Trainer(model=model, args=training_args, train_dataset=dataset)

trainer.train()
Building the Search Engine
Llama 3 is the central model for the generative search engine. It will be coupled with a backend server that responds to user queries.
Backend Server
The backend server handles user queries, uses Llama 3 to generate responses, and retrieves relevant information from indexed documents.

@app.post("/search/")
async def search(query: Query):
    query_embedding = model.encode([query.text])
    search_results = client.search(query_embedding)
    answer = generate_answer(search_results)
    return {"result": answer}
Generating Answers
Use the content of retrieved documents to generate answers with Llama 3.

def generate_answer(search_results):
    # Combine document contents
    combined_text = " ".join([result["content"] for result in search_results])
    inputs = tokenizer(combined_text, return_tensors="pt")
    outputs = model.generate(inputs["input_ids"])
    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return answer
User Interface Design
Create a user-friendly interface where users can input questions and receive answers.
Streamlit Interface
Set Up Input Bar:

query = st.text_input("Enter your query:")
Display Results:

if st.button("Search"):
    result = call_api(query)
    st.write(result)
Deployment and Testing
Use tools like as BrowserGym, Playwright, or Selenium to deploy your search engine. 
Make sure the search engine is functioning properly by conducting extensive testing after deployment.
Deployment and Testing
Deploy your search engine using platforms like Playwright, Selenium, or BrowserGym. After deployment, perform thorough testing to ensure the search engine's efficiency.

Deployment
Select a Platform: Select BrowserGym, Selenium, or Playwright for deployment.
Launch the Application: Observe the particular