Build Your Own Generative Search Engine with Llama 3
Introduction
Understanding Llama 3
Setting Up Your Environment
Downloading Llama 3
Downloading Llama 3
Install Hugging Face Transformers:
pip install transformers
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
, andhuggingface_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:
- 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.
- Large Language Model (LLM): To provide a condensed response by utilizing specific content from local documents.
- The purpose of the user interface (UI) is to communicate with the user by letting them submit questions and see answers.
Semantic Index
Initializing Qdrant
Install Qdrant Client:
pip install qdrant-client
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
from sentence_transformers import SentenceTransformer model = SentenceTransformer('all-MiniLM-L6-v2') documents = ["doc1 content", "doc2 content", "doc3 content"] embeddings = model.encode(documents)lems
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
Install FastAPI and Uvicorn:
pip install fastapi uvicorn
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"}
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