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LAB 3: Embeddings — Visualize Semantic Similarity
===================================================
FREE — No API key needed! Uses HuggingFace sentence-transformers.
"""
import gradio as gr
import numpy as np
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
model = SentenceTransformer("all-MiniLM-L6-v2")
CHUNKS = [
"RAG stands for Retrieval Augmented Generation.",
"Vector databases store embeddings for similarity search.",
"The fee for the AI bootcamp is ₹15,000.",
"LangChain is a framework for building LLM applications.",
"Chunking splits documents into smaller pieces for indexing.",
"Temperature controls the randomness of LLM outputs.",
"RAGAS is used to evaluate RAG pipeline quality.",
"Pavan Kumar is the instructor at pavancoaching.org",
]
def search(query: str, top_k: int) -> str:
query_emb = model.encode([query])
chunk_embs = model.encode(CHUNKS)
scores = cosine_similarity(query_emb, chunk_embs)[0]
ranked = sorted(zip(scores, CHUNKS), reverse=True)[:int(top_k)]
result = f"🔍 Query: '{query}'\n\nTop {int(top_k)} Results:\n{'='*50}\n\n"
for rank, (score, chunk) in enumerate(ranked, 1):
bar = "█" * int(score * 20)
result += f"#{rank} Score: {score:.4f} {bar}\n {chunk}\n\n"
return result
with gr.Blocks(title="Lab 3: Semantic Search", theme=gr.themes.Soft()) as demo:
gr.Markdown("## 🔢 Lab 3: Semantic Search with Embeddings (FREE)")
query = gr.Textbox(label="Your Question", value="How do I evaluate my RAG pipeline?")
top_k = gr.Slider(1, 8, value=3, step=1, label="Top-K Results")
btn = gr.Button("🔍 Search", variant="primary")
output = gr.Textbox(label="Results", lines=20)
btn.click(fn=search, inputs=[query, top_k], outputs=output)
demo.launch() |