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cef12fa 8d88add cef12fa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | import streamlit as st
from sentence_transformers import SentenceTransformer
from qdrant_client import QdrantClient
import google.generativeai as genai
# Qdrant details
QDRANT_URL = "https://807708a6-1d41-4ecb-a1f3-8a41fcd48ec3.us-east4-0.gcp.cloud.qdrant.io:6333"
QDRANT_API_KEY = "J3LJcoG3q_njIvu9OzjooR2VBD-tx_Zz553gGwMoUD_xzdYz1tFufA"
QDRANT_COLLECTION_NAME = "courses-data"
# Google Gemini API details
GEMINI_API_KEY = "API KEY"
genai.configure(api_key=GEMINI_API_KEY)
model = genai.GenerativeModel("gemini-1.5-flash")
# Initialize Qdrant client
qdrant_client = QdrantClient(url=QDRANT_URL, prefer_grpc=False, api_key=QDRANT_API_KEY)
# Load the SentenceTransformer model
embedder = SentenceTransformer('all-MiniLM-L6-v2') # Vector size = 384
def vector_search(query, collection_name, top_k):
"""Perform a vector search on the Qdrant collection."""
query_vector = embedder.encode(query).tolist()
search_result = qdrant_client.search(
collection_name=collection_name,
query_vector=query_vector,
limit=top_k
)
results = []
for result in search_result:
chunk_text = result.payload.get('page_content', 'No text found')
results.append(chunk_text)
return results
def gemini(query, chunks):
"""Generates an answer using Google's Generative AI (Gemini)."""
context = "\n".join([f"{i+1}. {chunk}" for i, chunk in enumerate(chunks)])
prompt = f"""
You are a highly knowledgeable assistant. Based on the given context, please provide a well-crafted answer to the query below. Use the provided information from the context as reference material.
### Context:
{context}
### Query:
{query}
Based on the context, provide a list of courses - course names and a short description.
Provide a concise, clear, and informative response based on the query.
"""
# Make the request to generate text
response = model.generate_content(prompt)
# Check if the response contains valid content
if response.candidates and len(response.candidates) > 0:
return response.text # Return the generated text as a string
else:
return "No valid content was returned. Please adjust your prompt or try again."
def getResult(input_query):
context = vector_search(input_query, QDRANT_COLLECTION_NAME, top_k=5)
return gemini(input_query, context)
# Streamlit App
st.title("Course Finder using RAG System")
st.write("Search for courses using a query. The system retrieves and generates relevant course details.")
# Search bar for user input
query = st.text_input("Enter your query:", "")
# Display the result when the user enters a query
if st.button("Search"):
if query.strip():
with st.spinner("Searching and generating results..."):
result = getResult(query)
st.subheader("Results:")
st.write(result)
else:
st.warning("Please enter a valid query!")
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