chat.py

from dotenv import load_dotenv
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from langchain_qdrant import QdrantVectorStore
from google import genai
import os

load_dotenv()

client = genai.Client(
    api_key = os.getenv("GOOGLE_API_KEY")
)


# vector embedding
embedding_model = GoogleGenerativeAIEmbeddings(
    model="models/gemini-embedding-2",
)

vector_db = QdrantVectorStore.from_existing_collection(
    embedding=embedding_model,
    url="http://localhost:6333",
    collection_name="DSA_75"
)

# user input
user_query = input("ask me question:: ")

# relevant chunks from vector db
search_result = vector_db.similarity_search(query=user_query)

context = "\n\n\n".join([
    (
        f"Page content: {result.page_content}\n"
        f"Page Number:{result.metadata['page_label']}\n"
        f"File Location: {result.metadata['source']}"
    )
    for result in search_result
    ])

SYSTEM_PROMPT = f"""
    You are a helpful AI assistant who answers user query based on available
    context retrieved from the Pdf file along with page_contents and page number.
    
    You should only ans the user based on the following context and navigate the
    user to open the right page number to know more.

    Context:
    {context}
"""

response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents=user_query,
    config={
        "system_instruction": SYSTEM_PROMPT,
    }
)

print(response.text)