chat.py

from dotenv import load_dotenv
from langchain_openai import OpenAIEmbeddings
from langchain_qdrant import QdrantVectorStore
from openai import OpenAI

from constants import COLLECTION_NAME, EMBEDDING_MODEL, QDRANT_URL

load_dotenv()

openai_client = OpenAI()

# Vector Embeddings
embedding_model = OpenAIEmbeddings(
    model=EMBEDDING_MODEL
)

vector_db = QdrantVectorStore.from_existing_collection(
    url=QDRANT_URL,
    collection_name=COLLECTION_NAME,
    embedding=embedding_model,
)

# Take user input
user_query = input("Ask something: ")

# Relevant chunks from the vector db
search_results = vector_db.similarity_search(query=user_query)

context = "\n\n\n".join(
    [f"Page Content: {result.page_content}\nPage Number: {result.metadata['page_label']}\nFile Location: {result.metadata['source']}" for result in search_results])


SYSTEM_PROMPT = f"""
 You are a helpfull AI Assistant who answeres user query based on the available context retrieved from a 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 = openai_client.chat.completions.create(
    model="gpt-5",
    messages=[
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": user_query},
    ]
)

print(f"🤖: {response.choices[0].message.content}")