Prev Next

Python / Python Modern Generative AI and Agents Interview Questions

What are vector databases and how do they enable semantic search in RAG pipelines?

Vector databases store numerical vector representations (embeddings) of documents and enable fast approximate nearest-neighbour (ANN) search — retrieving the vectors most similar to a query vector, typically measured by cosine similarity or inner product. This is the retrieval backbone of every RAG system.

The workflow has two phases. Indexing: each document chunk is passed through an embedding model (e.g. text-embedding-3-small or BAAI/bge-small-en-v1.5) to produce a fixed-size vector; the vector plus metadata is stored in the vector DB. Querying: the user's query is embedded with the same model, and the DB returns the k chunks whose vectors are closest to the query vector. Popular options include FAISS (in-memory, open-source), Chroma (embedded, easy local dev), and Pinecone / Weaviate (managed cloud).

# ── FAISS: local in-memory vector search
import faiss
import numpy as np
from openai import OpenAI

client = OpenAI()

def embed(texts: list[str]) -> np.ndarray:
    resp = client.embeddings.create(
        model='text-embedding-3-small',
        input=texts
    )
    return np.array([d.embedding for d in resp.data], dtype='float32')

docs = [
    'Python was created by Guido van Rossum in 1991.',
    'The Eiffel Tower is located in Paris, France.',
    'Machine learning is a subset of artificial intelligence.',
]

doc_vecs = embed(docs)          # (3, 1536)
faiss.normalize_L2(doc_vecs)    # normalise for cosine similarity via dot product

index = faiss.IndexFlatIP(doc_vecs.shape[1])  # inner product index
index.add(doc_vecs)

query_vec = embed(['Who invented Python?'])
faiss.normalize_L2(query_vec)
distances, indices = index.search(query_vec, k=2)  # top-2 results
for i in indices[0]:
    print(docs[i])
# Python was created by Guido van Rossum in 1991.  <- top match

# ── Chroma: persistent local vector DB
import chromadb

chroma = chromadb.PersistentClient(path='./chroma_db')
collection = chroma.get_or_create_collection('my_docs')
collection.add(
    documents=docs,
    ids=[f'doc_{i}' for i in range(len(docs))],
)
results = collection.query(query_texts=['Who invented Python?'], n_results=2)
print(results['documents'])

Why must the same embedding model be used for both indexing documents and embedding queries?
What does normalising vectors to unit length before storing them enable?

More Related questions...

What are Large Language Models (LLMs) and how do they generate text? What is the Hugging Face Transformers pipeline API and how do you use it for common NLP and vision tasks? How does tokenisation work in Hugging Face and what are the key tokenizer concepts? What is the Auto-class pattern in Hugging Face and how do you run inference with a raw model? What is prompt engineering and what are the most effective techniques for getting better outputs from LLMs? What is Retrieval-Augmented Generation (RAG) and why is it preferred over full fine-tuning for knowledge-intensive tasks? What are vector databases and how do they enable semantic search in RAG pipelines? How do you build a complete RAG pipeline using LangChain? What are the most important text splitting strategies in RAG, and how do chunk size and overlap affect retrieval quality? What are LangChain's core abstractions — Chains, Runnables, and the LangChain Expression Language? How do you add conversation memory to an LLM application with LangChain? What is an AI agent and how does function calling / tool use work in LLM-based agents? What is the ReAct agent pattern and how does LangChain implement it? How do you efficiently load large Hugging Face models for inference, including quantization and device placement? How do you use Hugging Face's text-generation pipeline with open-source chat models like Mistral or Llama? How do you use the Hugging Face Inference API and the InferenceClient for production deployments? What is LoRA and how does the Hugging Face PEFT library simplify fine-tuning large models? How do you use the Hugging Face Datasets library for training and evaluation? How do you fine-tune a model using the Hugging Face Trainer API? How do you evaluate LLM outputs for quality, factual accuracy, and hallucination? How do you stream LLM responses token by token for a better user experience? How do you use multimodal models (vision-language) with Hugging Face for image understanding tasks? How do you reliably get structured JSON output from LLMs, and what tools does LangChain provide? How do you compute semantic similarity between texts using Hugging Face and OpenAI embeddings? What document loaders does LangChain provide, and how do you handle different file types in a RAG pipeline? What is the OpenAI Assistants API and how does it differ from the Chat Completions API? What is the Parent Document Retriever pattern and when does it improve RAG performance? How do you manage, version, and reuse prompts in production LLM applications? How do you generate and manipulate images using Hugging Face's Diffusers library? How do you handle documents or conversations that exceed an LLM's context window? What is LangGraph and how does it differ from LangChain's AgentExecutor for building agents? What embedding models should you use for production RAG systems, and how do you choose between OpenAI and open-source options? How do you add safety guardrails and input/output validation to LLM applications? How do you manage LLM API costs and implement caching to reduce redundant calls? What is LlamaIndex and how does it compare to LangChain for RAG use cases? What is the Hugging Face Hub and how do you push a trained model to share it? How do you build a demo web interface for an LLM application using Gradio? How do you monitor and debug LLM applications in production using LangSmith?
Show more question and Answers...

FastAPI Basics Interview Questions

Comments & Discussions

Interviews Questions

About Javapedia.net

Javapedia.net is for Java and J2EE developers, technologist and college students who prepare of interview. Also this site includes many practical examples.

This site is developed using J2EE technologies by Steve Antony, a senior Developer/lead at one of the logistics based company.

Follow Javapedia updates

contact: javatutorials2016[at]gmail[dot]com

Kindly consider donating for maintaining this website. Thanks.

Copyright © 2026, javapedia.net, all rights reserved. · .

DMCA.com Protection Status