11 4 11

Tarun Jain

lucifertrj

https://youtube.com/@aiwithtarun

AI & ML interests

Deep Learning, FPGA, and ML

Recent Activity

posted an update 1 day ago

Bhagavad Gita GPT assistant - Build fast RAG pipeline to index 1000+ pages using Binary Optimization DeepSeek R-1 and Qdrant Binary Quantization Check out the latest tutorial where we build a Bhagavad Gita GPT assistant—covering: - DeepSeek R1 vs OpenAI O1 - Using Qdrant client with Binary Quantization - Building the RAG pipeline with LlamaIndex - Running inference with DeepSeek R1 Distill model on Groq - Develop Streamlit app for the chatbot inference Watch the full implementation here: https://www.youtube.com/watch?v=NK1wp3YVY4Q

updated a Space 15 days ago

lucifertrj/AI-Interview-Assistant

published a Space 15 days ago

lucifertrj/AI-Interview-Assistant

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lucifertrj's activity

posted an update 1 day ago

Post

353

Bhagavad Gita GPT assistant - Build fast RAG pipeline to index 1000+ pages using Binary Optimization

DeepSeek R-1 and Qdrant Binary Quantization

Check out the latest tutorial where we build a Bhagavad Gita GPT assistant—covering:
- DeepSeek R1 vs OpenAI O1
- Using Qdrant client with Binary Quantization
- Building the RAG pipeline with LlamaIndex
- Running inference with DeepSeek R1 Distill model on Groq
- Develop Streamlit app for the chatbot inference

Watch the full implementation here: https://www.youtube.com/watch?v=NK1wp3YVY4Q

1 reply

posted an update about 2 months ago

Post

529

Image Prompt Engineering Guide:
➡️ Artistic styling for Image generation
➡️ Prompt weighting using the parentheses method to generate realistic images.
➡️ Advanced features like style and positioning control[experimental].
➡️ Image placement on the generated AI image using Recraft V3 Mockup.

Watch: https://www.youtube.com/watch?v=d3nUG28-jIc

posted an update 4 months ago

Post

1548

AI Agents LlamaIndex in 40 minutes

The video covers code and workflow explanations for:

- Function Calling
- Function Calling Agents + Agent Runner
- Agentic RAG
- REAcT Agent: Build your own Search Assistant Agent

Watch: https://youtu.be/bHn4dLJYIqE

posted an update 8 months ago

Post

1699

Observability and Retrieval Augmented Generation in 10 lines of Code

Tutorial: https://www.youtube.com/watch?v=VCQ0Cw-GF2U

This video covers:
- Why we need observability?
- Implementation of RAG using BeyondLLM
- Monitor and Track LLM Observability using Phoenix

reacted to their post with 👀 8 months ago

Post

2278

Advanced RAG - Hybrid Search using HuggingFace Models

Chat with PDF in 10 lines of code:

# pip install beyondllm
# pip install llama-index-embeddings-fastembed

from beyondllm import source,retrieve,embeddings,llms,generator
import os
from getpass import getpass
os.environ['HUGGINGFACE_ACCESS_TOKEN'] = getpass("Enter your HF API token:")

data = source.fit("sample.pdf", dtype="pdf")
embed_model = embeddings.FastEmbedEmbeddings()

retriever = auto_retriever(
    data=data, embed_model=embed_model,
    type="hybrid", top_k=5, mode="OR"
)

llm = HuggingFaceHubModel(model="mistralai/Mistral-7B-Instruct-v0.2")
pipeline = generator.Generate(question="<replace-with-your-query>",llm=llm,retriever=retriever)
print(pipeline.call())

Cookbook: https://github.com/aiplanethub/beyondllm/blob/main/cookbook/Implementing_Hybrid_Search.ipynb

Support the project by giving a ⭐️ to the repo

posted an update 8 months ago

Post

2278

Advanced RAG - Hybrid Search using HuggingFace Models

Chat with PDF in 10 lines of code:

# pip install beyondllm
# pip install llama-index-embeddings-fastembed

from beyondllm import source,retrieve,embeddings,llms,generator
import os
from getpass import getpass
os.environ['HUGGINGFACE_ACCESS_TOKEN'] = getpass("Enter your HF API token:")

data = source.fit("sample.pdf", dtype="pdf")
embed_model = embeddings.FastEmbedEmbeddings()

retriever = auto_retriever(
    data=data, embed_model=embed_model,
    type="hybrid", top_k=5, mode="OR"
)

llm = HuggingFaceHubModel(model="mistralai/Mistral-7B-Instruct-v0.2")
pipeline = generator.Generate(question="<replace-with-your-query>",llm=llm,retriever=retriever)
print(pipeline.call())

Cookbook: https://github.com/aiplanethub/beyondllm/blob/main/cookbook/Implementing_Hybrid_Search.ipynb

Support the project by giving a ⭐️ to the repo

replied to their post 8 months ago

Thanks

replied to their post 8 months ago

This is not using beyondllm

posted an update 8 months ago

Post

1855

Evaluate RAG using Open Source from HuggingFace using BeyondLLM

# pip install beyondllm
# pip install huggingface_hub
# pip install llama-index-embeddings-fastembed

from beyondllm.source import fit
from beyondllm.embeddings import FastEmbedEmbeddings
from beyondllm.retrieve import auto_retriever
from beyondllm.llms import HuggingFaceHubModel
from beyondllm.generator import Generate

import os
from getpass import getpass
os.environ['HUGGINGFACE_ACCESS_TOKEN'] = getpass("Enter your HF API token:")

data = fit("RedHenLab_GSoC_Tarun.pdf",dtype="pdf")
embed_model = FastEmbedEmbeddings()
retriever = auto_retriever(data=data,embed_model=embed_model,type="normal",top_k=3)
llm = HuggingFaceHubModel(model="mistralai/Mistral-7B-Instruct-v0.2")
pipeline = Generate(question="what models has Tarun fine-tuned?",llm=llm,retriever=retriever)

print(pipeline.call()) # Return the AI response
print(pipeline.get_rag_triad_evals())

GitHub: https://github.com/aiplanethub/beyondllm

Don't forget to ⭐️ the repo

4 replies