Tarun Jain

lucifertrj

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posted an update about 1 month ago
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1485
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 5 months ago
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1678
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 ๐Ÿ‘€ 5 months ago
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2242
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 โญ๏ธ to the repo
posted an update 5 months ago
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2242
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 โญ๏ธ to the repo
replied to their post 5 months ago
replied to their post 5 months ago
posted an update 5 months ago
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1843
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 โญ๏ธ the repo
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