Advanced RAG: Fine-Tune Embeddings from HuggingFace for RAG
•
4
Tarun Jain
lucifertrj
AI & ML interests
Deep Learning, FPGA, and ML
Recent Activity
posted
an
update
8 days ago
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
authored
a paper
about 2 months ago
News Reporter: A Multi-lingual LLM Framework for Broadcast T.V News
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Organizations
lucifertrj's activity
posted
an
update
8 days ago
Post
477
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
➡️ 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
2 months ago
Post
1524
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
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
6 months ago
Post
1695
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
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
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post with 👀
6 months ago
Post
2255
Advanced RAG - Hybrid Search using HuggingFace Models
Chat with PDF in 10 lines of code:
Cookbook: https://github.com/aiplanethub/beyondllm/blob/main/cookbook/Implementing_Hybrid_Search.ipynb
Support the project by giving a ⭐️ to the repo
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
6 months ago
Post
2255
Advanced RAG - Hybrid Search using HuggingFace Models
Chat with PDF in 10 lines of code:
Cookbook: https://github.com/aiplanethub/beyondllm/blob/main/cookbook/Implementing_Hybrid_Search.ipynb
Support the project by giving a ⭐️ to the repo
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
7 months ago
Thanks
replied to
their
post
7 months ago
This is not using beyondllm
posted
an
update
7 months ago
Post
1852
Evaluate RAG using Open Source from HuggingFace using BeyondLLM
GitHub: https://github.com/aiplanethub/beyondllm
Don't forget to ⭐️ the repo
# 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
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