text
Evaluate RAG using Open Source from HuggingFace using BeyondLLM
Evaluate RAG using Open Source from HuggingFace using BeyondLLM
new_line
new_line
code_fence
```
# 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())
```
# 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())
new_line
new_line
text
GitHub: 
GitHub: 
link
https://github.com/aiplanethub/beyondllm
https://github.com/aiplanethub/beyondllm
new_line
new_line
text
Don't forget to ⭐️ the repo
Don't forget to ⭐️ the repo
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
🔥
fdaudens radames orkut simkjels umutphp victor thesven sadra-barikbin
👀
victor simkjels GPT007 thesven
https://cdn-avatars.huggingface.co/v1/production/uploads/5f17f0a0925b9863e28ad517/X7QKoiXbUtEZSG9jyvfk3.jpeg
Victor Mustar
victor
user
/avatars/c574b7c7ace902c4a613373d3a64e381.svg
Pavan Satish
Pavansatish
user
https://cdn-avatars.huggingface.co/v1/production/uploads/630f3058236215d0b7078806/TRTdqAZpT1bJg_RvGgxlg.jpeg
Tarun Jain
lucifertrj
user