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from huggingface_hub import hf_hub_download
n_gpu_layers = 40 # Change this value based on your model and your GPU VRAM pool.
n_batch = 256
import paperscraper
from paperqa import Docs
from langchain.llms import LlamaCpp
from langchain import PromptTemplate, LLMChain
from langchain.callbacks.manager import CallbackManager
from langchain.embeddings import LlamaCppEmbeddings
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from g4f import Provider, models
from langchain.llms.base import LLM
from langchain_g4f import G4FLLM
# Make sure the model path is correct for your system!
llm = LLM = G4FLLM(
model=models.gpt_35_turbo,
provider=Provider.Aichat,
)
from langchain.embeddings import HuggingFaceEmbeddings
model_name = "sentence-transformers/all-mpnet-base-v2"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': False}
embeddings = HuggingFaceEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs
)
docs = Docs(llm=llm, embeddings=embeddings)
docs.add("https://33bbf3d5-c3fe-409d-a723-d22ea129e9a0.usrfiles.com/ugd/33bbf3_a21b940230be4adbb8be48927b9dc92b.pdf",chunk_chars=500)
answer = docs.query("Que dis l'article 114 ?")
print(answer)
def re(r):
print(answer)
return r
gr.Interface(fn=re,inputs=gr.Textbox(),outputs=gr.Textbox).launch() |